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Wills C, Watts K, Houseman A, Maughan TS, Fisher D, Al-Tassan NA, Houlston RS, Escott-Price V, Cheadle JP. Relationship between inherited genetic variation and survival from colorectal cancer stratified by tumour location. Sci Rep 2025; 15:2423. [PMID: 39827162 PMCID: PMC11742712 DOI: 10.1038/s41598-024-77870-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 10/25/2024] [Indexed: 01/22/2025] Open
Abstract
The location of a patient's colorectal cancer (CRC) influences their outcome but inherited factors may also be involved. We studied 1899 patients with advanced CRC (514 had proximal colonic, 493 distal colonic and 892 rectal tumours) and carried out genome-wide association studies for survival. Single nucleotide polymorphisms (SNPs) suggestive of association (P < 1.0 × 10-5) were tested for replication in 5078 CRC patients from the UK Biobank. We investigated the relationship between Phosphatidylinositol 4-Kinase Type 2 Beta (PI4K2B) expression in colorectal tumours and survival in 597 patients from The Human Protein Atlas (THPA). We also analysed 3 SNPs previously associated with survival by anatomical site. We found that SNPs at 54 independent loci were suggestive of an association with survival when stratified by tumour location. rs76011559 replicated in patients with proximal tumours (COIN, COIN-B and UK Biobank combined Hazard Ratio [HR] = 1.53, 95% Confidence Intervals [CI] = 1.19-1.86, P = 7.5 × 10-7) and rs12273047 replicated in patients with rectal tumours (combined HR = 1.27, 95% CI = 1.09-1.46, P = 4.1 × 10-7). In gene analyses, PI4K2B associated with survival in patients with distal cancers (P = 2.1 × 10-6) and increased PI4K2B expression in colorectal tumours was associated with improved survival (P = 9.6 × 10-5). No previously associated SNPs were replicated. Our data identify novel loci associated with survival when stratified by tumour location.
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Affiliation(s)
- Christopher Wills
- Division of Cancer and Genetics, School of Medicine, Cardiff University, Heath Park, Cardiff, CF14 4XN, UK
| | - Katie Watts
- Division of Cancer and Genetics, School of Medicine, Cardiff University, Heath Park, Cardiff, CF14 4XN, UK
| | - Amy Houseman
- Division of Cancer and Genetics, School of Medicine, Cardiff University, Heath Park, Cardiff, CF14 4XN, UK
| | - Timothy S Maughan
- CRUK/MRC Oxford Institute for Radiation Oncology, University of Oxford, Roosevelt Drive, Oxford, OX3 7DQ, UK
| | - David Fisher
- MRC Clinical Trials Unit, University College of London, 125 Kingsway, London, WC2B 6NH, UK
| | - Nada A Al-Tassan
- Department of Genetics, King Faisal Specialist Hospital and Research Center, P.O. Box 3354, 11211, Riyadh, Saudi Arabia
| | - Richard S Houlston
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, SW7 3RP, UK
| | - Valentina Escott-Price
- Institute of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Hadyn Ellis Building, Maindy Road, Cardiff, CF24 4HQ, UK
| | - Jeremy P Cheadle
- Division of Cancer and Genetics, School of Medicine, Cardiff University, Heath Park, Cardiff, CF14 4XN, UK.
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Pearson NM, Novembre J. No evidence that ACE2 or TMPRSS2 drive population disparity in COVID risks. BMC Med 2024; 22:337. [PMID: 39183295 PMCID: PMC11346279 DOI: 10.1186/s12916-024-03539-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 07/22/2024] [Indexed: 08/27/2024] Open
Abstract
Early in the SARS-CoV2 pandemic, in this journal, Hou et al. (BMC Med 18:216, 2020) interpreted public genotype data, run through functional prediction tools, as suggesting that members of particular human populations carry potentially COVID-risk-increasing variants in genes ACE2 and TMPRSS2 far more often than do members of other populations. Beyond resting on predictions rather than clinical outcomes, and focusing on variants too rare to typify population members even jointly, their claim mistook a well known artifact (that large samples reveal more of a population's variants than do small samples) as if showing real and congruent population differences for the two genes, rather than lopsided population sampling in their shared source data. We explain that artifact, and contrast it with empirical findings, now ample, that other loci shape personal COVID risks far more significantly than do ACE2 and TMPRSS2-and that variation in ACE2 and TMPRSS2 per se unlikely exacerbates any net population disparity in the effects of such more risk-informative loci.
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Affiliation(s)
| | - John Novembre
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
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Bianchi A, Zelli V, D’Angelo A, Di Matteo A, Scoccia G, Cannita K, Dimas A, Glentis S, Zazzeroni F, Alesse E, Di Marco A, Tessitore A. A method to comprehensively identify germline SNVs, INDELs and CNVs from whole exome sequencing data of BRCA1/2 negative breast cancer patients. NAR Genom Bioinform 2024; 6:lqae033. [PMID: 38633426 PMCID: PMC11023157 DOI: 10.1093/nargab/lqae033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 03/22/2024] [Accepted: 04/03/2024] [Indexed: 04/19/2024] Open
Abstract
In the rapidly evolving field of genomics, understanding the genetic basis of complex diseases like breast cancer, particularly its familial/hereditary forms, is crucial. Current methods often examine genomic variants-such as Single Nucleotide Variants (SNVs), insertions/deletions (Indels), and Copy Number Variations (CNVs)-separately, lacking an integrated approach. Here, we introduced a robust, flexible methodology for a comprehensive variants' analysis using Whole Exome Sequencing (WES) data. Our approach uniquely combines meticulous validation with an effective variant filtering strategy. By reanalyzing two germline WES datasets from BRCA1/2 negative breast cancer patients, we demonstrated our tool's efficiency and adaptability, uncovering both known and novel variants. This contributed new insights for potential diagnostic, preventive, and therapeutic strategies. Our method stands out for its comprehensive inclusion of key genomic variants in a unified analysis, and its practical resolution of technical challenges, offering a pioneering solution in genomic research. This tool presents a breakthrough in providing detailed insights into the genetic alterations in genomes, with significant implications for understanding and managing hereditary breast cancer.
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Affiliation(s)
- Andrea Bianchi
- Department of Information Engineering, Computer Science and Mathematics, University of L’Aquila, L’Aquila 67100, Italy
| | - Veronica Zelli
- Department of Biotechnological and Applied Clinical Sciences, University of L’Aquila, L’Aquila 67100, Italy
| | - Andrea D’Angelo
- Department of Information Engineering, Computer Science and Mathematics, University of L’Aquila, L’Aquila 67100, Italy
| | - Alessandro Di Matteo
- Department of Information Engineering, Computer Science and Mathematics, University of L’Aquila, L’Aquila 67100, Italy
| | - Giulia Scoccia
- Department of Information Engineering, Computer Science and Mathematics, University of L’Aquila, L’Aquila 67100, Italy
| | - Katia Cannita
- Oncology Division, Mazzini Hospital, ASL Teramo, Teramo 64100, Italy
| | - Antigone S Dimas
- Institute for Bioinnovation, Biomedical Sciences Research Center, Alexander Fleming, Vari 16672, Greece
| | - Stavros Glentis
- Institute for Bioinnovation, Biomedical Sciences Research Center, Alexander Fleming, Vari 16672, Greece
- Pediatric Hematology/Oncology Unit (POHemU), First Department of Pediatrics, University of Athens, Aghia Sophia Children’s Hospital, Athens 11527, Grece
| | - Francesca Zazzeroni
- Department of Biotechnological and Applied Clinical Sciences, University of L’Aquila, L’Aquila 67100, Italy
| | - Edoardo Alesse
- Department of Biotechnological and Applied Clinical Sciences, University of L’Aquila, L’Aquila 67100, Italy
| | - Antinisca Di Marco
- Department of Information Engineering, Computer Science and Mathematics, University of L’Aquila, L’Aquila 67100, Italy
| | - Alessandra Tessitore
- Department of Biotechnological and Applied Clinical Sciences, University of L’Aquila, L’Aquila 67100, Italy
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Zhang S, Zhou Y, Geng P, Lu Q. Functional Neural Networks for High-Dimensional Genetic Data Analysis. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2024; 21:383-393. [PMID: 38507390 PMCID: PMC11301578 DOI: 10.1109/tcbb.2024.3364614] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/22/2024]
Abstract
Artificial intelligence (AI) is a thriving research field with many successful applications in areas such as computer vision and speech recognition. Machine learning methods, such as artificial neural networks (ANN), play a central role in modern AI technology. While ANN also holds great promise for human genetic research, the high-dimensional genetic data and complex genetic structure bring tremendous challenges. The vast majority of genetic variants on the genome have small or no effects on diseases, and fitting ANN on a large number of variants without considering the underlying genetic structure (e.g., linkage disequilibrium) could bring a serious overfitting issue. Furthermore, while a single disease phenotype is often studied in a classic genetic study, in emerging research fields (e.g., imaging genetics), researchers need to deal with different types of disease phenotypes. To address these challenges, we propose a functional neural networks (FNN) method. FNN uses a series of basis functions to model high-dimensional genetic data and a variety of phenotype data and further builds a multi-layer functional neural network to capture the complex relationships between genetic variants and disease phenotypes. Through simulations, we demonstrate the advantages of FNN for high-dimensional genetic data analysis in terms of robustness and accuracy. The real data applications also showed that FNN attained higher accuracy than the existing methods.
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He J, Li Q, Zhang Q. rvTWAS: identifying gene-trait association using sequences by utilizing transcriptome-directed feature selection. Genetics 2024; 226:iyad204. [PMID: 38001381 DOI: 10.1093/genetics/iyad204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 11/14/2023] [Accepted: 11/16/2023] [Indexed: 11/26/2023] Open
Abstract
Toward the identification of genetic basis of complex traits, transcriptome-wide association study (TWAS) is successful in integrating transcriptome data. However, TWAS is only applicable for common variants, excluding rare variants in exome or whole-genome sequences. This is partly because of the inherent limitation of TWAS protocols that rely on predicting gene expressions. Our previous research has revealed the insight into TWAS: the 2 steps in TWAS, building and applying the expression prediction models, are essentially genetic feature selection and aggregations that do not have to involve predictions. Based on this insight disentangling TWAS, rare variants' inability of predicting expression traits is no longer an obstacle. Herein, we developed "rare variant TWAS," or rvTWAS, that first uses a Bayesian model to conduct expression-directed feature selection and then uses a kernel machine to carry out feature aggregation, forming a model leveraging expressions for association mapping including rare variants. We demonstrated the performance of rvTWAS by thorough simulations and real data analysis in 3 psychiatric disorders, namely schizophrenia, bipolar disorder, and autism spectrum disorder. We confirmed that rvTWAS outperforms existing TWAS protocols and revealed additional genes underlying psychiatric disorders. Particularly, we formed a hypothetical mechanism in which zinc finger genes impact all 3 disorders through transcriptional regulations. rvTWAS will open a door for sequence-based association mappings integrating gene expressions.
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Affiliation(s)
- Jingni He
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary T2N 1N4, Canada
| | - Qing Li
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary T2N 1N4, Canada
| | - Qingrun Zhang
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary T2N 1N4, Canada
- Department of Mathematics and Statistics, University of Calgary, Calgary T2N 1N4, Canada
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary T2N 1N4, Canada
- Arnie Charbonneau Cancer Institute, University of Calgary, Calgary T2N 1N4, Canada
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Türk E, Ayaz A, Yüksek A, Süzek BE. DEVOUR: Deleterious Variants on Uncovered Regions in Whole-Exome Sequencing. PeerJ 2023; 11:e16026. [PMID: 37727687 PMCID: PMC10506587 DOI: 10.7717/peerj.16026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 08/13/2023] [Indexed: 09/21/2023] Open
Abstract
The discovery of low-coverage (i.e. uncovered) regions containing clinically significant variants, especially when they are related to the patient's clinical phenotype, is critical for whole-exome sequencing (WES) based clinical diagnosis. Therefore, it is essential to develop tools to identify the existence of clinically important variants in low-coverage regions. Here, we introduce a desktop application, namely DEVOUR (DEleterious Variants On Uncovered Regions), that analyzes read alignments for WES experiments, identifies genomic regions with no or low-coverage (read depth < 5) and then annotates known variants in the low-coverage regions using clinical variant annotation databases. As a proof of concept, DEVOUR was used to analyze a total of 28 samples from a publicly available Hirschsprung disease-related WES project (NCBI Bioproject: https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJEB19327), revealing the potential existence of 98 disease-associated variants in low-coverage regions. DEVOUR is available from https://github.com/projectDevour/DEVOUR under the MIT license.
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Affiliation(s)
- Erdem Türk
- Department of Computer Engineering, Muğla Sıtkı Koçman University, Muğla, Turkey
- Bioinformatics Graduate Program, Muğla Sıtkı Koçman University, Muğla, Turkey
| | - Akif Ayaz
- Department of Medical Genetics, School of Medicine, İstanbul Medipol University, İstanbul, Turkey
| | - Ayhan Yüksek
- Department of Computer Engineering, Muğla Sıtkı Koçman University, Muğla, Turkey
| | - Barış E. Süzek
- Department of Computer Engineering, Muğla Sıtkı Koçman University, Muğla, Turkey
- Bioinformatics Graduate Program, Muğla Sıtkı Koçman University, Muğla, Turkey
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Wills C, Watts K, Maughan TS, Fisher D, Al‐Tassan NA, Houlston RS, Escott‐Price V, Cheadle JP. Germline variation in RASAL2 may predict survival in patients with RAS-activated colorectal cancer. Genes Chromosomes Cancer 2023; 62:332-341. [PMID: 36790221 PMCID: PMC10805173 DOI: 10.1002/gcc.23133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 01/23/2023] [Accepted: 02/13/2023] [Indexed: 02/16/2023] Open
Abstract
BACKGROUND Therapeutic agents that specifically target patients with RAS mutant colorectal cancer (CRC) are needed. We sought potential drug targets by relating genome-wide association study and survival data in patients with advanced CRC profiled for mitogen-activated protein kinase (MAPK) pathway mutations. METHODS In total, 694 patients from the clinical trials COIN and COIN-B had MAPK-activated CRCs (assigned as KRAS, NRAS, or BRAF mutant). Genome-wide single nucleotide polymorphism (SNP), gene, and gene-set analyses were performed to identify determinants of survival. For rs12028023 in RAS protein activator-like 2 (RASAL2), we studied its effect by MAPK pathway activation status (by comparing to 760 patients without MAPK-activated CRCs), MAPK gene mutation status, surface area of the primary tumor (as a marker of proliferation), and expression on RASAL2. RESULTS In MAGMA genome-wide analyses, RASAL2 was the most significant gene associated with survival (p = 2.0 × 10-5 ). Patients carrying the minor (A) allele in the lead SNP, rs12028023 in intron 1 of RASAL2, had a median increase in survival of 167 days as compared with patients carrying the major allele. rs12028023 was predictive for survival by MAPK-activation status (pZ-test = 2.1 × 10-3 ). Furthermore, rs12028023 improved survival in patients with RAS mutant (hazard ratio [HR] = 0.62, 95% confidence intervals [CI] = 0.5-0.8, p = 3.4 × 10-5 ) but not BRAF mutant (p = 0.87) CRCs. The rs12028023 A-allele was associated with reduced surface area of the primary tumor (Beta = -0.037, standard error [SE] = 0.017, p = 3.2 × 10-2 ) and reduced RASAL2 expression in cultured fibroblasts (p = 1.6 × 10-11 ). CONCLUSION Our data demonstrate a prognostic role for RASAL2 in patients with MAPK-activated CRCs, with potential as a therapeutic target.
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Affiliation(s)
- Christopher Wills
- Division of Cancer and Genetics, School of MedicineCardiff UniversityCardiffUK
| | - Katie Watts
- Division of Cancer and Genetics, School of MedicineCardiff UniversityCardiffUK
| | - Timothy S. Maughan
- CRUK/MRC Oxford Institute for Radiation Oncology, University of OxfordOxfordUK
| | - David Fisher
- MRC Clinical Trials UnitUniversity College of LondonLondonUK
| | - Nada A. Al‐Tassan
- Department of GeneticsKing Faisal Specialist Hospital and Research CenterRiyadhSaudi Arabia
| | - Richard S. Houlston
- Division of Genetics and EpidemiologyThe Institute of Cancer ResearchLondonUK
| | - Valentina Escott‐Price
- Institute of Psychological Medicine and Clinical Neurosciences, School of MedicineCardiff UniversityCardiffUK
| | - Jeremy P. Cheadle
- Division of Cancer and Genetics, School of MedicineCardiff UniversityCardiffUK
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8
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He D, Pan C, Zhao Y, Wei W, Qin X, Cai Q, Shi S, Chu X, Zhang N, Jia Y, Wen Y, Cheng B, Liu H, Feng R, Zhang F, Xu P. Exome-wide screening identifies novel rare risk variants for bone mineral density. Osteoporos Int 2023; 34:965-975. [PMID: 36849660 DOI: 10.1007/s00198-023-06710-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Accepted: 02/14/2023] [Indexed: 03/01/2023]
Abstract
UNLABELLED Bone mineral density (BMD) is an independent risk factor of osteoporosis-related fractures. We performed gene-based burden tests to assess the association between rare variants and BMD, and identified several BMD candidate genes. PURPOSE BMD is highly heritable and a major predictor of osteoporotic fractures, but its genetic basis remains unclear. We aimed to identify rare risk variants contributing to BMD. METHODS Utilizing the newly released UK Biobank 200,643 exome dataset, we conducted a gene-based exome-wide association study in males and females, respectively. First, 100,639 males and 117,338 females with BMD values were included in the polygenic risk scores (PRS) analysis. Among individuals with lower 30% PRS, cases were individuals with top 10% BMD, and individuals with bottom 10% BMD were the controls. Considering the effects of vitamin D (VD), individuals with the highest 30% VD concentration were selected for VD-BMD analysis. After quality control, 741 males and 697 females were included in the BMD analysis, and 717 males and 708 females were included in the VD-BMD analysis. The variants were annotated by ANNOVAR software, then BMD and VD-BMD qualified variants were imported into the SKAT R-package to perform gene-based burden tests, respectively. RESULTS The gene-based burden test of the exonic variants identified genome-wide candidate associations in ANKRD18A (P = 1.60 × 10-5, PBonferroni adjust = 2.11 × 10-3), C22orf31 (P = 3.49 × 10-4, PBonferroni adjust = 3.17 × 10-2), and SPATC1L (P = 1.09 × 10-5, PBonferroni adjust = 8.80 × 10-3). For VD-BMD analysis, three genes were associated with BMD, such as NIPAL1 (P = 1.06 × 10-3, PBonferroni adjust = 3.91 × 10-2). CONCLUSIONS Our study suggested that rare variants contribute to BMD, providing new sights for broadening the genetic structure of BMD.
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Affiliation(s)
- D He
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - C Pan
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - Y Zhao
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - W Wei
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - X Qin
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - Q Cai
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - S Shi
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - X Chu
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - N Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - Y Jia
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - Y Wen
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - B Cheng
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - H Liu
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - R Feng
- Department of Joint Surgery, HongHui Hospital, Xi'an Jiaotong University, Xi'an, China
| | - F Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China.
- Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China.
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China.
- School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China.
| | - P Xu
- Department of Joint Surgery, HongHui Hospital, Xi'an Jiaotong University, Xi'an, China.
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Wadapurkar RM, Sivaram A, Vyas R. Computational studies reveal co-occurrence of two mutations in IL7R gene of high-grade serous carcinoma patients. J Biomol Struct Dyn 2022; 40:13310-13324. [PMID: 34657565 DOI: 10.1080/07391102.2021.1987326] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Major cause of mortality in ovarian cancer can be attributed to a lack of specific and sensitive biomarkers for diagnosis and prognosis of the disease. Uncovering the mutations in genes involved in crucial oncogenic pathways is a key step in discovery and development of novel biomarkers. Whole exome sequencing (WES) is a powerful method for the detection of cancer driver mutations. The present work focuses on identifying functionally damaging mutations in patients with high-grade serous ovarian carcinoma (HGSC) through computational analysis of WES. In this study, WES data of HGSC patients was retrieved from the genomic literature available in sequence read archive, the variants were identified and comprehensive structural and functional analysis was performed. Interestingly, I66T and V138I mutations were found to be co-occurring in the IL7R gene in four out of five HGSC patient samples investigated in this study. The V138I mutation was located in the fibronectin type-3 domain and computationally assessed to be causing disruptive effects on the structure and dynamics of IL7R protein. This mutation was found to be co-occurring with the neutral I66T mutation in the same domain which compensated the disruptive effects of V138I variant. These comprehensive studies point to a hitherto unexplored significant role of the IL7R gene in ovarian carcinoma. It is envisaged that the work will lay the foundation for the development of a novel biomarker with potential application in molecular profiling and in estimation of the disease prognosis.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Rucha M Wadapurkar
- MIT School of Bioengineering Sciences & Research, MIT-ADT University, Pune, Maharashtra, India
| | - Aruna Sivaram
- MIT School of Bioengineering Sciences & Research, MIT-ADT University, Pune, Maharashtra, India
| | - Renu Vyas
- MIT School of Bioengineering Sciences & Research, MIT-ADT University, Pune, Maharashtra, India
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Li Z, Li X, Zhou H, Gaynor SM, Selvaraj MS, Arapoglou T, Quick C, Liu Y, Chen H, Sun R, Dey R, Arnett DK, Auer PL, Bielak LF, Bis JC, Blackwell TW, Blangero J, Boerwinkle E, Bowden DW, Brody JA, Cade BE, Conomos MP, Correa A, Cupples LA, Curran JE, de Vries PS, Duggirala R, Franceschini N, Freedman BI, Göring HHH, Guo X, Kalyani RR, Kooperberg C, Kral BG, Lange LA, Lin BM, Manichaikul A, Manning AK, Martin LW, Mathias RA, Meigs JB, Mitchell BD, Montasser ME, Morrison AC, Naseri T, O'Connell JR, Palmer ND, Peyser PA, Psaty BM, Raffield LM, Redline S, Reiner AP, Reupena MS, Rice KM, Rich SS, Smith JA, Taylor KD, Taub MA, Vasan RS, Weeks DE, Wilson JG, Yanek LR, Zhao W, Rotter JI, Willer CJ, Natarajan P, Peloso GM, Lin X. A framework for detecting noncoding rare-variant associations of large-scale whole-genome sequencing studies. Nat Methods 2022; 19:1599-1611. [PMID: 36303018 PMCID: PMC10008172 DOI: 10.1038/s41592-022-01640-x] [Citation(s) in RCA: 54] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Accepted: 09/06/2022] [Indexed: 02/07/2023]
Abstract
Large-scale whole-genome sequencing studies have enabled analysis of noncoding rare-variant (RV) associations with complex human diseases and traits. Variant-set analysis is a powerful approach to study RV association. However, existing methods have limited ability in analyzing the noncoding genome. We propose a computationally efficient and robust noncoding RV association detection framework, STAARpipeline, to automatically annotate a whole-genome sequencing study and perform flexible noncoding RV association analysis, including gene-centric analysis and fixed window-based and dynamic window-based non-gene-centric analysis by incorporating variant functional annotations. In gene-centric analysis, STAARpipeline uses STAAR to group noncoding variants based on functional categories of genes and incorporate multiple functional annotations. In non-gene-centric analysis, STAARpipeline uses SCANG-STAAR to incorporate dynamic window sizes and multiple functional annotations. We apply STAARpipeline to identify noncoding RV sets associated with four lipid traits in 21,015 discovery samples from the Trans-Omics for Precision Medicine (TOPMed) program and replicate several of them in an additional 9,123 TOPMed samples. We also analyze five non-lipid TOPMed traits.
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Grants
- R01 DK078616 NIDDK NIH HHS
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- HHSN268201800001I U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (NHLBI)
- 75N92020D00002 NHLBI NIH HHS
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- T32 HL007374 NHLBI NIH HHS
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Affiliation(s)
- Zilin Li
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN, USA.
| | - Xihao Li
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Hufeng Zhou
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Sheila M Gaynor
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Margaret Sunitha Selvaraj
- Center for Genomic Medicine and Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Theodore Arapoglou
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Corbin Quick
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Yaowu Liu
- School of Statistics, Southwestern University of Finance and Economics, Chengdu, China
| | - Han Chen
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Ryan Sun
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Rounak Dey
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Donna K Arnett
- Dean's Office, University of Kentucky, College of Public Health, Lexington, KY, USA
| | - Paul L Auer
- Division of Biostatistics, Institute for Health & Equity and Cancer Center, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Lawrence F Bielak
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Joshua C Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Thomas W Blackwell
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - John Blangero
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, School of Medicine, The University of Texas Rio Grande Valley, Brownsville, TX, USA
| | - Eric Boerwinkle
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Donald W Bowden
- Department of Biochemistry, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Jennifer A Brody
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Brian E Cade
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
- Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA
| | - Matthew P Conomos
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Adolfo Correa
- Jackson Heart Study, Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | - L Adrienne Cupples
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Framingham Heart Study, National Heart, Lung, and Blood Institute and Boston University, Framingham, MA, USA
| | - Joanne E Curran
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, School of Medicine, The University of Texas Rio Grande Valley, Brownsville, TX, USA
| | - Paul S de Vries
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Ravindranath Duggirala
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, School of Medicine, The University of Texas Rio Grande Valley, Brownsville, TX, USA
| | - Nora Franceschini
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - Barry I Freedman
- Department of Internal Medicine, Nephrology, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Harald H H Göring
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, School of Medicine, The University of Texas Rio Grande Valley, Brownsville, TX, USA
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Rita R Kalyani
- GeneSTAR Research Program, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Brian G Kral
- GeneSTAR Research Program, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Leslie A Lange
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Bridget M Lin
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA
| | - Ani Manichaikul
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Alisa K Manning
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Metabolism Program, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Clinical and Translational Epidemiology Unit, Mongan Institute, Massachusetts General Hospital, Boston, MA, USA
| | - Lisa W Martin
- Division in Cardiology, George Washington School of Medicine and Health Sciences, Washington, DC, USA
| | - Rasika A Mathias
- GeneSTAR Research Program, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - James B Meigs
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Braxton D Mitchell
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
- Geriatrics Research and Education Clinical Center, Baltimore VA Medical Center, Baltimore, MD, USA
| | - May E Montasser
- Division of Endocrinology, Diabetes, and Nutrition, Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Alanna C Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Take Naseri
- Ministry of Health, Government of Samoa, Apia, Samoa
| | - Jeffrey R O'Connell
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Nicholette D Palmer
- Department of Biochemistry, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Patricia A Peyser
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Departments of Health Systems and Population Health, University of Washington, Seattle, WA, USA
| | - Laura M Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Susan Redline
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
- Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA
- Division of Pulmonary, Critical Care, and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Alexander P Reiner
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | | | - Kenneth M Rice
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Jennifer A Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Kent D Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Margaret A Taub
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Ramachandran S Vasan
- Framingham Heart Study, National Heart, Lung, and Blood Institute and Boston University, Framingham, MA, USA
- Department of Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Daniel E Weeks
- Department of Human Genetics and Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA
| | - James G Wilson
- Division of Cardiology, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Lisa R Yanek
- GeneSTAR Research Program, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Wei Zhao
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Cristen J Willer
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Pradeep Natarajan
- Center for Genomic Medicine and Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Gina M Peloso
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Framingham Heart Study, National Heart, Lung, and Blood Institute and Boston University, Framingham, MA, USA
| | - Xihong Lin
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Department of Statistics, Harvard University, Cambridge, MA, USA.
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Yang Z, Chen H, Lu Y, Gao Y, Sun H, Wang J, Jin L, Chu J, Xu S. Genetic evidence of tri-genealogy hypothesis on the origin of ethnic minorities in Yunnan. BMC Biol 2022; 20:166. [PMID: 35864541 PMCID: PMC9306206 DOI: 10.1186/s12915-022-01367-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 07/05/2022] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Yunnan is located in Southwest China and consists of great cultural, linguistic, and genetic diversity. However, the genomic diversity of ethnic minorities in Yunnan is largely under-investigated. To gain insights into population history and local adaptation of Yunnan minorities, we analyzed 242 whole-exome sequencing data with high coverage (~ 100-150 ×) of Yunnan minorities representing Achang, Jingpo, Dai, and Deang, who were linguistically assumed to be derived from three ancient lineages (the tri-genealogy hypothesis), i.e., Di-Qiang, Bai-Yue, and Bai-Pu. RESULTS Yunnan minorities show considerable genetic differences. Di-Qiang populations likely migrated from the Tibetan area about 6700 years ago. Genetic divergence between Bai-Yue and Di-Qiang was estimated to be 7000 years, and that between Bai-Yue and Bai-Pu was estimated to be 5500 years. Bai-Pu is relatively isolated, but gene flow from surrounding Di-Qiang and Bai-Yue populations was also found. Furthermore, we identified genetic variants that are differentiated within Yunnan minorities possibly due to the living circumstances and habits. Notably, we found that adaptive variants related to malaria and glucose metabolism suggest the adaptation to thalassemia and G6PD deficiency resulting from malaria resistance in the Dai population. CONCLUSIONS We provided genetic evidence of the tri-genealogy hypothesis as well as new insights into the genetic history and local adaptation of the Yunnan minorities.
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Affiliation(s)
- Zhaoqing Yang
- Department of Medical Genetics, Institute of Medical Biology, Chinese Academy of Medical Sciences, Kunming, 650118, China
| | - Hao Chen
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Yan Lu
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, Center for Evolutionary Biology, School of Life Sciences, Fudan University, Shanghai, 200438, China
| | - Yang Gao
- Human Phenome Institute, Zhangjiang Fudan International Innovation Center, and Ministry of Education Key Laboratory of Contemporary Anthropology, Fudan University, Shanghai, 201203, China
| | - Hao Sun
- Department of Medical Genetics, Institute of Medical Biology, Chinese Academy of Medical Sciences, Kunming, 650118, China
| | - Jiucun Wang
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, Center for Evolutionary Biology, School of Life Sciences, Fudan University, Shanghai, 200438, China
- Human Phenome Institute, Zhangjiang Fudan International Innovation Center, and Ministry of Education Key Laboratory of Contemporary Anthropology, Fudan University, Shanghai, 201203, China
| | - Li Jin
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, Center for Evolutionary Biology, School of Life Sciences, Fudan University, Shanghai, 200438, China
- Human Phenome Institute, Zhangjiang Fudan International Innovation Center, and Ministry of Education Key Laboratory of Contemporary Anthropology, Fudan University, Shanghai, 201203, China
| | - Jiayou Chu
- Department of Medical Genetics, Institute of Medical Biology, Chinese Academy of Medical Sciences, Kunming, 650118, China.
| | - Shuhua Xu
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, Center for Evolutionary Biology, School of Life Sciences, Fudan University, Shanghai, 200438, China.
- Human Phenome Institute, Zhangjiang Fudan International Innovation Center, and Ministry of Education Key Laboratory of Contemporary Anthropology, Fudan University, Shanghai, 201203, China.
- Department of Liver Surgery and Transplantation Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650223, China.
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12
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Dumont M, Weber-Lassalle N, Joly-Beauparlant C, Ernst C, Droit A, Feng BJ, Dubois S, Collin-Deschesnes AC, Soucy P, Vallée M, Fournier F, Lemaçon A, Adank MA, Allen J, Altmüller J, Arnold N, Ausems MGEM, Berutti R, Bolla MK, Bull S, Carvalho S, Cornelissen S, Dufault MR, Dunning AM, Engel C, Gehrig A, Geurts-Giele WRR, Gieger C, Green J, Hackmann K, Helmy M, Hentschel J, Hogervorst FBL, Hollestelle A, Hooning MJ, Horváth J, Ikram MA, Kaulfuß S, Keeman R, Kuang D, Luccarini C, Maier W, Martens JWM, Niederacher D, Nürnberg P, Ott CE, Peters A, Pharoah PDP, Ramirez A, Ramser J, Riedel-Heller S, Schmidt G, Shah M, Scherer M, Stäbler A, Strom TM, Sutter C, Thiele H, van Asperen CJ, van der Kolk L, van der Luijt RB, Volk AE, Wagner M, Waisfisz Q, Wang Q, Wang-Gohrke S, Weber BHF, Devilee P, Tavtigian S, Bader GD, Meindl A, Goldgar DE, Andrulis IL, Schmutzler RK, Easton DF, Schmidt MK, Hahnen E, Simard J. Uncovering the Contribution of Moderate-Penetrance Susceptibility Genes to Breast Cancer by Whole-Exome Sequencing and Targeted Enrichment Sequencing of Candidate Genes in Women of European Ancestry. Cancers (Basel) 2022; 14:cancers14143363. [PMID: 35884425 PMCID: PMC9317824 DOI: 10.3390/cancers14143363] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 06/29/2022] [Accepted: 07/01/2022] [Indexed: 01/27/2023] Open
Abstract
Rare variants in at least 10 genes, including BRCA1, BRCA2, PALB2, ATM, and CHEK2, are associated with increased risk of breast cancer; however, these variants, in combination with common variants identified through genome-wide association studies, explain only a fraction of the familial aggregation of the disease. To identify further susceptibility genes, we performed a two-stage whole-exome sequencing study. In the discovery stage, samples from 1528 breast cancer cases enriched for breast cancer susceptibility and 3733 geographically matched unaffected controls were sequenced. Using five different filtering and gene prioritization strategies, 198 genes were selected for further validation. These genes, and a panel of 32 known or suspected breast cancer susceptibility genes, were assessed in a validation set of 6211 cases and 6019 controls for their association with risk of breast cancer overall, and by estrogen receptor (ER) disease subtypes, using gene burden tests applied to loss-of-function and rare missense variants. Twenty genes showed nominal evidence of association (p-value < 0.05) with either overall or subtype-specific breast cancer. Our study had the statistical power to detect susceptibility genes with effect sizes similar to ATM, CHEK2, and PALB2, however, it was underpowered to identify genes in which susceptibility variants are rarer or confer smaller effect sizes. Larger sample sizes would be required in order to identify such genes.
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Affiliation(s)
- Martine Dumont
- Genomics Center, CHU de Québec-Université Laval Research Center, 2705 Laurier Boulevard, Quebec City, QC GIV 4G2, Canada; (M.D.); (C.J.-B.); (A.D.); (S.D.); (A.-C.C.-D.); (P.S.); (M.V.); (F.F.); (A.L.)
| | - Nana Weber-Lassalle
- Center for Familial Breast and Ovarian Cancer, Center for Integrated Oncology (CIO), Faculty of Medicine and University Hospital Cologne, University of Cologne, 50937 Cologne, Germany; (N.W.-L.); (C.E.); (R.K.S.); (E.H.)
| | - Charles Joly-Beauparlant
- Genomics Center, CHU de Québec-Université Laval Research Center, 2705 Laurier Boulevard, Quebec City, QC GIV 4G2, Canada; (M.D.); (C.J.-B.); (A.D.); (S.D.); (A.-C.C.-D.); (P.S.); (M.V.); (F.F.); (A.L.)
| | - Corinna Ernst
- Center for Familial Breast and Ovarian Cancer, Center for Integrated Oncology (CIO), Faculty of Medicine and University Hospital Cologne, University of Cologne, 50937 Cologne, Germany; (N.W.-L.); (C.E.); (R.K.S.); (E.H.)
| | - Arnaud Droit
- Genomics Center, CHU de Québec-Université Laval Research Center, 2705 Laurier Boulevard, Quebec City, QC GIV 4G2, Canada; (M.D.); (C.J.-B.); (A.D.); (S.D.); (A.-C.C.-D.); (P.S.); (M.V.); (F.F.); (A.L.)
| | - Bing-Jian Feng
- Department of Dermatology, University of Utah, Salt Lake City, UT 84103, USA; (B.-J.F.); (D.E.G.)
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA;
| | - Stéphane Dubois
- Genomics Center, CHU de Québec-Université Laval Research Center, 2705 Laurier Boulevard, Quebec City, QC GIV 4G2, Canada; (M.D.); (C.J.-B.); (A.D.); (S.D.); (A.-C.C.-D.); (P.S.); (M.V.); (F.F.); (A.L.)
| | - Annie-Claude Collin-Deschesnes
- Genomics Center, CHU de Québec-Université Laval Research Center, 2705 Laurier Boulevard, Quebec City, QC GIV 4G2, Canada; (M.D.); (C.J.-B.); (A.D.); (S.D.); (A.-C.C.-D.); (P.S.); (M.V.); (F.F.); (A.L.)
| | - Penny Soucy
- Genomics Center, CHU de Québec-Université Laval Research Center, 2705 Laurier Boulevard, Quebec City, QC GIV 4G2, Canada; (M.D.); (C.J.-B.); (A.D.); (S.D.); (A.-C.C.-D.); (P.S.); (M.V.); (F.F.); (A.L.)
| | - Maxime Vallée
- Genomics Center, CHU de Québec-Université Laval Research Center, 2705 Laurier Boulevard, Quebec City, QC GIV 4G2, Canada; (M.D.); (C.J.-B.); (A.D.); (S.D.); (A.-C.C.-D.); (P.S.); (M.V.); (F.F.); (A.L.)
| | - Frédéric Fournier
- Genomics Center, CHU de Québec-Université Laval Research Center, 2705 Laurier Boulevard, Quebec City, QC GIV 4G2, Canada; (M.D.); (C.J.-B.); (A.D.); (S.D.); (A.-C.C.-D.); (P.S.); (M.V.); (F.F.); (A.L.)
| | - Audrey Lemaçon
- Genomics Center, CHU de Québec-Université Laval Research Center, 2705 Laurier Boulevard, Quebec City, QC GIV 4G2, Canada; (M.D.); (C.J.-B.); (A.D.); (S.D.); (A.-C.C.-D.); (P.S.); (M.V.); (F.F.); (A.L.)
| | - Muriel A. Adank
- Family Cancer Clinic, The Netherlands Cancer Institute—Antoni van Leeuwenhoek Hospital, 1066 Amsterdam, The Netherlands; (M.A.A.); (F.B.L.H.); (L.v.d.K.)
| | - Jamie Allen
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK; (J.A.); (M.K.B.); (S.C.); (P.D.P.P.); (Q.W.); (D.F.E.)
| | - Janine Altmüller
- Cologne Center for Genomics (CCG), Faculty of Medicine, University Hospital Cologne, University of Cologne, 50931 Cologne, Germany; (J.A.); (H.T.)
| | - Norbert Arnold
- Institute of Clinical Molecular Biology, Department of Gynaecology and Obstetrics, University Hospital of Schleswig-Holstein, Campus Kiel, Christian-Albrechts University Kiel, 24105 Kiel, Germany;
| | - Margreet G. E. M. Ausems
- Division Laboratories, Pharmacy and Biomedical Genetics, Department of Genetics, University Medical Center Utrecht, 3584 Utrecht, The Netherlands;
| | - Riccardo Berutti
- Institute of Human Genetics, Technische Universität München, 81675 Munich, Germany; (R.B.); (T.M.S.)
| | - Manjeet K. Bolla
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK; (J.A.); (M.K.B.); (S.C.); (P.D.P.P.); (Q.W.); (D.F.E.)
| | - Shelley Bull
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON M5G 1X5, Canada; (S.B.); (J.G.); (G.D.B.); (I.L.A.)
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5T 3M7, Canada
| | - Sara Carvalho
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK; (J.A.); (M.K.B.); (S.C.); (P.D.P.P.); (Q.W.); (D.F.E.)
| | - Sten Cornelissen
- Division of Molecular Pathology, The Netherlands Cancer Institute—Antoni van Leeuwenhoek Hospital, 1066 Amsterdam, The Netherlands; (S.C.); (R.K.); (M.K.S.)
| | - Michael R. Dufault
- Precision Medicine and Computational Biology, Sanofi Genzyme, Cambridge, MA 02142, USA;
| | - Alison M. Dunning
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge CB1 8RN, UK; (A.M.D.); (C.L.); (M.S.)
| | - Christoph Engel
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, 04107 Leipzig, Germany;
| | - Andrea Gehrig
- Centre of Familial Breast and Ovarian Cancer, Department of Medical Genetics, Institute of Human Genetics, University of Würzburg, 97074 Würzburg, Germany;
| | | | - Christian Gieger
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Neuherberg, Germany; (C.G.); (A.P.)
- Research Unit Molecular Epidemiology, Helmholtz Zentrum München, German Research Centre for Environmental Health, 85764 Neuherberg, Germany
| | - Jessica Green
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON M5G 1X5, Canada; (S.B.); (J.G.); (G.D.B.); (I.L.A.)
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada;
| | - Karl Hackmann
- Institute for Clinical Genetics, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, 01307 Dresden, Germany;
| | - Mohamed Helmy
- The Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada;
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), Singapore 138632, Singapore
- Department of Computer Science, Lakehead University, Thunder Bay, ON P7B 5E1, Canada
| | - Julia Hentschel
- Institute of Human Genetics, University Leipzig, 04103 Leipzig, Germany;
| | - Frans B. L. Hogervorst
- Family Cancer Clinic, The Netherlands Cancer Institute—Antoni van Leeuwenhoek Hospital, 1066 Amsterdam, The Netherlands; (M.A.A.); (F.B.L.H.); (L.v.d.K.)
| | - Antoinette Hollestelle
- Department of Medical Oncology, Erasmus MC Cancer Institute, 3015 Rotterdam, The Netherlands; (A.H.); (M.J.H.); (J.W.M.M.)
| | - Maartje J. Hooning
- Department of Medical Oncology, Erasmus MC Cancer Institute, 3015 Rotterdam, The Netherlands; (A.H.); (M.J.H.); (J.W.M.M.)
| | - Judit Horváth
- Institute of Human Genetics, University of Münster, 48149 Münster, Germany;
| | - M. Arfan Ikram
- Department of Epidemiology, Erasmus MC University Medical Center, 3015 Rotterdam, The Netherlands;
| | - Silke Kaulfuß
- Institute of Human Genetics, University Medical Center Göttingen, 37075 Göttingen, Germany;
| | - Renske Keeman
- Division of Molecular Pathology, The Netherlands Cancer Institute—Antoni van Leeuwenhoek Hospital, 1066 Amsterdam, The Netherlands; (S.C.); (R.K.); (M.K.S.)
| | - Da Kuang
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada;
- The Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada;
| | - Craig Luccarini
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge CB1 8RN, UK; (A.M.D.); (C.L.); (M.S.)
| | - Wolfgang Maier
- German Center for Neurodegenerative Diseases (DZNE), Department of Neurodegenerative Diseases and Geriatric Psychiatry, Medical Faculty, University Hospital Bonn, 53127 Bonn, Germany;
| | - John W. M. Martens
- Department of Medical Oncology, Erasmus MC Cancer Institute, 3015 Rotterdam, The Netherlands; (A.H.); (M.J.H.); (J.W.M.M.)
| | - Dieter Niederacher
- Department of Gynecology and Obstetrics, University Hospital Düsseldorf, Heinrich-Heine University Düsseldorf, 40225 Düsseldorf, Germany;
| | - Peter Nürnberg
- Center for Molecular Medicine Cologne (CMMC), Cologne Center for Genomics (CCG), Faculty of Medicine, University Hospital Cologne, University of Cologne, 50931 Cologne, Germany;
| | - Claus-Eric Ott
- Institute of Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, 13353 Berlin, Germany;
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Neuherberg, Germany; (C.G.); (A.P.)
- Department of Epidemiology, Institute for Medical Information Processing, Biometry and Epidemiology, Medical Faculty, Ludwig-Maximilians-Universität München, 80539 Munich, Germany
| | - Paul D. P. Pharoah
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK; (J.A.); (M.K.B.); (S.C.); (P.D.P.P.); (Q.W.); (D.F.E.)
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge CB1 8RN, UK; (A.M.D.); (C.L.); (M.S.)
| | - Alfredo Ramirez
- Division for Neurogenetics and Molecular Psychiatry, Medical Faculty, University of Cologne, 50937 Cologne, Germany;
| | - Juliane Ramser
- Division of Gynaecology and Obstetrics, Klinikum Rechts der Isar der Technischen Universität München, 81675 Munich, Germany; (J.R.); (A.M.)
| | - Steffi Riedel-Heller
- Institute of Social Medicine, Occupational Health and Public Health, Faculty of Medicine, University of Leipzig, 04103 Leipzig, Germany;
| | - Gunnar Schmidt
- Institute of Human Genetics, Hannover Medical School, 30625 Hannover, Germany;
| | - Mitul Shah
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge CB1 8RN, UK; (A.M.D.); (C.L.); (M.S.)
| | - Martin Scherer
- Department of Primary Medical Care, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany;
| | - Antje Stäbler
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, 72076 Tübingen, Germany;
| | - Tim M. Strom
- Institute of Human Genetics, Technische Universität München, 81675 Munich, Germany; (R.B.); (T.M.S.)
| | - Christian Sutter
- Institute of Human Genetics, University Hospital Heidelberg, 69120 Heidelberg, Germany;
| | - Holger Thiele
- Cologne Center for Genomics (CCG), Faculty of Medicine, University Hospital Cologne, University of Cologne, 50931 Cologne, Germany; (J.A.); (H.T.)
| | - Christi J. van Asperen
- Department of Clinical Genetics, Leiden University Medical Center, 2333 Leiden, The Netherlands; (C.J.v.A.); (R.B.v.d.L.)
| | - Lizet van der Kolk
- Family Cancer Clinic, The Netherlands Cancer Institute—Antoni van Leeuwenhoek Hospital, 1066 Amsterdam, The Netherlands; (M.A.A.); (F.B.L.H.); (L.v.d.K.)
| | - Rob B. van der Luijt
- Department of Clinical Genetics, Leiden University Medical Center, 2333 Leiden, The Netherlands; (C.J.v.A.); (R.B.v.d.L.)
- Department of Medical Genetics, University Medical Center, 3584 Utrecht, The Netherlands
| | - Alexander E. Volk
- Institute of Human Genetics, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany;
| | - Michael Wagner
- Department of Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, 53127 Bonn, Germany;
| | - Quinten Waisfisz
- Department of Human Genetics, Amsterdam UMC, Vrije Universiteit Amsterdam, 1081 Amsterdam, The Netherlands;
| | - Qin Wang
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK; (J.A.); (M.K.B.); (S.C.); (P.D.P.P.); (Q.W.); (D.F.E.)
| | - Shan Wang-Gohrke
- Department of Gynaecology and Obstetrics, University of Ulm, 89081 Ulm, Germany;
| | - Bernhard H. F. Weber
- Institute of Human Genetics, Regensburg University, 93053 Regensburg, Germany;
- Institute of Clinical Human Genetics, University Hospital Regensburg, 93053 Regensburg, Germany
| | | | | | - Peter Devilee
- Department of Pathology, Department of Human Genetics, Leiden University Medical Center, 2333 Leiden, The Netherlands;
| | - Sean Tavtigian
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA;
- Department of Oncological Sciences, University of Utah School of Medicine, Salt Lake City, UT 84132, USA
| | - Gary D. Bader
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON M5G 1X5, Canada; (S.B.); (J.G.); (G.D.B.); (I.L.A.)
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada;
- The Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada;
- Department of Computer Science, University of Toronto, Toronto, ON M5S 3E1, Canada
- Princess Margaret Research Institute, University Health Network, Toronto, ON M5G 0A3, Canada
| | - Alfons Meindl
- Division of Gynaecology and Obstetrics, Klinikum Rechts der Isar der Technischen Universität München, 81675 Munich, Germany; (J.R.); (A.M.)
| | - David E. Goldgar
- Department of Dermatology, University of Utah, Salt Lake City, UT 84103, USA; (B.-J.F.); (D.E.G.)
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA;
| | - Irene L. Andrulis
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON M5G 1X5, Canada; (S.B.); (J.G.); (G.D.B.); (I.L.A.)
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada;
| | - Rita K. Schmutzler
- Center for Familial Breast and Ovarian Cancer, Center for Integrated Oncology (CIO), Faculty of Medicine and University Hospital Cologne, University of Cologne, 50937 Cologne, Germany; (N.W.-L.); (C.E.); (R.K.S.); (E.H.)
| | - Douglas F. Easton
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK; (J.A.); (M.K.B.); (S.C.); (P.D.P.P.); (Q.W.); (D.F.E.)
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge CB1 8RN, UK; (A.M.D.); (C.L.); (M.S.)
| | - Marjanka K. Schmidt
- Division of Molecular Pathology, The Netherlands Cancer Institute—Antoni van Leeuwenhoek Hospital, 1066 Amsterdam, The Netherlands; (S.C.); (R.K.); (M.K.S.)
- Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute—Antoni van Leeuwenhoek Hospital, 1066 Amsterdam, The Netherlands
| | - Eric Hahnen
- Center for Familial Breast and Ovarian Cancer, Center for Integrated Oncology (CIO), Faculty of Medicine and University Hospital Cologne, University of Cologne, 50937 Cologne, Germany; (N.W.-L.); (C.E.); (R.K.S.); (E.H.)
| | - Jacques Simard
- Genomics Center, CHU de Québec-Université Laval Research Center, 2705 Laurier Boulevard, Quebec City, QC GIV 4G2, Canada; (M.D.); (C.J.-B.); (A.D.); (S.D.); (A.-C.C.-D.); (P.S.); (M.V.); (F.F.); (A.L.)
- Department of Molecular Medicine, Faculty of Medicine, Université Laval, Quebec, QC G1V 0A6, Canada
- Correspondence: ; Tel.: +418-654-2264
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Van Der Merwe N, Ramesar R, De Vries J. Whole Exome Sequencing in South Africa: Stakeholder Views on Return of Individual Research Results and Incidental Findings. Front Genet 2022; 13:864822. [PMID: 35754817 PMCID: PMC9216214 DOI: 10.3389/fgene.2022.864822] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 03/30/2022] [Indexed: 11/17/2022] Open
Abstract
The use of whole exome sequencing (WES) in medical research is increasing in South Africa (SA), raising important questions about whether and which individual genetic research results, particularly incidental findings, should be returned to patients. Whilst some commentaries and opinions related to the topic have been published in SA, there is no qualitative data on the views of professional stakeholders on this topic. Seventeen participants including clinicians, genomics researchers, and genetic counsellors (GCs) were recruited from the Western Cape in SA. Semi-structured interviews were conducted, and the transcripts analysed using the framework approach for data analysis. Current roadblocks for the clinical adoption of WES in SA include a lack of standardised guidelines; complexities relating to variant interpretation due to lack of functional studies and underrepresentation of people of African ancestry in the reference genome, population and variant databases; lack of resources and skilled personnel for variant confirmation and follow-up. Suggestions to overcome these barriers include obtaining funding and buy-in from the private and public sectors and medical insurance companies; the generation of a locally relevant reference genome; training of health professionals in the field of genomics and bioinformatics; and multidisciplinary collaboration. Participants emphasised the importance of upscaling the accessibility to and training of GCs, as well as upskilling of clinicians and genetic nurses for return of genetic data in collaboration with GCs and medical geneticists. Future research could focus on exploring the development of stakeholder partnerships for increased access to trained specialists as well as community engagement and education, alongside the development of guidelines for result disclosure.
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Affiliation(s)
- Nicole Van Der Merwe
- UCT/MRC Genomic and Precision Medicine Research Unit, Division of Human Genetics, Institute for Infectious Diseases and Molecular Medicine, Department of Pathology, Faculty of Medicine and Health Sciences, University of Cape Town, Cape Town, South Africa.,Department of Pathology, Faculty of Medicine and Health Sciences, Stellenbosch University, Tygerberg, South Africa
| | - Raj Ramesar
- UCT/MRC Genomic and Precision Medicine Research Unit, Division of Human Genetics, Institute for Infectious Diseases and Molecular Medicine, Department of Pathology, Faculty of Medicine and Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Jantina De Vries
- Department of Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa.,Neuroscience Institute, Faculty of Health Sciences, University of Cape Town, Observatory, South Africa
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Larrivée-Vanier S, Jean-Louis M, Magne F, Bui H, Rouleau GA, Spiegelman D, Samuels ME, Kibar Z, Van Vliet G, Deladoëy J. Whole-Exome Sequencing in Congenital Hypothyroidism Due to Thyroid Dysgenesis. Thyroid 2022; 32:486-495. [PMID: 35272499 PMCID: PMC9145262 DOI: 10.1089/thy.2021.0597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Context: Congenital hypothyroidism due to thyroid dysgenesis (CHTD) is a predominantly sporadic and nonsyndromic (NS) condition of unknown etiology. NS-CHTD shows a 40-fold increase in relative risk among first-degree relatives (1 in 100 compared with a birth prevalence of 1 in 4000 in the general population), but a discordance rate between monozygotic (MZ) twins of 92%. This suggests a two-hit mechanism, combining a genetic predisposition (incomplete penetrance of inherited variants) with postzygotic events (accounting for MZ twin discordance). Objective: To evaluate whether whole-exome sequencing (WES) allows to identify new predisposing genes in NS-CHTD. Methods: We performed a case-control study by comparing the whole exome of 36 nonconsanguineous cases of NS-CHTD (33 with lingual thyroid ectopy and 3 with athyreosis, based on technetium pertechnetate scintigraphy at diagnosis) with that of 301 unaffected controls to assess for enrichment in rare protein-altering variants. We performed an unbiased approach using a gene-based burden with a false discovery rate correction. Moreover, we identified all rare pathogenic and likely pathogenic variants, based on in silico prediction tools, in 27 genes previously associated with congenital hypothyroidism (CH) (thyroid dysgenesis [TD] and dyshormonogenesis). Results: After correction for multiple testing, no enrichment in rare protein-altering variants was observed in NS-CHTD. Pathogenic or likely pathogenic variants (21 variants in 12 CH genes) were identified in 42% of cases. Eight percent of cases had variants in more than one gene (oligogenic group); these were not more severely affected than monogenic cases. Moreover, cases with protein-altering variants in dyshormonogenesis-related genes were not more severely affected than those without. Conclusions: No new predisposing genes were identified following an unbiased analysis of WES data in a well-characterized NS-CHTD cohort. Nonetheless, the discovery rate of rare pathogenic or likely pathogenic variants was 42%. Eight percent of the cases harbored multiple variants in genes associated with TD or dyshormonogenesis, but these variants did not explain the variability of hypothyroidism observed in dysgenesis. WES did not identify a genetic cause in NS-CHTD cases, confirming the complex etiology of this disease. Additional studies in larger cohorts and/or novel discovery approaches are required.
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Affiliation(s)
- Stéphanie Larrivée-Vanier
- Research Center of Centre Hospitalier Universitaire Sainte-Justine, Université de Montréal, Montréal, Canada
- Department of Biochemistry, Université de Montréal, Montréal, Canada
| | - Martineau Jean-Louis
- Research Center of Centre Hospitalier Universitaire Sainte-Justine, Université de Montréal, Montréal, Canada
| | - Fabien Magne
- Research Center of Centre Hospitalier Universitaire Sainte-Justine, Université de Montréal, Montréal, Canada
| | - Helen Bui
- Department of Endocrinology, McGill University Health Center, Montréal, Canada
| | - Guy A. Rouleau
- Montreal Neurological Institute, McGill University, Montréal, Canada
| | - Dan Spiegelman
- Montreal Neurological Institute, McGill University, Montréal, Canada
| | - Mark E. Samuels
- Research Center of Centre Hospitalier Universitaire Sainte-Justine, Université de Montréal, Montréal, Canada
- Department of Medicine, Université de Montréal, Montréal, Canada
| | - Zoha Kibar
- Research Center of Centre Hospitalier Universitaire Sainte-Justine, Université de Montréal, Montréal, Canada
- Department of Neurosciences, Université de Montréal, Montréal, Canada
| | - Guy Van Vliet
- Research Center of Centre Hospitalier Universitaire Sainte-Justine, Université de Montréal, Montréal, Canada
- Department of Pediatrics, Université de Montréal, Montréal, Canada
| | - Johnny Deladoëy
- Research Center of Centre Hospitalier Universitaire Sainte-Justine, Université de Montréal, Montréal, Canada
- Department of Pediatrics, Université de Montréal, Montréal, Canada
- Pediatric Institute of Southern Switzerland, Bellinzona, Switzerland
- Faculty of Biomedical Sciences, University of Southern Switzerland, Lugano, Switzerland
- Address correspondence to: Johnny Deladoëy, MD, PhD, Facoltà di Scienze Biomediche, Università della Svizzera Italiana, Campus Est, Lugano 6900, Switzerland
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15
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Cho MH, Hobbs BD, Silverman EK. Genetics of chronic obstructive pulmonary disease: understanding the pathobiology and heterogeneity of a complex disorder. THE LANCET. RESPIRATORY MEDICINE 2022; 10:485-496. [PMID: 35427534 PMCID: PMC11197974 DOI: 10.1016/s2213-2600(21)00510-5] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 10/20/2021] [Accepted: 11/09/2021] [Indexed: 12/20/2022]
Abstract
Chronic obstructive pulmonary disease (COPD) is a deadly and highly morbid disease. Susceptibility to and heterogeneity of COPD are incompletely explained by environmental factors such as cigarette smoking. Family-based and population-based studies have shown that a substantial proportion of COPD risk is related to genetic variation. Genetic association studies have identified hundreds of genetic variants that affect risk for COPD, decreased lung function, and other COPD-related traits. These genetic variants are associated with other pulmonary and non-pulmonary traits, demonstrate a genetic basis for at least part of COPD heterogeneity, have a substantial effect on COPD risk in aggregate, implicate early-life events in COPD pathogenesis, and often involve genes not previously suspected to have a role in COPD. Additional progress will require larger genetic studies with more ancestral diversity, improved profiling of rare variants, and better statistical methods. Through integration of genetic data with other omics data and comprehensive COPD phenotypes, as well as functional description of causal mechanisms for genetic risk variants, COPD genetics will continue to inform novel approaches to understanding the pathobiology of COPD and developing new strategies for management and treatment.
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Affiliation(s)
- Michael H Cho
- Channing Division of Network Medicine and Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
| | - Brian D Hobbs
- Channing Division of Network Medicine and Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Edwin K Silverman
- Channing Division of Network Medicine and Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
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16
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Current Understanding of the Genetics of Tourette Syndrome. Biomed J 2022; 45:271-279. [PMID: 35042017 PMCID: PMC9250083 DOI: 10.1016/j.bj.2022.01.008] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 01/09/2022] [Accepted: 01/10/2022] [Indexed: 12/13/2022] Open
Abstract
Gilles de la Tourette syndrome (TS) is a common, childhood-onset psychiatric disorder characterized by persistent motor and vocal tics. It is a heterogeneous disorder in which the phenotypic expression may be affected by environmental factors, such as immune responses. Furthermore, several studies have shown that genetic factors play a vital role in the etiology of TS, as well as its comorbidity with other disorders, including attention deficit hyperactivity disorder, obsessive-compulsive disorder, and autism spectrum disorder. TS has a complex inheritance pattern and, according to various genetic studies, several genes and loci have been correlated with TS. Genome-wide linkage studies have identified Slit and Trk-like 1 (SLITRK1) and histidine decarboxylase (HDC) genes, and candidate gene association studies have extensively investigated the dopamine and serotonin system genes, but there have been no consistent results. Moreover, genome-wide association studies have implicated several genetic loci; however, larger study cohorts are needed to confirm this. Copy number variations, which are polymorphisms in the number of gene copies due to chromosomal deletions or duplications, are considered another significant source of mutations in TS. In the last decade, whole genome/exome sequencing has identified several novel genetic mutations in patients with TS. In conclusion, more studies are needed to reveal the exact mechanisms of underlying TS, which may help to provide more information on the prognosis and therapeutic plans for TS.
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17
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Wills C, He Y, Summers MG, Lin Y, Phipps AI, Watts K, Law PJ, Al-Tassan NA, Maughan TS, Kaplan R, Houlston RS, Peters U, Newcomb PA, Chan AT, Buchanan DD, Gallinger S, Marchand LL, Pai RK, Shi Q, Alberts SR, Gray V, West HD, Escott-Price V, Dunlop MG, Cheadle JP. A genome-wide search for determinants of survival in 1926 patients with advanced colorectal cancer with follow-up in over 22,000 patients. Eur J Cancer 2021; 159:247-258. [PMID: 34794066 PMCID: PMC9132154 DOI: 10.1016/j.ejca.2021.09.047] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Revised: 09/22/2021] [Accepted: 09/26/2021] [Indexed: 01/03/2023]
Abstract
BACKGROUND While genome-wide association studies (GWAS) have identified germline variants influencing the risk of developing colorectal cancer (CRC), there has been limited examination of the possible role of inherited variation as a determinant of patient outcome. PATIENTS AND METHODS We performed a GWAS for overall survival (OS) in 1926 patients with advanced CRC from the COIN and COIN-B clinical trials. For single nucleotide polymorphisms (SNPs) showing an association with OS (P < 1.0 × 10-5), we conducted sensitivity analyses based on the time from diagnosis to death and sought independent replications in 5675 patients from the Study of Colorectal Cancer in Scotland (SOCCS) and 16,964 patients from the International Survival Analysis in Colorectal cancer Consortium (ISACC). We analysed the Human Protein Atlas to determine if ERBB4 expression was associated with survival in 438 patients with colon adenocarcinomas. RESULTS The most significant SNP associated with OS was rs79612564 in ERBB4 (hazard ratio [HR] = 1.24, 95% confidence interval [CI] = 1.16-1.32, P = 1.9 × 10-7). SNPs at 17 loci had suggestive associations for OS and all had similar effects on the time from diagnosis to death. No lead SNPs were independently replicated in the meta-analysis of all patients from SOCCS and ISACC. However, rs79612564 was significant in stage-IV patients from SOCCS (P = 2.1 × 10-2) but not ISACC (P = 0.89) and SOCCS combined with COIN and COIN-B attained genome-wide significance (P = 1.7 × 10-8). Patients with high ERBB4 expression in their colon adenocarcinomas had worse survival (HR = 1.50, 95% CI = 1.1-1.9, P = 4.6 × 10-2). CONCLUSIONS Genetic and expression data support a potential role for rs79612564 in the receptor tyrosine kinase ERBB4 as a predictive biomarker of survival.
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Affiliation(s)
- Christopher Wills
- Division of Cancer and Genetics, School of Medicine, Cardiff University, Heath Park, Cardiff, CF14 4XN, UK
| | - Yazhou He
- Colon Cancer Genetics Group, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh EH4 2XU, UK; Department of Oncology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, 610000, China
| | - Matthew G Summers
- Division of Cancer and Genetics, School of Medicine, Cardiff University, Heath Park, Cardiff, CF14 4XN, UK
| | - Yi Lin
- Epidemiology Department, University of Washington, Seattle, WA, USA; Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Amanda I Phipps
- Epidemiology Department, University of Washington, Seattle, WA, USA; Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Katie Watts
- Division of Cancer and Genetics, School of Medicine, Cardiff University, Heath Park, Cardiff, CF14 4XN, UK
| | - Philip J Law
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, SW7 3RP, UK
| | - Nada A Al-Tassan
- Department of Genetics, King Faisal Specialist Hospital and Research Center, P.O. Box 3354, Riyadh 11211, Saudi Arabia
| | - Timothy S Maughan
- CRUK/MRC Oxford Institute for Radiation Oncology, University of Oxford, Roosevelt Drive, Oxford OX3 7DQ, UK
| | - Richard Kaplan
- MRC Clinical Trials Unit, University College of London, 125 Kingsway, London, WC2B 6NH, UK
| | - Richard S Houlston
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, SW7 3RP, UK
| | - Ulrike Peters
- Epidemiology Department, University of Washington, Seattle, WA, USA; Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Polly A Newcomb
- Epidemiology Department, University of Washington, Seattle, WA, USA; Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Andrew T Chan
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Daniel D Buchanan
- Colorectal Oncogenomics Group, Department of Clinical Pathology, The University of Melbourne, Parkville, Victoria, Australia; Centre for Cancer Research, Victorian Comprehensive Cancer Centre, The University of Melbourne, Parkville, Victoria, Australia; Genomic Medicine and Family Cancer Clinic, The Royal Melbourne Hospital, Parkville, Victoria, Australia
| | - Steve Gallinger
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, University of Toronto, Toronto, Ontario, Canada; Ontario Institute for Cancer Research, Toronto, Ontario, Canada; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
| | - Loic L Marchand
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Rish K Pai
- Department of Laboratory Medicine and Pathology, Mayo Clinic Arizona, Scottsdale, AZ, USA
| | - Qian Shi
- Department of Quantitative Science, Mayo Clinic, Rochester, MN, USA
| | | | - Victoria Gray
- Division of Cancer and Genetics, School of Medicine, Cardiff University, Heath Park, Cardiff, CF14 4XN, UK
| | - Hannah D West
- Division of Cancer and Genetics, School of Medicine, Cardiff University, Heath Park, Cardiff, CF14 4XN, UK
| | - Valentina Escott-Price
- Institute of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Hadyn Ellis Building, Maindy Road, Cardiff, CF24 4HQ, UK
| | - Malcolm G Dunlop
- Colon Cancer Genetics Group, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh EH4 2XU, UK
| | - Jeremy P Cheadle
- Division of Cancer and Genetics, School of Medicine, Cardiff University, Heath Park, Cardiff, CF14 4XN, UK.
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18
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Hao M, Pu W, Li Y, Wen S, Sun C, Ma Y, Zheng H, Chen X, Tan J, Zhang G, Zhang M, Xu S, Wang Y, Li H, Wang J, Jin L. The HuaBiao project: whole-exome sequencing of 5000 Han Chinese individuals. J Genet Genomics 2021; 48:1032-1035. [PMID: 34416338 DOI: 10.1016/j.jgg.2021.07.013] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Revised: 07/08/2021] [Accepted: 07/13/2021] [Indexed: 01/23/2023]
Affiliation(s)
- Meng Hao
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, Human Phenome Institute, Fudan University, Shanghai 200433, China
| | - Weilin Pu
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, Human Phenome Institute, Fudan University, Shanghai 200433, China
| | - Yi Li
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, Human Phenome Institute, Institute for Six-sector Economy, Fudan University, Shanghai 200433, China
| | - Shaoqing Wen
- Ministry of Education Key Laboratory of Contemporary Anthropology, Department of Anthropology and Human Genetics, School of Life Sciences, Institute of Archaeological Science, Fudan University, Shanghai 200433, China
| | - Chang Sun
- Ministry of Education Key Laboratory of Contemporary Anthropology, Department of Anthropology and Human Genetics, School of Life Sciences, Fudan University, Shanghai 200433, China
| | - Yanyun Ma
- Ministry of Education Key Laboratory of Contemporary Anthropology, Department of Anthropology and Human Genetics, School of Life Sciences, Institute for Six-sector Economy, Fudan University, Shanghai 200433, China
| | - Hongxiang Zheng
- Ministry of Education Key Laboratory of Contemporary Anthropology, Department of Anthropology and Human Genetics, School of Life Sciences, Fudan University, Shanghai 200433, China
| | - Xingdong Chen
- Ministry of Education Key Laboratory of Contemporary Anthropology, Department of Anthropology and Human Genetics, School of Life Sciences, Fudan University, Shanghai 200433, China; Taizhou Institute of Health and Sciences, Fudan University, Taizhou, Jiangsu 225300, China
| | - Jingze Tan
- Ministry of Education Key Laboratory of Contemporary Anthropology, Department of Anthropology and Human Genetics, School of Life Sciences, Fudan University, Shanghai 200433, China
| | - Guoqing Zhang
- Key Laboratory of Computational Biology, Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Menghan Zhang
- Ministry of Education Key Laboratory of Contemporary Anthropology, Department of Anthropology and Human Genetics, School of Life Sciences, Fudan University, Shanghai 200433, China
| | - Shuhua Xu
- Ministry of Education Key Laboratory of Contemporary Anthropology, Department of Anthropology and Human Genetics, School of Life Sciences, Human Phenome Institute, Fudan University, Shanghai 200433, China
| | - Yi Wang
- Ministry of Education Key Laboratory of Contemporary Anthropology, Department of Anthropology and Human Genetics, School of Life Sciences, Fudan University, Shanghai 200433, China.
| | - Hui Li
- Ministry of Education Key Laboratory of Contemporary Anthropology, Department of Anthropology and Human Genetics, School of Life Sciences, Fudan University, Shanghai 200433, China.
| | - Jiucun Wang
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, Human Phenome Institute, Fudan University, Shanghai 200433, China; Taizhou Institute of Health and Sciences, Fudan University, Taizhou, Jiangsu 225300, China.
| | - Li Jin
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, Human Phenome Institute, Fudan University, Shanghai 200433, China; Taizhou Institute of Health and Sciences, Fudan University, Taizhou, Jiangsu 225300, China.
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19
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Lyra DH, Griffiths CA, Watson A, Joynson R, Molero G, Igna AA, Hassani-Pak K, Reynolds MP, Hall A, Paul MJ. Gene-based mapping of trehalose biosynthetic pathway genes reveals association with source- and sink-related yield traits in a spring wheat panel. Food Energy Secur 2021; 10:e292. [PMID: 34594548 PMCID: PMC8459250 DOI: 10.1002/fes3.292] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Revised: 04/12/2021] [Accepted: 04/12/2021] [Indexed: 12/11/2022] Open
Abstract
Trehalose 6‐phosphate (T6P) signalling regulates carbon use and allocation and is a target to improve crop yields. However, the specific contributions of trehalose phosphate synthase (TPS) and trehalose phosphate phosphatase (TPP) genes to source‐ and sink‐related traits remain largely unknown. We used enrichment capture sequencing on TPS and TPP genes to estimate and partition the genetic variation of yield‐related traits in a spring wheat (Triticum aestivum) breeding panel specifically built to capture the diversity across the 75,000 CIMMYT wheat cultivar collection. Twelve phenotypes were correlated to variation in TPS and TPP genes including plant height and biomass (source), spikelets per spike, spike growth and grain filling traits (sink) which showed indications of both positive and negative gene selection. Individual genes explained proportions of heritability for biomass and grain‐related traits. Three TPS1 homologues were particularly significant for trait variation. Epistatic interactions were found within and between the TPS and TPP gene families for both plant height and grain‐related traits. Gene‐based prediction improved predictive ability for grain weight when gene effects were combined with the whole‐genome markers. Our study has generated a wealth of information on natural variation of TPS and TPP genes related to yield potential which confirms the role for T6P in resource allocation and in affecting traits such as grain number and size confirming other studies which now opens up the possibility of harnessing natural genetic variation more widely to better understand the contribution of native genes to yield traits for incorporation into breeding programmes.
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Affiliation(s)
- Danilo H Lyra
- Computational & Analytical Sciences Rothamsted Research Harpenden UK
| | | | - Amy Watson
- Plant Sciences Rothamsted Research Harpenden UK
| | | | - Gemma Molero
- Global Wheat Program, International Maize and Wheat Improvement Centre (CIMMYT) Texcoco Mexico
| | | | | | - Matthew P Reynolds
- Global Wheat Program, International Maize and Wheat Improvement Centre (CIMMYT) Texcoco Mexico
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20
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DeMichele-Sweet MAA, Klei L, Creese B, Harwood JC, Weamer EA, McClain L, Sims R, Hernandez I, Moreno-Grau S, Tárraga L, Boada M, Alarcón-Martín E, Valero S, Liu Y, Hooli B, Aarsland D, Selbaek G, Bergh S, Rongve A, Saltvedt I, Skjellegrind HK, Engdahl B, Stordal E, Andreassen OA, Djurovic S, Athanasiu L, Seripa D, Borroni B, Albani D, Forloni G, Mecocci P, Serretti A, De Ronchi D, Politis A, Williams J, Mayeux R, Foroud T, Ruiz A, Ballard C, Holmans P, Lopez OL, Kamboh MI, Devlin B, Sweet RA. Genome-wide association identifies the first risk loci for psychosis in Alzheimer disease. Mol Psychiatry 2021; 26:5797-5811. [PMID: 34112972 PMCID: PMC8660923 DOI: 10.1038/s41380-021-01152-8] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Revised: 04/15/2021] [Accepted: 04/29/2021] [Indexed: 11/09/2022]
Abstract
Psychotic symptoms, defined as the occurrence of delusions or hallucinations, are frequent in Alzheimer disease (AD with psychosis, AD + P). AD + P affects ~50% of individuals with AD, identifies a subgroup with poor outcomes, and is associated with a greater degree of cognitive impairment and depressive symptoms, compared to subjects without psychosis (AD - P). Although the estimated heritability of AD + P is 61%, genetic sources of risk are unknown. We report a genome-wide meta-analysis of 12,317 AD subjects, 5445 AD + P. Results showed common genetic variation accounted for a significant portion of heritability. Two loci, one in ENPP6 (rs9994623, O.R. (95%CI) 1.16 (1.10, 1.22), p = 1.26 × 10-8) and one spanning the 3'-UTR of an alternatively spliced transcript of SUMF1 (rs201109606, O.R. 0.65 (0.56-0.76), p = 3.24 × 10-8), had genome-wide significant associations with AD + P. Gene-based analysis identified a significant association with APOE, due to the APOE risk haplotype ε4. AD + P demonstrated negative genetic correlations with cognitive and educational attainment and positive genetic correlation with depressive symptoms. We previously observed a negative genetic correlation with schizophrenia; instead, we now found a stronger negative correlation with the related phenotype of bipolar disorder. Analysis of polygenic risk scores supported this genetic correlation and documented a positive genetic correlation with risk variation for AD, beyond the effect of ε4. We also document a small set of SNPs likely to affect risk for AD + P and AD or schizophrenia. These findings provide the first unbiased identification of the association of psychosis in AD with common genetic variation and provide insights into its genetic architecture.
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Affiliation(s)
| | - Lambertus Klei
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Byron Creese
- University of Exeter Medical School, College of Medicine and Health, Exeter, UK
- Norwegian, Exeter and King's College Consortium for Genetics of Neuropsychiatric Symptoms in Dementia, Exeter, UK
| | - Janet C Harwood
- Division of Psychological Medicine and Clinical Neuroscience, School of Medicine, Cardiff University, Cardiff, UK
| | - Elise A Weamer
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Lora McClain
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Rebecca Sims
- Division of Psychological Medicine and Clinical Neuroscience, School of Medicine, Cardiff University, Cardiff, UK
| | - Isabel Hernandez
- Research Center and Memory Clinic Fundació ACE, Institut Català de Neurociències Aplicades, Universitat Internacional de Catalunya, Barcelona, Spain
- CIBERNED, Network Center for Biomedical Research in Neurodegenerative Diseases, National Institute of Health Carlos III, Madrid, Spain
| | - Sonia Moreno-Grau
- Research Center and Memory Clinic Fundació ACE, Institut Català de Neurociències Aplicades, Universitat Internacional de Catalunya, Barcelona, Spain
- CIBERNED, Network Center for Biomedical Research in Neurodegenerative Diseases, National Institute of Health Carlos III, Madrid, Spain
| | - Lluís Tárraga
- Research Center and Memory Clinic Fundació ACE, Institut Català de Neurociències Aplicades, Universitat Internacional de Catalunya, Barcelona, Spain
- CIBERNED, Network Center for Biomedical Research in Neurodegenerative Diseases, National Institute of Health Carlos III, Madrid, Spain
| | - Mercè Boada
- Research Center and Memory Clinic Fundació ACE, Institut Català de Neurociències Aplicades, Universitat Internacional de Catalunya, Barcelona, Spain
- CIBERNED, Network Center for Biomedical Research in Neurodegenerative Diseases, National Institute of Health Carlos III, Madrid, Spain
| | - Emilio Alarcón-Martín
- Research Center and Memory Clinic Fundació ACE, Institut Català de Neurociències Aplicades, Universitat Internacional de Catalunya, Barcelona, Spain
| | - Sergi Valero
- Research Center and Memory Clinic Fundació ACE, Institut Català de Neurociències Aplicades, Universitat Internacional de Catalunya, Barcelona, Spain
- CIBERNED, Network Center for Biomedical Research in Neurodegenerative Diseases, National Institute of Health Carlos III, Madrid, Spain
| | - Yushi Liu
- Global Statistical Science, Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, IN, USA
| | - Basavaraj Hooli
- Neurodegeneration Research, Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, IN, USA
| | - Dag Aarsland
- Department of Old Age Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London and Centre for Age-Related Medicine, Stavanger University Hospital, Stavanger, Norway
| | - Geir Selbaek
- Norwegian National Advisory Unit in Ageing and Health, Vestfold Hospital Trust, Tønsberg, Norway
- Department Geriatric Medicine, Oslo University Hospital, Oslo, Norway
- Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Sverre Bergh
- Research Centre of Age-related Functional Decline and Disease, Innlandet Hospital Trust, Pb 68, Ottestad, Norway
| | - Arvid Rongve
- Department of Research and Innovation, Helse Fonna, Haugesund and Department of Clinical Medicine (K1), University of Bergen, Bergen, Norway
| | - Ingvild Saltvedt
- Geriatric Department, St. Olav Hospital, University Hospital of Trondheim, Trondheim, Norway
- Department of Neuromedicine and Movement science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Håvard K Skjellegrind
- HUNT Research Centre, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Levanger, Norway
- Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway
| | - Bo Engdahl
- Norwegian Institute of Public Health, Oslo, Norway
| | - Eystein Stordal
- Department of Mental Health, Norwegian University of Science and Technology, Trondheim, Norway
| | - Ole A Andreassen
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, and Oslo University Hospital, Oslo, Norway
| | - Srdjan Djurovic
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
- NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Lavinia Athanasiu
- NORMENT, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Davide Seripa
- Department of Hematology and Stem Cell Transplant, Vito Fazzi Hospital, Lecce, Italy
| | - Barbara Borroni
- Centre for Neurodegenerative Disorders, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Diego Albani
- Neuroscience Department, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Gianluigi Forloni
- Neuroscience Department, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Patrizia Mecocci
- Institute of Gerontology and Geriatrics, Department of Medicine and Surgery, University of Perugia, Perugia, Italy
| | - Alessandro Serretti
- Department of Biomedical and NeuroMotor Sciences, University of Bologna, Bologna, Italy
| | - Diana De Ronchi
- Department of Biomedical and NeuroMotor Sciences, University of Bologna, Bologna, Italy
| | - Antonis Politis
- 1st Department of Psychiatry, Eginition Hospital, Medical School, National & Kapodistrian University of Athens, Athens, Greece
| | - Julie Williams
- Division of Psychological Medicine and Clinical Neuroscience, School of Medicine, Cardiff University, Cardiff, UK
- UK Dementia Research Institute @ Cardiff, School of Medicine, Cardiff University, Cardiff, UK
| | - Richard Mayeux
- Departments of Neurology, Psychiatry and Epidemiology, Columbia University, New York, NY, USA
| | - Tatiana Foroud
- Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Agustin Ruiz
- Research Center and Memory Clinic Fundació ACE, Institut Català de Neurociències Aplicades, Universitat Internacional de Catalunya, Barcelona, Spain
- CIBERNED, Network Center for Biomedical Research in Neurodegenerative Diseases, National Institute of Health Carlos III, Madrid, Spain
| | | | - Peter Holmans
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Oscar L Lopez
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA
| | - M Ilyas Kamboh
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Bernie Devlin
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Robert A Sweet
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA.
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Clifton NE, Collado-Torres L, Burke EE, Pardiñas AF, Harwood JC, Di Florio A, Walters JTR, Owen MJ, O'Donovan MC, Weinberger DR, Holmans PA, Jaffe AE, Hall J. Developmental Profile of Psychiatric Risk Associated With Voltage-Gated Cation Channel Activity. Biol Psychiatry 2021; 90:399-408. [PMID: 33965196 PMCID: PMC8375582 DOI: 10.1016/j.biopsych.2021.03.009] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Revised: 02/26/2021] [Accepted: 03/04/2021] [Indexed: 02/06/2023]
Abstract
BACKGROUND Recent breakthroughs in psychiatric genetics have implicated biological pathways onto which genetic risk for psychiatric disorders converges. However, these studies do not reveal the developmental time point(s) at which these pathways are relevant. METHODS We aimed to determine the relationship between psychiatric risk and developmental gene expression relating to discrete biological pathways. We used postmortem RNA sequencing data (BrainSeq and BrainSpan) from brain tissue at multiple prenatal and postnatal time points, with summary statistics from recent genome-wide association studies of schizophrenia, bipolar disorder, and major depressive disorder. We prioritized gene sets for overall enrichment of association with each disorder and then tested the relationship between the association of their constituent genes with their relative expression at each developmental stage. RESULTS We observed relationships between the expression of genes involved in voltage-gated cation channel activity during early midfetal, adolescence, and early adulthood time points and association with schizophrenia and bipolar disorder, such that genes more strongly associated with these disorders had relatively low expression during early midfetal development and higher expression during adolescence and early adulthood. The relationship with schizophrenia was strongest for the subset of genes related to calcium channel activity, while for bipolar disorder, the relationship was distributed between calcium and potassium channel activity genes. CONCLUSIONS Our results indicate periods during development when biological pathways related to the activity of calcium and potassium channels may be most vulnerable to the effects of genetic variants conferring risk for psychiatric disorders. Furthermore, they indicate key time points and potential targets for disorder-specific therapeutic interventions.
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Affiliation(s)
- Nicholas E Clifton
- Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, United Kingdom; MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, United Kingdom.
| | - Leonardo Collado-Torres
- Lieber Institute for Brain Development, Johns Hopkins University Medical Campus, Baltimore, Maryland; Centre for Computational Biology, Johns Hopkins University Medical Campus, Baltimore, Maryland
| | - Emily E Burke
- Lieber Institute for Brain Development, Johns Hopkins University Medical Campus, Baltimore, Maryland
| | - Antonio F Pardiñas
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, United Kingdom
| | - Janet C Harwood
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, United Kingdom
| | - Arianna Di Florio
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, United Kingdom
| | - James T R Walters
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, United Kingdom
| | - Michael J Owen
- Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, United Kingdom; MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, United Kingdom
| | - Michael C O'Donovan
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, United Kingdom
| | - Daniel R Weinberger
- Lieber Institute for Brain Development, Johns Hopkins University Medical Campus, Baltimore, Maryland; Departments of Psychiatry, Neurology, Neuroscience and Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland
| | - Peter A Holmans
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, United Kingdom
| | - Andrew E Jaffe
- Lieber Institute for Brain Development, Johns Hopkins University Medical Campus, Baltimore, Maryland; Centre for Computational Biology, Johns Hopkins University Medical Campus, Baltimore, Maryland; Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.
| | - Jeremy Hall
- Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, United Kingdom; MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, United Kingdom
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22
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Rare variants regulate expression of nearby individual genes in multiple tissues. PLoS Genet 2021; 17:e1009596. [PMID: 34061836 PMCID: PMC8195400 DOI: 10.1371/journal.pgen.1009596] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 06/11/2021] [Accepted: 05/11/2021] [Indexed: 12/30/2022] Open
Abstract
The rapid decrease in sequencing cost has enabled genetic studies to discover rare variants associated with complex diseases and traits. Once this association is identified, the next step is to understand the genetic mechanism of rare variants on how the variants influence diseases. Similar to the hypothesis of common variants, rare variants may affect diseases by regulating gene expression, and recently, several studies have identified the effects of rare variants on gene expression using heritability and expression outlier analyses. However, identifying individual genes whose expression is regulated by rare variants has been challenging due to the relatively small sample size of expression quantitative trait loci studies and statistical approaches not optimized to detect the effects of rare variants. In this study, we analyze whole-genome sequencing and RNA-seq data of 681 European individuals collected for the Genotype-Tissue Expression (GTEx) project (v8) to identify individual genes in 49 human tissues whose expression is regulated by rare variants. To improve statistical power, we develop an approach based on a likelihood ratio test that combines effects of multiple rare variants in a nonlinear manner and has higher power than previous approaches. Using GTEx data, we identify many genes regulated by rare variants, and some of them are only regulated by rare variants and not by common variants. We also find that genes regulated by rare variants are enriched for expression outliers and disease-causing genes. These results suggest the regulatory effects of rare variants, which would be important in interpreting associations of rare variants with complex traits. It has been shown that rare variants may affect many diseases including both rare and common diseases with the advent of next-generation sequencing technology. An important question is how rare variants affect diseases or traits, especially whether or how they regulate gene expression as they may affect diseases through gene regulation. However, it is challenging to identify the regulatory effects of rare variants because it often requires large sample sizes and the existing statistical approaches are not optimized for it. Here, we develop a novel method, LRT-q, based on a likelihood ratio test that aggregates the effects of multiple rare variants nonlinearly to achieve higher statistical power than previous rare variant association methods. We apply LRT-q to the latest GTEx v8 dataset and identify regulatory effect of rare variants on individual genes. We also observe that genes regulated by rare variants are likely to be disease-causing genes. These results demonstrate the functional effects of rare variants, especially on gene expression, which provides important biological insights in understanding the genetic mechanism of rare variants in complex traits and diseases.
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23
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Hubert JN, Suybeng V, Vallée M, Delhomme TM, Maubec E, Boland A, Bacq D, Deleuze JF, Jouenne F, Brennan P, McKay JD, Avril MF, Bressac-de Paillerets B, Chanudet E. The PI3K/mTOR Pathway Is Targeted by Rare Germline Variants in Patients with Both Melanoma and Renal Cell Carcinoma. Cancers (Basel) 2021; 13:2243. [PMID: 34067022 PMCID: PMC8125037 DOI: 10.3390/cancers13092243] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 04/26/2021] [Accepted: 04/28/2021] [Indexed: 12/24/2022] Open
Abstract
Background: Malignant melanoma and RCC have different embryonic origins, no common lifestyle risk factors but intriguingly share biological properties such as immune regulation and radioresistance. An excess risk of malignant melanoma is observed in RCC patients and vice versa. This bidirectional association is poorly understood, and hypothetic genetic co-susceptibility remains largely unexplored. Results: We hereby provide a clinical and genetic description of a series of 125 cases affected by both malignant melanoma and RCC. Clinical germline mutation testing identified a pathogenic variant in a melanoma and/or RCC predisposing gene in 17/125 cases (13.6%). This included mutually exclusive variants in MITF (p.E318K locus, N = 9 cases), BAP1 (N = 3), CDKN2A (N = 2), FLCN (N = 2), and PTEN (N = 1). A subset of 46 early-onset cases, without underlying germline variation, was whole-exome sequenced. In this series, thirteen genes were significantly enriched in mostly exclusive rare variants predicted to be deleterious, compared to 19,751 controls of similar ancestry. The observed variation mainly consisted of novel or low-frequency variants (<0.01%) within genes displaying strong evolutionary mutational constraints along the PI3K/mTOR pathway, including PIK3CD, NFRKB, EP300, MTOR, and related epigenetic modifier SETD2. The screening of independently processed germline exomes from The Cancer Genome Atlas confirmed an association with melanoma and RCC but not with cancers of established differing etiology such as lung cancers. Conclusions: Our study highlights that an exome-wide case-control enrichment approach may better characterize the rare variant-based missing heritability of multiple primary cancers. In our series, the co-occurrence of malignant melanoma and RCC was associated with germline variation in the PI3K/mTOR signaling cascade, with potential relevance for early diagnostic and clinical management.
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Affiliation(s)
- Jean-Noël Hubert
- Section of Genetics, International Agency for Research on Cancer (IARC-WHO), 69372 Lyon, France; (J.-N.H.); (M.V.); (T.M.D.); (P.B.); (J.D.M.)
| | - Voreak Suybeng
- Gustave Roussy, Département de Biopathologie, 94805 Villejuif, France; (V.S.); (F.J.)
| | - Maxime Vallée
- Section of Genetics, International Agency for Research on Cancer (IARC-WHO), 69372 Lyon, France; (J.-N.H.); (M.V.); (T.M.D.); (P.B.); (J.D.M.)
| | - Tiffany M. Delhomme
- Section of Genetics, International Agency for Research on Cancer (IARC-WHO), 69372 Lyon, France; (J.-N.H.); (M.V.); (T.M.D.); (P.B.); (J.D.M.)
| | - Eve Maubec
- Department of Dermatology, AP-HP, Hôpital Avicenne, University Paris 13, 93000 Bobigny, France;
- UMRS-1124, Campus Paris Saint-Germain-des-Prés, University of Paris, 75006 Paris, France
| | - Anne Boland
- Centre National de Recherche en Génomique Humaine, Université Paris-Saclay, CEA, 91057 Evry, France; (A.B.); (D.B.); (J.-F.D.)
| | - Delphine Bacq
- Centre National de Recherche en Génomique Humaine, Université Paris-Saclay, CEA, 91057 Evry, France; (A.B.); (D.B.); (J.-F.D.)
| | - Jean-François Deleuze
- Centre National de Recherche en Génomique Humaine, Université Paris-Saclay, CEA, 91057 Evry, France; (A.B.); (D.B.); (J.-F.D.)
| | - Fanélie Jouenne
- Gustave Roussy, Département de Biopathologie, 94805 Villejuif, France; (V.S.); (F.J.)
| | - Paul Brennan
- Section of Genetics, International Agency for Research on Cancer (IARC-WHO), 69372 Lyon, France; (J.-N.H.); (M.V.); (T.M.D.); (P.B.); (J.D.M.)
| | - James D. McKay
- Section of Genetics, International Agency for Research on Cancer (IARC-WHO), 69372 Lyon, France; (J.-N.H.); (M.V.); (T.M.D.); (P.B.); (J.D.M.)
| | | | - Brigitte Bressac-de Paillerets
- Gustave Roussy, Département de Biopathologie, 94805 Villejuif, France; (V.S.); (F.J.)
- INSERM U1279, Tumor Cell Dynamics, 94805 Villejuif, France
| | - Estelle Chanudet
- Section of Genetics, International Agency for Research on Cancer (IARC-WHO), 69372 Lyon, France; (J.-N.H.); (M.V.); (T.M.D.); (P.B.); (J.D.M.)
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Guan F, Han W, Ni T, Zhao L, Li X, Zhang B, Zhang T. Genetic Polymorphisms of RGS14 and Renal Stone Disease. Arch Med Res 2020; 52:332-338. [PMID: 33309307 DOI: 10.1016/j.arcmed.2020.11.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Revised: 11/12/2020] [Accepted: 11/19/2020] [Indexed: 12/22/2022]
Abstract
BACKGROUND Renal stone disease (RSD) is a common disease of the human urinary system and is regarded as a multifactorial condition affected by environmental and genetic factors. RGS14 encodes a complex scaffolding protein, known as regulator of G protein signaling 14, which is enriched in hippocampal area CA2 dendritic spines. AIM OF THE STUDY We aimed to investigate the association between genetic polymorphisms in RGS14 and the risk of RSD based on a large sample of the Chinese Han population. METHODS A total of 1,436 subjects, comprising 506 patients with RSD and 920 controls, were enrolled in the study. Ten tag SNPs located in the RGS14 gene region were chosen for genotyping. Genetic associations were evaluated at both the single marker and haplotype levels. Genotypic (χ2 test) and allelic analyses (Cochran-Armitage test for trend) were performed for single marker-based association. Two bioinformatics tools, RegulomeDB and GTEx, were used to examine the functional consequences of the target SNP. RESULTS SNP rs11746443 was found to be significantly associated with disease status (χ2 = 12.60, p = 0.0018). Moreover, the A allele of this SNP was significantly associated with an increased risk of RSD (OR [95%CI] = 1.36 [1.13-1.65]). Multiple significant eQTL signals of rs11746443 on RGS14 were identified. CONCLUSIONS This study replicated the association signal of RGS14 with RSD in a large sample of the Chinese Han population. The results suggest that the SNP rs11746443 of RGS14 might increase the risk of RSD by regulating the Ca2+ levels in humans.
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Affiliation(s)
- Fanglin Guan
- College of Medicine and Forensics, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - Wei Han
- College of Medicine and Forensics, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - Tong Ni
- College of Medicine and Forensics, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - Longrui Zhao
- College of Medicine and Forensics, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - Xiaoming Li
- Department of Emergency, Shaanxi People's Hospital, Xi'an, China
| | - Bo Zhang
- School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Tianxiao Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China.
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25
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Diez-Fairen M, Houle G, Ortega-Cubero S, Bandres-Ciga S, Alvarez I, Carcel M, Ibañez L, Fernandez MV, Budde JP, Trotta JR, Tonda R, Chong JX, Bamshad MJ, Nickerson DA, Aguilar M, Tartari JP, Gironell A, García-Martín E, Agundez JA, Alonso-Navarro H, Jimenez-Jimenez FJ, Fernandez M, Valldeoriola F, Marti MJ, Tolosa E, Coria F, Pastor MA, Vilariño-Güell C, Rajput A, Dion PA, Cruchaga C, Rouleau GA, Pastor P. Exome-wide rare variant analysis in familial essential tremor. Parkinsonism Relat Disord 2020; 82:109-116. [PMID: 33279834 DOI: 10.1016/j.parkreldis.2020.11.021] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Revised: 10/08/2020] [Accepted: 11/21/2020] [Indexed: 01/10/2023]
Abstract
INTRODUCTION Essential tremor (ET) is one of the most common movement disorders. Despite its high prevalence and heritability, its genetic etiology remains elusive with only a few susceptibility genes identified and poorly replicated. Our aim was to find novel candidate genes involved in ET predisposition through whole exome sequencing. METHODS We studied eight multigenerational families (N = 40 individuals) with an autosomal-dominant inheritance using a comprehensive strategy combining whole exome sequencing followed by case-control association testing of prioritized variants in a separate cohort comprising 521 ET cases and 596 controls. We further performed gene-based burden analyses in an additional dataset comprising 789 ET patients and 770 healthy individuals to investigate whether there was an enrichment of rare deleterious variants within our candidate genes. RESULTS Fifteen variants co-segregated with disease status in at least one of the families, among which rs749875462 in CCDC183, rs535864157 in MMP10 and rs114285050 in GPR151 showed a nominal association with ET. However, we found no significant enrichment of rare variants within these genes in cases compared with controls. Interestingly, MMP10 protein is involved in the inflammatory response to neuronal damage and has been previously associated with other neurological disorders. CONCLUSIONS We prioritized a set of promising genes, especially MMP10, for further genetic and functional studies in ET. Our study suggests that rare deleterious coding variants that markedly increase susceptibility to ET are likely to be found in many genes. Future studies are needed to replicate and further infer biological mechanisms and potential disease causality for our identified genes.
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Affiliation(s)
- Monica Diez-Fairen
- Fundació Docència i Recerca MútuaTerrassa, Movement Disorders Unit, Department of Neurology, University Hospital Mútua Terrassa, Terrassa, Barcelona, Spain
| | - Gabrielle Houle
- Department of Human Genetics, McGill University, Montréal, Quebec, Canada; Montreal Neurological Institute, McGill University, Montréal, Quebec, Canada
| | - Sara Ortega-Cubero
- Department of Neurology and Neurosurgery, Hospital Universitario de Burgos, Burgos, Spain
| | - Sara Bandres-Ciga
- Molecular Genetics Section, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA; Instituto de Investigación Biosanitaria de Granada (ibs. GRANADA), Granada, Spain
| | - Ignacio Alvarez
- Fundació Docència i Recerca MútuaTerrassa, Movement Disorders Unit, Department of Neurology, University Hospital Mútua Terrassa, Terrassa, Barcelona, Spain
| | - Maria Carcel
- Fundació Docència i Recerca MútuaTerrassa, Movement Disorders Unit, Department of Neurology, University Hospital Mútua Terrassa, Terrassa, Barcelona, Spain
| | - Laura Ibañez
- NeuroGenomics and Informatics, Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Maria Victoria Fernandez
- NeuroGenomics and Informatics, Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - John P Budde
- NeuroGenomics and Informatics, Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Jean-Rémi Trotta
- Centre Nacional d'Anàlisis Genòmic (CNAG-CRG), Center for Genomic Regulation, Barcelona Institute of Science and Technology (BIST), Barcelona, Spain & Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Raúl Tonda
- Centre Nacional d'Anàlisis Genòmic (CNAG-CRG), Center for Genomic Regulation, Barcelona Institute of Science and Technology (BIST), Barcelona, Spain & Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Jessica X Chong
- Division of Genetic Medicine, Department of Pediatrics, University of Washington, Seattle, WA, USA
| | - Michael J Bamshad
- Division of Genetic Medicine, Department of Pediatrics, University of Washington, Seattle, WA, USA; Seattle Children's Hospital, Seattle, WA, 98105, USA; Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | | | | | - Miquel Aguilar
- Fundació Docència i Recerca MútuaTerrassa, Movement Disorders Unit, Department of Neurology, University Hospital Mútua Terrassa, Terrassa, Barcelona, Spain
| | - Juan P Tartari
- Fundació Docència i Recerca MútuaTerrassa, Movement Disorders Unit, Department of Neurology, University Hospital Mútua Terrassa, Terrassa, Barcelona, Spain
| | - Alexandre Gironell
- Movement Disorders Unit, Neurology Department, Hospital de Sant Pau and Sant Pau Biomedical Research Institute, Barcelona, 08026, Spain
| | - Elena García-Martín
- University Institute of Molecular Pathology Biomarkers, UNEx. ARADyAL Instituto de Salud Carlos III, Cáceres, Spain
| | - Jose Ag Agundez
- University Institute of Molecular Pathology Biomarkers, UNEx. ARADyAL Instituto de Salud Carlos III, Cáceres, Spain
| | | | | | - Manel Fernandez
- María de Maeztu Unit of Excellence, Institute of Neurosciences, University of Barcelona, MDM-2017-0729, Ministry of Science, Innovation and Universities, Spain; Parkinson's Disease & Movement Disorders Unit, Department of Neurology, Hospital Clínic, IDIBAPS, Barcelona, Spain
| | - Francesc Valldeoriola
- Parkinson's Disease & Movement Disorders Unit, Department of Neurology, Hospital Clínic, IDIBAPS, Barcelona, Spain; Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Hospital Clínic de Barcelona, Barcelona, Spain
| | - Maria Jose Marti
- Parkinson's Disease & Movement Disorders Unit, Department of Neurology, Hospital Clínic, IDIBAPS, Barcelona, Spain; Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Hospital Clínic de Barcelona, Barcelona, Spain
| | - Eduard Tolosa
- Parkinson's Disease & Movement Disorders Unit, Department of Neurology, Hospital Clínic, IDIBAPS, Barcelona, Spain; Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Hospital Clínic de Barcelona, Barcelona, Spain
| | - Francisco Coria
- Clinic for Nervous Disorders, Service of Neurology, Son Espases University Hospital, Palma de Mallorca, Spain
| | - Maria A Pastor
- Department of Neurology, Clínica Universidad de Navarra, Pamplona, Spain
| | - Carles Vilariño-Güell
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
| | - Alex Rajput
- Saskatchewan Movement Disorders Program, University of Saskatchewan/Saskatchewan Health Authority, Saskatoon, Saskatchewan, Canada
| | - Patrick A Dion
- Department of Human Genetics, McGill University, Montréal, Quebec, Canada; Department of Neurology and Neurosurgery, McGill University, Montréal, H3A 2B4, Quebec, Canada
| | - Carlos Cruchaga
- NeuroGenomics and Informatics, Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Guy A Rouleau
- Montreal Neurological Institute, McGill University, Montréal, Quebec, Canada; Department of Neurology and Neurosurgery, McGill University, Montréal, H3A 2B4, Quebec, Canada
| | - Pau Pastor
- Fundació Docència i Recerca MútuaTerrassa, Movement Disorders Unit, Department of Neurology, University Hospital Mútua Terrassa, Terrassa, Barcelona, Spain.
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Xiang Y, Xiang X, Li Y. Identifying rare variants for quantitative traits in extreme samples of population via Kullback-Leibler distance. BMC Genet 2020; 21:130. [PMID: 33234108 PMCID: PMC7687851 DOI: 10.1186/s12863-020-00951-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Accepted: 11/10/2020] [Indexed: 11/23/2022] Open
Abstract
Background The rapid development of sequencing technology and simultaneously the availability of large quantities of sequence data has facilitated the identification of rare variant associated with quantitative traits. However, existing statistical methods depend on certain assumptions and thus lacking uniform power. The present study focuses on mapping rare variant associated with quantitative traits. Results In the present study, we proposed a two-stage strategy to identify rare variant of quantitative traits using phenotype extreme selection design and Kullback-Leibler distance, where the first stage was association analysis and the second stage was fine mapping. We presented a statistic and a linkage disequilibrium measure for the first stage and the second stage, respectively. Theory analysis and simulation study showed that (1) the power of the proposed statistic for association analysis increased with the stringency of the sample selection and was affected slightly by non-causal variants and opposite effect variants, (2) the statistic here achieved higher power than three commonly used methods, and (3) the linkage disequilibrium measure for fine mapping was independent of the frequencies of non-causal variants and simply dependent on the frequencies of causal variants. Conclusions We conclude that the two-stage strategy here can be used effectively to mapping rare variant associated with quantitative traits.
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Affiliation(s)
- Yang Xiang
- School of Mathematics and Computational Science, Huaihua University, Huaihua, Hunan, 418008, People's Republic of China.,Key Laboratory of Research and Utilization of Ethnomedicinal Plant Resources of Hunan Province, Huaihua University, Huaihua, 418008, China.,Key Laboratory of Hunan Higher Education for Western Hunan Medicinal Plant and Ethnobotany, Huaihua University, Huaihua, 418008, China
| | - Xinrong Xiang
- School of Mathematics and Statistics, Hunan Normal University, Changsha, Hunan, 410081, People's Republic of China
| | - Yumei Li
- School of Mathematics and Computational Science, Huaihua University, Huaihua, Hunan, 418008, People's Republic of China. .,Key Laboratory of Research and Utilization of Ethnomedicinal Plant Resources of Hunan Province, Huaihua University, Huaihua, 418008, China. .,Key Laboratory of Hunan Higher Education for Western Hunan Medicinal Plant and Ethnobotany, Huaihua University, Huaihua, 418008, China.
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27
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Li Z, Liu Y, Lin X. Simultaneous Detection of Signal Regions Using Quadratic Scan Statistics With Applications to Whole Genome Association Studies. J Am Stat Assoc 2020; 117:823-834. [PMID: 35845434 PMCID: PMC9285665 DOI: 10.1080/01621459.2020.1822849] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2019] [Revised: 06/18/2020] [Accepted: 08/25/2020] [Indexed: 01/03/2023]
Abstract
We consider in this paper detection of signal regions associated with disease outcomes in whole genome association studies. Gene- or region-based methods have become increasingly popular in whole genome association analysis as a complementary approach to traditional individual variant analysis. However, these methods test for the association between an outcome and the genetic variants in a pre-specified region, e.g., a gene. In view of massive intergenic regions in whole genome sequencing (WGS) studies, we propose a computationally efficient quadratic scan (Q-SCAN) statistic based method to detect the existence and the locations of signal regions by scanning the genome continuously. The proposed method accounts for the correlation (linkage disequilibrium) among genetic variants, and allows for signal regions to have both causal and neutral variants, and the effects of signal variants to be in different directions. We study the asymptotic properties of the proposed Q-SCAN statistics. We derive an empirical threshold that controls for the family-wise error rate, and show that under regularity conditions the proposed method consistently selects the true signal regions. We perform simulation studies to evaluate the finite sample performance of the proposed method. Our simulation results show that the proposed procedure outperforms the existing methods, especially when signal regions have causal variants whose effects are in different directions, or are contaminated with neutral variants. We illustrate Q-SCAN by analyzing the WGS data from the Atherosclerosis Risk in Communities study.
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Affiliation(s)
- Zilin Li
- Harvard University T H Chan School of Public Health, Biostatistics, 655 Huntington Avenue, Boston, 02115 United States
| | - Yaowu Liu
- Southwestern University of Finance and Economics School of Statistics, Chengdu, 610074 China
| | - Xihong Lin
- Harvard University T H Chan School of Public Health, Biostatistics, 655 Huntington Avenue, Boston, 02115 United States
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28
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Identification of candidate genetic variants and altered protein expression in neural stem and mature neural cells support altered microtubule function to be an essential component in bipolar disorder. Transl Psychiatry 2020; 10:390. [PMID: 33168801 PMCID: PMC7652854 DOI: 10.1038/s41398-020-01056-1] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 09/07/2020] [Accepted: 09/29/2020] [Indexed: 01/31/2023] Open
Abstract
Identification of causative genetic variants leading to the development of bipolar disorder (BD) could result in genetic tests that would facilitate diagnosis. A better understanding of affected genes and pathways is also necessary for targeting of genes that may improve treatment strategies. To date several susceptibility genes have been reported from genome-wide association studies (GWAS), but little is known about specific variants that affect disease development. Here, we performed quantitative proteomics and whole-genome sequencing (WGS). Quantitative proteomics revealed NLRP2 as the most significantly up-regulated protein in neural stem cells and mature neural cells obtained from BD-patient cell samples. These results are in concordance with our previously published transcriptome analysis. Furthermore, the levels of FEZ2 and CADM2 proteins were also significantly differentially expressed in BD compared to control derived cells. The levels of FEZ2 were significantly downregulated in neural stem cells (NSC) while CADM2 was significantly up-regulated in mature neuronal cell culture. Promising novel candidate mutations were identified in the ANK3, NEK3, NEK7, TUBB, ANKRD1, and BRD2 genes. A literature search of candidate variants and deregulated proteins revealed that there are several connections to microtubule function for the molecules putatively involved. Microtubule function in neurons is critical for axon structure and axonal transport. A functional dynamic microtubule is also needed for an advocate response to cellular and environmental stress. If microtubule dynamics is compromised by mutations, it could be followed by deregulated expression forming a possible explanation for the inherited vulnerability to stressful life events that have been proposed to trigger mood episodes in BD patients.
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29
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Wang Y, Liu Z, Yang G, Gao Q, Xiao L, Li J, Guo C, Troutwine BR, Gray RS, Xie L, Zhang H. Coding Variants Coupled With Rapid Modeling in Zebrafish Implicate Dynein Genes, dnaaf1 and zmynd10, as Adolescent Idiopathic Scoliosis Candidate Genes. Front Cell Dev Biol 2020; 8:582255. [PMID: 33251213 PMCID: PMC7672046 DOI: 10.3389/fcell.2020.582255] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2020] [Accepted: 10/07/2020] [Indexed: 12/11/2022] Open
Abstract
Adolescent idiopathic scoliosis (AIS) is the most common pediatric spine disorder affecting ∼3% of children worldwide. Human genetic studies suggest a complex polygenic disease model for AIS with large genetic and phenotypic heterogeneity. However, the overall genetic etiology of AIS remains poorly understood. To identify additional AIS susceptibility loci, we performed whole-exome sequencing (WES) on a cohort of 195 Southern Chinese AIS patients. Bioinformatics analysis identified 237 novel rare variants associated with AIS, located in 232 new susceptibility loci. Enrichment analysis of these variants revealed 10 gene families associated with our AIS cohort. We screened these gene families by comparing our candidate gene list with IS candidate genes in the Human Phenotype Ontology (HPO) database and previous reported studies. Two candidate gene families, axonemal dynein and axonemal dynein assembly factors, were retained for their associations with ciliary architecture and function. The damaging effects of candidate variants in dynein genes dnali1, dnah1, dnaaf, and zmynd10, as well as in one fibrillin-related gene tns1, were functionally analyzed in zebrafish using targeted CRISPR/Cas9 screening. Knockout of two candidate genes, dnaaf1 or zmynd10, recapitulated scoliosis in viable adult zebrafish. Altogether, our results suggest that the disruption of one or more dynein-associated factors may correlate with AIS susceptibility in the Southern Chinese population.
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Affiliation(s)
- Yunjia Wang
- Department of Spine Surgery and Orthopaedics, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.,Department of Pediatrics, Dell Pediatric Research Institute, The University of Texas at Austin, Dell Medical School, Austin, TX, United States
| | - Zhenhao Liu
- Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, Shanghai, China.,Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Key Laboratory of Carcinogenesis, National Health and Family Planning Commission, Xiangya Hospital, Central South University, Changsha, China
| | - Guanteng Yang
- Department of Spine Surgery and Orthopaedics, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Qile Gao
- Department of Spine Surgery and Orthopaedics, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Lige Xiao
- Department of Spine Surgery and Orthopaedics, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Jiong Li
- Department of Spine Surgery and Orthopaedics, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Chaofeng Guo
- Department of Spine Surgery and Orthopaedics, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Benjamin R Troutwine
- Department of Pediatrics, Dell Pediatric Research Institute, The University of Texas at Austin, Dell Medical School, Austin, TX, United States
| | - Ryan S Gray
- Department of Pediatrics, Dell Pediatric Research Institute, The University of Texas at Austin, Dell Medical School, Austin, TX, United States
| | - Lu Xie
- Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, Shanghai, China
| | - Hongqi Zhang
- Department of Spine Surgery and Orthopaedics, Xiangya Hospital, Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
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30
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Li X, Li Z, Zhou H, Gaynor SM, Liu Y, Chen H, Sun R, Dey R, Arnett DK, Aslibekyan S, Ballantyne CM, Bielak LF, Blangero J, Boerwinkle E, Bowden DW, Broome JG, Conomos MP, Correa A, Cupples LA, Curran JE, Freedman BI, Guo X, Hindy G, Irvin MR, Kardia SLR, Kathiresan S, Khan AT, Kooperberg CL, Laurie CC, Liu XS, Mahaney MC, Manichaikul AW, Martin LW, Mathias RA, McGarvey ST, Mitchell BD, Montasser ME, Moore JE, Morrison AC, O'Connell JR, Palmer ND, Pampana A, Peralta JM, Peyser PA, Psaty BM, Redline S, Rice KM, Rich SS, Smith JA, Tiwari HK, Tsai MY, Vasan RS, Wang FF, Weeks DE, Weng Z, Wilson JG, Yanek LR, Neale BM, Sunyaev SR, Abecasis GR, Rotter JI, Willer CJ, Peloso GM, Natarajan P, Lin X. Dynamic incorporation of multiple in silico functional annotations empowers rare variant association analysis of large whole-genome sequencing studies at scale. Nat Genet 2020; 52:969-983. [PMID: 32839606 PMCID: PMC7483769 DOI: 10.1038/s41588-020-0676-4] [Citation(s) in RCA: 150] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2019] [Accepted: 07/02/2020] [Indexed: 12/13/2022]
Abstract
Large-scale whole-genome sequencing studies have enabled the analysis of rare variants (RVs) associated with complex phenotypes. Commonly used RV association tests have limited scope to leverage variant functions. We propose STAAR (variant-set test for association using annotation information), a scalable and powerful RV association test method that effectively incorporates both variant categories and multiple complementary annotations using a dynamic weighting scheme. For the latter, we introduce 'annotation principal components', multidimensional summaries of in silico variant annotations. STAAR accounts for population structure and relatedness and is scalable for analyzing very large cohort and biobank whole-genome sequencing studies of continuous and dichotomous traits. We applied STAAR to identify RVs associated with four lipid traits in 12,316 discovery and 17,822 replication samples from the Trans-Omics for Precision Medicine Program. We discovered and replicated new RV associations, including disruptive missense RVs of NPC1L1 and an intergenic region near APOC1P1 associated with low-density lipoprotein cholesterol.
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Affiliation(s)
- Xihao Li
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Zilin Li
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Hufeng Zhou
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Sheila M Gaynor
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Yaowu Liu
- School of Statistics, Southwestern University of Finance and Economics, Chengdu, China
| | - Han Chen
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Center for Precision Health, School of Public Health and School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Ryan Sun
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Rounak Dey
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Donna K Arnett
- College of Public Health, University of Kentucky, Lexington, KY, USA
| | - Stella Aslibekyan
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | | | - Lawrence F Bielak
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - John Blangero
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, School of Medicine, The University of Texas Rio Grande Valley, Brownsville, TX, USA
| | - Eric Boerwinkle
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Donald W Bowden
- Department of Biochemistry, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Jai G Broome
- Division of Medical Genetics, University of Washington, Seattle, WA, USA
| | - Matthew P Conomos
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Adolfo Correa
- Jackson Heart Study, Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | - L Adrienne Cupples
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Framingham Heart Study, National Heart, Lung, and Blood Institute and Boston University, Framingham, MA, USA
| | - Joanne E Curran
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, School of Medicine, The University of Texas Rio Grande Valley, Brownsville, TX, USA
| | - Barry I Freedman
- Department of Internal Medicine, Nephrology, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - George Hindy
- Department of Population Medicine, Qatar University College of Medicine, QU Health, Doha, Qatar
| | - Marguerite R Irvin
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Sharon L R Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Sekar Kathiresan
- Verve Therapeutics, Cambridge, MA, USA
- Cardiology Division, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Alyna T Khan
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Charles L Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Cathy C Laurie
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - X Shirley Liu
- Department of Data Sciences, Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Statistics, Harvard University, Cambridge, MA, USA
| | - Michael C Mahaney
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, School of Medicine, The University of Texas Rio Grande Valley, Brownsville, TX, USA
| | - Ani W Manichaikul
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Lisa W Martin
- Division of Cardiology, George Washington School of Medicine and Health Sciences, Washington, DC, USA
| | - Rasika A Mathias
- GeneSTAR Research Program, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Stephen T McGarvey
- Department of Epidemiology, International Health Institute, Department of Anthropology, Brown University, Providence, RI, USA
| | - Braxton D Mitchell
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
- Geriatrics Research and Education Clinical Center, Baltimore VA Medical Center, Baltimore, MD, USA
| | - May E Montasser
- Division of Endocrinology, Diabetes, and Nutrition, Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Jill E Moore
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA, USA
| | - Alanna C Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Jeffrey R O'Connell
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Nicholette D Palmer
- Department of Biochemistry, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Akhil Pampana
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Center for Genomic Medicine and Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Juan M Peralta
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, School of Medicine, The University of Texas Rio Grande Valley, Brownsville, TX, USA
| | - Patricia A Peyser
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Services, University of Washington, Seattle, WA, USA
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Susan Redline
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
- Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA
- Division of Pulmonary, Critical Care, and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Kenneth M Rice
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Jennifer A Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Hemant K Tiwari
- Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Michael Y Tsai
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA
| | - Ramachandran S Vasan
- Framingham Heart Study, National Heart, Lung, and Blood Institute and Boston University, Framingham, MA, USA
- Department of Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Fei Fei Wang
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Daniel E Weeks
- Department of Human Genetics and Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Zhiping Weng
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA, USA
| | - James G Wilson
- Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS, USA
- Division of Cardiology, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Lisa R Yanek
- GeneSTAR Research Program, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Benjamin M Neale
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Shamil R Sunyaev
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Division of Genetics, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Gonçalo R Abecasis
- Regeneron Pharmaceuticals, Tarrytown, NY, USA
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Cristen J Willer
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Gina M Peloso
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Pradeep Natarajan
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Center for Genomic Medicine and Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Xihong Lin
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Department of Statistics, Harvard University, Cambridge, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
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31
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Bis-Brewer DM, Gan-Or Z, Sleiman P, Hakonarson H, Fazal S, Courel S, Cintra V, Tao F, Estiar MA, Tarnopolsky M, Boycott KM, Yoon G, Suchowersky O, Dupré N, Cheng A, Lloyd TE, Rouleau G, Schüle R, Züchner S. Assessing non-Mendelian inheritance in inherited axonopathies. Genet Med 2020; 22:2114-2119. [PMID: 32741968 PMCID: PMC7710562 DOI: 10.1038/s41436-020-0924-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Revised: 07/22/2020] [Accepted: 07/22/2020] [Indexed: 01/05/2023] Open
Abstract
PURPOSE Inherited axonopathies (IA) are rare, clinically and genetically heterogeneous diseases that lead to length-dependent degeneration of the long axons in central (hereditary spastic paraplegia [HSP]) and peripheral (Charcot-Marie-Tooth type 2 [CMT2]) nervous systems. Mendelian high-penetrance alleles in over 100 different genes have been shown to cause IA; however, about 50% of IA cases do not receive a genetic diagnosis. A more comprehensive spectrum of causative genes and alleles is warranted, including causative and risk alleles, as well as oligogenic multilocus inheritance. METHODS Through international collaboration, IA exome studies are beginning to be sufficiently powered to perform a pilot rare variant burden analysis. After extensive quality control, our cohort contained 343 CMT cases, 515 HSP cases, and 935 non-neurological controls. We assessed the cumulative mutational burden across disease genes, explored the evidence for multilocus inheritance, and performed an exome-wide rare variant burden analysis. RESULTS We replicated the previously described mutational burden in a much larger cohort of CMT cases, and observed the same effect in HSP cases. We identified a preliminary risk allele for CMT in the EXOC4 gene (p value= 6.9 × 10-6, odds ratio [OR] = 2.1) and explored the possibility of multilocus inheritance in IA. CONCLUSION Our results support the continuing emergence of complex inheritance mechanisms in historically Mendelian disorders.
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Affiliation(s)
- Dana M Bis-Brewer
- Dr. John T. Macdonald Foundation Department of Human Genetics, John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA.
| | - Ziv Gan-Or
- Department of Human Genetics, McGill University, Montréal, QC, Canada.,Montreal Neurological Institute and Hospital, McGill University, Montréal, QC, Canada.,Department of Neurology and Neurosurgery, McGill University, Montréal, QC, Canada
| | - Patrick Sleiman
- Center for Applied Genomics, The Children's Hospital of Philadelphia; Division of Human Genetics, Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Hakon Hakonarson
- Center for Applied Genomics, The Children's Hospital of Philadelphia; Division of Human Genetics, Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sarah Fazal
- Dr. John T. Macdonald Foundation Department of Human Genetics, John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Steve Courel
- Dr. John T. Macdonald Foundation Department of Human Genetics, John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Vivian Cintra
- Dr. John T. Macdonald Foundation Department of Human Genetics, John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Feifei Tao
- Dr. John T. Macdonald Foundation Department of Human Genetics, John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Mehrdad A Estiar
- Department of Human Genetics, McGill University, Montréal, QC, Canada.,Montreal Neurological Institute and Hospital, McGill University, Montréal, QC, Canada
| | - Mark Tarnopolsky
- Neuromuscular and Neurometabolics Division, Department of Pediatrics, McMaster University, Hamilton, ON, Canada
| | - Kym M Boycott
- Children's Hospital of Eastern Ontario Research Institute, University of Ottawa, Ottawa, ON, Canada
| | - Grace Yoon
- Division of Clinical and Metabolic Genetics, Department of Paediatrics, The Hospital for Sick Children, University of Toronto, Toronto, ON, Canada.,Division of Neurology, Department of Paediatrics, The Hospital for Sick Children, University of Toronto, Toronto, ON, Canada
| | - Oksana Suchowersky
- Department of Medicine, Medical Genetics and Pediatrics, University of Alberta, Edmonton, AB, Canada
| | - Nicolas Dupré
- Division of Neurosciences, CHU de Québec, Université Laval, Québec City, QC, Canada.,Department of Medicine, Faculty of Medicine, Université Laval, Québec City, QC, Canada
| | - Andrew Cheng
- Department of Neurology and Neuroscience, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Thomas E Lloyd
- Department of Neurology and Neuroscience, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Guy Rouleau
- Department of Human Genetics, McGill University, Montréal, QC, Canada.,Montreal Neurological Institute and Hospital, McGill University, Montréal, QC, Canada.,Department of Neurology and Neurosurgery, McGill University, Montréal, QC, Canada
| | - Rebecca Schüle
- Center for Neurology and Hertie Institute für Clinical Brain Research, University of Tübingen, German Center for Neurodegenerative Diseases, Tübingen, Germany
| | - Stephan Züchner
- Dr. John T. Macdonald Foundation Department of Human Genetics, John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
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32
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Jiang Y, Chiu CY, Yan Q, Chen W, Gorin MB, Conley YP, Lakhal-Chaieb ML, Cook RJ, Amos CI, Wilson AF, Bailey-Wilson JE, McMahon FJ, Vazquez AI, Yuan A, Zhong X, Xiong M, Weeks DE, Fan R. Gene-Based Association Testing of Dichotomous Traits With Generalized Functional Linear Mixed Models Using Extended Pedigrees: Applications to Age-Related Macular Degeneration. J Am Stat Assoc 2020; 116:531-545. [PMID: 34321704 PMCID: PMC8315575 DOI: 10.1080/01621459.2020.1799809] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2017] [Revised: 07/09/2020] [Accepted: 07/17/2020] [Indexed: 10/23/2022]
Abstract
Genetics plays a role in age-related macular degeneration (AMD), a common cause of blindness in the elderly. There is a need for powerful methods for carrying out region-based association tests between a dichotomous trait like AMD and genetic variants on family data. Here, we apply our new generalized functional linear mixed models (GFLMM) developed to test for gene-based association in a set of AMD families. Using common and rare variants, we observe significant association with two known AMD genes: CFH and ARMS2. Using rare variants, we find suggestive signals in four genes: ASAH1, CLEC6A, TMEM63C, and SGSM1. Intriguingly, ASAH1 is down-regulated in AMD aqueous humor, and ASAH1 deficiency leads to retinal inflammation and increased vulnerability to oxidative stress. These findings were made possible by our GFLMM which model the effect of a major gene as a fixed mean, the polygenic contributions as a random variation, and the correlation of pedigree members by kinship coefficients. Simulations indicate that the GFLMM likelihood ratio tests (LRTs) accurately control the Type I error rates. The LRTs have similar or higher power than existing retrospective kernel and burden statistics. Our GFLMM-based statistics provide a new tool for conducting family-based genetic studies of complex diseases. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
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Affiliation(s)
- Yingda Jiang
- Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA
| | - Chi-Yang Chiu
- Division of Biostatistics, Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, TN
- Computational and Statistical Genomics Branch, National Human Genome Research Institute, NIH, Baltimore, MD
| | - Qi Yan
- Division of Pulmonary Medicine, Allergy and Immunology, Children’s Hospital of Pittsburgh at The University of Pittsburgh, Pittsburgh, PA
| | - Wei Chen
- Division of Pulmonary Medicine, Allergy and Immunology, Children’s Hospital of Pittsburgh at The University of Pittsburgh, Pittsburgh, PA
| | - Michael B. Gorin
- Department of Ophthalmology, David Geffen School of Medicine, UCLA Stein Eye Institute, Los Angeles, CA
| | - Yvette P. Conley
- Department of Health Promotion and Development, University of Pittsburgh, Pittsburgh, PA
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA
| | | | - Richard J. Cook
- Department of Statistics and Actuarial Science, Waterloo, ON, Canada
| | | | - Alexander F. Wilson
- Computational and Statistical Genomics Branch, National Human Genome Research Institute, NIH, Baltimore, MD
| | - Joan E. Bailey-Wilson
- Computational and Statistical Genomics Branch, National Human Genome Research Institute, NIH, Baltimore, MD
| | - Francis J. McMahon
- Human Genetics Branch and Genetic Basis of Mood and Anxiety Disorders Section, National Institute of Mental Health, NIH, Bethesda, MD
| | - Ana I. Vazquez
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI
| | - Ao Yuan
- Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, DC
| | - Xiaogang Zhong
- Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, DC
| | - Momiao Xiong
- Human Genetics Center, University of Texas, Houston, TX
| | - Daniel E. Weeks
- Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA
| | - Ruzong Fan
- Computational and Statistical Genomics Branch, National Human Genome Research Institute, NIH, Baltimore, MD
- Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, DC
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33
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Guan F, Han W, Ni T, Zhao L, Zhang B, Li M, Luo X, Zhang L, Li X, Sun W, Zhang T. Risk of gastric ulcer contributed by genetic polymorphisms of PSCA: A case-control study based on Chinese Han population. Gene 2020; 757:144941. [PMID: 32640304 DOI: 10.1016/j.gene.2020.144941] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2020] [Revised: 05/29/2020] [Accepted: 07/01/2020] [Indexed: 02/07/2023]
Affiliation(s)
- Fanglin Guan
- College of Medicine and Forensics, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - Wei Han
- College of Medicine and Forensics, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - Tong Ni
- College of Medicine and Forensics, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - Longrui Zhao
- College of Medicine and Forensics, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - Bo Zhang
- School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Miao Li
- Department of Pathology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Xiaoqin Luo
- Department of Nutrition and Food Safety, School of Public Health, Xi'an Jiaotong University, Xi'an, China
| | - Lei Zhang
- Department of Laboratory Medicine, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Xiaoming Li
- Department of Emergency, Shaanxi People's Hospital, Xi'an, China
| | - Wei Sun
- Department of Digestion, Xi'an Central Hospital, Xi'an, China
| | - Tianxiao Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China.
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34
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Rotunno M, Barajas R, Clyne M, Hoover E, Simonds NI, Lam TK, Mechanic LE, Goldstein AM, Gillanders EM. A Systematic Literature Review of Whole Exome and Genome Sequencing Population Studies of Genetic Susceptibility to Cancer. Cancer Epidemiol Biomarkers Prev 2020; 29:1519-1534. [PMID: 32467344 DOI: 10.1158/1055-9965.epi-19-1551] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2019] [Revised: 03/17/2020] [Accepted: 05/13/2020] [Indexed: 01/03/2023] Open
Abstract
The application of next-generation sequencing (NGS) technologies in cancer research has accelerated the discovery of somatic mutations; however, progress in the identification of germline variation associated with cancer risk is less clear. We conducted a systematic literature review of cancer genetic susceptibility studies that used NGS technologies at an exome/genome-wide scale to obtain a fuller understanding of the research landscape to date and to inform future studies. The variability across studies on methodologies and reporting was considerable. Most studies sequenced few high-risk (mainly European) families, used a candidate analysis approach, and identified potential cancer-related germline variants or genes in a small fraction of the sequenced cancer cases. This review highlights the importance of establishing consensus on standards for the application and reporting of variants filtering strategies. It also describes the progress in the identification of cancer-related germline variation to date. These findings point to the untapped potential in conducting studies with appropriately sized and racially diverse families and populations, combining results across studies and expanding beyond a candidate analysis approach to advance the discovery of genetic variation that accounts for the unexplained cancer heritability.
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Affiliation(s)
- Melissa Rotunno
- National Cancer Institute, National Institutes of Health, U.S. Department of Health and Human Services, Bethesda, Maryland.
| | - Rolando Barajas
- National Cancer Institute, National Institutes of Health, U.S. Department of Health and Human Services, Bethesda, Maryland
| | - Mindy Clyne
- National Cancer Institute, National Institutes of Health, U.S. Department of Health and Human Services, Bethesda, Maryland
| | - Elise Hoover
- National Cancer Institute, National Institutes of Health, U.S. Department of Health and Human Services, Bethesda, Maryland
| | | | - Tram Kim Lam
- National Cancer Institute, National Institutes of Health, U.S. Department of Health and Human Services, Bethesda, Maryland
| | - Leah E Mechanic
- National Cancer Institute, National Institutes of Health, U.S. Department of Health and Human Services, Bethesda, Maryland
| | - Alisa M Goldstein
- National Cancer Institute, National Institutes of Health, U.S. Department of Health and Human Services, Bethesda, Maryland
| | - Elizabeth M Gillanders
- National Cancer Institute, National Institutes of Health, U.S. Department of Health and Human Services, Bethesda, Maryland
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35
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Wysocki T, Olesińska M, Paradowska-Gorycka A. Current Understanding of an Emerging Role of HLA-DRB1 Gene in Rheumatoid Arthritis-From Research to Clinical Practice. Cells 2020; 9:cells9051127. [PMID: 32370106 PMCID: PMC7291248 DOI: 10.3390/cells9051127] [Citation(s) in RCA: 56] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Revised: 04/28/2020] [Accepted: 04/30/2020] [Indexed: 12/22/2022] Open
Abstract
Rheumatoid arthritis (RA) is an autoimmune disease with an unclear pathogenic mechanism. However, it has been proven that the key underlying risk factor is a genetic predisposition. Association studies of the HLA-DRB1 gene clearly indicate its importance in RA morbidity. This review presents the current state of knowledge on the impact of HLA-DRB1 gene, functioning both as a component of the patient’s genome and as an environmental risk factor. The impact of known HLA-DRB1 risk variants on the specific structure of the polymorphic HLA-DR molecule, and epitope binding affinity, is presented. The issues of the potential influence of HLA-DRB1 on the occurrence of non-articular disease manifestations and response to treatment are also discussed. A deeper understanding of the role of the HLA-DRB1 gene is essential to explore the complex nature of RA, which is a result of multiple contributing factors, including genetic, epigenetic and environmental factors. It also creates new opportunities to develop modern and personalized forms of therapy.
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Affiliation(s)
- Tomasz Wysocki
- Department of Systemic Connective Tissue Diseases, National Institute of Geriatrics, Rheumatology and Rehabilitation, Spartańska 1, 02-637 Warsaw, Poland; or
- Correspondence:
| | - Marzena Olesińska
- Department of Systemic Connective Tissue Diseases, National Institute of Geriatrics, Rheumatology and Rehabilitation, Spartańska 1, 02-637 Warsaw, Poland; or
| | - Agnieszka Paradowska-Gorycka
- Department of Molecular Biology, National Institute of Geriatrics, Rheumatology and Rehabilitation, Spartańska 1, 02-637 Warsaw, Poland; or
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36
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Zhang H, Zhao N, Mehrotra DV, Shen J. Composite Kernel Association Test (CKAT) for SNP-set joint assessment of genotype and genotype-by-treatment interaction in Pharmacogenetics studies. Bioinformatics 2020; 36:3162-3168. [PMID: 32101275 DOI: 10.1093/bioinformatics/btaa125] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2019] [Revised: 02/14/2020] [Accepted: 02/19/2020] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION It is of substantial interest to discover novel genetic markers that influence drug response in order to develop personalized treatment strategies that maximize therapeutic efficacy and safety. To help enable such discoveries, we focus on testing the association between the cumulative effect of multiple single nucleotide polymorphisms (SNPs) in a particular genomic region and a drug response of interest. However, the currently existing methods are either computational inefficient or not able to control type I error and provide decent power for whole exome or genome analysis in Pharmacogenetics (PGx) studies with small sample sizes. RESULTS In this article, we propose the Composite Kernel Association Test (CKAT), a flexible and robust kernel machine-based approach to jointly test the genetic main effect and SNP-treatment interaction effect for SNP-sets in Pharmacogenetics (PGx) assessments embedded within randomized clinical trials. An analytic procedure is developed to accurately calculate the P-value so that computationally extensive procedures (e.g. permutation or perturbation) can be avoided. We evaluate CKAT through extensive simulation studies and application to the gene-level association test of the reduction in Clostridium difficile infection recurrence in patients treated with bezlotoxumab. The results demonstrate that the proposed CKAT controls type I error well for PGx studies, is efficient for whole exome/genome association analysis and provides better power performance than existing methods across multiple scenarios. AVAILABILITY AND IMPLEMENTATION The R package CKAT is publicly available on CRAN https://cran.r-project.org/web/packages/CKAT/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Hong Zhang
- Biostatistics and Research Decision Sciences, Merck Research Laboratories, Merck & Co., Inc., Rahway, NJ 07065, USA
| | - Ni Zhao
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Devan V Mehrotra
- Biostatistics and Research Decision Sciences, Merck Research Laboratories, Merck & Co., Inc., North Wales, PA 19454, USA
| | - Judong Shen
- Biostatistics and Research Decision Sciences, Merck Research Laboratories, Merck & Co., Inc., Rahway, NJ 07065, USA
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37
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Zaman S, Chobrutskiy BI, Patel JS, Diviney A, Tu YN, Tong WL, Gill T, Blanck G. Antiviral T Cell Receptor Complementarity Determining Region-3 Sequences Are Associated with a Worse Cancer Outcome: A Pancancer Analysis. Viral Immunol 2020; 33:404-412. [PMID: 32315578 DOI: 10.1089/vim.2019.0156] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
Human papilloma virus has a clearly demonstrated role in cervical and head and neck cancers, but viral etiology for other solid tumors is less well understood. To expand this area of research, we obtained and analyzed the immune receptor recombinations available from both blood and tumor samples, through mining of exome files produced from those sources, for 32 cancer types represented by the cancer genome atlas (TCGA). Among TCGA data sets, the recovery frequency for antiviral complementarity determining region-3 sequences (CDR3s), for T cell receptor-alpha and T cell receptor-beta, ranged from 0% to 21% of the patients, for the different cancer types, with breast, lung, pancreatic, and thymus cancers representing the highest of that range, particularly for tumor tissue resident T cells. In several cases, recovery of the antiviral CDR3s associated with distinct survival rates, and in all of these cases, the recovery of an antiviral CDR3 associated with a worse survival rate.
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Affiliation(s)
- Saif Zaman
- Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, Florida, USA
| | - Boris I Chobrutskiy
- Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, Florida, USA
| | - Jay S Patel
- Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, Florida, USA
| | - Andrea Diviney
- Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, Florida, USA
| | - Yaping N Tu
- Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, Florida, USA
| | - Wei Lue Tong
- Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, Florida, USA
| | - Tommy Gill
- Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, Florida, USA
| | - George Blanck
- Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, Florida, USA.,Immunology Program, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, USA
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38
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Riveros-Mckay F, Oliver-Williams C, Karthikeyan S, Walter K, Kundu K, Ouwehand WH, Roberts D, Di Angelantonio E, Soranzo N, Danesh J, Wheeler E, Zeggini E, Butterworth AS, Barroso I. The influence of rare variants in circulating metabolic biomarkers. PLoS Genet 2020; 16:e1008605. [PMID: 32150548 PMCID: PMC7108731 DOI: 10.1371/journal.pgen.1008605] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2019] [Revised: 03/31/2020] [Accepted: 01/10/2020] [Indexed: 12/19/2022] Open
Abstract
Circulating metabolite levels are biomarkers for cardiovascular disease (CVD). Here we studied, association of rare variants and 226 serum lipoproteins, lipids and amino acids in 7,142 (discovery plus follow-up) healthy participants. We leveraged the information from multiple metabolite measurements on the same participants to improve discovery in rare variant association analyses for gene-based and gene-set tests by incorporating correlated metabolites as covariates in the validation stage. Gene-based analysis corrected for the effective number of tests performed, confirmed established associations at APOB, APOC3, PAH, HAL and PCSK (p<1.32x10-7) and identified novel gene-trait associations at a lower stringency threshold with ACSL1, MYCN, FBXO36 and B4GALNT3 (p<2.5x10-6). Regulation of the pyruvate dehydrogenase (PDH) complex was associated for the first time, in gene-set analyses also corrected for effective number of tests, with IDL and LDL parameters, as well as circulating cholesterol (pMETASKAT<2.41x10-6). In conclusion, using an approach that leverages metabolite measurements obtained in the same participants, we identified novel loci and pathways involved in the regulation of these important metabolic biomarkers. As large-scale biobanks continue to amass sequencing and phenotypic information, analytical approaches such as ours will be useful to fully exploit the copious amounts of biological data generated in these efforts.
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Affiliation(s)
| | - Clare Oliver-Williams
- MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
- Homerton College, Cambridge, United Kingdom
| | - Savita Karthikeyan
- MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | | | - Kousik Kundu
- Wellcome Sanger Institute, Cambridge, United Kingdom
- Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | - Willem H. Ouwehand
- Wellcome Sanger Institute, Cambridge, United Kingdom
- Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
- NHS Blood and Transplant, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | - David Roberts
- The National Institute for Health Research Blood and Transplant Research Unit (NIHR BTRU) in Donor Health and Genomics, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
- NHS Blood and Transplant—Oxford Centre, Level 2, John Radcliffe Hospital, Oxford, United Kingdom
- Radcliffe Department of Medicine, University of Oxford, John Radcliffe Hospital, Oxford, United Kingdom
| | - Emanuele Di Angelantonio
- Wellcome Sanger Institute, Cambridge, United Kingdom
- MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
- The National Institute for Health Research Blood and Transplant Research Unit (NIHR BTRU) in Donor Health and Genomics, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, United Kingdom
- National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge and Cambridge University Hospitals, Cambridge, United Kingdom
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, United Kingdom
| | - Nicole Soranzo
- Wellcome Sanger Institute, Cambridge, United Kingdom
- Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | - John Danesh
- Wellcome Sanger Institute, Cambridge, United Kingdom
- MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
- The National Institute for Health Research Blood and Transplant Research Unit (NIHR BTRU) in Donor Health and Genomics, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, United Kingdom
- National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge and Cambridge University Hospitals, Cambridge, United Kingdom
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, United Kingdom
| | | | - Eleanor Wheeler
- Wellcome Sanger Institute, Cambridge, United Kingdom
- MRC Epidemiology Unit, Wellcome Trust-MRC Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, United Kingdom
| | - Eleftheria Zeggini
- Wellcome Sanger Institute, Cambridge, United Kingdom
- Institute of Translational Genomics, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
| | - Adam S. Butterworth
- Wellcome Sanger Institute, Cambridge, United Kingdom
- MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
- The National Institute for Health Research Blood and Transplant Research Unit (NIHR BTRU) in Donor Health and Genomics, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, United Kingdom
- National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge and Cambridge University Hospitals, Cambridge, United Kingdom
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, United Kingdom
| | - Inês Barroso
- Wellcome Sanger Institute, Cambridge, United Kingdom
- MRC Epidemiology Unit, Wellcome Trust-MRC Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, United Kingdom
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Zelli V, Compagnoni C, Cannita K, Capelli R, Capalbo C, Di Vito Nolfi M, Alesse E, Zazzeroni F, Tessitore A. Applications of Next Generation Sequencing to the Analysis of Familial Breast/Ovarian Cancer. High Throughput 2020; 9:ht9010001. [PMID: 31936873 PMCID: PMC7151204 DOI: 10.3390/ht9010001] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2019] [Revised: 01/02/2020] [Accepted: 01/07/2020] [Indexed: 12/24/2022] Open
Abstract
Next generation sequencing (NGS) provides a powerful tool in the field of medical genetics, allowing one to perform multi-gene analysis and to sequence entire exomes (WES), transcriptomes or genomes (WGS). The generated high-throughput data are particularly suitable for enhancing the understanding of the genetic bases of complex, multi-gene diseases, such as cancer. Among the various types of tumors, those with a familial predisposition are of great interest for the isolation of novel genes or gene variants, detectable at the germline level and involved in cancer pathogenesis. The identification of novel genetic factors would have great translational value, helping clinicians in defining risk and prevention strategies. In this regard, it is known that the majority of breast/ovarian cases with familial predisposition, lacking variants in the highly penetrant BRCA1 and BRCA2 genes (non-BRCA), remains unexplained, although several less penetrant genes (e.g., ATM, PALB2) have been identified. In this scenario, NGS technologies offer a powerful tool for the discovery of novel factors involved in familial breast/ovarian cancer. In this review, we summarize and discuss the state of the art applications of NGS gene panels, WES and WGS in the context of familial breast/ovarian cancer.
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Affiliation(s)
- Veronica Zelli
- Department of Biotechnological and Applied Clinical Sciences, University of L’Aquila, Via Vetoio, Coppito 2, 67100 L’Aquila, Italy; (V.Z.); (C.C.); (R.C.); (M.D.V.N.); (E.A.); (F.Z.)
- Center for Molecular Diagnostics and Advanced Therapies, University of L’Aquila, Via Petrini, 67100 L’Aquila, Italy
| | - Chiara Compagnoni
- Department of Biotechnological and Applied Clinical Sciences, University of L’Aquila, Via Vetoio, Coppito 2, 67100 L’Aquila, Italy; (V.Z.); (C.C.); (R.C.); (M.D.V.N.); (E.A.); (F.Z.)
| | - Katia Cannita
- Medical Oncology Unit, St Salvatore Hospital, Via L. Natali 1, 67100 L’Aquila, Italy;
| | - Roberta Capelli
- Department of Biotechnological and Applied Clinical Sciences, University of L’Aquila, Via Vetoio, Coppito 2, 67100 L’Aquila, Italy; (V.Z.); (C.C.); (R.C.); (M.D.V.N.); (E.A.); (F.Z.)
| | - Carlo Capalbo
- Department of Molecular Medicine, University of Rome “La Sapienza”, Viale Regina Elena 324, 00161 Rome, Italy;
| | - Mauro Di Vito Nolfi
- Department of Biotechnological and Applied Clinical Sciences, University of L’Aquila, Via Vetoio, Coppito 2, 67100 L’Aquila, Italy; (V.Z.); (C.C.); (R.C.); (M.D.V.N.); (E.A.); (F.Z.)
| | - Edoardo Alesse
- Department of Biotechnological and Applied Clinical Sciences, University of L’Aquila, Via Vetoio, Coppito 2, 67100 L’Aquila, Italy; (V.Z.); (C.C.); (R.C.); (M.D.V.N.); (E.A.); (F.Z.)
| | - Francesca Zazzeroni
- Department of Biotechnological and Applied Clinical Sciences, University of L’Aquila, Via Vetoio, Coppito 2, 67100 L’Aquila, Italy; (V.Z.); (C.C.); (R.C.); (M.D.V.N.); (E.A.); (F.Z.)
| | - Alessandra Tessitore
- Department of Biotechnological and Applied Clinical Sciences, University of L’Aquila, Via Vetoio, Coppito 2, 67100 L’Aquila, Italy; (V.Z.); (C.C.); (R.C.); (M.D.V.N.); (E.A.); (F.Z.)
- Center for Molecular Diagnostics and Advanced Therapies, University of L’Aquila, Via Petrini, 67100 L’Aquila, Italy
- Correspondence:
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40
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McGrath J, Ohyama M, Simpson M. PADI3
, hair disorders and genomic investigation. Br J Dermatol 2019; 181:1115-1116. [DOI: 10.1111/bjd.18463] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- J.A. McGrath
- St John's Institute of Dermatology King's College London (Guy's Campus) London U.K
| | - M. Ohyama
- Department of Dermatology Kyorin University Faculty of Medicine Tokyo Japan
| | - M.A. Simpson
- Department of Medical and Molecular Genetics King's College London (Guy's Campus) London U.K
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41
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Afzal M, Alghamdi SS, Migdadi HH, Khan MA, Nurmansyah, Mirza SB, El-Harty E. Legume genomics and transcriptomics: From classic breeding to modern technologies. Saudi J Biol Sci 2019; 27:543-555. [PMID: 31889880 PMCID: PMC6933173 DOI: 10.1016/j.sjbs.2019.11.018] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Revised: 11/16/2019] [Accepted: 11/17/2019] [Indexed: 02/06/2023] Open
Abstract
Legumes are essential and play a significant role in maintaining food standards and augmenting physiochemical soil properties through the biological nitrogen fixation process. Biotic and abiotic factors are the main factors limiting legume production. Classical breeding methodologies have been explored extensively about the problem of truncated yield in legumes but have not succeeded at the desired rate. Conventional breeding improved legume genotypes but with more resources and time. Recently, the invention of next-generation sequencing (NGS) and high-throughput methods for genotyping have opened new avenues for research and developments in legume studies. During the last decade, genome sequencing for many legume crops documented. Sequencing and re-sequencing of important legume species have made structural variation and functional genomics conceivable. NGS and other molecular techniques such as the development of markers; genotyping; high density genetic linkage maps; quantitative trait loci (QTLs) identification, expressed sequence tags (ESTs), single nucleotide polymorphisms (SNPs); and transcription factors incorporated into existing breeding technologies have made possible the accurate and accelerated delivery of information for researchers. The application of genome sequencing, RNA sequencing (transcriptome sequencing), and DNA sequencing (re-sequencing) provide considerable insights for legume development and improvement programs. Moreover, RNA-Seq helps to characterize genes, including differentially expressed genes, and can be applied for functional genomics studies, especially when there is limited information available for the studied genomes. Genome-based crop development studies and the availability of genomics data as well as decision-making gears look be specific for breeding programs. This review mainly presents an overview of the path from classical breeding to new emerging genomics tools, which will trigger and accelerate genomics-assisted breeding for recognition of novel genes for yield and quality characters for sustainable legume crop production.
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Affiliation(s)
- Muhammad Afzal
- Department of Plant Production, College of Food and Agricultural Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Salem S Alghamdi
- Department of Plant Production, College of Food and Agricultural Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Hussein H Migdadi
- Department of Plant Production, College of Food and Agricultural Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Muhammad Altaf Khan
- Department of Plant Production, College of Food and Agricultural Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Nurmansyah
- Department of Plant Production, College of Food and Agricultural Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Shaher Bano Mirza
- Computational Biology and Molecular Simulations Laboratory, Department of Biophysics, School of Medicine, Bahcesehir University (BAU), Istanbul, Turkey.,Department of Biosciences, COMSATS Institute of Information Technology (CIIT), Chak Shahzad, Islamabad, Pakistan
| | - Ehab El-Harty
- Department of Plant Production, College of Food and Agricultural Sciences, King Saud University, Riyadh, Saudi Arabia
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Akgun-Dogan O, Simsek-Kiper PO, Taskiran E, Lissewski C, Brinkmann J, Schanze D, Göçmen R, Cagdas D, Bilginer Y, Utine GE, Zenker M, Ozen S, Tezcan İ, Alikasifoglu M, Boduroğlu K. ADA2 deficiency in a patient with Noonan syndrome-like disorder with loose anagen hair: The co-occurrence of two rare syndromes. Am J Med Genet A 2019; 179:2474-2480. [PMID: 31584751 DOI: 10.1002/ajmg.a.61363] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2019] [Revised: 08/25/2019] [Accepted: 08/27/2019] [Indexed: 12/12/2022]
Abstract
Noonan syndrome-like disorder with loose anagen hair (NS/LAH) is one of the RASopathies, a group of clinically related developmental disorders caused by germline mutations in genes that encode components acting in the RAS/MAPK pathway. Among RASopathies, NS/LAH (OMIM 607721) is an extremely rare, multiple anomaly syndrome characterized by dysmorphic facial features similar to those observed in Noonan syndrome along with some distinctive ectodermal findings including easily pluckable, sparse, thin, and slow-growing hair. ADA2 deficiency (DADA2, OMIM 615688) is a monogenic autoinflammatory disorder caused by homozygous or compound heterozygous mutations in ADA2, with clinical features including recurrent fever, livedo racemosa, hepatosplenomegaly, and strokes as well as immune dysregulation. This is the first report of NS/LAH and ADA2 deficiency in the same individual. We report on a patient presenting with facial features, recurrent infections and ectodermal findings in whom both the clinical and molecular diagnoses of NS/LAH and ADA2 deficiency were established, respectively.
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Affiliation(s)
- Ozlem Akgun-Dogan
- Division of Pediatric Genetics, Department of Pediatrics, Hacettepe University Faculty of Medicine, Ankara, Turkey.,Division of Pediatric Genetics, Department of Pediatrics, Ümraniye Training and Research Hospital, Istanbul, Turkey
| | - Pelin O Simsek-Kiper
- Division of Pediatric Genetics, Department of Pediatrics, Hacettepe University Faculty of Medicine, Ankara, Turkey
| | - Ekim Taskiran
- Department of Medical Genetics, Hacettepe University Faculty of Medicine, Ankara, Turkey
| | - Christina Lissewski
- Institute of Human Genetics, University Hospital Magdeburg, Magdeburg, Germany
| | - Julia Brinkmann
- Institute of Human Genetics, University Hospital Magdeburg, Magdeburg, Germany
| | - Denny Schanze
- Institute of Human Genetics, University Hospital Magdeburg, Magdeburg, Germany
| | - Rahşan Göçmen
- Department of Radiology, Hacettepe University Faculty of Medicine, Ankara, Turkey
| | - Deniz Cagdas
- Division of Pediatric Immunology, Department of Pediatrics, Hacettepe University Faculty of Medicine, Ankara, Turkey
| | - Yelda Bilginer
- Division of Pediatric Rheumatology, Department of Pediatrics, Hacettepe University Faculty of Medicine, Ankara, Turkey
| | - Gülen E Utine
- Division of Pediatric Genetics, Department of Pediatrics, Hacettepe University Faculty of Medicine, Ankara, Turkey
| | - Martin Zenker
- Institute of Human Genetics, University Hospital Magdeburg, Magdeburg, Germany
| | - Seza Ozen
- Division of Pediatric Rheumatology, Department of Pediatrics, Hacettepe University Faculty of Medicine, Ankara, Turkey
| | - İlhan Tezcan
- Division of Pediatric Immunology, Department of Pediatrics, Hacettepe University Faculty of Medicine, Ankara, Turkey
| | - Mehmet Alikasifoglu
- Division of Pediatric Genetics, Department of Pediatrics, Hacettepe University Faculty of Medicine, Ankara, Turkey.,Department of Medical Genetics, Hacettepe University Faculty of Medicine, Ankara, Turkey
| | - Koray Boduroğlu
- Division of Pediatric Genetics, Department of Pediatrics, Hacettepe University Faculty of Medicine, Ankara, Turkey.,Department of Medical Genetics, Hacettepe University Faculty of Medicine, Ankara, Turkey
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43
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Gampawar P, Saba Y, Werner U, Schmidt R, Müller-Myhsok B, Schmidt H. Evaluation of the Performance of AmpliSeq and SureSelect Exome Sequencing Libraries for Ion Proton. Front Genet 2019; 10:856. [PMID: 31608108 PMCID: PMC6774276 DOI: 10.3389/fgene.2019.00856] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2019] [Accepted: 08/16/2019] [Indexed: 12/14/2022] Open
Abstract
Library preparation for whole-exome sequencing is a critical step serving the enrichment of the regions of interest. For Ion Proton, there are only two exome library preparation methods available, AmpliSeq and SureSelect. Although of major interest, a comparison of the two methods is hitherto missing in the literature. Here, we systematically evaluate the performance of AmpliSeq and SureSelect and present an improved variant calling pipeline. We used 12 in-house DNA samples with genome-wide and exome microarray data and a commercially available reference DNA (NA12878) for evaluation. Both methods had a high concordance (>97%) with microarray genotypes and, when validating against NA12878, a sensitivity and positive predictive values of >93% and >80%, respectively. Application of our variant calling pipeline decreased the number of false positive variants dramatically by 90% and resulted in positive predictive value of 97%. This improvement is highly relevant in research as well as clinical setting.
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Affiliation(s)
- Piyush Gampawar
- Research Unit-Genetic Epidemiology, Gottfried Schatz Research Centre for Cell Signaling, Metabolism and Aging, Molecular Biology and Biochemistry, Medical University Graz, Graz, Austria
| | - Yasaman Saba
- Research Unit-Genetic Epidemiology, Gottfried Schatz Research Centre for Cell Signaling, Metabolism and Aging, Molecular Biology and Biochemistry, Medical University Graz, Graz, Austria
| | - Ulrike Werner
- Research Unit-Genetic Epidemiology, Gottfried Schatz Research Centre for Cell Signaling, Metabolism and Aging, Molecular Biology and Biochemistry, Medical University Graz, Graz, Austria
| | - Reinhold Schmidt
- Department of Neurology, Clinical Division of Neurogeriatrics, Medical University Graz, Graz, Austria
| | - Bertram Müller-Myhsok
- Max Planck Institute of Psychiatry, Munich, Germany.,Munich Cluster for Systems Neurology (SyNergy), Munich, Germany.,Institute of Translational Medicine, University of Liverpool, Liverpool, United Kingdom
| | - Helena Schmidt
- Research Unit-Genetic Epidemiology, Gottfried Schatz Research Centre for Cell Signaling, Metabolism and Aging, Molecular Biology and Biochemistry, Medical University Graz, Graz, Austria
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44
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Variant calling and quality control of large-scale human genome sequencing data. Emerg Top Life Sci 2019; 3:399-409. [DOI: 10.1042/etls20190007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2019] [Revised: 06/28/2019] [Accepted: 07/16/2019] [Indexed: 12/12/2022]
Abstract
Abstract
Next-generation sequencing has allowed genetic studies to collect genome sequencing data from a large number of individuals. However, raw sequencing data are not usually interpretable due to fragmentation of the genome and technical biases; therefore, analysis of these data requires many computational approaches. First, for each sequenced individual, sequencing data are aligned and further processed to account for technical biases. Then, variant calling is performed to obtain information on the positions of genetic variants and their corresponding genotypes. Quality control (QC) is applied to identify individuals and genetic variants with sequencing errors. These procedures are necessary to generate accurate variant calls from sequencing data, and many computational approaches have been developed for these tasks. This review will focus on current widely used approaches for variant calling and QC.
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45
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Lee JM, Correia K, Loupe J, Kim KH, Barker D, Hong EP, Chao MJ, Long JD, Lucente D, Vonsattel JPG, Pinto RM, Abu Elneel K, Ramos EM, Mysore JS, Gillis T, Wheeler VC, MacDonald ME, Gusella JF, McAllister B, Massey T, Medway C, Stone TC, Hall L, Jones L, Holmans P, Kwak S, Ehrhardt AG, Sampaio C, Ciosi M, Maxwell A, Chatzi A, Monckton DG, Orth M, Landwehrmeyer GB, Paulsen JS, Dorsey ER, Shoulson I, Myers RH. CAG Repeat Not Polyglutamine Length Determines Timing of Huntington's Disease Onset. Cell 2019; 178:887-900.e14. [PMID: 31398342 PMCID: PMC6700281 DOI: 10.1016/j.cell.2019.06.036] [Citation(s) in RCA: 338] [Impact Index Per Article: 56.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2019] [Revised: 04/08/2019] [Accepted: 06/27/2019] [Indexed: 01/27/2023]
Abstract
Variable, glutamine-encoding, CAA interruptions indicate that a property of the uninterrupted HTT CAG repeat sequence, distinct from the length of huntingtin's polyglutamine segment, dictates the rate at which Huntington's disease (HD) develops. The timing of onset shows no significant association with HTT cis-eQTLs but is influenced, sometimes in a sex-specific manner, by polymorphic variation at multiple DNA maintenance genes, suggesting that the special onset-determining property of the uninterrupted CAG repeat is a propensity for length instability that leads to its somatic expansion. Additional naturally occurring genetic modifier loci, defined by GWAS, may influence HD pathogenesis through other mechanisms. These findings have profound implications for the pathogenesis of HD and other repeat diseases and question the fundamental premise that polyglutamine length determines the rate of pathogenesis in the "polyglutamine disorders."
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46
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Baker E, Sims R, Leonenko G, Frizzati A, Harwood JC, Grozeva D, Morgan K, Passmore P, Holmes C, Powell J, Brayne C, Gill M, Mead S, Bossù P, Spalletta G, Goate AM, Cruchaga C, Maier W, Heun R, Jessen F, Peters O, Dichgans M, FröLich L, Ramirez A, Jones L, Hardy J, Ivanov D, Hill M, Holmans P, Allen ND, Morgan BP, Seshadri S, Schellenberg GD, Amouyel P, Williams J, Escott-Price V. Gene-based analysis in HRC imputed genome wide association data identifies three novel genes for Alzheimer's disease. PLoS One 2019; 14:e0218111. [PMID: 31283791 PMCID: PMC6613773 DOI: 10.1371/journal.pone.0218111] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2019] [Accepted: 05/27/2019] [Indexed: 12/17/2022] Open
Abstract
Late onset Alzheimer's disease is the most common form of dementia for which about 30 susceptibility loci have been reported. The aim of the current study is to identify novel genes associated with Alzheimer's disease using the largest up-to-date reference single nucleotide polymorphism (SNP) panel, the most accurate imputation software and a novel gene-based analysis approach which tests for patterns of association within genes, in the powerful genome-wide association dataset of the International Genomics of Alzheimer's Project Consortium, comprising over 7 million genotypes from 17,008 Alzheimer's cases and 37,154 controls. In addition to earlier reported genes, we detected three novel gene-wide significant loci PPARGC1A (p = 2.2 × 10-6), RORA (p = 7.4 × 10-7) and ZNF423 (p = 2.1 × 10-6). PPARGC1A and RORA are involved in circadian rhythm; circadian disturbances are one of the earliest symptoms of Alzheimer's disease. PPARGC1A is additionally linked to energy metabolism and the generation of amyloid beta plaques. RORA is involved in a variety of functions apart from circadian rhythm, such as cholesterol metabolism and inflammation. The ZNF423 gene resides in an Alzheimer's disease-specific protein network and is likely involved with centrosomes and DNA damage repair.
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Affiliation(s)
- Emily Baker
- Medical Research Council Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, United Kingdom
- UK Dementia Research Institute at Cardiff University, Cardiff, United Kingdom
| | - Rebecca Sims
- Medical Research Council Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, United Kingdom
| | - Ganna Leonenko
- Medical Research Council Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, United Kingdom
| | - Aura Frizzati
- Medical Research Council Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, United Kingdom
| | - Janet C. Harwood
- Medical Research Council Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, United Kingdom
| | - Detelina Grozeva
- Medical Research Council Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, United Kingdom
| | | | | | | | | | | | - Kevin Morgan
- Human Genetics, School of Life Sciences, Life Sciences Building A27, University Park, University of Nottingham, Nottingham, NG7 2RD, United Kingdom
| | - Peter Passmore
- Centre for Public Health, School of Medicine, Dentistry and Biomedical Sciences, Queens University, Belfast, United Kingdom
| | - Clive Holmes
- Division of Clinical Neurosciences, School of Medicine, University of Southampton, Southampton, United Kingdom
| | - John Powell
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, Kings College London, London, United Kingdom
- Genetic Epidemiology, QIMR Berghofer Medical Research Institute, Herston, Queensland, Australia
| | - Carol Brayne
- Institute of Public Health, University of Cambridge, Cambridge, United Kingdom
| | - Michael Gill
- Mercer’s Institute for Research on Ageing, St. James’ Hospital, Dublin, Ireland
- James Hospital and Trinity College, Dublin, Ireland
| | - Simon Mead
- MRC Prion Unit at UCL, Institute of Prion Diseases, London, United Kingdom
| | - Paola Bossù
- Experimental Neuropsychobiology Laboratory, IRCCS Santa Lucia Foundation, Department of Clinical and Behavioral Neurology, Rome, Italy
| | | | - Alison M. Goate
- Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
- Hope Center Program on Protein Aggregation and Neurodegeneration, Washington University School of Medicine, St Louis, Missouri, United States of America
| | - Carlos Cruchaga
- Hope Center Program on Protein Aggregation and Neurodegeneration, Washington University School of Medicine, St Louis, Missouri, United States of America
- Department of Psychiatry, Washington University School of Medicine, St Louis, Missouri, United States of America
| | - Wolfgang Maier
- German Centre for Neurodegenerative Diseases (DZNE), 53127 Bonn, Germany
| | - Reinhard Heun
- Department of Psychiatry and Psychotherapy, University of Bonn, 53127, Bonn, Germany
| | - Frank Jessen
- German Centre for Neurodegenerative Diseases (DZNE), 53127 Bonn, Germany
- Department of Psychiatry and Psychotherapy, University of Cologne, 50937 Cologne, Germany
| | - Oliver Peters
- Department of Psychiatry and Psychotherapy, Charité Berlin, Berlin, Germany
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
| | - Martin Dichgans
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE, Munich), Munich, 80336, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Lutz FröLich
- Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Heidelberg, Germany
| | - Alfredo Ramirez
- Department of Psychiatry and Psychotherapy, University of Cologne, 50937 Cologne, Germany
- Department for Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn, Germany
| | - Lesley Jones
- Medical Research Council Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, United Kingdom
| | - John Hardy
- Department of Molecular Neuroscience, UCL, Institute of Neurology, London, United Kingdom
| | - Dobril Ivanov
- UK Dementia Research Institute at Cardiff University, Cardiff, United Kingdom
| | - Matthew Hill
- UK Dementia Research Institute at Cardiff University, Cardiff, United Kingdom
| | - Peter Holmans
- Medical Research Council Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, United Kingdom
| | - Nicholas D. Allen
- UK Dementia Research Institute at Cardiff University, Cardiff, United Kingdom
| | - B. Paul Morgan
- UK Dementia Research Institute at Cardiff University, Cardiff, United Kingdom
| | - Sudha Seshadri
- Department of Neurology, Boston University School of Medicine, Boston, Massachusetts, United States of America
| | - Gerard D. Schellenberg
- Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States of America
| | - Philippe Amouyel
- Univ. Lille, Inserm, CHU Lille University Hospital, Institut Pasteur de Lille, LabEx DISTALZ-UMR1167 - RID-AGE - Risk factors and molecular determinants of aging-related, F-59000 Lille, France
| | - Julie Williams
- Medical Research Council Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, United Kingdom
- UK Dementia Research Institute at Cardiff University, Cardiff, United Kingdom
| | - Valentina Escott-Price
- Medical Research Council Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, United Kingdom
- UK Dementia Research Institute at Cardiff University, Cardiff, United Kingdom
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Spracklen CN, Karaderi T, Yaghootkar H, Schurmann C, Fine RS, Kutalik Z, Preuss MH, Lu Y, Wittemans LBL, Adair LS, Allison M, Amin N, Auer PL, Bartz TM, Blüher M, Boehnke M, Borja JB, Bork-Jensen J, Broer L, Chasman DI, Chen YDI, Chirstofidou P, Demirkan A, van Duijn CM, Feitosa MF, Garcia ME, Graff M, Grallert H, Grarup N, Guo X, Haesser J, Hansen T, Harris TB, Highland HM, Hong J, Ikram MA, Ingelsson E, Jackson R, Jousilahti P, Kähönen M, Kizer JR, Kovacs P, Kriebel J, Laakso M, Lange LA, Lehtimäki T, Li J, Li-Gao R, Lind L, Luan J, Lyytikäinen LP, MacGregor S, Mackey DA, Mahajan A, Mangino M, Männistö S, McCarthy MI, McKnight B, Medina-Gomez C, Meigs JB, Molnos S, Mook-Kanamori D, Morris AP, de Mutsert R, Nalls MA, Nedeljkovic I, North KE, Pennell CE, Pradhan AD, Province MA, Raitakari OT, Raulerson CK, Reiner AP, Ridker PM, Ripatti S, Roberston N, Rotter JI, Salomaa V, Sandoval-Zárate AA, Sitlani CM, Spector TD, Strauch K, Stumvoll M, Taylor KD, Thuesen B, Tönjes A, Uitterlinden AG, Venturini C, Walker M, Wang CA, Wang S, Wareham NJ, Willems SM, Willems van Dijk K, Wilson JG, Wu Y, Yao J, Young KL, Langenberg C, Frayling TM, et alSpracklen CN, Karaderi T, Yaghootkar H, Schurmann C, Fine RS, Kutalik Z, Preuss MH, Lu Y, Wittemans LBL, Adair LS, Allison M, Amin N, Auer PL, Bartz TM, Blüher M, Boehnke M, Borja JB, Bork-Jensen J, Broer L, Chasman DI, Chen YDI, Chirstofidou P, Demirkan A, van Duijn CM, Feitosa MF, Garcia ME, Graff M, Grallert H, Grarup N, Guo X, Haesser J, Hansen T, Harris TB, Highland HM, Hong J, Ikram MA, Ingelsson E, Jackson R, Jousilahti P, Kähönen M, Kizer JR, Kovacs P, Kriebel J, Laakso M, Lange LA, Lehtimäki T, Li J, Li-Gao R, Lind L, Luan J, Lyytikäinen LP, MacGregor S, Mackey DA, Mahajan A, Mangino M, Männistö S, McCarthy MI, McKnight B, Medina-Gomez C, Meigs JB, Molnos S, Mook-Kanamori D, Morris AP, de Mutsert R, Nalls MA, Nedeljkovic I, North KE, Pennell CE, Pradhan AD, Province MA, Raitakari OT, Raulerson CK, Reiner AP, Ridker PM, Ripatti S, Roberston N, Rotter JI, Salomaa V, Sandoval-Zárate AA, Sitlani CM, Spector TD, Strauch K, Stumvoll M, Taylor KD, Thuesen B, Tönjes A, Uitterlinden AG, Venturini C, Walker M, Wang CA, Wang S, Wareham NJ, Willems SM, Willems van Dijk K, Wilson JG, Wu Y, Yao J, Young KL, Langenberg C, Frayling TM, Kilpeläinen TO, Lindgren CM, Loos RJF, Mohlke KL. Exome-Derived Adiponectin-Associated Variants Implicate Obesity and Lipid Biology. Am J Hum Genet 2019; 105:15-28. [PMID: 31178129 DOI: 10.1016/j.ajhg.2019.05.002] [Show More Authors] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2019] [Accepted: 04/30/2019] [Indexed: 12/25/2022] Open
Abstract
Circulating levels of adiponectin, an adipocyte-secreted protein associated with cardiovascular and metabolic risk, are highly heritable. To gain insights into the biology that regulates adiponectin levels, we performed an exome array meta-analysis of 265,780 genetic variants in 67,739 individuals of European, Hispanic, African American, and East Asian ancestry. We identified 20 loci associated with adiponectin, including 11 that had been reported previously (p < 2 × 10-7). Comparison of exome array variants to regional linkage disequilibrium (LD) patterns and prior genome-wide association study (GWAS) results detected candidate variants (r2 > .60) spanning as much as 900 kb. To identify potential genes and mechanisms through which the previously unreported association signals act to affect adiponectin levels, we assessed cross-trait associations, expression quantitative trait loci in subcutaneous adipose, and biological pathways of nearby genes. Eight of the nine loci were also associated (p < 1 × 10-4) with at least one obesity or lipid trait. Candidate genes include PRKAR2A, PTH1R, and HDAC9, which have been suggested to play roles in adipocyte differentiation or bone marrow adipose tissue. Taken together, these findings provide further insights into the processes that influence circulating adiponectin levels.
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Affiliation(s)
- Cassandra N Spracklen
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Tugce Karaderi
- The Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7FZ, UK; Department of Biological Sciences, Faculty of Arts and Sciences, Eastern Mediterranean University, Famagusta, Cyprus; Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen 2200, Denmark; DTU Health Technology, Technical University of Denmark, Lyngby 2800, Denmark
| | - Hanieh Yaghootkar
- Genetics of Complex Traits, University of Exeter Medical School, Royal Devon & Exeter Hospital, Exeter EX2 5DW, UK; Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - Claudia Schurmann
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Rebecca S Fine
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA; Division of Endocrinology and Center for Basic and Translational Obesity Research, Boston Children's Hospital, Boston, MA 02115, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Zoltan Kutalik
- Genetics of Complex Traits, University of Exeter Medical School, Royal Devon & Exeter Hospital, Exeter EX2 5DW, UK; University Center for Primary Care and Public Health, University of Lausanne, Lausanne 1010, Switzerland; Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland
| | - Michael H Preuss
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Yingchang Lu
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Division of Epidemiology, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt Epidemiology Center, Vanderbilt University School of Medicine, Nashville, TN 37203-1738, USA; Genetics of Obesity and Related Metabolic Traits Program, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Laura B L Wittemans
- The Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7FZ, UK; MRC Epidemiology Unit, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Linda S Adair
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27516, USA
| | - Matthew Allison
- Department of Family Medicine and Public Health, University of California, San Diego, CA 92093, USA
| | - Najaf Amin
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam 3015CN, the Netherlands
| | - Paul L Auer
- Joseph J. Zilber School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, WI 53201, USA
| | - Traci M Bartz
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA 98101, USA; Department of Biostatistics, University of Washington, Seattle, WA 98101, USA
| | - Matthias Blüher
- Medical Department III - Endocrinology, Nephrology, Rheumatology, University of Leipzig, Leipzig 4103, Germany
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Judith B Borja
- Office of Population Studies Foundation, Inc, Cebu City, Philippines; Department of Nutrition and Dietetics, University of San Carlos, Cebu City, Philippines
| | - Jette Bork-Jensen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen 2200, Denmark
| | - Linda Broer
- Department of Internal Medicine, Erasmus MC University Medical Center Rotterdam, Rotterdam 3000 CA, the Netherlands
| | - Daniel I Chasman
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA 02215, USA; Harvard Medical School, Boston, MA 02115, USA
| | - Yii-Der Ida Chen
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, LABioMed at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Paraskevi Chirstofidou
- Department of Twin Research and Genetic Epidemiology, Kings College London, London SE1 7EH, UK
| | - Ayse Demirkan
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam 3015CN, the Netherlands
| | - Cornelia M van Duijn
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam 3015CN, the Netherlands
| | - Mary F Feitosa
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Melissa E Garcia
- National Heart, Lung, and Blood Institute, Bethesda, MD 20892, USA
| | - Mariaelisa Graff
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Carolina Center for Genome Sciences, Chapel Hill, NC 27599, USA
| | - Harald Grallert
- Research Unit of Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München Research Center for Environmental Health, München-Neuherberg 85764, Germany; German Center for Diabetes Research, München-Neuherberg 85765, Germany
| | - Niels Grarup
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen 2200, Denmark
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, LABioMed at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Jeffrey Haesser
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Torben Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen 2200, Denmark
| | - Tamara B Harris
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIH, Bethesda, MD 20892, USA
| | - Heather M Highland
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jaeyoung Hong
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 2118, USA
| | - M Arfan Ikram
- Department of Internal Medicine, Erasmus MC University Medical Center Rotterdam, Rotterdam 3000 CA, the Netherlands; Department of Epidemiology, Erasmus MC University Medical Center Rotterdam, Rotterdam 3000 CA, the Netherlands
| | - Erik Ingelsson
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University, Palo Alto, CA 94304, USA; Stanford Cardiovascular Institute, Stanford University of Medicine, Palo Alto, CA 94304, USA; Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala 75185, Sweden; Stanford Diabetes Research Center, Stanford University, Stanford, CA 94305, USA
| | - Rebecca Jackson
- Division of Endocrinology, Diabetes, and Metabolism, Ohio State University, Columbus, OH 43210, USA
| | - Pekka Jousilahti
- Department of Public Health Solutions, National Institute for Health and Welfare, Helsinki 00271, Finland
| | - Mika Kähönen
- Department of Clinical Physiology, Tampere University Hospital, Tampere 33522, Finland; Department of Clinical Physiology, Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere 33522, Finland
| | - Jorge R Kizer
- Department of Medicine, Albert Einstein College of Medicine, Bronx, NY 10461, USA; Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Peter Kovacs
- Medical Department III - Endocrinology, Nephrology, Rheumatology, University of Leipzig, Leipzig 4103, Germany
| | - Jennifer Kriebel
- Research Unit of Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München Research Center for Environmental Health, München-Neuherberg 85764, Germany; German Center for Diabetes Research, München-Neuherberg 85765, Germany
| | - Markku Laakso
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University of Hospital, Kuopio 70029 KYS, Finland
| | - Leslie A Lange
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado-Denver, Denver, CO 80045, USA
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere 33520, Finland; Department of Clinical Chemistry, Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere 33522, Finland
| | - Jin Li
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University, Palo Alto, CA 94304, USA
| | - Ruifang Li-Gao
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden 2333 ZA, the Netherlands
| | - Lars Lind
- Department of Medical Sciences, Uppsala University, Uppsala 75185, Sweden
| | - Jian'an Luan
- MRC Epidemiology Unit, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Leo-Pekka Lyytikäinen
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere 33522, Finland; Department of Clinical Chemistry, Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere 33521, Finland
| | - Stuart MacGregor
- QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia
| | - David A Mackey
- Faculty of Health and Medical Sciences, The University of Western Australia, Perth, WA 6009, Australia; Centre for Ophthalmology and Visual Science, Lions Eye Institute, The University of Western Australia, Perth, WA 6009, Australia
| | - Anubha Mahajan
- The Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7FZ, UK; Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford OX3 7FZ, UK
| | - Massimo Mangino
- Department of Twin Research and Genetic Epidemiology, Kings College London, London SE1 7EH, UK; NIHR Biomedical Research Centre, Guy's and St Thomas' Foundation Trust, London SE1 9RT, UK
| | - Satu Männistö
- Department of Public Health Solutions, National Institute for Health and Welfare, Helsinki 00271, Finland
| | - Mark I McCarthy
- The Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7FZ, UK; Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford OX3 7FZ, UK; Oxford NIHR Biomedical Research Centre, Oxford University Hospitals Trust, Oxford OX3 7FZ, UK
| | - Barbara McKnight
- Department of Biostatistics, University of Washington, Seattle, WA 98101, USA
| | - Carolina Medina-Gomez
- Department of Internal Medicine, Erasmus MC University Medical Center Rotterdam, Rotterdam 3000 CA, the Netherlands; Department of Epidemiology, Erasmus MC University Medical Center Rotterdam, Rotterdam 3000 CA, the Netherlands
| | - James B Meigs
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA; Program in Population and Medical Genetics, Broad Institute, Cambridge, MA 02114, USA
| | - Sophie Molnos
- Research Unit of Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München Research Center for Environmental Health, München-Neuherberg 85764, Germany; German Center for Diabetes Research, München-Neuherberg 85765, Germany
| | - Dennis Mook-Kanamori
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden 2333 ZA, the Netherlands; Department of Public Health and Primary Care, Leiden University Medical Center, Leiden 2334 ZA, the Netherlands
| | - Andrew P Morris
- The Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7FZ, UK; Department of Biostatistics, University of Liverpool, Liverpool L69 3GL, UK
| | - Renee de Mutsert
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden 2333 ZA, the Netherlands
| | - Mike A Nalls
- Laboratory of Neurogenetics, National Institute on Aging, NIH, Bethesda, MD 20892, USA; Data Tecnica International, Glen Echo, MD 20812, USA
| | - Ivana Nedeljkovic
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam 3015CN, the Netherlands
| | - Kari E North
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Craig E Pennell
- School of Medicine and Public Health, Faculty of Medicine and Health, The University of Newcastle, Newcastle, NSW 2305, Australia
| | - Aruna D Pradhan
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA 02215, USA; Harvard Medical School, Boston, MA 02115, USA
| | - Michael A Province
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Olli T Raitakari
- Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland; Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland; Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
| | - Chelsea K Raulerson
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Alex P Reiner
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Paul M Ridker
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA 02215, USA; Harvard Medical School, Boston, MA 02115, USA
| | - Samuli Ripatti
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Public Health, University of Helsinki, Helsinki 00014, Finland; Institute for Molecular Medicine Finland, Helsinki 00014, Finland
| | - Neil Roberston
- The Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7FZ, UK; Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford OX3 7FZ, UK
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, LABioMed at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Veikko Salomaa
- Department of Public Health Solutions, National Institute for Health and Welfare, Helsinki 00271, Finland
| | | | - Colleen M Sitlani
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA 98101, USA
| | - Tim D Spector
- Department of Twin Research and Genetic Epidemiology, Kings College London, London SE1 7EH, UK
| | - Konstantin Strauch
- Institute of Genetic Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg 85764, Germany; Chair of Genetic Epidemiology, Institute of Medical Informatics, Biometry and Epidemiology, Ludwig-Maximilians-Universität, Munich 81377, Germany
| | - Michael Stumvoll
- Medical Department III - Endocrinology, Nephrology, Rheumatology, University of Leipzig, Leipzig 4103, Germany
| | - Kent D Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, LABioMed at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Betina Thuesen
- Center for Clinical Research and Disease Prevention, Bispebjerg and Frederiksberg Hospital, The Capital Region, Copenhagen 2400, Denmark
| | - Anke Tönjes
- Medical Department III - Endocrinology, Nephrology, Rheumatology, University of Leipzig, Leipzig 4103, Germany
| | - Andre G Uitterlinden
- Department of Internal Medicine, Erasmus MC University Medical Center Rotterdam, Rotterdam 3000 CA, the Netherlands; Department of Epidemiology, Erasmus MC University Medical Center Rotterdam, Rotterdam 3000 CA, the Netherlands
| | - Cristina Venturini
- Department of Twin Research and Genetic Epidemiology, Kings College London, London SE1 7EH, UK
| | - Mark Walker
- Institute of Cellular Medicine, The Medical School, Newcastle University, Newcastle, UK
| | - Carol A Wang
- School of Medicine and Public Health, Faculty of Medicine and Health, The University of Newcastle, Newcastle, NSW 2305, Australia
| | - Shuai Wang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 2118, USA
| | | | - Sara M Willems
- MRC Epidemiology Unit, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Ko Willems van Dijk
- Department of Internal Medicine, Division of Endocrinology, Leiden University Medical Center, Leiden 2333 ZA, the Netherlands; Einthoven Laboratory for Experimental Vascular Medicine, Leiden 2333 ZA, the Netherlands; Department of Human Genetics, Leiden University Medical Center, Leiden 2333 ZA, the Netherlands
| | - James G Wilson
- Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS 39216, USA
| | - Ying Wu
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jie Yao
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, LABioMed at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Kristin L Young
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | | | - Timothy M Frayling
- Genetics of Complex Traits, University of Exeter Medical School, Royal Devon & Exeter Hospital, Exeter EX2 5DW, UK
| | - Tuomas O Kilpeläinen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen 2200, Denmark; Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Cecilia M Lindgren
- The Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7FZ, UK; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7LF, UK
| | - Ruth J F Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; The Mindich Child Health and Development Institute, Ichan School of Medicine at Mount Sinai, New York, NY 10029, USA.
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
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Bope CD, Chimusa ER, Nembaware V, Mazandu GK, de Vries J, Wonkam A. Dissecting in silico Mutation Prediction of Variants in African Genomes: Challenges and Perspectives. Front Genet 2019; 10:601. [PMID: 31293624 PMCID: PMC6603221 DOI: 10.3389/fgene.2019.00601] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Accepted: 06/05/2019] [Indexed: 12/20/2022] Open
Abstract
Genomic medicine is set to drastically improve clinical care globally due to high throughput technologies which enable speedy in silico detection and analysis of clinically relevant mutations. However, the variability in the in silico prediction methods and categorization of functionally relevant genetic variants can pose specific challenges in some populations. In silico mutation prediction tools could lead to high rates of false positive/negative results, particularly in African genomes that harbor the highest genetic diversity and that are disproportionately underrepresented in public databases and reference panels. These issues are particularly relevant with the recent increase in initiatives, such as the Human Heredity and Health (H3Africa), that are generating huge amounts of genomic sequence data in the absence of policies to guide genomic researchers to return results of variants in so-called actionable genes to research participants. This report (i) provides an inventory of publicly available Whole Exome/Genome data from Africa which could help improve reference panels and explore the frequency of pathogenic variants in actionable genes and related challenges, (ii) reviews available in silico prediction mutation tools and the criteria for categorization of pathogenicity of novel variants, and (iii) proposes recommendations for analyzing pathogenic variants in African genomes for their use in research and clinical practice. In conclusion, this work proposes criteria to define mutation pathogenicity and actionability in human genetic research and clinical practice in Africa and recommends setting up an African expert panel to oversee the proposed criteria.
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Affiliation(s)
- Christian Domilongo Bope
- Department of Pathology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- Departments of Mathematics and Computer Sciences, Faculty of Sciences, University of Kinshasa, Kinshasa, Democratic Republic of Congo
| | - Emile R. Chimusa
- Department of Pathology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Victoria Nembaware
- Department of Pathology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Gaston K. Mazandu
- Department of Pathology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Jantina de Vries
- Department of Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Ambroise Wonkam
- Department of Pathology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- Department of Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- Institute of Infectious Diseases and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
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Li Z, Li X, Liu Y, Shen J, Chen H, Zhou H, Morrison AC, Boerwinkle E, Lin X. Dynamic Scan Procedure for Detecting Rare-Variant Association Regions in Whole-Genome Sequencing Studies. Am J Hum Genet 2019; 104:802-814. [PMID: 30982610 PMCID: PMC6507043 DOI: 10.1016/j.ajhg.2019.03.002] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2018] [Accepted: 03/01/2019] [Indexed: 11/19/2022] Open
Abstract
Whole-genome sequencing (WGS) studies are being widely conducted in order to identify rare variants associated with human diseases and disease-related traits. Classical single-marker association analyses for rare variants have limited power, and variant-set-based analyses are commonly used by researchers for analyzing rare variants. However, existing variant-set-based approaches need to pre-specify genetic regions for analysis; hence, they are not directly applicable to WGS data because of the large number of intergenic and intron regions that consist of a massive number of non-coding variants. The commonly used sliding-window method requires the pre-specification of fixed window sizes, which are often unknown as a priori, are difficult to specify in practice, and are subject to limitations given that the sizes of genetic-association regions are likely to vary across the genome and phenotypes. We propose a computationally efficient and dynamic scan-statistic method (Scan the Genome [SCANG]) for analyzing WGS data; this method flexibly detects the sizes and the locations of rare-variant association regions without the need to specify a prior, fixed window size. The proposed method controls for the genome-wise type I error rate and accounts for the linkage disequilibrium among genetic variants. It allows the detected sizes of rare-variant association regions to vary across the genome. Through extensive simulated studies that consider a wide variety of scenarios, we show that SCANG substantially outperforms several alternative methods for detecting rare-variant-associations while controlling for the genome-wise type I error rates. We illustrate SCANG by analyzing the WGS lipids data from the Atherosclerosis Risk in Communities (ARIC) study.
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Affiliation(s)
- Zilin Li
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Xihao Li
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Yaowu Liu
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Jincheng Shen
- Department of Population Health Sciences, University of Utah, Salt Lake City, UT 84108, USA
| | - Han Chen
- Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, the University of Texas Health Science Center at Houston, Houston, TX 77030, USA; Center for Precision Health, School of Public Health and School of Biomedical Informatics, the University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Hufeng Zhou
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Alanna C Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, the University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Eric Boerwinkle
- Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, the University of Texas Health Science Center at Houston, Houston, TX 77030, USA; Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Xihong Lin
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Department of Statistics, Harvard University, Cambridge, MA 02138, USA.
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Abstract
PURPOSE OF REVIEW Soon after the first genome-wide association study (GWAS) for type 2 diabetes (T2D) was published, it was hypothesized that rare and low-frequency variants might explain a substantial proportion of disease risk. Rare coding variants in particular were emphasized given their large expected role in disease. This review summarizes the extent to which recent T2D genetic studies provide evidence for or against this hypothesis. RECENT FINDINGS Following a comprehensive study of T2D genetic architecture using three sequencing and genotyping technologies, four even larger studies have provided a yet higher resolution view of the role of rare and low-frequency coding variation in T2D susceptibility. Empirical evidence strongly suggests that common regulatory variants are the dominant contributor to T2D heritability. However, rare coding variants may nonetheless be pervasive across T2D-relevant genes. A strategy using common variants to map disease genes, and rare coding variants to link molecular gene perturbations to cellular and phenotypic effects, may be an effective means to investigate T2D pathogenesis and potential new therapies.
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Affiliation(s)
- Jason Flannick
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, USA.
- Department of Pediatrics, Harvard Medical School, Boston, MA, 02115, USA.
- Programs in Medical and Population Genetics and Metabolism, Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA.
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