|
1
|
Lee SSY, Stapleton F, MacGregor S, Mackey DA. Genome-wide association studies, Polygenic Risk Scores and Mendelian randomisation: an overview of common genetic epidemiology methods for ophthalmic clinicians. Br J Ophthalmol 2025; 109:433-441. [PMID: 39622623 PMCID: PMC12013552 DOI: 10.1136/bjo-2024-326554] [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: 09/23/2024] [Accepted: 11/17/2024] [Indexed: 01/12/2025]
Abstract
Genetic information will be increasingly integrated into clinical eye care within the current generation of ophthalmologists. For monogenic diseases such as retinoblastoma, genetic studies have been relatively straightforward as these conditions result from pathogenic variants in a single gene resulting in large physiological effects. However, most eye diseases result from the cumulative effects of multiple genetic variants and environmental factors. In such diseases, because each variant usually has an individually small effect, genetic studies for complex diseases are comparatively more challenging. This article aims to provide an overview of three genetic epidemiology methods for polygenic (or complex) diseases: genome-wide association studies (GWAS), Polygenic Risk Scores (PRS) and Mendelian randomisation (MR). A GWAS systematically conducts association analyses of a trait of interest against millions of genetic variants, usually in the form of single nucleotide polymorphisms, across the genome. GWAS findings can then be used for PRS construction and MR analyses. To construct a PRS, the cumulative effect of many genetic variants associated with a trait from a prior GWAS is calculated and taken as a quantitative representation of an individual's genetic risk of a complex disease. MR studies analyse an outcome measure against the genetic variants of an exposure, and are particularly useful in investigating causal relations between two traits where randomised controlled trials are not possible or ethical. In addition to explaining the principles of these three genetic epidemiology concepts, this article provides a minimally technical description of their basic methodology that is accessible to the non-expert reader.
Collapse
Affiliation(s)
- Samantha Sze-Yee Lee
- Genetics and Epidemiology, Lions Eye Institute, Nedlands, Western Australia, Australia
- Centre for Ophthalmology and Visual Sciences, University of Western Australia, Nedlands, Western Australia, Australia
- School of Optometry and Vision Science, UNSW, Sydney, New South Wales, Australia
| | - Fiona Stapleton
- School of Optometry and Vision Science, UNSW, Sydney, New South Wales, Australia
| | - Stuart MacGregor
- QIMR Berghofer Medical Research Institute, Herston, Queensland, Australia
| | - David A Mackey
- Genetics and Epidemiology, Lions Eye Institute, Nedlands, Western Australia, Australia
- Centre for Ophthalmology and Visual Sciences, University of Western Australia, Nedlands, Western Australia, Australia
- Centre for Ophthalmology and Visual Science, Lions Eye Institute, Nedlands, Western Australia, Australia
- Centre for Eye Research Australia, Department of Ophthalmology, University of Melbourne, Melbourne, Victoria, Australia
| |
Collapse
|
|
2
|
Zhang C, Zha B, Yuan R, Zhao K, Sun J, Liu X, Wang X, Zhang F, Zhang B, Lamlom SF, Ren H, Qiu L. Identification of Quantitative Trait Loci for Node Number, Pod Number, and Seed Number in Soybean. Int J Mol Sci 2025; 26:2300. [PMID: 40076921 PMCID: PMC11900990 DOI: 10.3390/ijms26052300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2025] [Revised: 02/22/2025] [Accepted: 03/03/2025] [Indexed: 03/14/2025] Open
Abstract
Optimizing soybean yield remains a crucial challenge in meeting global food security demands. In this study, we report a comprehensive genetic analysis of yield-related traits in soybeans using a recombinant inbred line (RIL) population derived from crosses between 'Qihuang 34' (GH34) and 'Dongsheng 16' (DS16). Phenotypic analysis across two years (2023-2024) revealed significant variations between parental lines. Through high-density genetic mapping with 6297 SLAF markers spanning 2945.26 cM across 20 chromosomes, we constructed a genetic map with an average marker distance of 0.47 cM and 99.17% of gaps under 5 cM. QTL analysis identified ten significant loci across both years: in 2023, we detected six QTLs, including a major main stem node number (MSNN) QTL on chromosome 19 (LOD = 22.59, PVE = 24.57%), two seed number (SN) QTLs on chromosomes 14 and 18 (LOD = 2.52-2.85, PVE = 7.35% combined), and one pod number (PN) QTL on chromosome 20 (LOD = 4.68, PVE = 5.85%). The 2024 analysis revealed four major QTLs, notably a cluster on chromosome 19 harboring significant loci for MSNN (LOD = 37.92, PVE = 43.59%), PN (LOD = 18.16, PVE = 23.02%), and SN (LOD = 15.24, PVE = 19.59%). Within the stable chromosome 19 region, we identified seventeen candidate genes involved in crucial developmental processes. Gene expression analysis revealed distinct temporal patterns between parental lines during vegetative and reproductive stages, with GH34 showing dramatically higher expression of key reproductive genes Glyma.19G201300 and Glyma.19G201400 during the R1 stage. Our findings provide new insights into the genetic architecture of soybean stem node development and yield components, offering multiple promising targets for molecular breeding programs aimed at crop improvement.
Collapse
Affiliation(s)
- Chunlei Zhang
- Soybean Research Institute of Heilongjiang Academy of Agriculture Sciences, Harbin 150086, China; (C.Z.); (B.Z.); (R.Y.); (K.Z.); (X.L.); (X.W.); (F.Z.)
| | - Bire Zha
- Soybean Research Institute of Heilongjiang Academy of Agriculture Sciences, Harbin 150086, China; (C.Z.); (B.Z.); (R.Y.); (K.Z.); (X.L.); (X.W.); (F.Z.)
- College of Modern Agriculture and Ecological Environment, Heilongjiang University, Harbin 150080, China
| | - Rongqiang Yuan
- Soybean Research Institute of Heilongjiang Academy of Agriculture Sciences, Harbin 150086, China; (C.Z.); (B.Z.); (R.Y.); (K.Z.); (X.L.); (X.W.); (F.Z.)
| | - Kezhen Zhao
- Soybean Research Institute of Heilongjiang Academy of Agriculture Sciences, Harbin 150086, China; (C.Z.); (B.Z.); (R.Y.); (K.Z.); (X.L.); (X.W.); (F.Z.)
| | - Jianqiang Sun
- College of Agronomy, Shenyang Agricultural University, Shenyang 110065, China;
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRl), Ministry of Agriculture and Rural Affairs, Key Laboratory of Crop Gene Resource and Germplasm Enhancement, Ministry of Agriculture and Rural Affairs, Beijing 100081, China
| | - Xiulin Liu
- Soybean Research Institute of Heilongjiang Academy of Agriculture Sciences, Harbin 150086, China; (C.Z.); (B.Z.); (R.Y.); (K.Z.); (X.L.); (X.W.); (F.Z.)
| | - Xueyang Wang
- Soybean Research Institute of Heilongjiang Academy of Agriculture Sciences, Harbin 150086, China; (C.Z.); (B.Z.); (R.Y.); (K.Z.); (X.L.); (X.W.); (F.Z.)
| | - Fengyi Zhang
- Soybean Research Institute of Heilongjiang Academy of Agriculture Sciences, Harbin 150086, China; (C.Z.); (B.Z.); (R.Y.); (K.Z.); (X.L.); (X.W.); (F.Z.)
| | - Bixian Zhang
- Institute of Biotechnology, Heilongjiang Academy of Agricultural Sciences, Harbin 150086, China;
| | - Sobhi F. Lamlom
- Work Station of Science and Technique for Post-Doctoral in Sugar Beet Institute, Heilongjiang University, 74 Xuefu Road, Harbin 150000, China;
- Plant Production Department, Faculty of Agriculture Saba Basha, Alexandria University, Alexandria 21531, Egypt
| | - Honglei Ren
- Soybean Research Institute of Heilongjiang Academy of Agriculture Sciences, Harbin 150086, China; (C.Z.); (B.Z.); (R.Y.); (K.Z.); (X.L.); (X.W.); (F.Z.)
| | - Lijuan Qiu
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRl), Ministry of Agriculture and Rural Affairs, Key Laboratory of Crop Gene Resource and Germplasm Enhancement, Ministry of Agriculture and Rural Affairs, Beijing 100081, China
| |
Collapse
|
|
3
|
Mangnier L, Ruczinski I, Ricard J, Moreau C, Girard S, Maziade M, Bureau A. RetroFun-RVS: A Retrospective Family-Based Framework for Rare Variant Analysis Incorporating Functional Annotations. Genet Epidemiol 2025; 49:e70001. [PMID: 39876583 PMCID: PMC11775437 DOI: 10.1002/gepi.70001] [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: 04/03/2024] [Revised: 10/16/2024] [Accepted: 01/03/2025] [Indexed: 01/30/2025]
Abstract
A large proportion of genetic variations involved in complex diseases are rare and located within noncoding regions, making the interpretation of underlying biological mechanisms a daunting task. Although technical and methodological progress has been made to annotate the genome, current disease-rare-variant association tests incorporating such annotations suffer from two major limitations. First, they are generally restricted to case-control designs of unrelated individuals, which often require tens or hundreds of thousands of individuals to achieve sufficient power. Second, they were not evaluated with region-based annotations needed to interpret the causal regulatory mechanisms. In this work, we propose RetroFun-RVS, a new retrospective family-based score test, incorporating functional annotations. A critical feature of the proposed method is to aggregate genotypes to compare against rare variant-sharing expectations among affected family members. Through extensive simulations, we have demonstrated that RetroFun-RVS integrating networks based on 3D genome contacts as functional annotations reach greater power over the region-wide test, other strategies to include subregions and competing methods. Also, the proposed framework shows robustness to non-informative annotations, maintaining its power when causal variants are spread across regions. Asymptotic p-values are susceptible to Type I error inflation when the number of families with rare variants is small, and a bootstrap procedure is recommended in these instances. Application of RetroFun-RVS is illustrated on whole genome sequence in the Eastern Quebec Schizophrenia and Bipolar Disorder Kindred Study with networks constructed from 3D contacts and epigenetic data on neurons. In summary, the integration of functional annotations corresponding to regions or networks with transcriptional impacts in rare variant tests appears promising to highlight regulatory mechanisms involved in complex diseases.
Collapse
Affiliation(s)
- Loïc Mangnier
- Department of Social and Preventive MedicineLaval UniversityQuebec CityQuebecCanada
- CERVO Brain Research CenterQuebec CityQuebecCanada
- Big Data Research CenterLaval UniversityQuebec CityQuebecCanada
| | - Ingo Ruczinski
- Department of BiostatisticsJohns Hopkins Bloomberg School of Public HealthBaltimoreMarylandUSA
| | | | - Claudia Moreau
- Department of Fundamental SciencesUniversity of Quebec in ChicoutimiSaguenayQuebecCanada
| | - Simon Girard
- Department of Fundamental SciencesUniversity of Quebec in ChicoutimiSaguenayQuebecCanada
| | - Michel Maziade
- CERVO Brain Research CenterQuebec CityQuebecCanada
- Department of Psychiatry and NeurosciencesLaval UniversityQuebec CityQuebecCanada
| | - Alexandre Bureau
- Department of Social and Preventive MedicineLaval UniversityQuebec CityQuebecCanada
- CERVO Brain Research CenterQuebec CityQuebecCanada
- Big Data Research CenterLaval UniversityQuebec CityQuebecCanada
| |
Collapse
|
|
4
|
Gou Y, Jing Y, Wang Y, Li X, Yang J, Wang K, He H, Yang Y, Tang Y, Wang C, Xu J, Yang F, Li M, Tang Q. AnimalGWASAtlas: Annotation and prioritization of GWAS loci and quantitative trait loci for animal complex traits. J Biol Chem 2025; 301:108267. [PMID: 39909383 PMCID: PMC11904539 DOI: 10.1016/j.jbc.2025.108267] [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: 06/28/2024] [Revised: 01/28/2025] [Accepted: 01/30/2025] [Indexed: 02/07/2025] Open
Abstract
Genome-wide association study (GWAS) and quantitative trait locus (QTL) mapping methods provide valuable insights and opportunities for identifying functional gene underlying phenotype formation. However, the majority of GWAS risk loci and QTLs located in noncoding regions poses significant challenges in pinpointing the protein-coding genes associated with specific traits. Moreover, growing evidence suggests not all GWAS risk loci and QTLs are functional, emphasizing the critical need for prioritizing causal sites-a task of paramount importance for biologists. The accumulation of publicly available multiomics data provides an unprecedented opportunity to annotate and prioritize GWAS risk loci and QTLs. Therefore, we developed a comprehensive multiomics database encompassing four major agricultural species-pig, sheep, cattle, and chicken. This database integrates publicly accessible datasets, including 140 GWAS studies (covering 471 traits), 2625 QTL datasets (spanning 1235 traits), 86 Hi-C datasets (from eight cells/tissue types), 95 epigenomic datasets (from four cells/tissue types), and 769 transcription factor motifs. The database aims to link GWAS-QTL loci located in the noncoding regions to the target genes they regulate and prioritize functional and causal regulatory elements. Ultimately, it provides a valuable resource and potential validation targets for elucidating the genes and molecular pathways underlying economically important traits in agricultural animals.
Collapse
Affiliation(s)
- Yuwei Gou
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, China
| | - Yunhan Jing
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, China
| | - Yifei Wang
- Department of Zoology, College of Life Science, Sichuan Agricultural University, Ya'an, Sichuan, China
| | - Xingyu Li
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, China
| | - Jing Yang
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, China
| | - Kai Wang
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, China
| | - Hengdong He
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, China
| | - Yuan Yang
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, China
| | - Yuanling Tang
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, China
| | - Chen Wang
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, China
| | - Jun Xu
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, China
| | - Fan Yang
- Mianyang Sanrun Cultural Technology Co, Ltd, Mianyang, Sichuan, China
| | - Mingzhou Li
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, China.
| | - Qianzi Tang
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, China.
| |
Collapse
|
|
5
|
Davies NM, Hemani G, Neiderhiser JM, Martin HC, Mills MC, Visscher PM, Yengo L, Young AS, Keller MC. The importance of family-based sampling for biobanks. Nature 2024; 634:795-803. [PMID: 39443775 PMCID: PMC11623399 DOI: 10.1038/s41586-024-07721-5] [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: 04/06/2023] [Accepted: 06/13/2024] [Indexed: 10/25/2024]
Abstract
Biobanks aim to improve our understanding of health and disease by collecting and analysing diverse biological and phenotypic information in large samples. So far, biobanks have largely pursued a population-based sampling strategy, where the individual is the unit of sampling, and familial relatedness occurs sporadically and by chance. This strategy has been remarkably efficient and successful, leading to thousands of scientific discoveries across multiple research domains, and plans for the next wave of biobanks are underway. In this Perspective, we discuss the strengths and limitations of a complementary sampling strategy for future biobanks based on oversampling of close genetic relatives. Such family-based samples facilitate research that clarifies causal relationships between putative risk factors and outcomes, particularly in estimates of genetic effects, because they enable analyses that reduce or eliminate confounding due to familial and demographic factors. Family-based biobank samples would also shed new light on fundamental questions across multiple fields that are often difficult to explore in population-based samples. Despite the potential for higher costs and greater analytical complexity, the many advantages of family-based samples should often outweigh their potential challenges.
Collapse
Affiliation(s)
- Neil M Davies
- Division of Psychiatry, University College London, London, UK.
- Department of Statistical Science, University College London, London, UK.
- Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway.
| | - Gibran Hemani
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Jenae M Neiderhiser
- Department of Psychology, The Pennsylvania State University, University Park, PA, USA
| | - Hilary C Martin
- Human Genetics Programme, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Melinda C Mills
- Department of Economics, Econometrics & Finance, University of Groningen, Groningen, The Netherlands
- Department of Genetics, University Medical Centre Groningen, Groningen, The Netherlands
- Leverhulme Centre for Demographic Science, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Loïc Yengo
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Alexander Strudwick Young
- UCLA Anderson School of Management, Los Angeles, CA, USA
- Human Genetics Department, UCLA David Geffen School of Medicine, Los Angeles, CA, USA
| | - Matthew C Keller
- Institute for Behavioral Genetics, University of Colorado at Boulder, Boulder, CO, USA.
- Department of Psychology and Neuroscience, University of Colorado at Boulder, Boulder, CO, USA.
| |
Collapse
|
|
6
|
Ghavi Hossein-Zadeh N. An overview of recent technological developments in bovine genomics. Vet Anim Sci 2024; 25:100382. [PMID: 39166173 PMCID: PMC11334705 DOI: 10.1016/j.vas.2024.100382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/22/2024] Open
Abstract
Cattle are regarded as highly valuable animals because of their milk, beef, dung, fur, and ability to draft. The scientific community has tried a number of strategies to improve the genetic makeup of bovine germplasm. To ensure higher returns for the dairy and beef industries, researchers face their greatest challenge in improving commercially important traits. One of the biggest developments in the last few decades in the creation of instruments for cattle genetic improvement is the discovery of the genome. Breeding livestock is being revolutionized by genomic selection made possible by the availability of medium- and high-density single nucleotide polymorphism (SNP) arrays coupled with sophisticated statistical techniques. It is becoming easier to access high-dimensional genomic data in cattle. Continuously declining genotyping costs and an increase in services that use genomic data to increase return on investment have both made a significant contribution to this. The field of genomics has come a long way thanks to groundbreaking discoveries such as radiation-hybrid mapping, in situ hybridization, synteny analysis, somatic cell genetics, cytogenetic maps, molecular markers, association studies for quantitative trait loci, high-throughput SNP genotyping, whole-genome shotgun sequencing to whole-genome mapping, and genome editing. These advancements have had a significant positive impact on the field of cattle genomics. This manuscript aimed to review recent advances in genomic technologies for cattle breeding and future prospects in this field.
Collapse
Affiliation(s)
- Navid Ghavi Hossein-Zadeh
- Department of Animal Science, Faculty of Agricultural Sciences, University of Guilan, Rasht, 41635-1314, Iran
| |
Collapse
|
|
7
|
Treccani M, Veschetti L, Patuzzo C, Malerba G, Vaglio A, Martorana D. Genetic and Non-Genetic Contributions to Eosinophilic Granulomatosis with Polyangiitis: Current Knowledge and Future Perspectives. Curr Issues Mol Biol 2024; 46:7516-7529. [PMID: 39057087 PMCID: PMC11275403 DOI: 10.3390/cimb46070446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2024] [Revised: 07/12/2024] [Accepted: 07/15/2024] [Indexed: 07/28/2024] Open
Abstract
In this work, we present a comprehensive overview of the genetic and non-genetic complexity of eosinophilic granulomatosis with polyangiitis (EGPA). EGPA is a rare complex systemic disease that occurs in people presenting with severe asthma and high eosinophilia. After briefly introducing EGPA and its relationship with the anti-neutrophil cytoplasmic autoantibodies (ANCA)-associated vasculitis (AAVs), we delve into the complexity of this disease. At first, the two main biological actors, ANCA and eosinophils, are presented. Biological and clinical phenotypes related to ANCA positivity or negativity are explained, as well as the role of eosinophils and their pathological subtypes, pointing out their intricate relations with EGPA. Then, the genetics of EGPA are described, providing an overview of the research effort to unravel them. Candidate gene studies have investigated biologically relevant candidate genes; the more recent genome-wide association studies and meta-analyses, able to analyze the whole genome, have confirmed previous associations and discovered novel risk loci; in the end, family-based studies have dissected the contribution of rare variants and the heritability of EGPA. Then, we briefly present the environmental contribution to EGPA, reporting seasonal events and pollutants as triggering factors. In the end, the latest omic research is discussed and the most recent epigenomic, transcriptomic and microbiome studies are presented, highlighting the current challenges, open questions and suggesting approaches to unraveling this complex disease.
Collapse
Affiliation(s)
- Mirko Treccani
- GM Lab, Department of Surgery, Dentistry, Gynaecology and Paediatrics, University of Verona, 37134 Verona, Italy;
| | - Laura Veschetti
- Infections and Cystic Fibrosis Unit, Division of Immunology, Transplantation and Infectious Diseases, IRCCS San Raffaele Scientific Institute, 20132 Milano, Italy;
- Vita-Salute San Raffaele University, 20132 Milano, Italy
| | - Cristina Patuzzo
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, 37134 Verona, Italy;
| | - Giovanni Malerba
- GM Lab, Department of Surgery, Dentistry, Gynaecology and Paediatrics, University of Verona, 37134 Verona, Italy;
| | - Augusto Vaglio
- Nephrology and Dialysis Unit, Meyer Children’s Hospital IRCCS, 50139 Florence, Italy;
- Department of Biomedical Experimental and Clinical Sciences “Mario Serio”, University of Florence, 50121 Florence, Italy
| | - Davide Martorana
- Medical Genetics Unit, Department of Onco-Hematology, University Hospital of Parma, 43126 Parma, Italy;
- CoreLab Unit, Research Center, University Hospital of Parma, 43126 Parma, Italy
| |
Collapse
|
|
8
|
Wang P, Xu X, Li M, Lou XY, Xu S, Wu B, Gao G, Yin P, Liu N. Gene-based association tests in family samples using GWAS summary statistics. Genet Epidemiol 2024; 48:103-113. [PMID: 38317324 DOI: 10.1002/gepi.22548] [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: 07/12/2023] [Revised: 11/18/2023] [Accepted: 01/08/2024] [Indexed: 02/07/2024]
Abstract
Genome-wide association studies (GWAS) have led to rapid growth in detecting genetic variants associated with various phenotypes. Owing to a great number of publicly accessible GWAS summary statistics, and the difficulty in obtaining individual-level genotype data, many existing gene-based association tests have been adapted to require only GWAS summary statistics rather than individual-level data. However, these association tests are restricted to unrelated individuals and thus do not apply to family samples directly. Moreover, due to its flexibility and effectiveness, the linear mixed model has been increasingly utilized in GWAS to handle correlated data, such as family samples. However, it remains unknown how to perform gene-based association tests in family samples using the GWAS summary statistics estimated from the linear mixed model. In this study, we show that, when family size is negligible compared to the total sample size, the diagonal block structure of the kinship matrix makes it possible to approximate the correlation matrix of marginal Z scores by linkage disequilibrium matrix. Based on this result, current methods utilizing summary statistics for unrelated individuals can be directly applied to family data without any modifications. Our simulation results demonstrate that this proposed strategy controls the type 1 error rate well in various situations. Finally, we exemplify the usefulness of the proposed approach with a dental caries GWAS data set.
Collapse
Affiliation(s)
- Peng Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Hubei, People's Republic of China
| | - Xiao Xu
- Department of Epidemiology and Biostatistics, Indiana University School of Public Health-Bloomington, Bloomington, Indiana, USA
| | - Ming Li
- Department of Epidemiology and Biostatistics, Indiana University School of Public Health-Bloomington, Bloomington, Indiana, USA
| | - Xiang-Yang Lou
- Department of Biostatistics, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Siqi Xu
- Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong, Hong Kong
| | - Baolin Wu
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA
| | - Guimin Gao
- Department of Public Health Sciences, University of Chicago, Chicago, Illinois, USA
| | - Ping Yin
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Hubei, People's Republic of China
| | - Nianjun Liu
- Department of Epidemiology and Biostatistics, Indiana University School of Public Health-Bloomington, Bloomington, Indiana, USA
| |
Collapse
|
|
9
|
Dossa HRG, Bureau A, Maziade M, Lakhal-Chaieb L, Oualkacha K. A novel rare variants association test for binary traits in family-based designs via copulas. Stat Methods Med Res 2023; 32:2096-2122. [PMID: 37832140 PMCID: PMC10683345 DOI: 10.1177/09622802231197977] [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] [Indexed: 10/15/2023]
Abstract
With the cost-effectiveness technology in whole-genome sequencing, more sophisticated statistical methods for testing genetic association with both rare and common variants are being investigated to identify the genetic variation between individuals. Several methods which group variants, also called gene-based approaches, are developed. For instance, advanced extensions of the sequence kernel association test, which is a widely used variant-set test, have been proposed for unrelated samples and extended for family data. Family data have been shown to be powerful when analyzing rare variants. However, most of such methods capture familial relatedness using a random effect component within the generalized linear mixed model framework. Therefore, there is a need to develop unified and flexible methods to study the association between a set of genetic variants and a trait, especially for a binary outcome. Copulas are multivariate distribution functions with uniform margins on the [ 0 , 1 ] interval and they provide suitable models to capture familial dependence structure. In this work, we propose a flexible family-based association test for both rare and common variants in the presence of binary traits. The method, termed novel rare variant association test (NRVAT), uses a marginal logistic model and a Gaussian Copula. The latter is employed to model the dependence between relatives. An analytic score-type test is derived. Through simulations, we show that our method can achieve greater power than existing approaches. The proposed model is applied to investigate the association between schizophrenia and bipolar disorder in a family-based cohort consisting of 17 extended families from Eastern Quebec.
Collapse
Affiliation(s)
- Houssou R. G. Dossa
- Département de Mathématiques, Université du Québec à Montréal (UQAM) et, Québec, Canada
| | - Alexandre Bureau
- Département de Médecine Sociale et Préventive, Université Laval, Québec, Canada
- Centre de Recherche CERVO, Quebec, Canada
| | - Michel Maziade
- Centre de Recherche CERVO, Quebec, Canada
- Département de Psychiatrie et Neuroscience, Université Laval, Québec, Canada
| | - Lajmi Lakhal-Chaieb
- Département de Mathématiques et Statistique, Université Laval, Québec, Canada
| | - Karim Oualkacha
- Département de Mathématiques, Université du Québec à Montréal (UQAM) et, Québec, Canada
| |
Collapse
|
|
10
|
Georgina-Pérez L, Ribas-Pérez D, Dehesa-Santos A, Mendoza-Mendoza A. Relationship between the TGFBR1 Gene and Molar Incisor Hypomineralization. J Pers Med 2023; 13:jpm13050777. [PMID: 37240947 DOI: 10.3390/jpm13050777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Revised: 04/21/2023] [Accepted: 04/26/2023] [Indexed: 05/28/2023] Open
Abstract
BACKGROUND Molar Incisor Hypomineralization Syndrome (MIH) is a problem of increasing incidence that represents a new challenge in the dental treatment of many of the children we see in our dental offices. Understanding the etiology of this syndrome (still unknown) will help us to prevent the appearance of this process. Lately a certain genetic relationship has been suggested in the syndrome. The aim of the present study was to explore the relationship between activation of the TGFBR1 gene and the development of MIH, as recent studies suggest that there may be an association in this regard. MATERIALS AND METHODS The study sample consisted of 50 children between 6-17 years of age with MIH, each with at least one parent and a sibling with or without MIH, and a group control of 100 children without MIH. The condition of the permanent molars and incisors was evaluated and recorded based on the criteria of Mathu-Muju and Wright. Saliva samples were collected after washing and rinsing of the oral cavity. Genotyping was performed with the saliva samples for the selection of a target polymorphism of the studied gene (TGFBR1). RESULTS The mean age was 9.7 years (SD 2.36). Of the 50 children with MIH, 56% were boys and 44% girls. The degree of MIH was predominantly severe (58%), with moderate and mild involvement in 22% and 20% of the cases, respectively, according to the classification of Mathu-Muju. The allelic frequencies were seen to behave as expected. The logistic regression analysis aimed to relate each polymorphism to the presence or absence of the factors. These results were inconclusive, with no evidence suggesting an alteration of the TGFBR1 gene to be related to the appearance of MIH. CONCLUSIONS Within the limitations posed by a study of these characteristics, it can be affirmed that no relationship has been found between the TGFBR1 gene and the appearance of molar incisor hypomineralization.
Collapse
Affiliation(s)
| | - David Ribas-Pérez
- Department of Stomatology, Universidad de Sevilla, 41080 Seville, Spain
| | - Alexandra Dehesa-Santos
- Department of Clinical Dental Specialities, Universidad Complutense de Madrid, 28040 Madrid, Spain
| | | |
Collapse
|
|
11
|
He GQ, Huang XX, Pei MS, Jin HY, Cheng YZ, Wei TL, Liu HN, Yu YH, Guo DL. Dissection of the Pearl of Csaba pedigree identifies key genomic segments related to early ripening in grape. PLANT PHYSIOLOGY 2023; 191:1153-1166. [PMID: 36440478 PMCID: PMC9922404 DOI: 10.1093/plphys/kiac539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 11/01/2022] [Indexed: 06/16/2023]
Abstract
Pearl of Csaba (PC) is a valuable backbone parent for early-ripening grapevine (Vitis vinifera) breeding, from which many excellent early ripening varieties have been bred. However, the genetic basis of the stable inheritance of its early ripening trait remains largely unknown. Here, the pedigree, consisting of 40 varieties derived from PC, was re-sequenced for an average depth of ∼30×. Combined with the resequencing data of 24 other late-ripening varieties, 5,795,881 high-quality single nucleotide polymorphisms (SNPs) were identified following a strict filtering pipeline. The population genetic analysis showed that these varieties could be distinguished clearly, and the pedigree was characterized by lower nucleotide diversity and stronger linkage disequilibrium than the non-pedigree varieties. The conserved haplotypes (CHs) transmitted in the pedigree were obtained via identity-by-descent analysis. Subsequently, the key genomic segments were identified based on the combination analysis of haplotypes, selective signatures, known ripening-related quantitative trait loci (QTLs), and transcriptomic data. The results demonstrated that varieties with a superior haplotype, H1, significantly (one-way ANOVA, P < 0.001) exhibited early grapevine berry development. Further analyses indicated that H1 encompassed VIT_16s0039g00720 encoding a folate/biopterin transporter protein (VvFBT) with a missense mutation. VvFBT was specifically and highly expressed during grapevine berry development, particularly at veraison. Exogenous folate treatment advanced the veraison of "Kyoho". This work uncovered core haplotypes and genomic segments related to the early ripening trait of PC and provided an important reference for the molecular breeding of early-ripening grapevine varieties.
Collapse
Affiliation(s)
- Guang-Qi He
- College of Horticulture and Plant Protection, Henan University of Science and Technology, Luoyang 471023, China
- Henan Engineering Technology Research Center of Quality Regulation of Horticultural Plants, Henan University of Science and Technology, Luoyang 471023, China
| | - Xi-Xi Huang
- College of Horticulture and Plant Protection, Henan University of Science and Technology, Luoyang 471023, China
| | - Mao-Song Pei
- College of Horticulture and Plant Protection, Henan University of Science and Technology, Luoyang 471023, China
- Henan Engineering Technology Research Center of Quality Regulation of Horticultural Plants, Henan University of Science and Technology, Luoyang 471023, China
| | - Hui-Ying Jin
- College of Horticulture and Plant Protection, Henan University of Science and Technology, Luoyang 471023, China
- Henan Engineering Technology Research Center of Quality Regulation of Horticultural Plants, Henan University of Science and Technology, Luoyang 471023, China
| | - Yi-Zhe Cheng
- College of Horticulture and Plant Protection, Henan University of Science and Technology, Luoyang 471023, China
- Henan Engineering Technology Research Center of Quality Regulation of Horticultural Plants, Henan University of Science and Technology, Luoyang 471023, China
| | - Tong-Lu Wei
- College of Horticulture and Plant Protection, Henan University of Science and Technology, Luoyang 471023, China
- Henan Engineering Technology Research Center of Quality Regulation of Horticultural Plants, Henan University of Science and Technology, Luoyang 471023, China
| | - Hai-Nan Liu
- College of Horticulture and Plant Protection, Henan University of Science and Technology, Luoyang 471023, China
- Henan Engineering Technology Research Center of Quality Regulation of Horticultural Plants, Henan University of Science and Technology, Luoyang 471023, China
| | - Yi-He Yu
- College of Horticulture and Plant Protection, Henan University of Science and Technology, Luoyang 471023, China
- Henan Engineering Technology Research Center of Quality Regulation of Horticultural Plants, Henan University of Science and Technology, Luoyang 471023, China
| | - Da-Long Guo
- College of Horticulture and Plant Protection, Henan University of Science and Technology, Luoyang 471023, China
- Henan Engineering Technology Research Center of Quality Regulation of Horticultural Plants, Henan University of Science and Technology, Luoyang 471023, China
| |
Collapse
|
|
12
|
Abstract
PURPOSE OF REVIEW Genetic studies in humans and animal models have improved our understanding of the role of numerous genes in the etiology of nonsyndromic tooth agenesis (TA). The purpose of this review is to discuss recently identified genes potentially contributing to TA. RECENT FINDINGS Despite research progress, understanding the genetic factors underlying nonsyndromic TA has been challenging given the genetic heterogeneity, variable expressivity, and incomplete penetrance of putatively pathogenic variants often observed associated with the condition. Next-generation sequencing technologies have provided a platform for novel gene and variant discoveries and informed paradigm-shifting concepts in the etiology of TA. This review summarizes the current knowledge on genes and pathways related to nonsyndromic TA with a focus on recently identified genes/variants. Evidence suggesting possible multi-locus variation in TA is also presented.
Collapse
Affiliation(s)
- Ariadne Letra
- Department of Oral and Craniofacial Sciences, and Center for Craniofacial and Dental Genetics, University of Pittsburgh School of Dental Medicine, Pittsburgh, PA, 15219, USA.
| |
Collapse
|
|
13
|
Oskarsson GR, Magnusson MK, Oddsson A, Jensson BO, Fridriksdottir R, Arnadottir GA, Katrinardottir H, Rognvaldsson S, Halldorsson GH, Sveinbjornsson G, Ivarsdottir EV, Stefansdottir L, Ferkingstad E, Norland K, Tragante V, Saemundsdottir J, Jonasdottir A, Jonasdottir A, Sigurjonsdottir S, Petursdottir KO, Davidsson OB, Rafnar T, Holm H, Olafsson I, Onundarson PT, Vidarsson B, Sigurdardottir O, Masson G, Gudbjartsson DF, Jonsdottir I, Norddahl GL, Thorsteinsdottir U, Sulem P, Stefansson K. Genetic architecture of band neutrophil fraction in Iceland. Commun Biol 2022; 5:525. [PMID: 35650273 PMCID: PMC9160026 DOI: 10.1038/s42003-022-03462-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Accepted: 05/09/2022] [Indexed: 11/22/2022] Open
Abstract
The characteristic lobulated nuclear morphology of granulocytes is partially determined by composition of nuclear envelope proteins. Abnormal nuclear morphology is primarily observed as an increased number of hypolobulated immature neutrophils, called band cells, during infection or in rare envelopathies like Pelger-Huët anomaly. To search for sequence variants affecting nuclear morphology of granulocytes, we performed a genome-wide association study using band neutrophil fraction from 88,101 Icelanders. We describe 13 sequence variants affecting band neutrophil fraction at nine loci. Five of the variants are at the Lamin B receptor (LBR) locus, encoding an inner nuclear membrane protein. Mutations in LBR are linked to Pelger-Huët anomaly. In addition, we identify cosegregation of a rare stop-gain sequence variant in LBR and Pelger Huët anomaly in an Icelandic eight generation pedigree, initially reported in 1963. Two of the other loci include genes which, like LBR, play a role in the nuclear membrane function and integrity. These GWAS results highlight the role proteins of the inner nuclear membrane have as important for neutrophil nuclear morphology.
Collapse
Affiliation(s)
- Gudjon R Oskarsson
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
| | - Magnus K Magnusson
- deCODE genetics/Amgen Inc., Reykjavik, Iceland.
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland.
| | | | | | | | | | | | | | | | | | | | | | | | | | - Vinicius Tragante
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | | | | | | | | | | | | | | | - Hilma Holm
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
| | - Isleifur Olafsson
- Department of Clinical Biochemistry, Landspitali, The National University Hospital of Iceland, Reykjavik, Iceland
| | - Pall T Onundarson
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
- Department of Laboratory Hematology, Landspitali, The National University Hospital of Iceland, Reykjavik, Iceland
| | - Brynjar Vidarsson
- Department of Laboratory Hematology, Landspitali, The National University Hospital of Iceland, Reykjavik, Iceland
- The Laboratory in Mjodd, RAM, Reykjavik, Iceland
| | | | | | - Daniel F Gudbjartsson
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | - Ingileif Jonsdottir
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
- Department of Immunology of Landspitali, The National University Hospital of Iceland, Reykjavik, Iceland
| | | | - Unnur Thorsteinsdottir
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
| | | | - Kari Stefansson
- deCODE genetics/Amgen Inc., Reykjavik, Iceland.
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland.
| |
Collapse
|
|
14
|
Dattani S, Howard DM, Lewis CM, Sham PC. Clarifying the causes of consistent and inconsistent findings in genetics. Genet Epidemiol 2022; 46:372-389. [PMID: 35652173 PMCID: PMC9544854 DOI: 10.1002/gepi.22459] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 04/12/2022] [Accepted: 04/22/2022] [Indexed: 11/29/2022]
Abstract
As research in genetics has advanced, some findings have been unexpected or shown to be inconsistent between studies or datasets. The reasons these inconsistencies arise are complex. Results from genetic studies can be affected by various factors including statistical power, linkage disequilibrium, quality control, confounding and selection bias, as well as real differences from interactions and effect modifiers, which may be informative about the mechanisms of traits and disease. Statistical artefacts can manifest as differences between results but they can also conceal underlying differences, which implies that their critical examination is important for understanding the underpinnings of traits. In this review, we examine these factors and outline how they can be identified and conceptualised with structural causal models. We explain the consequences they have on genetic estimates, such as genetic associations, polygenic scores, family‐ and genome‐wide heritability, and describe methods to address them to aid in the estimation of true effects of genetic variation. Clarifying these factors can help researchers anticipate when results are likely to diverge and aid researchers' understanding of causal relationships between genes and complex traits.
Collapse
Affiliation(s)
- Saloni Dattani
- Social Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.,Department of Psychiatry, Li Ka Shing (LKS) Faculty of Medicine, University of Hong Kong, Hong Kong, China
| | - David M Howard
- Social Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.,Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
| | - Cathryn M Lewis
- Social Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.,Department of Medical and Molecular Genetics, Faculty of Life Sciences and Medicine, King's College London, London, UK
| | - Pak C Sham
- Department of Psychiatry, State Key Laboratory of Brain and Cognitive Sciences, and Centre for Panoromic Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| |
Collapse
|
|
15
|
Guo Y, Zhang W, He R, Zheng C, Liu X, Ge M, Xu J. Investigating the Association Between rs2439302 Polymorphism and Thyroid Cancer: A Systematic Review and Meta-Analysis. Front Surg 2022; 9:877206. [PMID: 35558387 PMCID: PMC9086625 DOI: 10.3389/fsurg.2022.877206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 03/15/2022] [Indexed: 11/30/2022] Open
Abstract
Background and Aims The extent of surgical treatment for most patients with thyroid cancer (TC) remains controversial and varies widely. As an emerging technology, genetic testing facilitates tumor typing and disease progression monitoring and is expected to influence the choice of surgical approach for patients with TC. Recent genome-wide association studies (GWASs) have identified that rs2439302 (8p12) variants near NRG1 are associated with TC risk; however, the results remain inconclusive. Therefore, we aimed to perform a meta-analysis to clarify the association between rs2439302 variants and the risk of TC. Methods We search eligible studies using Pubmed, Scopus, Embase, Web of Science, and Cochrane library by July 2021. We analyzed the pooled OR and the corresponding 95% confidence interval (95% CI) of the included studies and then conducted subgroup analysis according to the ethnicity. We also performed a sensitivity analysis to validate the findings. Results This meta-analysis finally included 7 studies involving 6,090 cases and 14,461 controls. Results showed that the G allele of the rs2439302 polymorphism was a significant risk factor of TC in Allele (G/C), Dominant (GG+GC/CC), Recessive (GG/GC+CC), Homozygote (GG/CC), Heterozygote (GC/CC) models, with pooled ORs of 1.38 (95%CI, 1.31–1.45), 1.51 (95%CI, 1.41–1.62), 1.52 (95%CI, 1.40–1.66), 1.90 (95%CI, 1.71–2.10), and 1.40 (95%CI, 1.30–1.51), respectively. The subgroup analysis showed that rs2439302 polymorphism was associated with higher TC risk in different ethnicities with OR > 1. The sensitivity analysis exhibited that the results were stable by omitting any included studies. Conclusions The study revealed that rs2439302 variants were associated with higher TC risk and may have a major influence on the choice of operative approach for patients with TC.
Collapse
Affiliation(s)
- Yawen Guo
- Department of Head and Neck Surgery, Otolaryngology & Head and Neck Center, Cancer Center, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, China
- Department of Public Health, Zhejiang University School of Medicine, Hangzhou, China
- Key Laboratory of Endocrine Gland Diseases of Zhejiang Province, Hangzhou, China
| | - Wanchen Zhang
- Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, China
| | - Ru He
- School of Basic Medical Sciences and Forensic Medicine, Hangzhou Medical College, Hangzhou, China
| | - Chuanming Zheng
- Department of Head and Neck Surgery, Otolaryngology & Head and Neck Center, Cancer Center, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, China
- Key Laboratory of Endocrine Gland Diseases of Zhejiang Province, Hangzhou, China
| | - Xuefeng Liu
- Neck and Breast Department 3, Tumour Hospital of Mudanjiang City, Mudanjiang, China
| | - Minghua Ge
- Department of Head and Neck Surgery, Otolaryngology & Head and Neck Center, Cancer Center, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, China
- Key Laboratory of Endocrine Gland Diseases of Zhejiang Province, Hangzhou, China
| | - Jiajie Xu
- Department of Head and Neck Surgery, Otolaryngology & Head and Neck Center, Cancer Center, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, China
- Key Laboratory of Endocrine Gland Diseases of Zhejiang Province, Hangzhou, China
- *Correspondence: Jiajie Xu
| |
Collapse
|
|
16
|
Mdyogolo S, MacNeil MD, Neser FWC, Scholtz MM, Makgahlela ML. Assessing accuracy of genotype imputation in the Afrikaner and Brahman cattle breeds of South Africa. Trop Anim Health Prod 2022; 54:90. [PMID: 35133512 DOI: 10.1007/s11250-022-03102-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Accepted: 02/01/2022] [Indexed: 11/26/2022]
Abstract
Imputation may be used to rescue genomic data from animals that would otherwise be eliminated due to a lower than desired call rate. The aim of this study was to compare the accuracy of genotype imputation for Afrikaner, Brahman, and Brangus cattle of South Africa using within- and multiple-breed reference populations. A total of 373, 309, and 101 Afrikaner, Brahman, and Brangus cattle, respectively, were genotyped using the GeneSeek Genomic Profiler 150 K panel that contained 141,746 markers. Markers with MAF ≤ 0.02 and call rates ≤ 0.95 or that deviated from Hardy Weinberg Equilibrium frequency with a probability of ≤ 0.0001 were excluded from the data as were animals with a call rate ≤ 0.90. The remaining data included 99,086 SNPs and 360 Afrikaner, 75,291 SNPs and 288 animals Brahman, and 97,897 SNPs and 99 Brangus animals. A total of 7986, 7002, and 7000 SNP from 50 Afrikaner and Brahman and 30 Brangus cattle, respectively, were masked and then imputed using BEAGLE v3 and FImpute v2. The within-breed imputation yielded accuracies ranging from 89.9 to 96.6% for the three breeds. The multiple-breed imputation yielded corresponding accuracies from 69.21 to 88.35%. The results showed that population homogeneity and numerical representation for within and across breed strategies, respectively, are crucial components for improving imputation accuracies.
Collapse
Affiliation(s)
- S Mdyogolo
- Department of Animal Breeding and Genetics, Agricultural Research Council, Irene, South Africa.
- Department of Animal, Wildlife and Grassland Sciences, University of the Free State, Bloemfontein, South Africa.
| | - M D MacNeil
- Department of Animal Breeding and Genetics, Agricultural Research Council, Irene, South Africa
- Department of Animal, Wildlife and Grassland Sciences, University of the Free State, Bloemfontein, South Africa
- Delta G, Miles City, MT, USA
| | - F W C Neser
- Department of Animal, Wildlife and Grassland Sciences, University of the Free State, Bloemfontein, South Africa
| | - M M Scholtz
- Department of Animal Breeding and Genetics, Agricultural Research Council, Irene, South Africa
- Department of Animal, Wildlife and Grassland Sciences, University of the Free State, Bloemfontein, South Africa
| | - M L Makgahlela
- Department of Animal Breeding and Genetics, Agricultural Research Council, Irene, South Africa
- Department of Animal, Wildlife and Grassland Sciences, University of the Free State, Bloemfontein, South Africa
| |
Collapse
|
|
17
|
Ahmadi N. Genetic Bases of Complex Traits: From Quantitative Trait Loci to Prediction. Methods Mol Biol 2022; 2467:1-44. [PMID: 35451771 DOI: 10.1007/978-1-0716-2205-6_1] [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] [Indexed: 06/14/2023]
Abstract
Conceived as a general introduction to the book, this chapter is a reminder of the core concepts of genetic mapping and molecular marker-based prediction. It provides an overview of the principles and the evolution of methods for mapping the variation of complex traits, and methods for QTL-based prediction of human disease risk and animal and plant breeding value. The principles of linkage-based and linkage disequilibrium-based QTL mapping methods are described in the context of the simplest, single-marker, methods. Methodological evolutions are analysed in relation with their ability to account for the complexity of the genotype-phenotype relations. Main characteristics of the genetic architecture of complex traits, drawn from QTL mapping works using large populations of unrelated individuals, are presented. Methods combining marker-QTL association data into polygenic risk score that captures part of an individual's susceptibility to complex diseases are reviewed. Principles of best linear mixed model-based prediction of breeding value in animal- and plant-breeding programs using phenotypic and pedigree data, are summarized and methods for moving from BLUP to marker-QTL BLUP are presented. Factors influencing the additional genetic progress achieved by using molecular data and rules for their optimization are discussed.
Collapse
Affiliation(s)
- Nourollah Ahmadi
- CIRAD, UMR AGAP Institut, Montpellier, France.
- AGAP Institut, Univ Montpellier, CIRAD, INRAE, Montpellier SupAgro, Montpellier, France.
| |
Collapse
|
|
18
|
Shen L, Lv X, Huang H, Li M, Huai C, Wu X, Wu H, Ma J, Chen L, Wang T, Tan J, Sun Y, Li L, Shi Y, Yang C, Cai L, Lu Y, Zhang Y, Weng S, Tai S, Zhang N, He L, Wan C, Qin S. Genome-wide analysis of DNA methylation in 106 schizophrenia family trios in Han Chinese. EBioMedicine 2021; 72:103609. [PMID: 34628353 PMCID: PMC8511801 DOI: 10.1016/j.ebiom.2021.103609] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Revised: 09/05/2021] [Accepted: 09/17/2021] [Indexed: 12/26/2022] Open
Abstract
Background Schizophrenia (SCZ) is a severe psychiatric disorder that affects approximately 0.75% of the global population. Both genetic and environmental factors contribute to development of SCZ. SCZ tends to run in family while both genetic and environmental factor contribute to its etiology. Much evidence suggested that alterations in DNA methylations occurred in SCZ patients. Methods To investigate potential inheritable pattern of DNA methylation in SCZ family, we performed a genome-wide analysis of DNA methylation of peripheral blood samples from 106 Chinese SCZ family trios. Genome-wide DNA methylations were quantified by Agilent 1 × 244 k Human Methylation Microarray. Findings In this study, we proposed a loci inheritance frequency model that allows characterization of differential methylated regions as SCZ biomarkers. Based on this model, 112 hypermethylated and 125 hypomethylated regions were identified. Additionally, 121 hypermethylated and 139 hypomethylated genes were annotated. The results of functional enrichment analysis indicated that multiple differentially methylated genes (DMGs) involved in Notch/HH/Wnt signaling, MAPK signaling, GPCR signaling, immune response signaling. Notably, a number of hypomethylated genes were significantly enriched in cerebral cortex and functionally enriched in nervous system development. Interpretation Our findings not only validated previously discovered risk genes of SCZ but also identified novel candidate DMGs in SCZ. These results may further the understanding of altered DNA methylations in SCZ.
Collapse
Affiliation(s)
- Lu Shen
- Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Bio-X Institutes, Shanghai Jiao Tong University, Shanghai 200030, PR China; Institutes of Biomedical Sciences, Fudan University, Shanghai 200032, China
| | - Xiaoying Lv
- DCH Technologies Inc, Cambridge, MA 02142, USA
| | - Hailiang Huang
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences (SIBS), Chinese Academy of Sciences, Shanghai 200031, PR China; Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Mo Li
- Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Bio-X Institutes, Shanghai Jiao Tong University, Shanghai 200030, PR China
| | - Cong Huai
- Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Bio-X Institutes, Shanghai Jiao Tong University, Shanghai 200030, PR China
| | - Xi Wu
- Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Bio-X Institutes, Shanghai Jiao Tong University, Shanghai 200030, PR China
| | - Hao Wu
- Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Bio-X Institutes, Shanghai Jiao Tong University, Shanghai 200030, PR China
| | - Jingsong Ma
- Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Bio-X Institutes, Shanghai Jiao Tong University, Shanghai 200030, PR China
| | - Luan Chen
- Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Bio-X Institutes, Shanghai Jiao Tong University, Shanghai 200030, PR China
| | - Ting Wang
- Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Bio-X Institutes, Shanghai Jiao Tong University, Shanghai 200030, PR China
| | - Jie Tan
- Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Bio-X Institutes, Shanghai Jiao Tong University, Shanghai 200030, PR China
| | - Yidan Sun
- Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Bio-X Institutes, Shanghai Jiao Tong University, Shanghai 200030, PR China
| | - Lixing Li
- Department of General Surgery, School of Medicine, Shanghai General Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Yi Shi
- Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Bio-X Institutes, Shanghai Jiao Tong University, Shanghai 200030, PR China
| | - Chao Yang
- Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Bio-X Institutes, Shanghai Jiao Tong University, Shanghai 200030, PR China
| | - Lei Cai
- Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Bio-X Institutes, Shanghai Jiao Tong University, Shanghai 200030, PR China
| | - Yana Lu
- Wuxi Mental Health Center, Nanjing Medical University, Wuxi 214151, China
| | - Yan Zhang
- The Second People's Hospital of Lishui, Lishui 323020, China
| | - Saizheng Weng
- Fuzhou Neuro-psychiatric hospital, Fujian Medical University, Fuzhou 350026, China
| | - Shaobin Tai
- The Second People's Hospital of Huangshan, Huangshan 245021, China
| | - Na Zhang
- Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Bio-X Institutes, Shanghai Jiao Tong University, Shanghai 200030, PR China
| | - Lin He
- Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Bio-X Institutes, Shanghai Jiao Tong University, Shanghai 200030, PR China; Department of General Surgery, School of Medicine, Shanghai General Hospital, Shanghai Jiao Tong University, Shanghai, China; Institutes of Biomedical Sciences, Fudan University, Shanghai 200032, China.
| | - Chunling Wan
- Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Bio-X Institutes, Shanghai Jiao Tong University, Shanghai 200030, PR China.
| | - Shengying Qin
- Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Bio-X Institutes, Shanghai Jiao Tong University, Shanghai 200030, PR China.
| |
Collapse
|
|
19
|
Pattan V, Kashyap R, Bansal V, Candula N, Koritala T, Surani S. Genomics in medicine: A new era in medicine. World J Methodol 2021; 11:231-242. [PMID: 34631481 PMCID: PMC8472545 DOI: 10.5662/wjm.v11.i5.231] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Revised: 06/18/2021] [Accepted: 07/19/2021] [Indexed: 02/06/2023] Open
Abstract
The sequencing of complete human genome revolutionized the genomic medicine. However, the complex interplay of gene-environment-lifestyle and influence of non-coding genomic regions on human health remain largely unexplored. Genomic medicine has great potential for diagnoses or disease prediction, disease prevention and, targeted treatment. However, many of the promising tools of genomic medicine are still in their infancy and their application may be limited because of the limited knowledge we have that precludes its use in many clinical settings. In this review article, we have reviewed the evolution of genomic methodologies/tools, their limitations, and scope, for current and future clinical application.
Collapse
Affiliation(s)
- Vishwanath Pattan
- Division of Endocrinology, Wyoming Medical Center, Casper, WY 82601, United States
| | - Rahul Kashyap
- Department of Anesthesiology and Peri-operative Medicine, Mayo Clinic, Rochester, MN 55905, United States
| | - Vikas Bansal
- Department of Anesthesiology and Peri-operative Medicine, Mayo Clinic, Rochester, MN 55905, United States
| | - Narsimha Candula
- Hospital Medicine, University Florida Health, Jacksonville, FL 32209, United States
| | - Thoyaja Koritala
- Hospital Medicine, Mayo Clinic Health System, Mankato, MN 56001, United States
| | - Salim Surani
- Department of Internal Medicine, Texas A&M University, Corpus Christi, TX 78405, United States
| |
Collapse
|
|
20
|
Fears SC, Service SK, Kremeyer B, Araya C, Araya X, Bejarano J, Ramirez M, Castrillón G, Gomez-Franco J, Lopez MC, Montoya G, Montoya P, Aldana I, Teshiba TM, Al-Sharif NB, Jalbrzikowski M, Tishler TA, Escobar J, Ruiz-Linares A, Lopez-Jaramillo C, Macaya G, Molina J, Reus VI, Cantor RM, Sabatti C, Freimer NB, Bearden CE. Genome-wide mapping of brain phenotypes in extended pedigrees with strong genetic loading for bipolar disorder. Mol Psychiatry 2021; 26:5229-5238. [PMID: 32606377 DOI: 10.1038/s41380-020-0805-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2019] [Revised: 05/26/2020] [Accepted: 05/29/2020] [Indexed: 02/08/2023]
Abstract
Bipolar disorder is a highly heritable illness, associated with alterations of brain structure. As such, identification of genes influencing inter-individual differences in brain morphology may help elucidate the underlying pathophysiology of bipolar disorder (BP). To identify quantitative trait loci (QTL) that contribute to phenotypic variance of brain structure, structural neuroimages were acquired from family members (n = 527) of extended pedigrees heavily loaded for bipolar disorder ascertained from genetically isolated populations in Latin America. Genome-wide linkage and association analysis were conducted on the subset of heritable brain traits that showed significant evidence of association with bipolar disorder (n = 24) to map QTL influencing regional measures of brain volume and cortical thickness. Two chromosomal regions showed significant evidence of linkage; a QTL on chromosome 1p influencing corpus callosum volume and a region on chromosome 7p linked to cortical volume. Association analysis within the two QTLs identified three SNPs correlated with the brain measures.
Collapse
Affiliation(s)
- Scott C Fears
- Department of Psychiatry and Biobehavioral Science, University of California, Los Angeles, CA, USA.
- Section of Mental Health, Greater Los Angeles Veterans Administration, Los Angeles, CA, USA.
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, Los Angeles, CA, USA.
| | - Susan K Service
- Department of Psychiatry and Biobehavioral Science, University of California, Los Angeles, CA, USA
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, Los Angeles, CA, USA
| | - Barbara Kremeyer
- Department of Genetics, Evolution and Environment, University College London, London, WC1E 6BT, UK
| | - Carmen Araya
- Cell and Molecular Biology Research Center, Universidad de Costa Rica, San Pedro de Montes de Oca, Costa Rica
| | - Xinia Araya
- Cell and Molecular Biology Research Center, Universidad de Costa Rica, San Pedro de Montes de Oca, Costa Rica
| | - Julio Bejarano
- Cell and Molecular Biology Research Center, Universidad de Costa Rica, San Pedro de Montes de Oca, Costa Rica
| | - Margarita Ramirez
- Cell and Molecular Biology Research Center, Universidad de Costa Rica, San Pedro de Montes de Oca, Costa Rica
| | | | | | - Maria C Lopez
- Grupo de Investigación en Psiquiatría (Research Group in Psychiatry (GIPSI)), Departamento de Psiquiatría, Facultad de Medicina, Universidad de Antioquia, Medellín, Colombia
| | - Gabriel Montoya
- Grupo de Investigación en Psiquiatría (Research Group in Psychiatry (GIPSI)), Departamento de Psiquiatría, Facultad de Medicina, Universidad de Antioquia, Medellín, Colombia
| | - Patricia Montoya
- Grupo de Investigación en Psiquiatría (Research Group in Psychiatry (GIPSI)), Departamento de Psiquiatría, Facultad de Medicina, Universidad de Antioquia, Medellín, Colombia
| | - Ileana Aldana
- Department of Psychiatry and Biobehavioral Science, University of California, Los Angeles, CA, USA
| | - Terri M Teshiba
- Department of Psychiatry and Biobehavioral Science, University of California, Los Angeles, CA, USA
| | - Noor B Al-Sharif
- Department of Psychiatry and Biobehavioral Science, University of California, Los Angeles, CA, USA
| | - Maria Jalbrzikowski
- Department of Psychiatry and Biobehavioral Science, University of California, Los Angeles, CA, USA
| | - Todd A Tishler
- Department of Psychiatry and Biobehavioral Science, University of California, Los Angeles, CA, USA
| | - Javier Escobar
- Department of Psychiatry and Family Medicine, Rutgers-Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA
| | - Andrés Ruiz-Linares
- Ministry of Education Key Laboratory of Contemporary Anthropology and Collaborative Innovation Center of Genetics and Development, School of Life Sciences and Human Phenome Institute, Fudan University, Yangpu District, Shanghai, China
- UMR 7268 ADES, CNRS, Aix-Marseille Université, EFS, Faculté de médecine Timone, Marseille, 13005, France
- Department of Genetics, Evolution and Environment, and UCL Genetics Institute, University College London, London, WC1E 6BT, UK
| | - Carlos Lopez-Jaramillo
- Grupo de Investigación en Psiquiatría (Research Group in Psychiatry (GIPSI)), Departamento de Psiquiatría, Facultad de Medicina, Universidad de Antioquia, Medellín, Colombia
| | - Gabriel Macaya
- Cell and Molecular Biology Research Center, Universidad de Costa Rica, San Pedro de Montes de Oca, Costa Rica
| | - Julio Molina
- Department of Psychiatry and Biobehavioral Science, University of California, Los Angeles, CA, USA
- BioCiencias Lab, Guatemala, Guatemala
| | - Victor I Reus
- Department of Psychiatry, University of California, San Francisco, CA, USA
| | - Rita M Cantor
- Department of Psychiatry and Biobehavioral Science, University of California, Los Angeles, CA, USA
- Department of Human Genetics, University of California, Los Angeles, CA, USA
| | - Chiara Sabatti
- Department of Health Research and Policy, Stanford University, Stanford, CA, USA
| | - Nelson B Freimer
- Department of Psychiatry and Biobehavioral Science, University of California, Los Angeles, CA, USA
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, Los Angeles, CA, USA
| | - Carrie E Bearden
- Department of Psychiatry and Biobehavioral Science, University of California, Los Angeles, CA, USA
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, Los Angeles, CA, USA
| |
Collapse
|
|
21
|
Wu X, Huai C, Shen L, Li M, Yang C, Zhang J, Chen L, Zhu W, Fan L, Zhou W, Xing Q, He L, Wan C, Qin S. Genome-wide study of copy number variation implicates multiple novel loci for schizophrenia risk in Han Chinese family trios. iScience 2021; 24:102894. [PMID: 34401673 PMCID: PMC8358640 DOI: 10.1016/j.isci.2021.102894] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 06/17/2021] [Accepted: 07/19/2021] [Indexed: 01/22/2023] Open
Abstract
Schizophrenia (SCZ) is a severe neuropsychiatric disorder that affects 1% of the global population. Copy number variations (CNVs) have been shown to play a critical role in its pathophysiology; however, only case-control studies on SCZ susceptibility CNVs have been conducted in Han Chinese. Here, we performed an array comparative genomic hybridization-based genome-wide CNV analysis in 100 Chinese family trios with SCZ. Burden test suggested that the SCZ probands carried more duplications than their healthy parents and unrelated healthy controls. Besides, five CNV loci were firstly reported to be associated with SCZ here, including both unbalanced transmitted CNVs and enriched de novo CNVs. Moreover, two genes (CTDSPL and MGAM) in these CNVs showed significant SCZ relevance in the expression level. Our findings support the crucial role of CNVs in the etiology of SCZ and provide new insights into the underlying mechanism of SCZ pathogenesis.
Collapse
Affiliation(s)
- Xi Wu
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, 200030, China
| | - Cong Huai
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, 200030, China
| | - Lu Shen
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, 200030, China
| | - Mo Li
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, 200030, China
| | - Chao Yang
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, 200030, China
| | - Juan Zhang
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, 200030, China
| | - Luan Chen
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, 200030, China
| | - Wenli Zhu
- The Fourth People's Hospital of Wuhu, Wuhu, Anhui, 241000, China
| | - Lingzi Fan
- Zhumadian Psychiatric Hospital, Zhumadian, Henan, 463000, China
| | - Wei Zhou
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, 200030, China
| | - Qinghe Xing
- Children's Hospital & Institutes of Biomedical Sciences, Fudan University, Shanghai, 200032, China
| | - Lin He
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, 200030, China
- Corresponding author
| | - Chunling Wan
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, 200030, China
- Corresponding author
| | - Shengying Qin
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, 200030, China
- Corresponding author
| |
Collapse
|
|
22
|
Li WX, Wang P, Zhao H, Sun X, Yang T, Li H, Hou Y, Liu C, Siyal M, Raja veesar R, Hu B, Ning H. QTL for Main Stem Node Number and Its Response to Plant Densities in 144 Soybean FW-RILs. FRONTIERS IN PLANT SCIENCE 2021; 12:666796. [PMID: 34489989 PMCID: PMC8417731 DOI: 10.3389/fpls.2021.666796] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Accepted: 07/21/2021] [Indexed: 06/13/2023]
Abstract
Although the main stem node number of soybean [Glycine max (L.) Merr. ] is an important yield-related trait, there have been limited studies on the effect of plant density on the identification of quantitative trait loci (QTL) for main stem node number (MSNN). To address this issue, here, 144 four-way recombinant inbred lines (FW-RILs) derived from Kenfeng 14, Kenfeng 15, Heinong 48, and Kenfeng 19 were used to identify QTL for MSNN with densities of 2.2 × 105 (D1) and 3 × 105 (D2) plants/ha in five environments by linkage and association studies. As a result, the linkage and association studies identified 40 and 28 QTL in D1 and D2, respectively, indicating the difference in QTL in various densities. Among these QTL, five were common in the two densities; 36 were singly identified for response to density; 12 were repeatedly identified by both response to density and phenotype of two densities. Thirty-one were repeatedly detected across various methods, densities, and environments in the linkage and association studies. Among the 24 common QTL in the linkage and association studies, 15 explained a phenotypic variation of more than 10%. Finally, Glyma.06G094400, Glyma.06G147600, Glyma.19G160800.1, and Glyma.19G161100 were predicted to be associated with MSNN. These findings will help to elucidate the genetic basis of MSNN and improve molecular assistant selection in high-yield soybean breeding.
Collapse
Affiliation(s)
- Wen-Xia Li
- Key Laboratory of Soybean Biology, Ministry of Education, Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Ping Wang
- Key Laboratory of Soybean Biology, Ministry of Education, Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
- High Education Institute, Huaiyin Institute of Technology, Huai'an, China
| | - Hengxing Zhao
- Key Laboratory of Soybean Biology, Ministry of Education, Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Xu Sun
- Key Laboratory of Soybean Biology, Ministry of Education, Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Tao Yang
- Key Laboratory of Soybean Biology, Ministry of Education, Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Haoran Li
- Key Laboratory of Soybean Biology, Ministry of Education, Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Yongqin Hou
- Key Laboratory of Soybean Biology, Ministry of Education, Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Cuiqiao Liu
- Key Laboratory of Soybean Biology, Ministry of Education, Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Mahfishan Siyal
- Key Laboratory of Soybean Biology, Ministry of Education, Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Rameez Raja veesar
- Key Laboratory of Soybean Biology, Ministry of Education, Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Bo Hu
- Key Laboratory of Soybean Biology, Ministry of Education, Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Hailong Ning
- Key Laboratory of Soybean Biology, Ministry of Education, Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| |
Collapse
|
|
23
|
Benchek P, Igo RP, Voss-Hoynes H, Wren Y, Miller G, Truitt B, Zhang W, Osterman M, Freebairn L, Tag J, Taylor HG, Chan ER, Roussos P, Lewis B, Stein CM, Iyengar SK. Association between genes regulating neural pathways for quantitative traits of speech and language disorders. NPJ Genom Med 2021; 6:64. [PMID: 34315907 PMCID: PMC8316336 DOI: 10.1038/s41525-021-00225-5] [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: 01/05/2021] [Accepted: 06/22/2021] [Indexed: 02/06/2023] Open
Abstract
Speech sound disorders (SSD) manifest as difficulties in phonological memory and awareness, oral motor function, language, vocabulary, reading, and spelling. Families enriched for SSD are rare, and typically display a cluster of deficits. We conducted a genome-wide association study (GWAS) in 435 children from 148 families in the Cleveland Family Speech and Reading study (CFSRS), examining 16 variables representing 6 domains. Replication was conducted using the Avon Longitudinal Study of Parents and Children (ALSPAC). We identified 18 significant loci (combined p < 10-8) that we pursued bioinformatically. We prioritized 5 novel gene regions with likely functional repercussions on neural pathways, including those which colocalized with differentially methylated regions in our sample. Polygenic risk scores for receptive language, expressive vocabulary, phonological awareness, phonological memory, spelling, and reading decoding associated with increasing clinical severity. In summary, neural-genetic influence on SSD is primarily multigenic and acts on genomic regulatory elements, similar to other neurodevelopmental disorders.
Collapse
Affiliation(s)
- Penelope Benchek
- Department of Population & Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Robert P Igo
- Department of Population & Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Heather Voss-Hoynes
- Department of Population & Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Yvonne Wren
- Bristol Dental School, Faculty of Health Sciences, University of Bristol, and Bristol Speech and Language Therapy Research Unit, North Bristol NHS Trust, Bristol, UK
| | - Gabrielle Miller
- Department of Psychological Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Barbara Truitt
- Department of Population & Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Wen Zhang
- Department of Psychiatry, Friedman Brain Institute, and Department of Genetics and Genomic Science and Institute for Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Michael Osterman
- Department of Population & Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Lisa Freebairn
- Department of Psychological Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Jessica Tag
- Department of Psychological Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - H Gerry Taylor
- Department of Pediatrics, Case Western Reserve University, and Rainbow Babies & Children's Hospital, University Hospital Case Medical Center, Cleveland, OH, USA
- Nationwide Children's Hospital Research Institute and Department of Pediatrics, The Ohio State University, Columbus, OH, USA
| | - E Ricky Chan
- Department of Population & Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Panos Roussos
- Department of Psychiatry, Friedman Brain Institute, and Department of Genetics and Genomic Science and Institute for Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, NY, USA
| | - Barbara Lewis
- Department of Psychological Sciences, Case Western Reserve University, Cleveland, OH, USA
- Cleveland Hearing and Speech Center, Cleveland, OH, USA
| | - Catherine M Stein
- Department of Population & Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA.
| | - Sudha K Iyengar
- Department of Population & Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA.
| |
Collapse
|
|
24
|
Teerlink CC, Miller JB, Vance EL, Staley LA, Stevens J, Tavana JP, Cloward ME, Page ML, Dayton L, Cannon-Albright LA, Kauwe JSK. Analysis of high-risk pedigrees identifies 11 candidate variants for Alzheimer's disease. Alzheimers Dement 2021; 18:307-317. [PMID: 34151536 PMCID: PMC9291865 DOI: 10.1002/alz.12397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Revised: 04/15/2021] [Accepted: 05/11/2021] [Indexed: 11/08/2022]
Abstract
Introduction Analysis of sequence data in high‐risk pedigrees is a powerful approach to detect rare predisposition variants. Methods Rare, shared candidate predisposition variants were identified from exome sequencing 19 Alzheimer's disease (AD)‐affected cousin pairs selected from high‐risk pedigrees. Variants were further prioritized by risk association in various external datasets. Candidate variants emerging from these analyses were tested for co‐segregation to additional affected relatives of the original sequenced pedigree members. Results AD‐affected high‐risk cousin pairs contained 564 shared rare variants. Eleven variants spanning 10 genes were prioritized in external datasets: rs201665195 (ABCA7), and rs28933981 (TTR) were previously implicated in AD pathology; rs141402160 (NOTCH3) and rs140914494 (NOTCH3) were previously reported; rs200290640 (PIDD1) and rs199752248 (PIDD1) were present in more than one cousin pair; rs61729902 (SNAP91), rs140129800 (COX6A2, AC026471), and rs191804178 (MUC16) were not present in a longevity cohort; and rs148294193 (PELI3) and rs147599881 (FCHO1) approached significance from analysis of AD‐related phenotypes. Three variants were validated via evidence of co‐segregation to additional relatives (PELI3, ABCA7, and SNAP91). Discussion These analyses support ABCA7 and TTR as AD risk genes, expand on previously reported NOTCH3 variant identification, and prioritize seven additional candidate variants.
Collapse
Affiliation(s)
- Craig C Teerlink
- Department of Internal Medicine, University of Utah, Salt Lake City, Utah, USA
| | - Justin B Miller
- Department of Biomedical Informatics, University of Kentucky Sanders-Brown Center on Aging, Lexington, Kentucky, USA
| | | | - Lyndsay A Staley
- Department of Biology, Brigham Young University, Provo, Utah, USA
| | - Jeffrey Stevens
- Department of Internal Medicine, University of Utah, Salt Lake City, Utah, USA
| | - Justina P Tavana
- Department of Biology, Brigham Young University, Provo, Utah, USA
| | | | - Madeline L Page
- Department of Biology, Brigham Young University, Provo, Utah, USA
| | - Louisa Dayton
- Department of Biology, Brigham Young University, Provo, Utah, USA
| | | | - Lisa A Cannon-Albright
- Department of Internal Medicine, University of Utah, Salt Lake City, Utah, USA.,George E. Wahlen Department of Veterans Affairs Medical Center, Salt Lake City, Utah, USA.,Huntsman Cancer Institute, Salt Lake City, Utah, USA
| | - John S K Kauwe
- Department of Biology, Brigham Young University, Provo, Utah, USA
| |
Collapse
|
|
25
|
Martin AM, Cassirer EF, Waits LP, Plowright RK, Cross PC, Andrews KR. Genomic association with pathogen carriage in bighorn sheep ( Ovis canadensis). Ecol Evol 2021; 11:2488-2502. [PMID: 33767816 PMCID: PMC7981200 DOI: 10.1002/ece3.7159] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 12/15/2020] [Accepted: 12/16/2020] [Indexed: 12/03/2022] Open
Abstract
Genetic composition can influence host susceptibility to, and transmission of, pathogens, with potential population-level consequences. In bighorn sheep (Ovis canadensis), pneumonia epidemics caused by Mycoplasma ovipneumoniae have been associated with severe population declines and limited recovery across North America. Adult survivors either clear the infection or act as carriers that continually shed M. ovipneumoniae and expose their susceptible offspring, resulting in high rates of lamb mortality for years following the outbreak event. Here, we investigated the influence of genomic composition on persistent carriage of M. ovipneumoniae in a well-studied bighorn sheep herd in the Wallowa Mountains of Oregon, USA. Using 10,605 SNPs generated using RADseq technology for 25 female bighorn sheep, we assessed genomic diversity metrics and employed family-based genome-wide association methodologies to understand variant association and genetic architecture underlying chronic carriage. We observed no differences among genome-wide diversity metrics (heterozygosity and allelic richness) between groups. However, we identified two variant loci of interest and seven associated candidate genes, which may influence carriage status. Further, we found that the SNP panel explained ~55% of the phenotypic variance (SNP-based heritability) for M. ovipneumoniae carriage, though there was considerable uncertainty in these estimates. While small sample sizes limit conclusions drawn here, our study represents one of the first to assess the genomic factors influencing chronic carriage of a pathogen in a wild population and lays a foundation for understanding genomic influence on pathogen persistence in bighorn sheep and other wildlife populations. Future research should incorporate additional individuals as well as distinct herds to further explore the genomic basis of chronic carriage.
Collapse
Affiliation(s)
- Alynn M. Martin
- United States Geological SurveyNorthern Rocky Mountain Science CenterBozemanMTUSA
| | | | | | - Raina K. Plowright
- Department of Microbiology and ImmunologyMontana State UniversityBozemanMTUSA
| | - Paul C. Cross
- United States Geological SurveyNorthern Rocky Mountain Science CenterBozemanMTUSA
| | - Kimberly R. Andrews
- Institute for Bioinformatics and Evolutionary Studies (IBEST)University of IdahoMoscowIDUSA
| |
Collapse
|
|
26
|
Lou XY, Hou TT, Liu SY, Xu HM, Lin F, Tang X, MacLeod SL, Cleves MA, Hobbs CA. Innovative approach to identify multigenomic and environmental interactions associated with birth defects in family-based hybrid designs. Genet Epidemiol 2021; 45:171-189. [PMID: 32996630 PMCID: PMC8495752 DOI: 10.1002/gepi.22363] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Revised: 09/08/2020] [Accepted: 09/11/2020] [Indexed: 11/09/2022]
Abstract
Genes, including those with transgenerational effects, work in concert with behavioral, environmental, and social factors via complex biological networks to determine human health. Understanding complex relationships between causal factors underlying human health is an essential step towards deciphering biological mechanisms. We propose a new analytical framework to investigate the interactions between maternal and offspring genetic variants or their surrogate single nucleotide polymorphisms (SNPs) and environmental factors using family-based hybrid study design. The proposed approach can analyze diverse genetic and environmental factors and accommodate samples from a variety of family units, including case/control-parental triads, and case/control-parental dyads, while minimizing potential bias introduced by population admixture. Comprehensive simulations demonstrated that our innovative approach outperformed the log-linear approach, the best available method for case-control family data. The proposed approach had greater statistical power and was capable to unbiasedly estimate the maternal and child genetic effects and the effects of environmental factors, while controlling the Type I error rate against population stratification. Using our newly developed approach, we analyzed the associations between maternal and fetal SNPs and obstructive and conotruncal heart defects, with adjustment for demographic and lifestyle factors and dietary supplements. Fourteen and 11 fetal SNPs were associated with obstructive and conotruncal heart defects, respectively. Twenty-seven and 17 maternal SNPs were associated with obstructive and conotruncal heart defects, respectively. In addition, maternal body mass index was a significant risk factor for obstructive defects. The proposed approach is a powerful tool for interrogating the etiological mechanism underlying complex traits.
Collapse
Affiliation(s)
- Xiang-Yang Lou
- Department of Biostatistics, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Ting-Ting Hou
- Department of Biostatistics, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, Florida, USA
- Institute of Bioinformatics and Institute of Crop Science, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
| | - Shou-Ye Liu
- Institute of Bioinformatics and Institute of Crop Science, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
| | - Hai-Ming Xu
- Institute of Bioinformatics and Institute of Crop Science, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
| | - Feng Lin
- Institute of Bioinformatics and Institute of Crop Science, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
| | - Xinyu Tang
- The US Food and Drug Administration, Silver Spring, Maryland, USA
| | | | - Mario A. Cleves
- Department of Pediatrics, Morsani College of Medicine, Health Informatics Institute, University of South Florida, Tampa, Florida, USA
| | - Charlotte A. Hobbs
- Rady Children’s Institute for Genomic Medicine, San Diego, California, USA
| |
Collapse
|
|
27
|
Turkmen AS, Lin S. Detecting X-linked common and rare variant effects in family-based sequencing studies. Genet Epidemiol 2021; 45:36-45. [PMID: 32864779 DOI: 10.1002/gepi.22352] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2019] [Revised: 06/26/2020] [Accepted: 08/03/2020] [Indexed: 11/08/2022]
Abstract
The breakthroughs in next generation sequencing have allowed us to access data consisting of both common and rare variants, and in particular to investigate the impact of rare genetic variation on complex diseases. Although rare genetic variants are thought to be important components in explaining genetic mechanisms of many diseases, discovering these variants remains challenging, and most studies are restricted to population-based designs. Further, despite the shift in the field of genome-wide association studies (GWAS) towards studying rare variants due to the "missing heritability" phenomenon, little is known about rare X-linked variants associated with complex diseases. For instance, there is evidence that X-linked genes are highly involved in brain development and cognition when compared with autosomal genes; however, like most GWAS for other complex traits, previous GWAS for mental diseases have provided poor resources to deal with identification of rare variant associations on X-chromosome. In this paper, we address the two issues described above by proposing a method that can be used to test X-linked variants using sequencing data on families. Our method is much more general than existing methods, as it can be applied to detect both common and rare variants, and is applicable to autosomes as well. Our simulation study shows that the method is efficient, and exhibits good operational characteristics. An application to the University of Miami Study on Genetics of Autism and Related Disorders also yielded encouraging results.
Collapse
Affiliation(s)
- Asuman S Turkmen
- Statistics Department, The Ohio State University, Columbus, Ohio
- Statistics Department, The Ohio State University, Newark, Ohio
| | - Shili Lin
- Statistics Department, The Ohio State University, Columbus, Ohio
| |
Collapse
|
|
28
|
Wang P, Sun X, Zhang K, Fang Y, Wang J, Yang C, Li WX, Ning H. Mapping QTL/QTN and mining candidate genes for plant height and its response to planting densities in soybean [ Glycine max (L.) Merr.] through a FW-RIL population. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2021; 41:12. [PMID: 37309477 PMCID: PMC10236039 DOI: 10.1007/s11032-021-01209-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Accepted: 01/26/2021] [Indexed: 06/13/2023]
Abstract
Plant height (PH) determines the morphology and seed yield of soybean, so it is an important breeding target, which is controlled by multiple genes and affected by plant density. In this research, it was used about a four-way recombinant inbred lines (FW-RIL) with 144 families constructed by double cross (Kenfeng 14 × Kenfeng 15) × (Heinong 48 × Kenfeng 19) as experimental materials, with the purpose to map QTL/QTN associated with PH under densities of 2.2×105 plant/ha (D1) and 3×105 plant/ha (D2) in five environments. The results showed that response of PH to densities varied in accordance to genotypes among environments. A total of 26 QTLs and 13 QTNs were identified specifically in D1; 20 QTLs and 21 QTNs were identified specifically in D2. Nine QTLs and one QTN were discovered commonly in two densities. Fifteen QTLs and 9 QTNs were repeatedly detected by multiple statistical methods, densities, or environments, which could be considered stable. Eighteen QTLs were detected, as well as 7 QTNs underlying responses of PH to density increment. Six QTNs, co-located in the interval of QTL, were detected in more than two environments or methods with a longer genome length over 3000 kb. Based on gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, five genes were predicted as candidates, which were likely to be involved in growth and development of PH. The results will help elucidate the genetic basis and improve molecular assistant selection of PH. Supplementary Information The online version contains supplementary material available at 10.1007/s11032-021-01209-0.
Collapse
Affiliation(s)
- Ping Wang
- Key Laboratory of Soybean Biology, Ministry of Education/Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
- Huaiyin Institute of Technology, Huai’an, China
| | - Xu Sun
- Key Laboratory of Soybean Biology, Ministry of Education/Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Kaixin Zhang
- Key Laboratory of Soybean Biology, Ministry of Education/Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Yanlong Fang
- Key Laboratory of Soybean Biology, Ministry of Education/Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Jiajing Wang
- Key Laboratory of Soybean Biology, Ministry of Education/Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Chang Yang
- Key Laboratory of Soybean Biology, Ministry of Education/Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Wen-Xia Li
- Key Laboratory of Soybean Biology, Ministry of Education/Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Hailong Ning
- Key Laboratory of Soybean Biology, Ministry of Education/Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| |
Collapse
|
|
29
|
Clinical and genetic differences between bipolar disorder type 1 and 2 in multiplex families. Transl Psychiatry 2021; 11:31. [PMID: 33431802 PMCID: PMC7801527 DOI: 10.1038/s41398-020-01146-0] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 11/30/2020] [Accepted: 12/04/2020] [Indexed: 01/29/2023] Open
Abstract
The two major subtypes of bipolar disorder (BD), BD-I and BD-II, are distinguished based on the presence of manic or hypomanic episodes. Historically, BD-II was perceived as a less severe form of BD-I. Recent research has challenged this concept of a severity continuum. Studies in large samples of unrelated patients have described clinical and genetic differences between the subtypes. Besides an increased schizophrenia polygenic risk load in BD-I, these studies also observed an increased depression risk load in BD-II patients. The present study assessed whether such clinical and genetic differences are also found in BD patients from multiplex families, which exhibit reduced genetic and environmental heterogeneity. Comparing 252 BD-I and 75 BD-II patients from the Andalusian Bipolar Family (ABiF) study, the clinical course, symptoms during depressive and manic episodes, and psychiatric comorbidities were analyzed. Furthermore, polygenic risk scores (PRS) for BD, schizophrenia, and depression were assessed. BD-I patients not only suffered from more severe symptoms during manic episodes but also more frequently showed incapacity during depressive episodes. A higher BD PRS was significantly associated with suicidal ideation. Moreover, BD-I cases exhibited lower depression PRS. In line with a severity continuum from BD-II to BD-I, our results link BD-I to a more pronounced clinical presentation in both mania and depression and indicate that the polygenic risk load of BD predisposes to more severe disorder characteristics. Nevertheless, our results suggest that the genetic risk burden for depression also shapes disorder presentation and increases the likelihood of BD-II subtype development.
Collapse
|
|
30
|
Lonjou C, Eon-Marchais S, Truong T, Dondon MG, Karimi M, Jiao Y, Damiola F, Barjhoux L, Le Gal D, Beauvallet J, Mebirouk N, Cavaciuti E, Chiesa J, Floquet A, Audebert-Bellanger S, Giraud S, Frebourg T, Limacher JM, Gladieff L, Mortemousque I, Dreyfus H, Lejeune-Dumoulin S, Lasset C, Venat-Bouvet L, Bignon YJ, Pujol P, Maugard CM, Luporsi E, Bonadona V, Noguès C, Berthet P, Delnatte C, Gesta P, Lortholary A, Faivre L, Buecher B, Caron O, Gauthier-Villars M, Coupier I, Mazoyer S, Monraz LC, Kondratova M, Kuperstein I, Guénel P, Barillot E, Stoppa-Lyonnet D, Andrieu N, Lesueur F. Gene- and pathway-level analyses of iCOGS variants highlight novel signaling pathways underlying familial breast cancer susceptibility. Int J Cancer 2021; 148:1895-1909. [PMID: 33368296 PMCID: PMC9290690 DOI: 10.1002/ijc.33457] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 11/20/2020] [Accepted: 12/07/2020] [Indexed: 12/17/2022]
Abstract
Single‐nucleotide polymorphisms (SNPs) in over 180 loci have been associated with breast cancer (BC) through genome‐wide association studies involving mostly unselected population‐based case‐control series. Some of them modify BC risk of women carrying a BRCA1 or BRCA2 (BRCA1/2) mutation and may also explain BC risk variability in BC‐prone families with no BRCA1/2 mutation. Here, we assessed the contribution of SNPs of the iCOGS array in GENESIS consisting of BC cases with no BRCA1/2 mutation and a sister with BC, and population controls. Genotyping data were available for 1281 index cases, 731 sisters with BC, 457 unaffected sisters and 1272 controls. In addition to the standard SNP‐level analysis using index cases and controls, we performed pedigree‐based association tests to capture transmission information in the sibships. We also performed gene‐ and pathway‐level analyses to maximize the power to detect associations with lower‐frequency SNPs or those with modest effect sizes. While SNP‐level analyses identified 18 loci, gene‐level analyses identified 112 genes. Furthermore, 31 Kyoto Encyclopedia of Genes and Genomes and 7 Atlas of Cancer Signaling Network pathways were highlighted (false discovery rate of 5%). Using results from the “index case‐control” analysis, we built pathway‐derived polygenic risk scores (PRS) and assessed their performance in the population‐based CECILE study and in a data set composed of GENESIS‐affected sisters and CECILE controls. Although these PRS had poor predictive value in the general population, they performed better than a PRS built using our SNP‐level findings, and we found that the joint effect of family history and PRS needs to be considered in risk prediction models.
What's new?
Genetic studies have identified more than 180 single‐nucleotide polymorphisms (SNPs) associated with breast cancer susceptibility, but these studies are reaching their limits. Here, the authors evaluated SNPs in the iCOGS genotyping array using a multilevel approach, including single variant, gene, and pathway analyses. They measured the contribution of the SNPs to breast cancer in patients who have a sister with breast cancer but do not carry a BRCA1/2 mutation. They showed that a pathway‐derived polygenic risk score performed poorly in the general population, and that the best predictive model must include family history.
Collapse
Affiliation(s)
- Christine Lonjou
- Inserm, U900, Institut Curie, Paris, France.,Mines ParisTech, Fontainebleau, France.,PSL Research University, Paris, France
| | - Séverine Eon-Marchais
- Inserm, U900, Institut Curie, Paris, France.,Mines ParisTech, Fontainebleau, France.,PSL Research University, Paris, France
| | - Thérèse Truong
- Université Paris-Saclay, UVSQ, Inserm, CESP, Villejuif, France.,Inserm U1018, CESP, Team Exposome and Heredity, Villejuif, France
| | - Marie-Gabrielle Dondon
- Inserm, U900, Institut Curie, Paris, France.,Mines ParisTech, Fontainebleau, France.,PSL Research University, Paris, France
| | - Mojgan Karimi
- Université Paris-Saclay, UVSQ, Inserm, CESP, Villejuif, France.,Inserm U1018, CESP, Team Exposome and Heredity, Villejuif, France
| | - Yue Jiao
- Inserm, U900, Institut Curie, Paris, France.,Mines ParisTech, Fontainebleau, France.,PSL Research University, Paris, France
| | | | - Laure Barjhoux
- Department of BioPathology, Centre Léon Bérard, Lyon, France
| | - Dorothée Le Gal
- Inserm, U900, Institut Curie, Paris, France.,Mines ParisTech, Fontainebleau, France.,PSL Research University, Paris, France
| | - Juana Beauvallet
- Inserm, U900, Institut Curie, Paris, France.,Mines ParisTech, Fontainebleau, France.,PSL Research University, Paris, France
| | - Noura Mebirouk
- Inserm, U900, Institut Curie, Paris, France.,Mines ParisTech, Fontainebleau, France.,PSL Research University, Paris, France
| | - Eve Cavaciuti
- Inserm, U900, Institut Curie, Paris, France.,Mines ParisTech, Fontainebleau, France.,PSL Research University, Paris, France
| | | | | | | | - Sophie Giraud
- Service de Génétique, Hospices Civils de Lyon, Groupement Hospitalier Est, Bron, France
| | - Thierry Frebourg
- Département de Génétique, Hôpital Universitaire de Rouen, Rouen, France
| | | | - Laurence Gladieff
- Service d'Oncologie Médicale, Institut Claudius Regaud-IUCT-Oncopole, Toulouse, France
| | | | - Hélène Dreyfus
- Clinique Sainte Catherine, Avignon, France.,Département de Génétique, CHU de Grenoble, Hôpital Couple-Enfant, Grenoble, France
| | | | - Christine Lasset
- Université Claude Bernard Lyon 1, Villeurbanne, France.,CNRS UMR 5558, Lyon, France.,Centre Léon Bérard, Unité de Prévention et Epidémiologie Génétique, Lyon, France
| | | | - Yves-Jean Bignon
- Département d'Oncogénétique, Université Clermont Auvergne, UMR INSERM, U1240, Centre Jean Perrin, Clermont Ferrand, France
| | - Pascal Pujol
- Hôpital Arnaud de Villeneuve, CHU Montpellier, Service de Génétique Médicale et Oncogénétique, Montpellier, France.,INSERM 896, CRCM Val d'Aurelle, Montpellier, France
| | - Christine M Maugard
- Département d'Oncobiologie, LBBM, Hôpitaux Universitaires de Strasbourg, Génétique Oncologique Moléculaire, UF1422, Strasbourg, France.,Hôpitaux Universitaires de Strasbourg, UF6948 Génétique Oncologique Clinique, Évaluation Familiale et Suivi, Strasbourg, France
| | - Elisabeth Luporsi
- ICL Alexis Vautrin, Unité d'Oncogénétique, Vandœuvre-lès-Nancy, France
| | - Valérie Bonadona
- Université Claude Bernard Lyon 1, Villeurbanne, France.,CNRS UMR 5558, Lyon, France.,Centre Léon Bérard, Unité de Prévention et Epidémiologie Génétique, Lyon, France
| | - Catherine Noguès
- Département d'Anticipation et de Suivi des Cancers, Oncogénétique Clinique, Institut Paoli-Calmettes, Marseille, France.,Aix Marseille University, INSERM, IRD, SESSTIM, Marseille, France
| | - Pascaline Berthet
- Département de Biopathologie, Centre François Baclesse, Oncogénétique, Caen, France
| | - Capucine Delnatte
- Institut de Cancérologie de l'Ouest, Unité d'Oncogénétique, Saint Herblain, France
| | - Paul Gesta
- CH Georges Renon, Service d'Oncogénétique Régional Poitou-Charentes, Niort, France
| | - Alain Lortholary
- Centre Catherine de Sienne, Service d'Oncologie Médicale, Nantes, France
| | - Laurence Faivre
- Institut GIMI, CHU de Dijon, Hôpital d'Enfants, Dijon, France.,Oncogénétique, Centre de Lutte contre le Cancer Georges François Leclerc, Dijon, France
| | | | - Olivier Caron
- Département de Médecine Oncologique, Gustave Roussy, Villejuif, France
| | | | - Isabelle Coupier
- Hôpital Arnaud de Villeneuve, CHU Montpellier, Service de Génétique Médicale et Oncogénétique, Montpellier, France.,INSERM 896, CRCM Val d'Aurelle, Montpellier, France
| | - Sylvie Mazoyer
- Equipe GENDEV, Centre de Recherche en Neurosciences de Lyon, Inserm U1028, CNRS UMR5292, Université Lyon 1, Université St Etienne, Lyon, France
| | - Luis-Cristobal Monraz
- Inserm, U900, Institut Curie, Paris, France.,Mines ParisTech, Fontainebleau, France.,PSL Research University, Paris, France
| | - Maria Kondratova
- Inserm, U900, Institut Curie, Paris, France.,Mines ParisTech, Fontainebleau, France.,PSL Research University, Paris, France
| | - Inna Kuperstein
- Inserm, U900, Institut Curie, Paris, France.,Mines ParisTech, Fontainebleau, France.,PSL Research University, Paris, France
| | - Pascal Guénel
- Université Paris-Saclay, UVSQ, Inserm, CESP, Villejuif, France.,Inserm U1018, CESP, Team Exposome and Heredity, Villejuif, France
| | - Emmanuel Barillot
- Inserm, U900, Institut Curie, Paris, France.,Mines ParisTech, Fontainebleau, France.,PSL Research University, Paris, France
| | - Dominique Stoppa-Lyonnet
- Institut Curie, Service de Génétique, Paris, France.,Inserm, U830, Université Paris-Descartes, Paris, France
| | - Nadine Andrieu
- Inserm, U900, Institut Curie, Paris, France.,Mines ParisTech, Fontainebleau, France.,PSL Research University, Paris, France
| | - Fabienne Lesueur
- Inserm, U900, Institut Curie, Paris, France.,Mines ParisTech, Fontainebleau, France.,PSL Research University, Paris, France
| |
Collapse
|
|
31
|
Yang K, Lu K, Wu Y, Yu J, Liu B, Zhao Y, Chen J, Zhou X. A network-based machine-learning framework to identify both functional modules and disease genes. Hum Genet 2021; 140:897-913. [PMID: 33409574 DOI: 10.1007/s00439-020-02253-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Accepted: 12/22/2020] [Indexed: 01/20/2023]
Abstract
Disease gene identification is a critical step towards uncovering the molecular mechanisms of diseases and systematically investigating complex disease phenotypes. Despite considerable efforts to develop powerful computing methods, candidate gene identification remains a severe challenge owing to the connectivity of an incomplete interactome network, which hampers the discovery of true novel candidate genes. We developed a network-based machine-learning framework to identify both functional modules and disease candidate genes. In this framework, we designed a semi-supervised non-negative matrix factorization model to obtain the functional modules related to the diseases and genes. Of note, we proposed a disease gene-prioritizing method called MapGene that integrates the correlations from both functional modules and network closeness. Our framework identified a set of functional modules with highly functional homogeneity and close gene interactions. Experiments on a large-scale benchmark dataset showed that MapGene performs significantly better than the state-of-the-art algorithms. Further analysis demonstrates MapGene can effectively relieve the impact of the incompleteness of interactome networks and obtain highly reliable rankings of candidate genes. In addition, disease cases on Parkinson's disease and diabetes mellitus confirmed the generalization of MapGene for novel candidate gene identification. This work proposed, for the first time, an integrated computing framework to predict both functional modules and disease candidate genes. The methodology and results support that our framework has the potential to help discover underlying functional modules and reliable candidate genes in human disease.
Collapse
Affiliation(s)
- Kuo Yang
- School of Computer and Information Technology, Institute of Medical Intelligence, Beijing Jiaotong University, Beijing, 100044, China.,Institute for TCM-X, MOE Key Laboratory of Bioinformatics / Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, 10084, China
| | - Kezhi Lu
- School of Computer and Information Technology, Institute of Medical Intelligence, Beijing Jiaotong University, Beijing, 100044, China.,imec-DistriNet, KU Leuven, Leuven, 3001, Belgium
| | - Yang Wu
- Key Laboratory of Intelligent Information Processing, Advanced Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China
| | - Jian Yu
- Beijing Key Laboratory of Traffic Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, China
| | - Baoyan Liu
- Data Center of Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, 100700, China
| | - Yi Zhao
- Key Laboratory of Intelligent Information Processing, Advanced Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China
| | - Jianxin Chen
- Beijing University of Chinese Medicine, Beijing, 100029, China
| | - Xuezhong Zhou
- School of Computer and Information Technology, Institute of Medical Intelligence, Beijing Jiaotong University, Beijing, 100044, China. .,Data Center of Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, 100700, China.
| |
Collapse
|
|
32
|
Nyangiri OA, Edwige SA, Koffi M, Mewamba E, Simo G, Namulondo J, Mulindwa J, Nassuuna J, Elliott A, Karume K, Mumba D, Corstjens P, Casacuberta-Partal M, van Dam G, Bucheton B, Noyes H, Matovu E, TrypanoGEN+ Research Group of the H3Africa Consortium. Candidate gene family-based and case-control studies of susceptibility to high Schistosoma mansoni worm burden in African children: a protocol. AAS Open Res 2021; 4:36. [PMID: 35252746 PMCID: PMC8861467 DOI: 10.12688/aasopenres.13203.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/04/2021] [Indexed: 11/20/2022] Open
Abstract
Background: Approximately 25% of the risk of Schistosoma mansoni is associated with host genetic variation. We will test 24 candidate genes, mainly in the T h2 and T h17 pathways, for association with S. mansoni infection intensity in four African countries, using family based and case-control approaches. Methods: Children aged 5-15 years will be recruited in S. mansoni endemic areas of Ivory Coast, Cameroon, Uganda and the Democratic Republic of Congo (DRC). We will use family based (study 1) and case-control (study 2) designs. Study 1 will take place in Ivory Coast, Cameroon, Uganda and the DRC. We aim to recruit 100 high worm burden families from each country except Uganda, where a previous study recruited at least 40 families. For phenotyping, cases will be defined as the 20% of children in each community with heaviest worm burdens as measured by the circulating cathodic antigen (CCA) assay. Study 2 will take place in Uganda. We will recruit 500 children in a highly endemic community. For phenotyping, cases will be defined as the 20% of children with heaviest worm burdens as measured by the CAA assay, while controls will be the 20% of infected children with the lightest worm burdens. Deoxyribonucleic acid (DNA) will be genotyped on the Illumina H3Africa SNP (single nucleotide polymorphisms) chip and genotypes will be converted to sets of haplotypes that span the gene region for analysis. We have selected 24 genes for genotyping that are mainly in the Th2 and Th17 pathways and that have variants that have been demonstrated to be or could be associated with Schistosoma infection intensity. Analysis: In the family-based design, we will identify SNP haplotypes disproportionately transmitted to children with high worm burden. Case-control analysis will detect overrepresentation of haplotypes in extreme phenotypes with correction for relatedness by using whole genome principal components.
Collapse
Affiliation(s)
- Oscar A. Nyangiri
- College of Veterinary Medicine, Animal Resources and Biosecurity, Makerere University, Kampala, Uganda
| | - Sokouri A. Edwige
- Université Jean Lorougnon Guédé (UJLoG) de Daloa, Daloa, Cote d'Ivoire
| | - Mathurin Koffi
- Université Jean Lorougnon Guédé (UJLoG) de Daloa, Daloa, Cote d'Ivoire
| | - Estelle Mewamba
- Faculty of Science, University of Dschang, Dschang, Cameroon
| | - Gustave Simo
- Faculty of Science, University of Dschang, Dschang, Cameroon
| | - Joyce Namulondo
- College of Veterinary Medicine, Animal Resources and Biosecurity, Makerere University, Kampala, Uganda
| | - Julius Mulindwa
- College of Veterinary Medicine, Animal Resources and Biosecurity, Makerere University, Kampala, Uganda
| | - Jacent Nassuuna
- Medical Research Council/Uganda Virus Research Institute and London School of Hygiene & Tropical Medicine Uganda Research Unit, Entebbe, Uganda
| | - Alison Elliott
- Medical Research Council/Uganda Virus Research Institute and London School of Hygiene & Tropical Medicine Uganda Research Unit, Entebbe, Uganda
- London School of Hygiene and Tropical Medicine, London, WC1E, UK
| | - Kévin Karume
- Institut National de Recherche Biomedicale, Kinshasa, Democratic Republic of the Congo
| | - Dieudonne Mumba
- Institut National de Recherche Biomedicale, Kinshasa, Democratic Republic of the Congo
| | - P.L.A.M Corstjens
- Dept. of Cell and Chemical Biology, Leiden University Medical Center, Leiden, The Netherlands
| | | | - G.J. van Dam
- Dept. of Parasitology, Leiden University Medical Center, Leiden, The Netherlands
| | - Bruno Bucheton
- Institut de Recherche pour le Développement (IRD), IRD-CIRAD, Montpellier, France
| | - Harry Noyes
- Centre for Genomic Research, University of Liverpool, Liverpool, UK
| | - Enock Matovu
- College of Veterinary Medicine, Animal Resources and Biosecurity, Makerere University, Kampala, Uganda
| | | |
Collapse
|
|
33
|
Nyangiri OA, Edwige SA, Koffi M, Mewamba E, Simo G, Namulondo J, Mulindwa J, Nassuuna J, Elliott A, Karume K, Mumba D, Corstjens P, Casacuberta-Partal M, van Dam G, Bucheton B, Noyes H, Matovu E, TrypanoGEN+ Research Group of the H3Africa Consortium. Candidate gene family-based and case-control studies of susceptibility to high Schistosoma mansoni worm burden in African children: a protocol. AAS Open Res 2021; 4:36. [PMID: 35252746 PMCID: PMC8861467 DOI: 10.12688/aasopenres.13203.2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/15/2021] [Indexed: 11/20/2022] Open
Abstract
Background: Approximately 25% of the risk of Schistosoma mansoni is associated with host genetic variation. We will test 24 candidate genes, mainly in the T h2 and T h17 pathways, for association with S. mansoni infection intensity in four African countries, using family based and case-control approaches. Methods: Children aged 5-15 years will be recruited in S. mansoni endemic areas of Ivory Coast, Cameroon, Uganda and the Democratic Republic of Congo (DRC). We will use family based (study 1) and case-control (study 2) designs. Study 1 will take place in Ivory Coast, Cameroon, Uganda and the DRC. We aim to recruit 100 high worm burden families from each country except Uganda, where a previous study recruited at least 40 families. For phenotyping, cases will be defined as the 20% of children in each community with heaviest worm burdens as measured by the circulating cathodic antigen (CCA) assay. Study 2 will take place in Uganda. We will recruit 500 children in a highly endemic community. For phenotyping, cases will be defined as the 20% of children with heaviest worm burdens as measured by the CAA assay, while controls will be the 20% of infected children with the lightest worm burdens. Deoxyribonucleic acid (DNA) will be genotyped on the Illumina H3Africa SNP (single nucleotide polymorphisms) chip and genotypes will be converted to sets of haplotypes that span the gene region for analysis. We have selected 24 genes for genotyping that are mainly in the Th2 and Th17 pathways and that have variants that have been demonstrated to be or could be associated with Schistosoma infection intensity. Analysis: In the family-based design, we will identify SNP haplotypes disproportionately transmitted to children with high worm burden. Case-control analysis will detect overrepresentation of haplotypes in extreme phenotypes with correction for relatedness by using whole genome principal components.
Collapse
Affiliation(s)
- Oscar A. Nyangiri
- College of Veterinary Medicine, Animal Resources and Biosecurity, Makerere University, Kampala, Uganda
| | - Sokouri A. Edwige
- Université Jean Lorougnon Guédé (UJLoG) de Daloa, Daloa, Cote d'Ivoire
| | - Mathurin Koffi
- Université Jean Lorougnon Guédé (UJLoG) de Daloa, Daloa, Cote d'Ivoire
| | - Estelle Mewamba
- Faculty of Science, University of Dschang, Dschang, Cameroon
| | - Gustave Simo
- Faculty of Science, University of Dschang, Dschang, Cameroon
| | - Joyce Namulondo
- College of Veterinary Medicine, Animal Resources and Biosecurity, Makerere University, Kampala, Uganda
| | - Julius Mulindwa
- College of Veterinary Medicine, Animal Resources and Biosecurity, Makerere University, Kampala, Uganda
| | - Jacent Nassuuna
- Medical Research Council/Uganda Virus Research Institute and London School of Hygiene & Tropical Medicine Uganda Research Unit, Entebbe, Uganda
| | - Alison Elliott
- Medical Research Council/Uganda Virus Research Institute and London School of Hygiene & Tropical Medicine Uganda Research Unit, Entebbe, Uganda
- London School of Hygiene and Tropical Medicine, London, WC1E, UK
| | - Kévin Karume
- Institut National de Recherche Biomedicale, Kinshasa, Democratic Republic of the Congo
| | - Dieudonne Mumba
- Institut National de Recherche Biomedicale, Kinshasa, Democratic Republic of the Congo
| | - P.L.A.M Corstjens
- Dept. of Cell and Chemical Biology, Leiden University Medical Center, Leiden, The Netherlands
| | | | - G.J. van Dam
- Dept. of Parasitology, Leiden University Medical Center, Leiden, The Netherlands
| | - Bruno Bucheton
- Institut de Recherche pour le Développement (IRD), IRD-CIRAD, Montpellier, France
| | - Harry Noyes
- Centre for Genomic Research, University of Liverpool, Liverpool, UK
| | - Enock Matovu
- College of Veterinary Medicine, Animal Resources and Biosecurity, Makerere University, Kampala, Uganda
| | | |
Collapse
|
|
34
|
Andres EM, Earnest KK, Smith SD, Rice ML, Raza MH. Pedigree-Based Gene Mapping Supports Previous Loci and Reveals Novel Suggestive Loci in Specific Language Impairment. JOURNAL OF SPEECH, LANGUAGE, AND HEARING RESEARCH : JSLHR 2020; 63:4046-4061. [PMID: 33186502 PMCID: PMC8608229 DOI: 10.1044/2020_jslhr-20-00102] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Purpose Specific language impairment (SLI) is characterized by a delay in language acquisition despite a lack of other developmental delays or hearing loss. Genetics of SLI is poorly understood. The purpose of this study is to identify SLI genetic loci through family-based linkage mapping. Method We performed genome-wide parametric linkage analysis in six families segregating with SLI. An age-appropriate standardized omnibus language measure was used to categorically define the SLI phenotype. Results A suggestive linkage region replicated a previous region of interest with the highest logarithm of odds (LOD) score of 2.40 at 14q11.2-q13.3 in Family 489. A paternal parent-of-origin effect associated with SLI and language phenotypes on a nonsynonymous single nucleotide polymorphism (SNP) in NOP9 (14q12) was reported previously. Linkage analysis identified a new SLI locus at 15q24.3-25.3 with the highest parametric LOD score of 3.06 in Family 315 under a recessive mode of inheritance. Suggestive evidence of linkage was also revealed at 4q31.23-q35.2 in Family 300, with the highest LOD score of 2.41. Genetic linkage was not identified in the other three families included in parametric linkage analysis. Conclusions These results are the first to report genome-wide suggestive linkage with a total language standard score on an age-appropriate omnibus language measure across a wide age range. Our findings confirm previous reports of a language-associated locus on chromosome 14q, report new SLI loci, and validate the pedigree-based parametric linkage analysis approach to mapping genes for SLI. Supplemental Material https://doi.org/10.23641/asha.13203218.
Collapse
Affiliation(s)
- Erin M. Andres
- Child Language Doctoral Program, University of Kansas, Lawrence
| | | | - Shelley D. Smith
- Department of Neurological Sciences, University of Nebraska Medical Center, Omaha
| | - Mabel L. Rice
- Child Language Doctoral Program, University of Kansas, Lawrence
| | | |
Collapse
|
|
35
|
Kanzi AM, San JE, Chimukangara B, Wilkinson E, Fish M, Ramsuran V, de Oliveira T. Next Generation Sequencing and Bioinformatics Analysis of Family Genetic Inheritance. Front Genet 2020; 11:544162. [PMID: 33193618 PMCID: PMC7649788 DOI: 10.3389/fgene.2020.544162] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2020] [Accepted: 09/21/2020] [Indexed: 12/29/2022] Open
Abstract
Mendelian and complex genetic trait diseases continue to burden and affect society both socially and economically. The lack of effective tests has hampered diagnosis thus, the affected lack proper prognosis. Mendelian diseases are caused by genetic mutations in a singular gene while complex trait diseases are caused by the accumulation of mutations in either linked or unlinked genomic regions. Significant advances have been made in identifying novel diseases associated mutations especially with the introduction of next generation and third generation sequencing. Regardless, some diseases are still without diagnosis as most tests rely on SNP genotyping panels developed from population based genetic analyses. Analysis of family genetic inheritance using whole genomes, whole exomes or a panel of genes has been shown to be effective in identifying disease-causing mutations. In this review, we discuss next generation and third generation sequencing platforms, bioinformatic tools and genetic resources commonly used to analyze family based genomic data with a focus on identifying inherited or novel disease-causing mutations. Additionally, we also highlight the analytical, ethical and regulatory challenges associated with analyzing personal genomes which constitute the data used for family genetic inheritance.
Collapse
Affiliation(s)
- Aquillah M. Kanzi
- Kwazulu-Natal Research and Innovation Sequencing Platform (KRISP), School of Laboratory Medicine and Medical Sciences, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa
| | | | | | | | | | | | | |
Collapse
|
|
36
|
Khan MI, CS P. Case-Parent Trio Studies in Cleft Lip and Palate. Glob Med Genet 2020; 7:75-79. [PMID: 33392609 PMCID: PMC7772012 DOI: 10.1055/s-0040-1722097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Cleft lip with or without cleft palate (CL/P) is one of the most common congenital malformations in humans involving various genetic and environmental risk factors. The prevalence of CL/P varies according to geographical location, ethnicity, race, gender, and socioeconomic status, affecting approximately 1 in 800 live births worldwide. Genetic studies aim to understand the mechanisms contributory to a phenotype by measuring the association between genetic variants and also between genetic variants and phenotype population. Genome-wide association studies are standard tools used to discover genetic loci related to a trait of interest. Genetic association studies are generally divided into two main design types: population-based studies and family-based studies. The epidemiological population-based studies comprise unrelated individuals that directly compare the frequency of genetic variants between (usually independent) cases and controls. The alternative to population-based studies (case-control designs) includes various family-based study designs that comprise related individuals. An example of such a study is a case-parent trio design study, which is commonly employed in genetics to identify the variants underlying complex human disease where transmission of alleles from parents to offspring is studied. This article describes the fundamentals of case-parent trio study, trio design and its significances, statistical methods, and limitations of the trio studies.
Collapse
Affiliation(s)
- Mahamad Irfanulla Khan
- Department of Orthodontics & Dentofacial Orthopedics, The Oxford Dental College, Bangalore, Karnataka, India
| | - Prashanth CS
- Department of Orthodontics & Dentofacial Orthopedics, DAPM RV Dental College, Bangalore, Karnataka, India
| |
Collapse
|
|
37
|
Bates S, Sesia M, Sabatti C, Candès E. Causal inference in genetic trio studies. Proc Natl Acad Sci U S A 2020; 117:24117-24126. [PMID: 32948695 PMCID: PMC7533659 DOI: 10.1073/pnas.2007743117] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Accepted: 08/11/2020] [Indexed: 12/26/2022] Open
Abstract
We introduce a method to draw causal inferences-inferences immune to all possible confounding-from genetic data that include parents and offspring. Causal conclusions are possible with these data because the natural randomness in meiosis can be viewed as a high-dimensional randomized experiment. We make this observation actionable by developing a conditional independence test that identifies regions of the genome containing distinct causal variants. The proposed digital twin test compares an observed offspring to carefully constructed synthetic offspring from the same parents to determine statistical significance, and it can leverage any black-box multivariate model and additional nontrio genetic data to increase power. Crucially, our inferences are based only on a well-established mathematical model of recombination and make no assumptions about the relationship between the genotypes and phenotypes. We compare our method to the widely used transmission disequilibrium test and demonstrate enhanced power and localization.
Collapse
Affiliation(s)
- Stephen Bates
- Department of Statistics, Stanford University, Stanford, CA 94305;
| | - Matteo Sesia
- Department of Data Sciences and Operations, Marshall School of Business, University of Southern California, Los Angeles, CA 90089
| | - Chiara Sabatti
- Department of Statistics, Stanford University, Stanford, CA 94305
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305
| | - Emmanuel Candès
- Department of Statistics, Stanford University, Stanford, CA 94305;
- Department of Mathematics, Stanford University, Stanford, CA 94305
| |
Collapse
|
|
38
|
Miller JB, Ward E, Staley LA, Stevens J, Teerlink CC, Tavana JP, Cloward M, Page M, Dayton L, Cannon-Albright LA, Kauwe JSK. Identification and genomic analysis of pedigrees with exceptional longevity identifies candidate rare variants. Neurobiol Dis 2020; 143:104972. [PMID: 32574725 PMCID: PMC7461696 DOI: 10.1016/j.nbd.2020.104972] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Revised: 05/05/2020] [Accepted: 06/12/2020] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND Longevity as a phenotype entails living longer than average and typically includes living without chronic age-related diseases. Recently, several common genetic components to longevity have been identified. This study aims to identify additional genetic variants associated with longevity using unique and powerful analyses of pedigrees with a statistical excess of healthy elderly individuals identified in the Utah Population Database (UPDB). METHODS From an existing biorepository of Utah pedigrees, six independent cousin pairs were selected from four extended pedigrees that exhibited an excess of healthy elderly individuals; whole exome sequencing (WES) was performed on two elderly individuals from each pedigree who were either first cousins or first cousins once removed. Rare (<.01 population frequency) variants shared by at least one elderly cousin pair in a region likely to be identical by descent were identified as candidates. Ingenuity Variant Analysis was used to prioritize putative causal variants based on quality control, frequency, and gain or loss of function. The variant frequency was compared in healthy cohorts and in an Alzheimer's disease cohort. Remaining variants were filtered based on their presence in genes reported to have an effect on the aging process, aging of cells, or the longevity process. Validation of these candidate variants included tests of segregation on other elderly relatives. RESULTS Fifteen rare candidate genetic variants spanning 17 genes shared within cousins were identified as having passed prioritization criteria. Of those variants, six were present in genes that are known or predicted to affect the aging process: rs78408340 (PAM), rs112892337 (ZFAT), rs61737629 (ESPL1), rs141903485 (CEBPE), rs144369314 (UTP4), and rs61753103 (NUP88 and RABEP1). ESPL1 rs61737629 and CEBPE rs141903485 show additional evidence of segregation with longevity in expanded pedigree analyses (p-values = .001 and .0001, respectively). DISCUSSION This unique pedigree analysis efficiently identified several novel rare candidate variants that may affect the aging process and added support to seven genes that likely contribute to longevity. Further analyses showed evidence for segregation for two rare variants, ESPL1 rs61737629 and CEBPE rs141903485, in the original longevity pedigrees in which they were initially observed. These candidate genes and variants warrant further investigation.
Collapse
Affiliation(s)
- Justin B Miller
- Department of Biology, Brigham Young University, Provo, UT 84602, USA
| | - Elizabeth Ward
- Department of Biology, Brigham Young University, Provo, UT 84602, USA
| | - Lyndsay A Staley
- Department of Biology, Brigham Young University, Provo, UT 84602, USA
| | - Jeffrey Stevens
- Genetic Epidemiology, Department of Internal Medicine, University of Utah, Salt Lake City, UT 84132, USA
| | - Craig C Teerlink
- Genetic Epidemiology, Department of Internal Medicine, University of Utah, Salt Lake City, UT 84132, USA
| | - Justina P Tavana
- Department of Biology, Brigham Young University, Provo, UT 84602, USA
| | - Matthew Cloward
- Department of Biology, Brigham Young University, Provo, UT 84602, USA
| | - Madeline Page
- Department of Biology, Brigham Young University, Provo, UT 84602, USA
| | - Louisa Dayton
- Department of Biology, Brigham Young University, Provo, UT 84602, USA
| | - Lisa A Cannon-Albright
- Genetic Epidemiology, Department of Internal Medicine, University of Utah, Salt Lake City, UT 84132, USA
| | - John S K Kauwe
- Department of Biology, Brigham Young University, Provo, UT 84602, USA.
| |
Collapse
|
|
39
|
Yokouchi Y, Suzuki S, Ohtsuki N, Yamamoto K, Noguchi S, Soejima Y, Goto M, Ishioka K, Nakamura I, Suzuki S, Takenoshita S, Era T. Rapid repair of human disease-specific single-nucleotide variants by One-SHOT genome editing. Sci Rep 2020; 10:13927. [PMID: 32811847 PMCID: PMC7435196 DOI: 10.1038/s41598-020-70401-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2019] [Accepted: 07/24/2020] [Indexed: 12/26/2022] Open
Abstract
Many human diseases ranging from cancer to hereditary disorders are caused by single-nucleotide mutations in critical genes. Repairing these mutations would significantly improve the quality of life for patients with hereditary diseases. However, current procedures for repairing deleterious single-nucleotide mutations are not straightforward, requiring multiple steps and taking several months to complete. In the current study, we aimed to repair pathogenic allele-specific single-nucleotide mutations using a single round of genome editing. Using high-fidelity, site-specific nuclease AsCas12a/Cpf1, we attempted to repair pathogenic single-nucleotide variants (SNVs) in disease-specific induced pluripotent stem cells. As a result, we achieved repair of the Met918Thr SNV in human oncogene RET with the inclusion of a single-nucleotide marker, followed by absolute markerless, scarless repair of the RET SNV with no detected off-target effects. The markerless method was then confirmed in human type VII collagen-encoding gene COL7A1. Thus, using this One-SHOT method, we successfully reduced the number of genetic manipulations required for genome repair from two consecutive events to one, resulting in allele-specific repair that can be completed within 3 weeks, with or without a single-nucleotide marker. Our findings suggest that One-SHOT can be used to repair other types of mutations, with potential beyond human medicine.
Collapse
Affiliation(s)
- Yuji Yokouchi
- Pluripotent Stem Cell Research Unit in Department of Thyroid and Endocrinology, School of Medicine, Fukushima Medical University, 1 Hikariga-oka, Fukushima, 960-1295, Japan. .,Department of Thyroid and Endocrinology, School of Medicine, Fukushima Medical University, Fukushima, Japan.
| | - Shinichi Suzuki
- Department of Thyroid and Endocrinology, School of Medicine, Fukushima Medical University, Fukushima, Japan
| | - Noriko Ohtsuki
- Pluripotent Stem Cell Research Unit in Department of Thyroid and Endocrinology, School of Medicine, Fukushima Medical University, 1 Hikariga-oka, Fukushima, 960-1295, Japan.,Department of Thyroid and Endocrinology, School of Medicine, Fukushima Medical University, Fukushima, Japan
| | - Kei Yamamoto
- Pluripotent Stem Cell Research Unit in Department of Thyroid and Endocrinology, School of Medicine, Fukushima Medical University, 1 Hikariga-oka, Fukushima, 960-1295, Japan.,Department of Thyroid and Endocrinology, School of Medicine, Fukushima Medical University, Fukushima, Japan
| | - Satomi Noguchi
- Pluripotent Stem Cell Research Unit in Department of Thyroid and Endocrinology, School of Medicine, Fukushima Medical University, 1 Hikariga-oka, Fukushima, 960-1295, Japan.,Department of Thyroid and Endocrinology, School of Medicine, Fukushima Medical University, Fukushima, Japan
| | - Yumi Soejima
- Department of Cell Modulation, Institute of Molecular Embryology and Genetics (IMEG), Kumamoto University, Kumamoto, Japan
| | - Mizuki Goto
- Department of Cell Modulation, Institute of Molecular Embryology and Genetics (IMEG), Kumamoto University, Kumamoto, Japan.,Department of Dermatology, Faculty of Medicine, Oita University, Yufu, Japan
| | - Ken Ishioka
- Department of Microbiology, School of Medicine, Fukushima Medical University, Fukushima, Japan
| | - Izumi Nakamura
- Pluripotent Stem Cell Research Unit in Department of Thyroid and Endocrinology, School of Medicine, Fukushima Medical University, 1 Hikariga-oka, Fukushima, 960-1295, Japan.,Department of Thyroid and Endocrinology, School of Medicine, Fukushima Medical University, Fukushima, Japan
| | - Satoru Suzuki
- Office of Thyroid Ultrasound Examination Promotion, Radiation Medical Science Centre for the Fukushima Health Management Survey, Fukushima Medical University, Fukushima, Japan
| | | | - Takumi Era
- Pluripotent Stem Cell Research Unit in Department of Thyroid and Endocrinology, School of Medicine, Fukushima Medical University, 1 Hikariga-oka, Fukushima, 960-1295, Japan.,Department of Thyroid and Endocrinology, School of Medicine, Fukushima Medical University, Fukushima, Japan.,Department of Cell Modulation, Institute of Molecular Embryology and Genetics (IMEG), Kumamoto University, Kumamoto, Japan
| |
Collapse
|
|
40
|
Brumpton B, Sanderson E, Heilbron K, Hartwig FP, Harrison S, Vie GÅ, Cho Y, Howe LD, Hughes A, Boomsma DI, Havdahl A, Hopper J, Neale M, Nivard MG, Pedersen NL, Reynolds CA, Tucker-Drob EM, Grotzinger A, Howe L, Morris T, Li S, Auton A, Windmeijer F, Chen WM, Bjørngaard JH, Hveem K, Willer C, Evans DM, Kaprio J, Davey Smith G, Åsvold BO, Hemani G, Davies NM. Avoiding dynastic, assortative mating, and population stratification biases in Mendelian randomization through within-family analyses. Nat Commun 2020; 11:3519. [PMID: 32665587 PMCID: PMC7360778 DOI: 10.1038/s41467-020-17117-4] [Citation(s) in RCA: 230] [Impact Index Per Article: 46.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Accepted: 06/12/2020] [Indexed: 01/24/2023] Open
Abstract
Estimates from Mendelian randomization studies of unrelated individuals can be biased due to uncontrolled confounding from familial effects. Here we describe methods for within-family Mendelian randomization analyses and use simulation studies to show that family-based analyses can reduce such biases. We illustrate empirically how familial effects can affect estimates using data from 61,008 siblings from the Nord-Trøndelag Health Study and UK Biobank and replicated our findings using 222,368 siblings from 23andMe. Both Mendelian randomization estimates using unrelated individuals and within family methods reproduced established effects of lower BMI reducing risk of diabetes and high blood pressure. However, while Mendelian randomization estimates from samples of unrelated individuals suggested that taller height and lower BMI increase educational attainment, these effects were strongly attenuated in within-family Mendelian randomization analyses. Our findings indicate the necessity of controlling for population structure and familial effects in Mendelian randomization studies.
Collapse
Affiliation(s)
- Ben Brumpton
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway.
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, BS8 2BN, Bristol, UK.
- Clinic of Thoracic and Occupational Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway.
| | - Eleanor Sanderson
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, BS8 2BN, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Karl Heilbron
- 23andMe, Inc., 223 N Mathilda Avenue, Sunnyvale, CA, 94086, USA
| | - Fernando Pires Hartwig
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, BS8 2BN, Bristol, UK
- Postgraduate Program in Epidemiology, Federal University of Pelotas, Pelotas, Brazil
| | - Sean Harrison
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, BS8 2BN, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Gunnhild Åberge Vie
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Yoonsu Cho
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, BS8 2BN, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Laura D Howe
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, BS8 2BN, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Amanda Hughes
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, BS8 2BN, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Dorret I Boomsma
- Netherlands Twin Register, Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Alexandra Havdahl
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, BS8 2BN, Bristol, UK
- Nic Waals Institute, Lovisenberg Diaconal Hospital, Spångbergveien 25, 0853, Oslo, Norway
- Department of Mental Disorders, Norwegian Institute of Public Health, Sandakerveien 24 C, 0473, Oslo, Norway
| | - John Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, 207 Bouverie Street, Carlton, VIC, 3053, Australia
| | - Michael Neale
- Virginia Institute for Psychiatric and Behavior Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Michel G Nivard
- Netherlands Twin Register, Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Nancy L Pedersen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Chandra A Reynolds
- Department of Psychology, University of California Riverside, Riverside, CA, USA
| | - Elliot M Tucker-Drob
- Department of Psychology and Population Research Center, University of Texas at Austin, 108 E. Dean Keeton Stop A8000,, Austin, TX, 78712, USA
| | - Andrew Grotzinger
- Department of Psychology and Population Research Center, University of Texas at Austin, 108 E. Dean Keeton Stop A8000,, Austin, TX, 78712, USA
| | - Laurence Howe
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, BS8 2BN, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Tim Morris
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, BS8 2BN, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Shuai Li
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, 207 Bouverie Street, Carlton, VIC, 3053, Australia
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Worts Causeway, Cambridge, CB1 8RN, UK
| | - Adam Auton
- 23andMe, Inc., 223 N Mathilda Avenue, Sunnyvale, CA, 94086, USA
| | - Frank Windmeijer
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, BS8 2BN, Bristol, UK
- Department of Economics, University of Bristol, 2 Priory Road, Bristol, BS8 1TU, UK
| | - Wei-Min Chen
- Center for public health genomics, Department of public health sciences, University of Virginia, Charlottesville, VA, USA
| | - Johan Håkon Bjørngaard
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- Faculty of Nursing and Health Sciences, Nord University, Levanger, Norway
| | - Kristian Hveem
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Cristen Willer
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA
| | - David M Evans
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, BS8 2BN, Bristol, UK
- University of Queensland Diamantina Institute, University of Queensland, Brisbane, QLD, Australia
| | - Jaakko Kaprio
- Department of Public Health, University of Helsinki, Helsinki, Finland
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - George Davey Smith
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, BS8 2BN, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Bjørn Olav Åsvold
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Endocrinology, St Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Gibran Hemani
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, BS8 2BN, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Neil M Davies
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway.
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, BS8 2BN, Bristol, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Barley House, Oakfield Grove, Bristol, BS8 2BN, UK.
| |
Collapse
|
|
41
|
Rotroff DM. A Bioinformatics Crash Course for Interpreting Genomics Data. Chest 2020; 158:S113-S123. [PMID: 32658646 PMCID: PMC8176646 DOI: 10.1016/j.chest.2020.03.004] [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: 09/05/2019] [Revised: 11/11/2019] [Accepted: 03/09/2020] [Indexed: 10/23/2022] Open
Abstract
Reductions in genotyping costs and improvements in computational power have made conducting genome-wide association studies (GWAS) standard practice for many complex diseases. GWAS is the assessment of genetic variants across the genome of many individuals to determine which, if any, genetic variants are associated with a specific trait. As with any analysis, there are evolving best practices that should be followed to ensure scientific rigor and reliability in the conclusions. This article presents a brief summary for many of the key bioinformatics considerations when either planning or evaluating GWAS. This review is meant to serve as a guide to those without deep expertise in bioinformatics and GWAS and give them tools to critically evaluate this popular approach to investigating complex diseases. In addition, a checklist is provided that can be used by investigators to evaluate whether a GWAS has appropriately accounted for the many potential sources of bias and generally followed current best practices.
Collapse
Affiliation(s)
- Daniel M Rotroff
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH.
| |
Collapse
|
|
42
|
Hao D, Wang X, Thomsen B, Kadarmideen HN, Wang X, Lan X, Huang Y, Qi X, Chen H. Copy Number Variations and Expression Levels of Guanylate-Binding Protein 6 Gene Associated with Growth Traits of Chinese Cattle. Animals (Basel) 2020; 10:E566. [PMID: 32230930 PMCID: PMC7222342 DOI: 10.3390/ani10040566] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Revised: 03/18/2020] [Accepted: 03/22/2020] [Indexed: 11/16/2022] Open
Abstract
Association studies have indicated profound effects of copy number variations (CNVs) on various phenotypes in different species. In this study, we identified the CNV distributions and expression levels of guanylate-binding protein 6 (GBP6) associated with the growth traits of Chinese cattle. The results showed that the phenotypic values of body size and weight of Xianan (XN) cattle were higher than those of Nanyang (NY) cattle. The medium CNV types were mostly identified in the XN and NY breeds, but their CNV distributions were significantly different (adjusted p < 0.05). The association analysis revealed that the body weight, cannon circumference and chest circumference of XN cattle had significantly different values in different CNV types (p < 0.05), with CNV gain types (Log22-ΔΔCt > 0.5) displaying superior phenotypic values. We also found that transcription levels varied in different tissues (p < 0.001) and the CNV gain types showed the highest relative gene expression levels in the muscle tissue, consistent with the highest phenotypic values of body weight and cannon circumference among the three CNV types. Consequently, our results suggested that CNV gain types of GBP6 could be used as the candidate markers in the cattle-breeding program for growth traits.
Collapse
Affiliation(s)
- Dan Hao
- College of Animal Science and Technology, Northwest A&F University, Shaanxi Key Laboratory of Animal Genetics, Breeding and Reproduction, Yangling 712100, Shaanxi, China; (D.H.); (X.W.); (X.L.); (Y.H.)
- Department of Molecular Biology and Genetics, Aarhus University, 8000 Aarhus C, Denmark;
| | - Xiao Wang
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Kongens Lyngby, Denmark; (X.W.); (H.N.K.)
| | - Bo Thomsen
- Department of Molecular Biology and Genetics, Aarhus University, 8000 Aarhus C, Denmark;
| | - Haja N. Kadarmideen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Kongens Lyngby, Denmark; (X.W.); (H.N.K.)
| | - Xiaogang Wang
- College of Animal Science and Technology, Northwest A&F University, Shaanxi Key Laboratory of Animal Genetics, Breeding and Reproduction, Yangling 712100, Shaanxi, China; (D.H.); (X.W.); (X.L.); (Y.H.)
| | - Xianyong Lan
- College of Animal Science and Technology, Northwest A&F University, Shaanxi Key Laboratory of Animal Genetics, Breeding and Reproduction, Yangling 712100, Shaanxi, China; (D.H.); (X.W.); (X.L.); (Y.H.)
| | - Yongzhen Huang
- College of Animal Science and Technology, Northwest A&F University, Shaanxi Key Laboratory of Animal Genetics, Breeding and Reproduction, Yangling 712100, Shaanxi, China; (D.H.); (X.W.); (X.L.); (Y.H.)
| | - Xinglei Qi
- Bureau of Animal Husbandry of Biyang County, Biyang 463700, Henan, China;
| | - Hong Chen
- College of Animal Science and Technology, Northwest A&F University, Shaanxi Key Laboratory of Animal Genetics, Breeding and Reproduction, Yangling 712100, Shaanxi, China; (D.H.); (X.W.); (X.L.); (Y.H.)
| |
Collapse
|
|
43
|
Fang Y, Liu S, Dong Q, Zhang K, Tian Z, Li X, Li W, Qi Z, Wang Y, Tian X, Song J, Wang J, Yang C, Jiang S, Li WX, Ning H. Linkage Analysis and Multi-Locus Genome-Wide Association Studies Identify QTNs Controlling Soybean Plant Height. FRONTIERS IN PLANT SCIENCE 2020; 11:9. [PMID: 32117360 PMCID: PMC7033546 DOI: 10.3389/fpls.2020.00009] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Accepted: 01/07/2020] [Indexed: 05/05/2023]
Abstract
Plant height is an important target for soybean breeding. It is a typical quantitative trait controlled by multiple genes and is susceptible to environmental influences. Here, we carried out phenotypic analysis of 156 recombinant inbred lines derived from "Dongnong L13" and "Henong 60" in nine environments at four locations over 6 years using interval mapping and inclusive composite interval mapping methods. We performed quantitative trait locus (QTL) analysis by applying pre-built simple-sequence repeat maps. We detected 48 QTLs, including nine significant QTLs detected by multiple methods and in multiple environments. Meanwhile, genotyping of all lines using the SoySNP660k BeadChip produced 54,836 non-redundant single-nucleotide polymorphism (SNP) genotypes. We used five multi-locus genome-wide association analysis methods to locate 10 quantitative trait nucleotides (QTNs), four of which overlap with previously located QTLs. Five candidate genes related to plant height are predicted to lie within 200 kb of these four QTNs. We identified 19 homologous genes in Arabidopsis, two of which may be associated with plant height. These findings further our understanding of the multi-gene regulatory network and genetic determinants of soybean plant height, which will be important for breeding high-yielding soybean.
Collapse
Affiliation(s)
- Yanlong Fang
- Key Laboratory of Soybean Biology, Ministry of Education/Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Shulin Liu
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China
| | - Quanzhong Dong
- Key Laboratory of Soybean Biology, Ministry of Education/Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Kaixin Zhang
- Key Laboratory of Soybean Biology, Ministry of Education/Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Zhixi Tian
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China
| | - Xiyu Li
- Key Laboratory of Soybean Biology, Ministry of Education/Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Wenbin Li
- Key Laboratory of Soybean Biology, Ministry of Education/Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Zhongying Qi
- Key Laboratory of Soybean Biology, Ministry of Education/Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Yue Wang
- Key Laboratory of Soybean Biology, Ministry of Education/Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Xiaocui Tian
- Key Laboratory of Soybean Biology, Ministry of Education/Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Jie Song
- Key Laboratory of Soybean Biology, Ministry of Education/Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Jiajing Wang
- Key Laboratory of Soybean Biology, Ministry of Education/Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Chang Yang
- Key Laboratory of Soybean Biology, Ministry of Education/Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Sitong Jiang
- Key Laboratory of Soybean Biology, Ministry of Education/Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Wen-Xia Li
- Key Laboratory of Soybean Biology, Ministry of Education/Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| | - Hailong Ning
- Key Laboratory of Soybean Biology, Ministry of Education/Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin, China
| |
Collapse
|
|
44
|
Schena FP, Serino G, Sallustio F, Falchi M, Cox SN. Omics studies for comprehensive understanding of immunoglobulin A nephropathy: state-of-the-art and future directions. Nephrol Dial Transplant 2019; 33:2101-2112. [PMID: 29905852 DOI: 10.1093/ndt/gfy130] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2018] [Accepted: 04/17/2018] [Indexed: 12/11/2022] Open
Abstract
Immunoglobulin A nephropathy (IgAN) is the most common worldwide primary glomerulonephritis with a strong autoimmune component. The disease shows variability in both clinical phenotypes and endpoints and can be potentially subdivided into more homogeneous subtypes through the identification of specific molecular biomarkers. This review focuses on the role of omics in driving the identification of potential molecular subtypes of the disease through the integration of multilevel data from genomics, transcriptomics, epigenomics, proteomics and metabolomics. First, the identification of molecular biomarkers, including mapping of the full spectrum of common and rare IgAN risk alleles, could permit a more precise stratification of IgAN patients. Second, the analysis of transcriptomic patterns and their modulation by epigenetic factors like microRNAs has the potential to increase our understanding in the pathogenic mechanisms of the disease. Third, the specificity of urinary proteomic and metabolomic signatures and the understanding of their functional relevance may contribute to the development of new non-invasive biomarkers for a better molecular characterization of the renal damage and its follow-up. All these approaches can give information for targeted therapeutic decisions and will support novel clinical decision making. In conclusion, we offer a framework of omic studies and outline barriers and potential solutions that should be used for improving the diagnosis and treatment of the disease. The ongoing decade is exploiting novel high-throughput molecular technologies and computational analyses for improving the diagnosis (precision nephrology) and treatment (personalized therapy) of the IgAN subtypes.
Collapse
Affiliation(s)
- Francesco Paolo Schena
- Division of Nephrology, Dialysis, and Transplantation, Department of Emergency and Organ Transplantation, University of Bari, Bari, Italy.,Schena Foundation, Valenzano, Bari, Italy
| | - Grazia Serino
- National Institute of Gastroenterology 'S. de Bellis', Research Hospital, Castellana Grotte, Bari, Italy
| | - Fabio Sallustio
- Division of Nephrology, Dialysis, and Transplantation, Department of Emergency and Organ Transplantation, University of Bari, Bari, Italy
| | - Mario Falchi
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Sharon N Cox
- Division of Nephrology, Dialysis, and Transplantation, Department of Emergency and Organ Transplantation, University of Bari, Bari, Italy.,Schena Foundation, Valenzano, Bari, Italy
| |
Collapse
|
|
45
|
Kuipers AL, Wojczynski MK, Barinas-Mitchell E, Minster RL, Wang L, Feitosa MF, Kulminski A, Thyagarajan B, Lee JH, Province MA, Newman AB, Zmuda JM. Genome-wide linkage analysis of carotid artery traits in exceptionally long-lived families. Atherosclerosis 2019; 291:19-26. [PMID: 31634740 DOI: 10.1016/j.atherosclerosis.2019.10.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2019] [Revised: 10/03/2019] [Accepted: 10/09/2019] [Indexed: 12/11/2022]
Abstract
BACKGROUND AND AIMS Atherosclerosis develops with age and is partially controlled by genetics. Research to date has identified common variants with small effects on atherosclerosis related traits. We aimed to use family-based genome-wide linkage analysis to identify chromosomal regions potentially harboring rare variants with larger effects for atherosclerosis related traits. METHODS Participants included 2205 individuals from the Long Life Family Study (LLFS), which recruited families with exceptional longevity from Boston, New York, Pittsburgh, and Denmark. Participants underwent B-mode ultrasonography of the carotid arteries to measure intima-media thickness (IMT), inter-adventitial diameter (IAD), and plaque presence and severity. We conducted residual heritability and genome-wide linkage analyses adjusted for age, age2, sex, and field center using pedigree-based maximum-likelihood methods in SOLAR. RESULTS All carotid traits were significantly heritable with a range of 0.68 for IAD to 0.38 for IMT. We identified three chromosomal regions with linkage to IAD (3q13; max LOD 5.3), plaque severity (17q22-q23, max LOD 3.2), and plaque presence (17q24, max LOD 3.1). No common allelic variants within these linkage peaks were associated with the carotid artery traits. CONCLUSIONS We identified three chromosomal regions with evidence of linkage to carotid artery diameter and atherosclerotic plaque in exceptionally long-lived families. Since common allelic variants within our linkage peaks did not account for our findings, future follow-up resequencing of these regions in LLFS families should help advance our understanding of atherosclerosis, CVD, and healthy vascular aging.
Collapse
Affiliation(s)
- Allison L Kuipers
- Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA.
| | - Mary K Wojczynski
- Department of Genetics, Washington University in St Louis, St. Louis, MO, USA
| | | | - Ryan L Minster
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Lihua Wang
- Department of Genetics, Washington University in St Louis, St. Louis, MO, USA
| | - Mary F Feitosa
- Department of Genetics, Washington University in St Louis, St. Louis, MO, USA
| | | | - Bharat Thyagarajan
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA
| | - Joseph H Lee
- Sergievsky Center, Taub Institute, Departments of Epidemiology and Neurology, Columbia University, New York, NY, USA
| | - Michael A Province
- Department of Genetics, Washington University in St Louis, St. Louis, MO, USA
| | - Anne B Newman
- Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Joseph M Zmuda
- Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA; Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA
| | | |
Collapse
|
|
46
|
Zhao L, He Z, Zhang D, Wang GT, Renton AE, Vardarajan BN, Nothnagel M, Goate AM, Mayeux R, Leal SM. A Rare Variant Nonparametric Linkage Method for Nuclear and Extended Pedigrees with Application to Late-Onset Alzheimer Disease via WGS Data. Am J Hum Genet 2019; 105:822-835. [PMID: 31585107 DOI: 10.1016/j.ajhg.2019.09.006] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2019] [Accepted: 09/05/2019] [Indexed: 10/25/2022] Open
Abstract
To analyze family-based whole-genome sequence (WGS) data for complex traits, we developed a rare variant (RV) non-parametric linkage (NPL) analysis method, which has advantages over association methods. The RV-NPL differs from the NPL in that RVs are analyzed, and allele sharing among affected relative-pairs is estimated only for minor alleles. Analyzing families can increase power because causal variants with familial aggregation usually have larger effect sizes than those underlying sporadic diseases. Differing from association analysis, for NPL only affected individuals are analyzed, which can increase power, since unaffected family members can be susceptibility variant carriers. RV-NPL is robust to population substructure and admixture, inclusion of nonpathogenic variants, as well as allelic and locus heterogeneity and can readily be applied outside of coding regions. In contrast to analyzing common variants using NPL, where loci localize to large genomic regions (e.g., >50 Mb), mapped regions are well defined for RV-NPL. Using simulation studies, we demonstrate that RV-NPL is substantially more powerful than applying traditional NPL methods to analyze RVs. The RV-NPL was applied to analyze 107 late-onset Alzheimer disease (LOAD) pedigrees of Caribbean Hispanic and European ancestry with WGS data, and statistically significant linkage (LOD ≥ 3.8) was found with RVs in PSMF1 and PTPN21 which have been shown to be involved in LOAD etiology. Additionally, nominally significant linkage was observed with RVs in ABCA7, ACE, EPHA1, and SORL1, genes that were previously reported to be associated with LOAD. RV-NPL is an ideal method to elucidate the genetic etiology of complex familial diseases.
Collapse
|
|
47
|
Identification and characterization of SEC24D as a susceptibility gene for hepatitis B virus infection. Sci Rep 2019; 9:13425. [PMID: 31530870 PMCID: PMC6748997 DOI: 10.1038/s41598-019-49777-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2018] [Accepted: 08/30/2019] [Indexed: 12/15/2022] Open
Abstract
Single nucleotide polymorphisms (SNPs) and genes associated with susceptibility to hepatitis B virus (HBV) infection that have been identified by genome-wide association studies explain only a limited portion of the known heritability, indicating more genetic variants remain to be discovered. In this study, we adopted a new research strategy to identify more susceptibility genes and variants for HBV infection. We first performed genetic association analysis of 300 sib-pairs and 3,087 case-control samples, which revealed that 36 SNPs located in 31 genes showed nominal associations with HBV infection in both samples. Of these genes, we selected SEC24D for further molecular analysis according to the following two main lines of evidence. First, a time course analysis of the expression profiles from HBV-infected primary human hepatocytes (PHH) demonstrated that SEC24D expression increased markedly as time passed after HBV infection (P = 4.0 × 10−4). Second, SNP rs76459466 in SEC24D was adversely associated with HBV risk (ORmeta = 0.82; Pmeta = 0.002), which again indicated that SEC24D represents a novel susceptibility gene for HBV infection. Moreover, SEC24D appeared to be protective against HBV infection in vitro. Consistently, we found that SEC24D expression was significantly enhanced in non-infected liver tissues (P = 0.002). We conclude that SEC24D is a novel candidate gene linked to susceptibility to HBV infection.
Collapse
|
|
48
|
Genetic Variation Underpinning ADHD Risk in a Caribbean Community. Cells 2019; 8:cells8080907. [PMID: 31426340 PMCID: PMC6721689 DOI: 10.3390/cells8080907] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Revised: 04/07/2019] [Accepted: 08/12/2019] [Indexed: 12/13/2022] Open
Abstract
Attention Deficit Hyperactivity Disorder (ADHD) is a highly heritable and prevalent neurodevelopmental disorder that frequently persists into adulthood. Strong evidence from genetic studies indicates that single nucleotide polymorphisms (SNPs) harboured in the ADGRL3 (LPHN3), SNAP25, FGF1, DRD4, and SLC6A2 genes are associated with ADHD. We genotyped 26 SNPs harboured in genes previously reported to be associated with ADHD and evaluated their potential association in 386 individuals belonging to 113 nuclear families from a Caribbean community in Barranquilla, Colombia, using family-based association tests. SNPs rs362990-SNAP25 (T allele; p = 2.46 × 10−4), rs2282794-FGF1 (A allele; p = 1.33 × 10−2), rs2122642-ADGRL3 (C allele, p = 3.5 × 10−2), and ADGRL3 haplotype CCC (markers rs1565902-rs10001410-rs2122642, OR = 1.74, Ppermuted = 0.021) were significantly associated with ADHD. Our results confirm the susceptibility to ADHD conferred by SNAP25, FGF1, and ADGRL3 variants in a community with a significant African American component, and provide evidence supporting the existence of specific patterns of genetic stratification underpinning the susceptibility to ADHD. Knowledge of population genetics is crucial to define risk and predict susceptibility to disease.
Collapse
|
|
49
|
Zeng Y, Zhu X, Chen C, Banerjee K, Sun L, Yu W, Zheng B, Wu R. A unified DNA sequence and non-DNA sequence mapping model of complex traits. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2019; 99:784-795. [PMID: 31009159 DOI: 10.1111/tpj.14354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2018] [Revised: 03/10/2019] [Accepted: 04/01/2019] [Indexed: 06/09/2023]
Abstract
Increasing evidence shows that quantitative inheritance is based on both DNA sequence and non-DNA sequence variants. However, how to simultaneously detect these variants from a mapping study has been unexplored, hampering our effort to illustrate the detailed genetic architecture of complex traits. We address this issue by developing a unified model of quantitative trait locus (QTL) mapping based on an open-pollinated design composed of randomly sampling maternal plants from a natural population and their half-sib seeds. This design forms a two-level hierarchical platform for a joint linkage-linkage disequilibrium analysis of population structure. The EM algorithm was implemented to estimate and test DNA sequence-based effects and non-DNA sequence-based effects of QTLs. We applied this model to analyze genetic mapping data from the OP design of a gymnosperm coniferous species, Torreya grandis, identifying 25 significant DNA sequence and non-DNA sequence QTLs for seedling height and diameter growth in different years. Results from computer simulation show that the unified model has good statistical properties and is powerful for QTL detection. Our model enables the tests of how a complex trait is affected differently by DNA-based effects and non-DNA sequence-based transgenerational effects, thus allowing a more comprehensive picture of genetic architecture to be charted and quantified.
Collapse
Affiliation(s)
- Yanru Zeng
- State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Lin'an, Zhejiang, 311300, China
| | - Xuli Zhu
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, 100083, China
| | - Chixiang Chen
- Center for Statistical Genetics, Departments of Public Health Sciences and Statistics, Pennsylvania State University, Hershey, PA, 17033, USA
| | - Kalins Banerjee
- Center for Statistical Genetics, Departments of Public Health Sciences and Statistics, Pennsylvania State University, Hershey, PA, 17033, USA
| | - Lidan Sun
- Center for Statistical Genetics, Departments of Public Health Sciences and Statistics, Pennsylvania State University, Hershey, PA, 17033, USA
| | - Weiwu Yu
- State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Lin'an, Zhejiang, 311300, China
| | - Bingsong Zheng
- State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Lin'an, Zhejiang, 311300, China
| | - Rongling Wu
- Center for Statistical Genetics, Departments of Public Health Sciences and Statistics, Pennsylvania State University, Hershey, PA, 17033, USA
- State Key Laboratory of Tree Genetics and Breeding, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, 100091, China
| |
Collapse
|
|
50
|
Ko A, Nielsen R. Joint Estimation of Pedigrees and Effective Population Size Using Markov Chain Monte Carlo. Genetics 2019; 212:855-868. [PMID: 31123041 PMCID: PMC6614905 DOI: 10.1534/genetics.119.302280] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2018] [Accepted: 05/16/2019] [Indexed: 12/31/2022] Open
Abstract
Pedigrees provide the genealogical relationships among individuals at a fine resolution and serve an important function in many areas of genetic studies. One such use of pedigree information is in the estimation of the short-term effective population size [Formula: see text], which is of great relevance in fields such as conservation genetics. Despite the usefulness of pedigrees, however, they are often an unknown parameter and must be inferred from genetic data. In this study, we present a Bayesian method to jointly estimate pedigrees and [Formula: see text] from genetic markers using Markov Chain Monte Carlo. Our method supports analysis of a large number of markers and individuals within a single generation with the use of a composite likelihood, which significantly increases computational efficiency. We show, on simulated data, that our method is able to jointly estimate relationships up to first cousins and [Formula: see text] with high accuracy. We also apply the method on a real dataset of house sparrows to reconstruct their previously unreported pedigree.
Collapse
Affiliation(s)
- Amy Ko
- Department of Integrative Biology, University of California, Berkeley, 94720 California
| | - Rasmus Nielsen
- Department of Integrative Biology, University of California, Berkeley, 94720 California
- Department of Statistics, University of California, Berkeley, 94720 California
- Museum of Natural History, University of Copenhagen, 1123 Denmark
| |
Collapse
|