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Yin M, Feng C, Yu Z, Zhang Y, Li Y, Wang X, Song C, Guo M, Li C. sc2GWAS: a comprehensive platform linking single cell and GWAS traits of human. Nucleic Acids Res 2025; 53:D1151-D1161. [PMID: 39565208 PMCID: PMC11701642 DOI: 10.1093/nar/gkae1008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2024] [Revised: 10/01/2024] [Accepted: 10/23/2024] [Indexed: 11/21/2024] Open
Abstract
Identifying cell populations associated with risk variants is essential for uncovering cell-specific mechanisms that drive disease development and progression. Integrating genome-wide association studies (GWAS) with single-cell RNA sequencing (scRNA-seq) has become an effective strategy for detecting trait-cell relationships. The accumulation of trait-related single cell data has led to an urgent need for its comprehensively processing. To address this, we developed sc2GWAS (https://bio.liclab.net/sc2GWAS/), which aims to document large-scale GWAS trait-cell regulatory pairs at single-cell resolution and provide comprehensive annotations and enrichment analyses for these related pairs. The current version of sc2GWAS curates a total of 15 078 310 candidate trait-cell pairs from > 6 300 000 individual cells, offering a valuable resource for exploring complex regulatory relationships between traits and cells. We applied strict quality control measures on both scRNA-seq data and GWAS data, ensuring the reliability and accuracy of the datasets for the identification of trait-relevant cells and genes. In addition, sc2GWAS provides ranked lists of trait-relevant genes and extensive (epi) genetic annotations, making it a valuable resource for downstream analyses. We demonstrate the utility of the platform by investigating Alzheimer's disease, where we identified significant associations between the disease and microglial cells, with the APOE gene emerging as particularly significant. This platform facilitates detailed research into complex trait-cell and trait-gene interactions, we anticipate that sc2GWAS will become a comprehensive and valuable platform for exploring GWAS trait-cell regulatory mechanisms.
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Affiliation(s)
- Mingxue Yin
- The First Affiliated Hospital & National Health Commission Key Laboratory of Birth Defect Research and Prevention, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Insititute of Biochemistry and Molecular Biology, Hengyang Medical College, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan 421001, China
| | - Chenchen Feng
- School of Computer, University of South China, Hengyang, Hunan 421001, China
| | - Zhengmin Yu
- The First Affiliated Hospital & National Health Commission Key Laboratory of Birth Defect Research and Prevention, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Insititute of Biochemistry and Molecular Biology, Hengyang Medical College, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan 421001, China
- School of Computer, University of South China, Hengyang, Hunan 421001, China
| | - Yuexin Zhang
- The First Affiliated Hospital & National Health Commission Key Laboratory of Birth Defect Research and Prevention, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Insititute of Biochemistry and Molecular Biology, Hengyang Medical College, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan 421001, China
| | - Ye Li
- The First Affiliated Hospital & National Health Commission Key Laboratory of Birth Defect Research and Prevention, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Insititute of Biochemistry and Molecular Biology, Hengyang Medical College, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan 421001, China
| | - Xuan Wang
- The First Affiliated Hospital & National Health Commission Key Laboratory of Birth Defect Research and Prevention, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan 421001, China
| | - Chao Song
- The First Affiliated Hospital & National Health Commission Key Laboratory of Birth Defect Research and Prevention, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan 421001, China
| | - Maozu Guo
- School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
| | - Chunquan Li
- The First Affiliated Hospital & National Health Commission Key Laboratory of Birth Defect Research and Prevention, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Insititute of Biochemistry and Molecular Biology, Hengyang Medical College, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan 421001, China
- School of Computer, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, China
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan421001, China
- Key Laboratory of Rare Pediatric Diseases, Ministry of Education, University of South China, Hengyang, Hunan 421001, China
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Omar M, Omar M, Patt YS, Ukashi O, Sharif Y, Lahat A, Selinger CP, Sharif K. Genetic Risk of Ankylosing Spondylitis and Second-Line Therapy Need in Crohn's Disease: A Mendelian Randomization Study. J Clin Med 2024; 13:7496. [PMID: 39768419 PMCID: PMC11678710 DOI: 10.3390/jcm13247496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2024] [Revised: 12/02/2024] [Accepted: 12/08/2024] [Indexed: 01/11/2025] Open
Abstract
Background: Crohn's disease (CD) and Ankylosing Spondylitis (AS) are chronic conditions with overlapping inflammatory pathways. This research investigates the genetic association between AS and the requirement for more aggressive therapeutic interventions in CD, suggesting a likelihood of increased severity in CD progression among individuals diagnosed with AS. Methods: This study utilized two-sample Mendelian randomization (TSMR) to analyze GWAS datasets for AS and CD requiring second-line treatment. Instrumental variables were selected based on single-nucleotide polymorphisms of genome-wide significance. Analytical methods included inverse-variance weighted (IVW), MR Egger, and other MR approaches, alongside sensitivity analysis, to validate the findings. Results: Our results indicated a significant association between AS genetic predisposition and the increased need for second-line treatments in CD. The IVW method showed an Odds Ratio (OR) of 2.16, and MR Egger provided an OR of 2.71, both were statistically significant. This association persisted even after the exclusion of influential outlier SNP rs2517655, confirming the robustness of our findings. Conclusions: This study suggests that genetic factors contributing to AS may influence the progression of CD, potentially necessitating more intensive treatment strategies. These findings underscore the importance of early screening in patients with co-existing AS and CD for tailoring treatment approaches, thus advancing personalized medicine in the management of these complex conditions.
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Affiliation(s)
- Mahmud Omar
- Faculty of Medicine, Tel-Aviv University, Tel-Aviv 6997801, Israel;
| | - Mohammad Omar
- School of Medicine, V. N. Karazin Kharkiv National University, 61022 Kharkiv, Ukraine;
| | | | - Offir Ukashi
- Department of Gastroenterology, Sheba Medical Center, Tel-Hashomer 5262000, Israel; (O.U.); (A.L.)
| | - Yousra Sharif
- Department of Gastroenterology, Hadassah Medical Center, Jerusalem 91120, Israel;
| | - Adi Lahat
- Department of Gastroenterology, Sheba Medical Center, Tel-Hashomer 5262000, Israel; (O.U.); (A.L.)
| | | | - Kassem Sharif
- Internal Medicine B, Sheba Medical Centre, Ramat Gan 5262000, Israel;
- Department of Gastroenterology, Sheba Medical Center, Tel-Hashomer 5262000, Israel; (O.U.); (A.L.)
- Leeds Gastroenterology Institute, Leeds Teaching Hospitals, Leeds LS1 3EX, UK
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Went M, Duran-Lozano L, Halldorsson GH, Gunnell A, Ugidos-Damboriena N, Law P, Ekdahl L, Sud A, Thorleifsson G, Thodberg M, Olafsdottir T, Lamarca-Arrizabalaga A, Cafaro C, Niroula A, Ajore R, Lopez de Lapuente Portilla A, Ali Z, Pertesi M, Goldschmidt H, Stefansdottir L, Kristinsson SY, Stacey SN, Love TJ, Rognvaldsson S, Hajek R, Vodicka P, Pettersson-Kymmer U, Späth F, Schinke C, Van Rhee F, Sulem P, Ferkingstad E, Hjorleifsson Eldjarn G, Mellqvist UH, Jonsdottir I, Morgan G, Sonneveld P, Waage A, Weinhold N, Thomsen H, Försti A, Hansson M, Juul-Vangsted A, Thorsteinsdottir U, Hemminki K, Kaiser M, Rafnar T, Stefansson K, Houlston R, Nilsson B. Deciphering the genetics and mechanisms of predisposition to multiple myeloma. Nat Commun 2024; 15:6644. [PMID: 39103364 PMCID: PMC11300596 DOI: 10.1038/s41467-024-50932-7] [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: 01/03/2024] [Accepted: 07/24/2024] [Indexed: 08/07/2024] Open
Abstract
Multiple myeloma (MM) is an incurable malignancy of plasma cells. Epidemiological studies indicate a substantial heritable component, but the underlying mechanisms remain unclear. Here, in a genome-wide association study totaling 10,906 cases and 366,221 controls, we identify 35 MM risk loci, 12 of which are novel. Through functional fine-mapping and Mendelian randomization, we uncover two causal mechanisms for inherited MM risk: longer telomeres; and elevated levels of B-cell maturation antigen (BCMA) and interleukin-5 receptor alpha (IL5RA) in plasma. The largest increase in BCMA and IL5RA levels is mediated by the risk variant rs34562254-A at TNFRSF13B. While individuals with loss-of-function variants in TNFRSF13B develop B-cell immunodeficiency, rs34562254-A exerts a gain-of-function effect, increasing MM risk through amplified B-cell responses. Our results represent an analysis of genetic MM predisposition, highlighting causal mechanisms contributing to MM development.
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Affiliation(s)
- Molly Went
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, SW7 3RP, UK
| | - Laura Duran-Lozano
- Department of Laboratory Medicine, Lund University, SE-221 84, Lund, Sweden
- Lund Stem Cell Center, Lund University, SE-221 84, Lund, Sweden
| | | | - Andrea Gunnell
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, SW7 3RP, UK
| | - Nerea Ugidos-Damboriena
- Department of Laboratory Medicine, Lund University, SE-221 84, Lund, Sweden
- Lund Stem Cell Center, Lund University, SE-221 84, Lund, Sweden
| | - Philip Law
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, SW7 3RP, UK
| | - Ludvig Ekdahl
- Department of Laboratory Medicine, Lund University, SE-221 84, Lund, Sweden
- Lund Stem Cell Center, Lund University, SE-221 84, Lund, Sweden
| | - Amit Sud
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, SW7 3RP, UK
| | | | - Malte Thodberg
- Department of Laboratory Medicine, Lund University, SE-221 84, Lund, Sweden
- Lund Stem Cell Center, Lund University, SE-221 84, Lund, Sweden
| | | | - Antton Lamarca-Arrizabalaga
- Department of Laboratory Medicine, Lund University, SE-221 84, Lund, Sweden
- Lund Stem Cell Center, Lund University, SE-221 84, Lund, Sweden
| | - Caterina Cafaro
- Department of Laboratory Medicine, Lund University, SE-221 84, Lund, Sweden
- Lund Stem Cell Center, Lund University, SE-221 84, Lund, Sweden
| | - Abhishek Niroula
- Department of Laboratory Medicine, Lund University, SE-221 84, Lund, Sweden
- Lund Stem Cell Center, Lund University, SE-221 84, Lund, Sweden
| | - Ram Ajore
- Department of Laboratory Medicine, Lund University, SE-221 84, Lund, Sweden
- Lund Stem Cell Center, Lund University, SE-221 84, Lund, Sweden
| | - Aitzkoa Lopez de Lapuente Portilla
- Department of Laboratory Medicine, Lund University, SE-221 84, Lund, Sweden
- Lund Stem Cell Center, Lund University, SE-221 84, Lund, Sweden
| | - Zain Ali
- Department of Laboratory Medicine, Lund University, SE-221 84, Lund, Sweden
- Lund Stem Cell Center, Lund University, SE-221 84, Lund, Sweden
| | - Maroulio Pertesi
- Department of Laboratory Medicine, Lund University, SE-221 84, Lund, Sweden
- Lund Stem Cell Center, Lund University, SE-221 84, Lund, Sweden
| | - Hartmut Goldschmidt
- Department of Internal Medicine V, University of Heidelberg, 69120, Heidelberg, Germany
| | | | - Sigurdur Y Kristinsson
- Landspitali, National University Hospital of Iceland, IS-101, Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, IS-101, Reykjavik, Iceland
| | - Simon N Stacey
- deCODE Genetics/Amgen, Sturlugata 8, IS-101, Reykjavik, Iceland
| | - Thorvardur J Love
- Landspitali, National University Hospital of Iceland, IS-101, Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, IS-101, Reykjavik, Iceland
| | - Saemundur Rognvaldsson
- Landspitali, National University Hospital of Iceland, IS-101, Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, IS-101, Reykjavik, Iceland
| | - Roman Hajek
- University Hospital Ostrava and University of Ostrava, Ostrava, Czech Republic
| | - Pavel Vodicka
- Institute of Experimental Medicine, Academy of Sciences of the Czech Republic, Prague, Czech Republic
| | | | - Florentin Späth
- Department of Radiation Sciences, Umeå University, SE-901 87, Umeå, Sweden
| | - Carolina Schinke
- Myeloma Center, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Frits Van Rhee
- Myeloma Center, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Patrick Sulem
- deCODE Genetics/Amgen, Sturlugata 8, IS-101, Reykjavik, Iceland
| | | | | | | | | | - Gareth Morgan
- Perlmutter Cancer Center, Langone Health, New York University, New York, NY, USA
| | - Pieter Sonneveld
- Department of Hematology, Erasmus MC Cancer Institute, 3075 EA, Rotterdam, The Netherlands
| | - Anders Waage
- Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology, Box 8905, N-7491, Trondheim, Norway
| | - Niels Weinhold
- Department of Internal Medicine V, University of Heidelberg, 69120, Heidelberg, Germany
- German Cancer Research Center (DKFZ), D-69120, Heidelberg, Germany
| | | | - Asta Försti
- German Cancer Research Center (DKFZ), D-69120, Heidelberg, Germany
- Hopp Children's Cancer Center, Heidelberg, Germany
| | - Markus Hansson
- Department of Laboratory Medicine, Lund University, SE-221 84, Lund, Sweden
- Section of Hematology, Sahlgrenska University Hospital, Gothenburg, SE-413 45, Sweden
- Skåne University Hospital, SE-221 85, Lund, Sweden
| | - Annette Juul-Vangsted
- Department of Haematology, University Hospital of Copenhagen at Rigshospitalet, Blegdamsvej 9, DK-2100, Copenhagen, Denmark
| | - Unnur Thorsteinsdottir
- deCODE Genetics/Amgen, Sturlugata 8, IS-101, Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, IS-101, Reykjavik, Iceland
| | - Kari Hemminki
- German Cancer Research Center (DKFZ), D-69120, Heidelberg, Germany
- Faculty of Medicine in Pilsen, Charles University, 30605, Pilsen, Czech Republic
| | - Martin Kaiser
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, SW7 3RP, UK
| | - Thorunn Rafnar
- deCODE Genetics/Amgen, Sturlugata 8, IS-101, Reykjavik, Iceland
| | - Kari Stefansson
- deCODE Genetics/Amgen, Sturlugata 8, IS-101, Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, IS-101, Reykjavik, Iceland
| | - Richard Houlston
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, SW7 3RP, UK.
| | - Björn Nilsson
- Department of Laboratory Medicine, Lund University, SE-221 84, Lund, Sweden.
- Lund Stem Cell Center, Lund University, SE-221 84, Lund, Sweden.
- Broad Institute, 415 Main Street, Cambridge, MA, 02142, USA.
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Feng C, Song C, Song S, Zhang G, Yin M, Zhang Y, Qian F, Wang Q, Guo M, Li C. KnockTF 2.0: a comprehensive gene expression profile database with knockdown/knockout of transcription (co-)factors in multiple species. Nucleic Acids Res 2024; 52:D183-D193. [PMID: 37956336 PMCID: PMC10767813 DOI: 10.1093/nar/gkad1016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 10/17/2023] [Accepted: 10/28/2023] [Indexed: 11/15/2023] Open
Abstract
Transcription factors (TFs), transcription co-factors (TcoFs) and their target genes perform essential functions in diseases and biological processes. KnockTF 2.0 (http://www.licpathway.net/KnockTF/index.html) aims to provide comprehensive gene expression profile datasets before/after T(co)F knockdown/knockout across multiple tissue/cell types of different species. Compared with KnockTF 1.0, KnockTF 2.0 has the following improvements: (i) Newly added T(co)F knockdown/knockout datasets in mice, Arabidopsis thaliana and Zea mays and also an expanded scale of datasets in humans. Currently, KnockTF 2.0 stores 1468 manually curated RNA-seq and microarray datasets associated with 612 TFs and 172 TcoFs disrupted by different knockdown/knockout techniques, which are 2.5 times larger than those of KnockTF 1.0. (ii) Newly added (epi)genetic annotations for T(co)F target genes in humans and mice, such as super-enhancers, common SNPs, methylation sites and chromatin interactions. (iii) Newly embedded and updated search and analysis tools, including T(co)F Enrichment (GSEA), Pathway Downstream Analysis and Search by Target Gene (BLAST). KnockTF 2.0 is a comprehensive update of KnockTF 1.0, which provides more T(co)F knockdown/knockout datasets and (epi)genetic annotations across multiple species than KnockTF 1.0. KnockTF 2.0 facilitates not only the identification of functional T(co)Fs and target genes but also the investigation of their roles in the physiological and pathological processes.
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Affiliation(s)
- Chenchen Feng
- National Health Commission Key Laboratory of Birth Defect Research and Prevention & School of Computer, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan, 421001, China
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, 163319, China
| | - Chao Song
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, China
| | - Shuang Song
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan, 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Guorui Zhang
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan, 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Mingxue Yin
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan, 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Yuexin Zhang
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, China
| | - Fengcui Qian
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, China
| | - Qiuyu Wang
- National Health Commission Key Laboratory of Birth Defect Research and Prevention & School of Computer, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan, 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Maozu Guo
- School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
| | - Chunquan Li
- National Health Commission Key Laboratory of Birth Defect Research and Prevention & School of Computer, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, University of South China, Hengyang, Hunan, 421001, China
- MOE Key Lab of Rare Pediatric Diseases, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
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5
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Song C, Zhang G, Mu X, Feng C, Zhang Q, Song S, Zhang Y, Yin M, Zhang H, Tang H, Li C. eRNAbase: a comprehensive database for decoding the regulatory eRNAs in human and mouse. Nucleic Acids Res 2024; 52:D81-D91. [PMID: 37889077 PMCID: PMC10767853 DOI: 10.1093/nar/gkad925] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 09/26/2023] [Accepted: 10/12/2023] [Indexed: 10/28/2023] Open
Abstract
Enhancer RNAs (eRNAs) transcribed from distal active enhancers serve as key regulators in gene transcriptional regulation. The accumulation of eRNAs from multiple sequencing assays has led to an urgent need to comprehensively collect and process these data to illustrate the regulatory landscape of eRNAs. To address this need, we developed the eRNAbase (http://bio.liclab.net/eRNAbase/index.php) to store the massive available resources of human and mouse eRNAs and provide comprehensive annotation and analyses for eRNAs. The current version of eRNAbase cataloged 10 399 928 eRNAs from 1012 samples, including 858 human samples and 154 mouse samples. These eRNAs were first identified and uniformly processed from 14 eRNA-related experiment types manually collected from GEO/SRA and ENCODE. Importantly, the eRNAbase provides detailed and abundant (epi)genetic annotations in eRNA regions, such as super enhancers, enhancers, common single nucleotide polymorphisms, expression quantitative trait loci, transcription factor binding sites, CRISPR/Cas9 target sites, DNase I hypersensitivity sites, chromatin accessibility regions, methylation sites, chromatin interactions regions, topologically associating domains and RNA spatial interactions. Furthermore, the eRNAbase provides users with three novel analyses including eRNA-mediated pathway regulatory analysis, eRNA-based variation interpretation analysis and eRNA-mediated TF-target gene analysis. Hence, eRNAbase is a powerful platform to query, browse and visualize regulatory cues associated with eRNAs.
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Affiliation(s)
- Chao Song
- The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Guorui Zhang
- The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences & MOE Key Lab of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Xinxin Mu
- The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Chenchen Feng
- School of Computer, University of South China, Hengyang, Hunan, 421001, China
| | - Qinyi Zhang
- The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences & MOE Key Lab of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Shuang Song
- The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences & MOE Key Lab of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Yuexin Zhang
- The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Mingxue Yin
- The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences & MOE Key Lab of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Hang Zhang
- The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- School of Computer, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Huifang Tang
- The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Clinical Research Center for Myocardial Injury in Hunan Province, Hengyang, Hunan, 421001, China
| | - Chunquan Li
- The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences & MOE Key Lab of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Hunan Provincial Maternal and Child Health Care Hospital, National Health Commission Key Laboratory of Birth Defect Research and Prevention, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- School of Computer, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, China
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6
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Zhang Y, Zhang Y, Song C, Zhao X, Ai B, Wang Y, Zhou L, Zhu J, Feng C, Xu L, Wang Q, Sun H, Fang Q, Xu X, Li E, Li C. CRdb: a comprehensive resource for deciphering chromatin regulators in human. Nucleic Acids Res 2023; 51:D88-D100. [PMID: 36318256 PMCID: PMC9825595 DOI: 10.1093/nar/gkac960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 10/04/2022] [Accepted: 10/12/2022] [Indexed: 11/05/2022] Open
Abstract
Chromatin regulators (CRs) regulate epigenetic patterns on a partial or global scale, playing a critical role in affecting multi-target gene expression. As chromatin immunoprecipitation sequencing (ChIP-seq) data associated with CRs are rapidly accumulating, a comprehensive resource of CRs needs to be built urgently for collecting, integrating, and processing these data, which can provide abundant annotated information on CR upstream and downstream regulatory analyses as well as CR-related analysis functions. This study established an integrative CR resource, named CRdb (http://cr.liclab.net/crdb/), with the aim of curating a large number of available resources for CRs and providing extensive annotations and analyses of CRs to help biological researchers clarify the regulation mechanism and function of CRs. The CRdb database comprised a total of 647 CRs and 2,591 ChIP-seq samples from more than 300 human tissues and cell types. These samples have been manually curated from NCBI GEO/SRA and ENCODE. Importantly, CRdb provided the abundant and detailed genetic annotations in CR-binding regions based on ChIP-seq. Furthermore, CRdb supported various functional annotations and upstream regulatory information on CRs. In particular, it embedded four types of CR regulatory analyses: CR gene set enrichment, CR-binding genomic region annotation, CR-TF co-occupancy analysis, and CR regulatory axis analysis. CRdb is a useful and powerful resource that can help in exploring the potential functions of CRs and their regulatory mechanism in diseases and biological processes.
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Affiliation(s)
- Yimeng Zhang
- The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan Area, Shantou University Medical College, Shantou 515041, China
| | | | | | - Xilong Zhao
- School of Medical Informatics, Daqing Campus, Harbin Medical University.Daqing 163319, China
| | - Bo Ai
- School of Medical Informatics, Daqing Campus, Harbin Medical University.Daqing 163319, China
| | - Yuezhu Wang
- School of Medical Informatics, Daqing Campus, Harbin Medical University.Daqing 163319, China
| | - Liwei Zhou
- School of Medical Informatics, Daqing Campus, Harbin Medical University.Daqing 163319, China
| | - Jiang Zhu
- School of Medical Informatics, Daqing Campus, Harbin Medical University.Daqing 163319, China
| | - Chenchen Feng
- School of Medical Informatics, Daqing Campus, Harbin Medical University.Daqing 163319, China
| | - Liyan Xu
- The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan Area, Shantou University Medical College, Shantou 515041, China
- Guangdong Provincial Key Laboratory of Infectious Diseases and Molecular Immunopathology, Institute of Oncologic Pathology, Cancer Research Center, Shantou University Medical College, Shantou 515041, China
| | - Qiuyu Wang
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- School of Computer, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Hunan Provincial Base for Scientific and Technological Innovation Cooperation, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
| | - Hong Sun
- The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan Area, Shantou University Medical College, Shantou 515041, China
- Department of Biochemistry and Molecular Biology, Shantou University Medical College, Shantou 515041, China
| | - Qiaoli Fang
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- School of Computer, University of South China, Hengyang, Hunan 421001, China
| | - Xiaozheng Xu
- School of Medical Informatics, Daqing Campus, Harbin Medical University.Daqing 163319, China
| | - Enmin Li
- The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan Area, Shantou University Medical College, Shantou 515041, China
- Department of Biochemistry and Molecular Biology, Shantou University Medical College, Shantou 515041, China
| | - Chunquan Li
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- School of Computer, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Hunan Provincial Base for Scientific and Technological Innovation Cooperation, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South
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7
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Elghzaly AA, Sun C, Looger LL, Hirose M, Salama M, Khalil NM, Behiry ME, Hegazy MT, Hussein MA, Salem MN, Eltoraby E, Tawhid Z, Alwasefy M, Allam W, El-Shiekh I, Elserafy M, Abdelnaser A, Hashish S, Shebl N, Shahba AA, Elgirby A, Hassab A, Refay K, El-Touchy HM, Youssef A, Shabacy F, Hashim AA, Abdelzaher A, Alshebini E, Fayez D, El-Bakry SA, Elzohri MH, Abdelsalam EN, El-Khamisy SF, Ibrahim S, Ragab G, Nath SK. Genome-wide association study for systemic lupus erythematosus in an egyptian population. Front Genet 2022; 13:948505. [PMID: 36324510 PMCID: PMC9619055 DOI: 10.3389/fgene.2022.948505] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 09/30/2022] [Indexed: 04/11/2024] Open
Abstract
Systemic lupus erythematosus (SLE) susceptibility has a strong genetic component. Genome-wide association studies (GWAS) across trans-ancestral populations show both common and distinct genetic variants of susceptibility across European and Asian ancestries, while many other ethnic populations remain underexplored. We conducted the first SLE GWAS on Egyptians-an admixed North African/Middle Eastern population-using 537 patients and 883 controls. To identify novel susceptibility loci and replicate previously known loci, we performed imputation-based association analysis with 6,382,276 SNPs while accounting for individual admixture. We validated the association analysis using adaptive permutation tests (n = 109). We identified a novel genome-wide significant locus near IRS1/miR-5702 (Pcorrected = 1.98 × 10-8) and eight novel suggestive loci (Pcorrected < 1.0 × 10-5). We also replicated (Pperm < 0.01) 97 previously known loci with at least one associated nearby SNP, with ITGAM, DEF6-PPARD and IRF5 the top three replicated loci. SNPs correlated (r 2 > 0.8) with lead SNPs from four suggestive loci (ARMC9, DIAPH3, IFLDT1, and ENTPD3) were associated with differential gene expression (3.5 × 10-95 < p < 1.0 × 10-2) across diverse tissues. These loci are involved in cellular proliferation and invasion-pathways prominent in lupus and nephritis. Our study highlights the utility of GWAS in an admixed Egyptian population for delineating new genetic associations and for understanding SLE pathogenesis.
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Affiliation(s)
- Ashraf A. Elghzaly
- Department of Clinical Pathology, Faculty of Medicine, Mansoura University, El-Mansoura, Egypt
| | - Celi Sun
- Arthritis and Clinical Immunology Research Program, Oklahoma Medical Research Foundation, Oklahoma City, OK, United States
| | - Loren L. Looger
- Department of Neurosciences, Howard Hughes Medical Institute, University of California, San Diego, San Diego, CA, United States
| | - Misa Hirose
- Division of Genetics, Lübeck Institute of Experimental Dermatology, University of Lübeck, Lübeck, Germany
| | - Mohamed Salama
- Institute of Global Health and Human Ecology, The American University in Cairo, New Cairo, Egypt
| | - Noha M. Khalil
- Rheumatology and Clinical Immunology Unit, Department of Internal Medicine, Faculty of Medicine, Cairo University, Cairo, Egypt
| | - Mervat Essam Behiry
- Rheumatology and Clinical Immunology Unit, Department of Internal Medicine, Faculty of Medicine, Cairo University, Cairo, Egypt
| | - Mohamed Tharwat Hegazy
- Rheumatology and Clinical Immunology Unit, Department of Internal Medicine, Faculty of Medicine, Cairo University, Cairo, Egypt
| | - Mohamed Ahmed Hussein
- Rheumatology and Clinical Immunology Unit, Department of Internal Medicine, Faculty of Medicine, Cairo University, Cairo, Egypt
| | - Mohamad Nabil Salem
- Department of Internal Medicine, Faculty of Medicine, Beni-Suef University, Beni Suef, Egypt
| | - Ehab Eltoraby
- Department of Internal Medicine, Faculty of Medicine, Mansoura University, El-Mansoura, Egypt
| | - Ziyad Tawhid
- Department of Clinical Pathology, Faculty of Medicine, Mansoura University, El-Mansoura, Egypt
| | - Mona Alwasefy
- Department of Clinical Pathology, Faculty of Medicine, Mansoura University, El-Mansoura, Egypt
| | - Walaa Allam
- Center for Genomics, Helmy Institute for Medical Sciences, Zewail City of Science and Technology, Giza, Egypt
| | - Iman El-Shiekh
- Center for Genomics, Helmy Institute for Medical Sciences, Zewail City of Science and Technology, Giza, Egypt
| | - Menattallah Elserafy
- Center for Genomics, Helmy Institute for Medical Sciences, Zewail City of Science and Technology, Giza, Egypt
| | - Anwar Abdelnaser
- Institute of Global Health and Human Ecology, The American University in Cairo, New Cairo, Egypt
| | - Sara Hashish
- Institute of Global Health and Human Ecology, The American University in Cairo, New Cairo, Egypt
| | - Nourhan Shebl
- Institute of Global Health and Human Ecology, The American University in Cairo, New Cairo, Egypt
| | | | - Amira Elgirby
- Department of Internal Medicine, Faculty of Medicine, Alexandria University, Bab Sharqi, Egypt
| | - Amina Hassab
- Department of Clinical Pathology, Faculty of Medicine, Alexandria University, Bab Sharqi, Egypt
| | - Khalida Refay
- Department of Internal Medicine, Faculty of Medicine, Al-Azhar University, Cairo, Egypt
| | | | - Ali Youssef
- Department of Rheumatology and Immunology, Faculty of Medicine, Benha University Hospital, Benha, Egypt
| | - Fatma Shabacy
- Department of Rheumatology and Immunology, Faculty of Medicine, Benha University Hospital, Benha, Egypt
| | | | - Asmaa Abdelzaher
- Department of Clinical Pathology, Faculty of Medicine, South Valley University, Qena, Egypt
| | - Emad Alshebini
- Department of Internal Medicine, Faculty of Medicine, Menoufia University, Al Minufiyah, Egypt
| | - Dalia Fayez
- Rheumatology and Clinical Immunology Unit, Department of Internal Medicine, Faculty of Medicine, Ain Shams University, Cairo, Egypt
| | - Samah A. El-Bakry
- Rheumatology and Clinical Immunology Unit, Department of Internal Medicine, Faculty of Medicine, Ain Shams University, Cairo, Egypt
| | - Mona H. Elzohri
- Department of Internal Medicine, Faculty of Medicine, Assiut University, Asyut, Egypt
| | | | - Sherif F. El-Khamisy
- Center for Genomics, Helmy Institute for Medical Sciences, Zewail City of Science and Technology, Giza, Egypt
- The Healthy Lifespan Institute, University of Sheffield, Sheffield, United Kingdom
- The Institute of Cancer Therapeutics, University of Bradford, Bradford, United Kingdom
| | - Saleh Ibrahim
- Division of Genetics, Lübeck Institute of Experimental Dermatology, University of Lübeck, Lübeck, Germany
| | - Gaafar Ragab
- Rheumatology and Clinical Immunology Unit, Department of Internal Medicine, Faculty of Medicine, Cairo University, Cairo, Egypt
| | - Swapan K. Nath
- Arthritis and Clinical Immunology Research Program, Oklahoma Medical Research Foundation, Oklahoma City, OK, United States
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8
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Zhang Y, Song C, Zhang Y, Wang Y, Feng C, Chen J, Wei L, Pan Q, Shang D, Zhu Y, Zhu J, Fang S, Zhao J, Yang Y, Zhao X, Xu X, Wang Q, Guo J, Li C. TcoFBase: a comprehensive database for decoding the regulatory transcription co-factors in human and mouse. Nucleic Acids Res 2022; 50:D391-D401. [PMID: 34718747 PMCID: PMC8728270 DOI: 10.1093/nar/gkab950] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2021] [Revised: 09/21/2021] [Accepted: 10/04/2021] [Indexed: 02/05/2023] Open
Abstract
Transcription co-factors (TcoFs) play crucial roles in gene expression regulation by communicating regulatory cues from enhancers to promoters. With the rapid accumulation of TcoF associated chromatin immunoprecipitation sequencing (ChIP-seq) data, the comprehensive collection and integrative analyses of these data are urgently required. Here, we developed the TcoFBase database (http://tcof.liclab.net/TcoFbase), which aimed to document a large number of available resources for mammalian TcoFs and provided annotations and enrichment analyses of TcoFs. TcoFBase curated 2322 TcoFs and 6759 TcoFs associated ChIP-seq data from over 500 tissues/cell types in human and mouse. Importantly, TcoFBase provided detailed and abundant (epi) genetic annotations of ChIP-seq based TcoF binding regions. Furthermore, TcoFBase supported regulatory annotation information and various functional annotations for TcoFs. Meanwhile, TcoFBase embedded five types of TcoF regulatory analyses for users, including TcoF gene set enrichment, TcoF binding genomic region annotation, TcoF regulatory network analysis, TcoF-TF co-occupancy analysis and TcoF regulatory axis analysis. TcoFBase was designed to be a useful resource that will help reveal the potential biological effects of TcoFs and elucidate TcoF-related regulatory mechanisms.
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Affiliation(s)
| | | | | | | | - Chenchen Feng
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Jiaxin Chen
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Ling Wei
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Qi Pan
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Desi Shang
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- School of Computer, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Hunan Provincial Base for Scientific and Technological Innovation Cooperation, University of South China, Hengyang, Hunan 421001, China
| | - Yanbing Zhu
- Experimental and Translational Research Center, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China
| | - Jiang Zhu
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Shuangsang Fang
- Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Jun Zhao
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Yongsan Yang
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Xilong Zhao
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Xiaozheng Xu
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Qiuyu Wang
- Correspondence may also be addressed to Qiuyu Wang. Tel: +86 13351294769; Fax: +86 0734 8279018;
| | - Jincheng Guo
- Correspondence may also be addressed to Jincheng Guo. Tel: +86 1062600822; Fax: +86 1062601356;
| | - Chunquan Li
- To whom correspondence should be addressed. Tel: +86 15004591078; Fax: +86 0734 8279018;
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9
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Li B, Ritchie MD. From GWAS to Gene: Transcriptome-Wide Association Studies and Other Methods to Functionally Understand GWAS Discoveries. Front Genet 2021; 12:713230. [PMID: 34659337 PMCID: PMC8515949 DOI: 10.3389/fgene.2021.713230] [Citation(s) in RCA: 67] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Accepted: 07/27/2021] [Indexed: 12/12/2022] Open
Abstract
Since their inception, genome-wide association studies (GWAS) have identified more than a hundred thousand single nucleotide polymorphism (SNP) loci that are associated with various complex human diseases or traits. The majority of GWAS discoveries are located in non-coding regions of the human genome and have unknown functions. The valley between non-coding GWAS discoveries and downstream affected genes hinders the investigation of complex disease mechanism and the utilization of human genetics for the improvement of clinical care. Meanwhile, advances in high-throughput sequencing technologies reveal important genomic regulatory roles that non-coding regions play in the transcriptional activities of genes. In this review, we focus on data integrative bioinformatics methods that combine GWAS with functional genomics knowledge to identify genetically regulated genes. We categorize and describe two types of data integrative methods. First, we describe fine-mapping methods. Fine-mapping is an exploratory approach that calibrates likely causal variants underneath GWAS signals. Fine-mapping methods connect GWAS signals to potentially causal genes through statistical methods and/or functional annotations. Second, we discuss gene-prioritization methods. These are hypothesis generating approaches that evaluate whether genetic variants regulate genes via certain genetic regulatory mechanisms to influence complex traits, including colocalization, mendelian randomization, and the transcriptome-wide association study (TWAS). TWAS is a gene-based association approach that investigates associations between genetically regulated gene expression and complex diseases or traits. TWAS has gained popularity over the years due to its ability to reduce multiple testing burden in comparison to other variant-based analytic approaches. Multiple types of TWAS methods have been developed with varied methodological designs and biological hypotheses over the past 5 years. We dive into discussions of how TWAS methods differ in many aspects and the challenges that different TWAS methods face. Overall, TWAS is a powerful tool for identifying complex trait-associated genes. With the advent of single-cell sequencing, chromosome conformation capture, gene editing technologies, and multiplexing reporter assays, we are expecting a more comprehensive understanding of genomic regulation and genetically regulated genes underlying complex human diseases and traits in the future.
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Affiliation(s)
- Binglan Li
- Department of Biomedical Data Science, Stanford University, Stanford, CA, United States
| | - Marylyn D Ritchie
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, United States.,Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, United States
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10
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Calonga‐Solís V, Amorim LM, Farias TDJ, Petzl‐Erler ML, Malheiros D, Augusto DG. Variation in genes implicated in B-cell development and antibody production affects susceptibility to pemphigus. Immunology 2021; 162:58-67. [PMID: 32926429 PMCID: PMC7730027 DOI: 10.1111/imm.13259] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Revised: 08/23/2020] [Accepted: 08/29/2020] [Indexed: 12/12/2022] Open
Abstract
Pemphigus foliaceus (PF) is an autoimmune blistering skin disease characterized by the presence of pathogenic autoantibodies against desmoglein 1, a component of intercellular desmosome junctions. PF occurs sporadically across the globe and is endemic in some Brazilian regions. Because PF is a B-cell-mediated disease, we aimed to study the impact of variants within genes encoding molecules involved in the different steps of B-cell development and antibody production on the susceptibility of endemic PF. We analysed 3,336 single nucleotide polymorphisms (SNPs) from 167 candidate genes genotyped with Illumina microarray in a cohort of 227 PF patients and 193 controls. After quality control and exclusion of non-informative and redundant SNPs, 607 variants in 149 genes remained in the logistic regression analysis, in which sex and ancestry were included as covariates. Our results revealed 10 SNPs within or nearby 11 genes that were associated with susceptibility to endemic PF (OR >1.56; p < 0.005): rs6657275*G (TGFB2); rs1818545*A (RAG1/RAG2/IFTAP);rs10781530*A (PAXX), rs10870140*G and rs10781522*A (TRAF2); rs535068*A (TNFRSF1B); rs324011*A (STAT6);rs6432018*C (YWHAQ); rs17149161*C (YWHAG); and rs2070729*C (IRF1). Interestingly, these SNPs have been previously associated with differential gene expression, mostly in peripheral blood, in publicly available databases. For the first time, we show that polymorphisms in genes involved in B-cell development and antibody production confer differential susceptibility to endemic PF, and therefore are candidates for possible functional studies to understand immunoglobulin gene rearrangement and its impact on diseases.
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Affiliation(s)
- Verónica Calonga‐Solís
- Programa de Pós‐Graduação em GenéticaDepartamento de GenéticaUniversidade Federal do ParanáCuritibaBrasil
| | - Leonardo M. Amorim
- Programa de Pós‐Graduação em GenéticaDepartamento de GenéticaUniversidade Federal do ParanáCuritibaBrasil
| | - Ticiana D. J. Farias
- Programa de Pós‐Graduação em GenéticaDepartamento de GenéticaUniversidade Federal do ParanáCuritibaBrasil
| | - Maria Luiza Petzl‐Erler
- Programa de Pós‐Graduação em GenéticaDepartamento de GenéticaUniversidade Federal do ParanáCuritibaBrasil
| | - Danielle Malheiros
- Programa de Pós‐Graduação em GenéticaDepartamento de GenéticaUniversidade Federal do ParanáCuritibaBrasil
| | - Danillo G. Augusto
- Programa de Pós‐Graduação em GenéticaDepartamento de GenéticaUniversidade Federal do ParanáCuritibaBrasil
- Department of NeurologyUniversity of California San FranciscoSan FranciscoCAUSA
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11
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Abstract
Exploration of genetic variant-to-gene relationships by quantitative trait loci such as expression QTLs is a frequently used tool in genome-wide association studies. However, the wide range of public QTL databases and the lack of batch annotation features complicate a comprehensive annotation of GWAS results. In this work, we introduce the tool “Qtlizer” for annotating lists of variants in human with associated changes in gene expression and protein abundance using an integrated database of published QTLs. Features include incorporation of variants in linkage disequilibrium and reverse search by gene names. Analyzing the database for base pair distances between best significant eQTLs and their affected genes suggests that the commonly used cis-distance limit of 1,000,000 base pairs might be too restrictive, implicating a substantial amount of wrongly and yet undetected eQTLs. We also ranked genes with respect to the maximum number of tissue-specific eQTL studies in which a most significant eQTL signal was consistent. For the top 100 genes we observed the strongest enrichment with housekeeping genes (P = 2 × 10–6) and with the 10% highest expressed genes (P = 0.005) after grouping eQTLs by r2 > 0.95, underlining the relevance of LD information in eQTL analyses. Qtlizer can be accessed via https://genehopper.de/qtlizer or by using the respective Bioconductor R-package (https://doi.org/10.18129/B9.bioc.Qtlizer).
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12
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Zhang H, Liu L, Ni JJ, Wei XT, Feng GJ, Yang XL, Xu Q, Zhang ZJ, Hai R, Tian Q, Shen H, Deng HW, Pei YF, Zhang L. Pleiotropic loci underlying bone mineral density and bone size identified by a bivariate genome-wide association analysis. Osteoporos Int 2020; 31:1691-1701. [PMID: 32314116 PMCID: PMC7883523 DOI: 10.1007/s00198-020-05389-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Accepted: 03/11/2020] [Indexed: 01/30/2023]
Abstract
UNLABELLED Aiming to identify pleiotropic genomic loci for bone mineral density and bone size, we performed a bivariate GWAS in five discovery samples and replicated in two large-scale samples. We identified 2 novel loci at 2q37.1 and 6q26. Our findings provide insight into common genetic architecture underlying both traits. INTRODUCTION Bone mineral density (BMD) and bone size (BS) are two important factors that contribute to the development of osteoporosis and osteoporotic fracture. Both BMD and BS are highly heritable and they are genetically correlated. In this study, we aim to identify pleiotropic loci associated with BMD and BS. METHODS We conducted a bivariate genome-wide association (GWA) analysis of hip BMD and hip BS in 6180 participants from 5 samples, followed by in silico replication in the UK Biobank study of BMD (N = 426,824) and the deCODE study of BS (N = 28,954), respectively. RESULTS SNPs from 2 genomic loci were significant at the genome-wide significance (GWS) level (p lt; 5 × 10-8) in the discovery samples and were successfully replicated in the replication samples (2q37.1, lead SNP rs7575512, discovery p = 1.49 × 10-10, replication p = 0.05; 6q26, lead SNP rs1040724, discovery p = 1.95 × 10-8, replication p = 0.03). Functional annotations suggested functional relevance of the identified variants to bone development. CONCLUSION Our findings provide insight into the common genetic architecture underlying BMD and BS, and enhance our understanding of the potential mechanism of osteoporosis fracture.
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Affiliation(s)
- H Zhang
- Center for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, 199 Ren-ai Rd., SuZhou City, 215123, Jiangsu Province, People's Republic of China
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, 199 Ren-ai Rd., SuZhou City, 215123, Jiangsu Province, People's Republic of China
| | - L Liu
- Center for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, 199 Ren-ai Rd., SuZhou City, 215123, Jiangsu Province, People's Republic of China
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, 199 Ren-ai Rd., SuZhou City, 215123, Jiangsu Province, People's Republic of China
- Kunshan Hospital of Traditional Chinese Medicine, SuZhou, Jiangsu, People's Republic of China
| | - J-J Ni
- Center for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, 199 Ren-ai Rd., SuZhou City, 215123, Jiangsu Province, People's Republic of China
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, 199 Ren-ai Rd., SuZhou City, 215123, Jiangsu Province, People's Republic of China
| | - X-T Wei
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, 199 Ren-ai Rd., SuZhou City, 215123, Jiangsu Province, People's Republic of China
- Department of Epidemiology and Health Statistics, School of Public Health, Medical College of Soochow University, SuZhou, Jiangsu, People's Republic of China
| | - G-J Feng
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, 199 Ren-ai Rd., SuZhou City, 215123, Jiangsu Province, People's Republic of China
- Department of Epidemiology and Health Statistics, School of Public Health, Medical College of Soochow University, SuZhou, Jiangsu, People's Republic of China
| | - X-L Yang
- Center for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, 199 Ren-ai Rd., SuZhou City, 215123, Jiangsu Province, People's Republic of China
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, 199 Ren-ai Rd., SuZhou City, 215123, Jiangsu Province, People's Republic of China
| | - Q Xu
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, 199 Ren-ai Rd., SuZhou City, 215123, Jiangsu Province, People's Republic of China
- Department of Epidemiology and Health Statistics, School of Public Health, Medical College of Soochow University, SuZhou, Jiangsu, People's Republic of China
| | - Z-J Zhang
- People's Hospital of Inner Mongolia Autonomous Region, Hohhot, Inner Mongolia, People's Republic of China
| | - R Hai
- People's Hospital of Inner Mongolia Autonomous Region, Hohhot, Inner Mongolia, People's Republic of China
| | - Q Tian
- Department of Biostatistics and Bioinformatics, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, USA
| | - H Shen
- Department of Biostatistics and Bioinformatics, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, USA
| | - H-W Deng
- Department of Biostatistics and Bioinformatics, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, USA.
- Center for Bioinformatics and Genomics, School of Public Health and Tropical Medicine, Tulane University, 1440 Canal St., Suite 2001, New Orleans, LA, 70112, USA.
| | - Y-F Pei
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, 199 Ren-ai Rd., SuZhou City, 215123, Jiangsu Province, People's Republic of China.
- Department of Epidemiology and Health Statistics, School of Public Health, Medical College of Soochow University, SuZhou, Jiangsu, People's Republic of China.
| | - L Zhang
- Center for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, 199 Ren-ai Rd., SuZhou City, 215123, Jiangsu Province, People's Republic of China.
- Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, 199 Ren-ai Rd., SuZhou City, 215123, Jiangsu Province, People's Republic of China.
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High carbohydrate and noodle/meat-rich dietary patterns interact with the minor haplotype in the 22q13 loci to increase its association with non-alcoholic fatty liver disease risk in Koreans. Nutr Res 2020; 82:88-98. [PMID: 32977255 DOI: 10.1016/j.nutres.2020.08.011] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Revised: 08/01/2020] [Accepted: 08/18/2020] [Indexed: 12/29/2022]
Abstract
Non-alcoholic fatty liver(NAFLD) is prevalent in Asians despite the low obesity rate. We hypothesized that the haplotype of genetic variants in the 22q13 loci has a strong association with non-alcoholic fatty liver disease (NAFLD) that can be identified by genome-wide association study and that lifestyles may interact with the haplotype. We tested the hypothesis in middle-aged and elderly adults in a large city hospital-based cohort from the KoGES study. Men and women diagnosed with fatty liver, but who respectively consumed over 40 and 30 g ethanol per day were excluded. The haplotype of the selected SNPs from the 22q13 loci that influences NAFLD risk was generated. Among the 27374 participants, 1486 (5.4%) were diagnosed with NAFLD. LARGE_rs240072, RBFOX2_rs11089778, TRIOBP_rs12628603, PNPLA3_rs738409, and PARVB_rs2073080 in the 22q13 loci were included in the haplotype. Participants with the minor haplotype had 1.8, 2.3, and 1.8 times higher in the risk for NAFLD and serum AST and ALT activities, respectively, than those with the major haplotype. BMI, waist circumferences, serum glucose concentrations, and blood pressure interacted with the haplotype for NAFLD risk. We also found that a high carbohydrate intake and a dietary pattern characterized by high noodle and meat consumption significantly interacted with the minor haplotype to increase the risk of NAFLD. We hypothesized that the high incidence of NAFLD among Koreans, despite a relatively low incidence of obesity, might be due to genetic factors and perhaps their interactions with dietary patterns. The hypothesis was accepted since this study confirmed that participants with the minor allele of the haplotype in the 22q13 loci had a higher NAFLD risk that was exacerbated by high intakes of carbohydrates and a dietary pattern characterized by high noodle and meat consumption.
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14
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Li X, Shi L, Wang Y, Zhong J, Zhao X, Teng H, Shi X, Yang H, Ruan S, Li M, Sun ZS, Zhan Q, Mao F. OncoBase: a platform for decoding regulatory somatic mutations in human cancers. Nucleic Acids Res 2020; 47:D1044-D1055. [PMID: 30445567 PMCID: PMC6323961 DOI: 10.1093/nar/gky1139] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2018] [Accepted: 11/11/2018] [Indexed: 12/16/2022] Open
Abstract
Whole-exome and whole-genome sequencing have revealed millions of somatic mutations associated with different human cancers, and the vast majority of them are located outside of coding sequences, making it challenging to directly interpret their functional effects. With the rapid advances in high-throughput sequencing technologies, genome-scale long-range chromatin interactions were detected, and distal target genes of regulatory elements were determined using three-dimensional (3D) chromatin looping. Herein, we present OncoBase (http://www.oncobase.biols.ac.cn/), an integrated database for annotating 81 385 242 somatic mutations in 68 cancer types from more than 120 cancer projects by exploring their roles in distal interactions between target genes and regulatory elements. OncoBase integrates local chromatin signatures, 3D chromatin interactions in different cell types and reconstruction of enhancer-target networks using state-of-the-art algorithms. It employs informative visualization tools to display the integrated local and 3D chromatin signatures and effects of somatic mutations on regulatory elements. Enhancer-promoter interactions estimated from chromatin interactions are integrated into a network diffusion system that quantitatively prioritizes somatic mutations and target genes from a large pool. Thus, OncoBase is a useful resource for the functional annotation of regulatory noncoding regions and systematically benchmarking the regulatory effects of embedded noncoding somatic mutations in human carcinogenesis.
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Affiliation(s)
- Xianfeng Li
- Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Laboratory of Molecular Oncology, Peking University Cancer Hospital & Institute, Beijing 100142, China.,Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing 100101, China
| | - Leisheng Shi
- Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Yan Wang
- Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Laboratory of Molecular Oncology, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Jianing Zhong
- Key Laboratory of Prevention and Treatment of Cardiovascular and Cerebrovascular Diseases of Ministry of Education, Gannan Medical University, Ganzhou 341000,China
| | - Xiaolu Zhao
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Huajing Teng
- Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing 100101, China
| | - Xiaohui Shi
- Sino-Danish college, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Haonan Yang
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Shasha Ruan
- Department of Clinical Oncology, Renmin Hospital of Wuhan University, Wuhan, Hubei 430072, China
| | - MingKun Li
- Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
| | - Zhong Sheng Sun
- Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing 100101, China
| | - Qimin Zhan
- Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Laboratory of Molecular Oncology, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Fengbiao Mao
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA
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15
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Kelly J, Moyeed R, Carroll C, Luo S, Li X. Genetic networks in Parkinson's and Alzheimer's disease. Aging (Albany NY) 2020; 12:5221-5243. [PMID: 32205467 PMCID: PMC7138567 DOI: 10.18632/aging.102943] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Accepted: 03/09/2020] [Indexed: 12/14/2022]
Abstract
Parkinson’s disease (PD) and Alzheimer’s disease (AD) are the most common neurodegenerative diseases and there is increasing evidence that they share common physiological and pathological links. Here we have conducted the largest network analysis of PD and AD based on their gene expressions in blood to date. We identified modules that were not preserved between disease and healthy control (HC) networks, and important hub genes and transcription factors (TFs) in these modules. We highlighted that the PD module not preserved in HCs was associated with insulin resistance, and HDAC6 was identified as a hub gene in this module which may have the role of influencing tau phosphorylation and autophagic flux in neurodegenerative disease. The AD module associated with regulation of lipolysis in adipocytes and neuroactive ligand-receptor interaction was not preserved in healthy and mild cognitive impairment networks and the key hubs TRPC5 and BRAP identified as potential targets for therapeutic treatments of AD. Our study demonstrated that PD and AD share common disrupted genetics and identified novel pathways, hub genes and TFs that may be new areas for mechanistic study and important targets in both diseases.
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Affiliation(s)
- Jack Kelly
- Faculty of Health: Medicine, Dentistry and Human Sciences, Plymouth University, Plymouth PL6 8BU, UK
| | - Rana Moyeed
- Faculty of Science and Engineering, Plymouth University, Plymouth PL6 8BU, UK
| | - Camille Carroll
- Faculty of Health: Medicine, Dentistry and Human Sciences, Plymouth University, Plymouth PL6 8BU, UK
| | - Shouqing Luo
- Faculty of Health: Medicine, Dentistry and Human Sciences, Plymouth University, Plymouth PL6 8BU, UK
| | - Xinzhong Li
- School of Science, Engineering and Design, Teesside University, Middlesbrough TS1 3BX, UK
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16
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Shang Z, Sun W, Zhang M, Xu L, Jia X, Zhang R, Fu S. Identification of key genes associated with multiple sclerosis based on gene expression data from peripheral blood mononuclear cells. PeerJ 2020; 8:e8357. [PMID: 32117605 PMCID: PMC7003695 DOI: 10.7717/peerj.8357] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Accepted: 12/04/2019] [Indexed: 11/20/2022] Open
Abstract
The aim of this study was to identify the potential key candidate genes of multiple sclerosis (MS) and uncover mechanisms in MS. We combined data from the microarray expression profile of three MS stages and performed bioinformatics analysis. Differentially expressed genes (DEGs) were identified among the distinct stages of MS and healthy controls, and a total of 349 shared DEGs were identified. Gene ontology (GO) and pathway enrichment analyses showed that the DEGs were significantly enriched in the biological processes (BPs) of purine-related metabolic processes and signaling, especially the common DEGs, which were enriched in some immunological processes. Most of the DEGs were enriched in signaling pathways associated with the immune system, some immune diseases and infectious disease pathways. Through a protein-protein interaction (PPI) network analysis and a gene expression regulatory network constructed with MS-related miRNAs, we confirmed FOS, TP53, VEGFA, JUN, HIF1A, RB1, PTGS2, CXCL8, OAS2, NFKBIA and OAS1 as candidate genes of MS. Furthermore , we explored the potential SNPs associated with MS by database mining. In conclusion, this study provides the identified genes, SNPs, biological processes, and cellular pathways associated with MS. The uncovered candidate genes may be potential biomarkers involved in the diagnosis and therapy of MS.
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Affiliation(s)
- Zhenwei Shang
- Harbin Medical University, Laboratory of Medical Genetics, Harbin, China.,Harbin Medical University, Key Laboratory of Preservation of Human Genetic Resources and Disease Control in China, Ministry of Education, Harbin, China.,Harbin Medical University, College of Bioinformatics Science and Technology, Harbin, China
| | - Wenjing Sun
- Harbin Medical University, Laboratory of Medical Genetics, Harbin, China.,Harbin Medical University, Key Laboratory of Preservation of Human Genetic Resources and Disease Control in China, Ministry of Education, Harbin, China
| | - Mingming Zhang
- Harbin Medical University, Laboratory of Medical Genetics, Harbin, China.,Harbin Medical University, Key Laboratory of Preservation of Human Genetic Resources and Disease Control in China, Ministry of Education, Harbin, China.,Harbin Medical University, College of Bioinformatics Science and Technology, Harbin, China
| | - Lidan Xu
- Harbin Medical University, Laboratory of Medical Genetics, Harbin, China.,Harbin Medical University, Key Laboratory of Preservation of Human Genetic Resources and Disease Control in China, Ministry of Education, Harbin, China
| | - Xueyuan Jia
- Harbin Medical University, Laboratory of Medical Genetics, Harbin, China.,Harbin Medical University, Key Laboratory of Preservation of Human Genetic Resources and Disease Control in China, Ministry of Education, Harbin, China
| | - Ruijie Zhang
- Harbin Medical University, College of Bioinformatics Science and Technology, Harbin, China
| | - Songbin Fu
- Harbin Medical University, Laboratory of Medical Genetics, Harbin, China.,Harbin Medical University, Key Laboratory of Preservation of Human Genetic Resources and Disease Control in China, Ministry of Education, Harbin, China
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17
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Abstract
In this review we critically summarize the evidence base and the progress to date regarding the genomic basis of periodontal disease and tooth morbidity (ie, dental caries and tooth loss), and discuss future applications and research directions in the context of precision oral health and care. Evidence for these oral/dental traits from genome-wide association studies first emerged less than a decade ago. Basic and translational research activities in this domain are now under way by multiple groups around the world. Key departure points in the oral health genomics discourse are: (a) some heritable variation exists for periodontal and dental diseases; (b) the environmental component (eg, social determinants of health and behavioral risk factors) has a major influence on the population distribution but probably interacts with factors of innate susceptibility at the person-level; (c) sizeable, multi-ethnic, well-characterized samples or cohorts with high-quality measures on oral health outcomes and genomics information are required to make decisive discoveries; (d) challenges remain in the measurement of oral health and disease, with current periodontitis and dental caries traits capturing only a part of the health-disease continuum, and are little or not informed by the underlying biology; (e) the substantial individual heterogeneity that exists in the clinical presentation and lifetime trajectory of oral disease can be identified and leveraged in a precision medicine framework or, if unappreciated, can hamper translational efforts. In this review we discuss how composite or biologically informed traits may offer improvements over clinically defined ones for the genomic interrogation of oral diseases. We demonstrate the utility of the results of genome-wide association studies for the development and testing of a genetic risk score for severe periodontitis. We conclude that exciting opportunities lie ahead for improvements in the oral health of individual patients and populations via advances in our understanding of the genomic basis of oral health and disease. The pace of new discoveries and their equitable translation to practice will largely depend on investments in the education and training of the oral health care workforce, basic and population research, and sustained collaborative efforts..
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Affiliation(s)
- Thiago Morelli
- Department of PeriodontologySchool of DentistryUniversity of North Carolina at Chapel HillChapel HillNorth Carolina, USA
| | - Cary S. Agler
- Department of Oral and Craniofacial Health SciencesSchool of DentistryUniversity of North Carolina at Chapel HillChapel HillNorth Carolina, USA
| | - Kimon Divaris
- Department of Pediatric DentistrySchool of DentistryUniversity of North Carolina at Chapel HillChapel HillNorth Carolina, USA
- Department of EpidemiologyGillings School of Global Public HealthUniversity of North Carolina at Chapel HillChapel HillNorth Carolina, USA
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18
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Pendergrass SA, Buyske S, Jeff JM, Frase A, Dudek S, Bradford Y, Ambite JL, Avery CL, Buzkova P, Deelman E, Fesinmeyer MD, Haiman C, Heiss G, Hindorff LA, Hsu CN, Jackson RD, Lin Y, Le Marchand L, Matise TC, Monroe KR, Moreland L, North KE, Park SL, Reiner A, Wallace R, Wilkens LR, Kooperberg C, Ritchie MD, Crawford DC. A phenome-wide association study (PheWAS) in the Population Architecture using Genomics and Epidemiology (PAGE) study reveals potential pleiotropy in African Americans. PLoS One 2019; 14:e0226771. [PMID: 31891604 PMCID: PMC6938343 DOI: 10.1371/journal.pone.0226771] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Accepted: 12/03/2019] [Indexed: 12/11/2022] Open
Abstract
We performed a hypothesis-generating phenome-wide association study (PheWAS) to identify and characterize cross-phenotype associations, where one SNP is associated with two or more phenotypes, between thousands of genetic variants assayed on the Metabochip and hundreds of phenotypes in 5,897 African Americans as part of the Population Architecture using Genomics and Epidemiology (PAGE) I study. The PAGE I study was a National Human Genome Research Institute-funded collaboration of four study sites accessing diverse epidemiologic studies genotyped on the Metabochip, a custom genotyping chip that has dense coverage of regions in the genome previously associated with cardio-metabolic traits and outcomes in mostly European-descent populations. Here we focus on identifying novel phenome-genome relationships, where SNPs are associated with more than one phenotype. To do this, we performed a PheWAS, testing each SNP on the Metabochip for an association with up to 273 phenotypes in the participating PAGE I study sites. We identified 133 putative pleiotropic variants, defined as SNPs associated at an empirically derived p-value threshold of p<0.01 in two or more PAGE study sites for two or more phenotype classes. We further annotated these PheWAS-identified variants using publicly available functional data and local genetic ancestry. Amongst our novel findings is SPARC rs4958487, associated with increased glucose levels and hypertension. SPARC has been implicated in the pathogenesis of diabetes and is also known to have a potential role in fibrosis, a common consequence of multiple conditions including hypertension. The SPARC example and others highlight the potential that PheWAS approaches have in improving our understanding of complex disease architecture by identifying novel relationships between genetic variants and an array of common human phenotypes.
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Affiliation(s)
| | - Steven Buyske
- Department of Statistics, Rutgers University, Piscataway, New Jersey, United States of America
- Department of Genetics, Rutgers University, Piscataway, New Jersey, United States of America
| | - Janina M. Jeff
- Illumina, Inc., San Diego, California, United States of America
| | - Alex Frase
- Department of Genetics, Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Scott Dudek
- Department of Genetics, Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Yuki Bradford
- Department of Genetics, Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Jose-Luis Ambite
- Information Sciences Institute; University of Southern California, Marina del Rey, California, United States of America
| | - Christy L. Avery
- Department of Epidemiology, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Petra Buzkova
- Department of Biostatistics, University of Washington, Seattle, Washington, United States of America
| | - Ewa Deelman
- Information Sciences Institute; University of Southern California, Marina del Rey, California, United States of America
| | | | - Christopher Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, California, United States of America
| | - Gerardo Heiss
- Department of Epidemiology, University of North Carolina, Chapel Hill, North Carolina, United States of America
- Carolina Center for Genome Sciences, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Lucia A. Hindorff
- National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Chun-Nan Hsu
- Center for Research in Biological Systems, Department of Neurosciences, University of California, San Diego, La Jolla, California, United States of America
| | | | - Yi Lin
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Loic Le Marchand
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, Hawaii, United States of America
| | - Tara C. Matise
- Department of Genetics, Rutgers University, Piscataway, New Jersey, United States of America
| | - Kristine R. Monroe
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, California, United States of America
| | - Larry Moreland
- University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Kari E. North
- Department of Epidemiology, University of North Carolina, Chapel Hill, North Carolina, United States of America
- Carolina Center for Genome Sciences, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Sungshim L. Park
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, California, United States of America
| | - Alex Reiner
- Department of Epidemiology, University of Washington, Seattle, Washington, United States of America
| | - Robert Wallace
- Departments of Epidemiology and Internal Medicine, University of Iowa, Iowa City, Iowa, United States of America
| | - Lynne R. Wilkens
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, Hawaii, United States of America
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Marylyn D. Ritchie
- Department of Genetics, Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Dana C. Crawford
- Cleveland Institute for Computational Biology, Cleveland, Ohio, United States of America
- Departments of Population and Quantitative Health Sciences and Genetics and Genome Sciences, Case Western Reserve University, Cleveland, Ohio, United States of America
- * E-mail:
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Breitbach ME, Greenspan S, Resnick NM, Perera S, Gurkar AU, Absher D, Levine AS. Exonic Variants in Aging-Related Genes Are Predictive of Phenotypic Aging Status. Front Genet 2019; 10:1277. [PMID: 31921313 PMCID: PMC6931058 DOI: 10.3389/fgene.2019.01277] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2019] [Accepted: 11/19/2019] [Indexed: 01/31/2023] Open
Abstract
Background: Recent studies investigating longevity have revealed very few convincing genetic associations with increased lifespan. This is, in part, due to the complexity of biological aging, as well as the limited power of genome-wide association studies, which assay common single nucleotide polymorphisms (SNPs) and require several thousand subjects to achieve statistical significance. To overcome such barriers, we performed comprehensive DNA sequencing of a panel of 20 genes previously associated with phenotypic aging in a cohort of 200 individuals, half of whom were clinically defined by an "early aging" phenotype, and half of whom were clinically defined by a "late aging" phenotype based on age (65-75 years) and the ability to walk up a flight of stairs or walk for 15 min without resting. A validation cohort of 511 late agers was used to verify our results. Results: We found early agers were not enriched for more total variants in these 20 aging-related genes than late agers. Using machine learning methods, we identified the most predictive model of aging status, both in our discovery and validation cohorts, to be a random forest model incorporating damaging exon variants [Combined Annotation-Dependent Depletion (CADD) > 15]. The most heavily weighted variants in the model were within poly(ADP-ribose) polymerase 1 (PARP1) and excision repair cross complementation group 5 (ERCC5), both of which are involved in a canonical aging pathway, DNA damage repair. Conclusion: Overall, this study implemented a framework to apply machine learning to identify sequencing variants associated with complex phenotypes such as aging. While the small sample size making up our cohort inhibits our ability to make definitive conclusions about the ability of these genes to accurately predict aging, this study offers a unique method for exploring polygenic associations with complex phenotypes.
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Affiliation(s)
- Megan E. Breitbach
- HudsonAlpha Institute for Biotechnology, Hunstville, AL, United States
- Department of Biotechnology Science and Engineering, University of Alabama in Huntsville, Hunstville, AL, United States
| | - Susan Greenspan
- Division of Geriatric Medicine, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States
| | - Neil M. Resnick
- Division of Geriatric Medicine, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States
- Institute on Aging of UPMC, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States
| | - Subashan Perera
- Division of Geriatric Medicine, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States
- Department of Biostatistics, University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA, United States
| | - Aditi U. Gurkar
- Division of Geriatric Medicine, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States
- Institute on Aging of UPMC, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States
| | - Devin Absher
- HudsonAlpha Institute for Biotechnology, Hunstville, AL, United States
| | - Arthur S. Levine
- Department of Microbiology and Molecular Genetics, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States
- UPMC Hillman Cancer Center, Pittsburgh, PA, United States
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Srivastava A, Singh P, Gupta H, Kaur H, Kanojia N, Guin D, Sood M, Chadda RK, Yadav J, Vohora D, Saso L, Kukreti R. Systems Approach to Identify Common Genes and Pathways Associated with Response to Selective Serotonin Reuptake Inhibitors and Major Depression Risk. Int J Mol Sci 2019; 20:1993. [PMID: 31018568 PMCID: PMC6514561 DOI: 10.3390/ijms20081993] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2019] [Revised: 04/17/2019] [Accepted: 04/20/2019] [Indexed: 12/27/2022] Open
Abstract
Despite numerous studies on major depressive disorder (MDD) susceptibility, the precise underlying molecular mechanism has not been elucidated which restricts the development of etiology-based disease-modifying drug. Major depressive disorder treatment is still symptomatic and is the leading cause of (~30%) failure of the current antidepressant therapy. Here we comprehended the probable genes and pathways commonly associated with antidepressant response and MDD. A systematic review was conducted, and candidate genes/pathways associated with antidepressant response and MDD were identified using an integrative genetics approach. Initially, single nucleotide polymorphisms (SNPs)/genes found to be significantly associated with antidepressant response were systematically reviewed and retrieved from the candidate studies and genome-wide association studies (GWAS). Also, significant variations concerning MDD susceptibility were extracted from GWAS only. We found 245 (Set A) and 800 (Set B) significantly associated genes with antidepressant response and MDD, respectively. Further, gene set enrichment analysis revealed the top five co-occurring molecular pathways (p ≤ 0.05) among the two sets of genes: Cushing syndrome, Axon guidance, cAMP signaling pathway, Insulin secretion, and Glutamatergic synapse, wherein all show a very close relation to synaptic plasticity. Integrative analyses of candidate gene and genome-wide association studies would enable us to investigate the putative targets for the development of disease etiology-based antidepressant that might be more promising than current ones.
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Affiliation(s)
- Ankit Srivastava
- Genomics and Molecular Medicine Unit, Institute of Genomics and Integrative Biology (IGIB), Council of Scientific and Industrial Research (CSIR), Delhi 110007, India.
- Department of Pharmacology, School of Pharmaceutical Education and Research, Jamia Hamdard, New Delhi 110062, India.
| | - Priyanka Singh
- Genomics and Molecular Medicine Unit, Institute of Genomics and Integrative Biology (IGIB), Council of Scientific and Industrial Research (CSIR), Delhi 110007, India.
- Academy of Scientific and Innovative Research (AcSIR), CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB) Campus, New Delhi 110007, India.
| | - Hitesh Gupta
- Genomics and Molecular Medicine Unit, Institute of Genomics and Integrative Biology (IGIB), Council of Scientific and Industrial Research (CSIR), Delhi 110007, India.
| | - Harpreet Kaur
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic Foundation, 9500 Euclid Avenue, Cleveland, OH 44195, USA.
| | - Neha Kanojia
- Genomics and Molecular Medicine Unit, Institute of Genomics and Integrative Biology (IGIB), Council of Scientific and Industrial Research (CSIR), Delhi 110007, India.
- Academy of Scientific and Innovative Research (AcSIR), CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB) Campus, New Delhi 110007, India.
| | - Debleena Guin
- Genomics and Molecular Medicine Unit, Institute of Genomics and Integrative Biology (IGIB), Council of Scientific and Industrial Research (CSIR), Delhi 110007, India.
- Department of Bioinformatics, Delhi Technological University, Shahbad Daulatpur, Main Bawana Road, Delhi 110042, India.
| | - Mamta Sood
- Department of Psychiatry, All India Institute of Medical Sciences, Ansari Nagar, New Delhi 110029, India.
| | - Rakesh Kumar Chadda
- Department of Psychiatry, All India Institute of Medical Sciences, Ansari Nagar, New Delhi 110029, India.
| | - Jyoti Yadav
- Genomics and Molecular Medicine Unit, Institute of Genomics and Integrative Biology (IGIB), Council of Scientific and Industrial Research (CSIR), Delhi 110007, India.
| | - Divya Vohora
- Department of Pharmacology, School of Pharmaceutical Education and Research, Jamia Hamdard, New Delhi 110062, India.
| | - Luciano Saso
- Department of Physiology and Pharmacology "Vittorio Erspamer", Sapienza University of Rome, P. le Aldo Moro 5, 00185 Rome, Italy.
| | - Ritushree Kukreti
- Genomics and Molecular Medicine Unit, Institute of Genomics and Integrative Biology (IGIB), Council of Scientific and Industrial Research (CSIR), Delhi 110007, India.
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21
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Genomic landscape analyses of reprogrammed cells using integrative and non-integrative methods reveal variable cancer-associated alterations. Oncotarget 2019; 10:2693-2708. [PMID: 31105870 PMCID: PMC6505633 DOI: 10.18632/oncotarget.26857] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2018] [Accepted: 03/23/2019] [Indexed: 12/13/2022] Open
Abstract
Recent development of cell reprogramming technologies brought a major hope for future cell therapy applications by the use of these cells or their derivatives. For this purpose, one of the major requirements is the absence of genomic alterations generating a risk of cell transformation. Here we analyzed by microarray-based comparative genomic hybridization human iPSC generated by two non-integrative and one integrative method at pluripotent stage as well as in corresponding teratomas. We show that all iPSC lines exhibit copy number variations (CNV) of several genes deregulated in oncogenesis. These cancer-associated genomic alterations were more pronounced in virally programmed hiPSCs and their derivative teratoma as compared to those found in iPSC generated by mRNA-mediated reprogramming. Bioinformatics analysis showed the involvement of these genes in human leukemia and carcinoma. We conclude that genetic screening should become a standard procedure to ensure that hiPSCs are free from cancer-associated genomic alterations before clinical use.
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22
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Agler CS, Shungin D, Ferreira Zandoná AG, Schmadeke P, Basta PV, Luo J, Cantrell J, Pahel TD, Meyer BD, Shaffer JR, Schaefer AS, North KE, Divaris K. Protocols, Methods, and Tools for Genome-Wide Association Studies (GWAS) of Dental Traits. Methods Mol Biol 2019; 1922:493-509. [PMID: 30838596 PMCID: PMC6613560 DOI: 10.1007/978-1-4939-9012-2_38] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Oral health and disease are known to be influenced by complex interactions between environmental (e.g., social and behavioral) factors and innate susceptibility. Although the exact contribution of genomics and other layers of "omics" to oral health is an area of active research, it is well established that the susceptibility to dental caries, periodontal disease, and other oral and craniofacial traits is substantially influenced by the human genome. A comprehensive understanding of these genomic factors is necessary for the realization of precision medicine in the oral health domain. To aid in this direction, the advent and increasing affordability of high-throughput genotyping has enabled the simultaneous interrogation of millions of genetic polymorphisms for association with oral and craniofacial traits. Specifically, genome-wide association studies (GWAS) of dental caries and periodontal disease have provided initial insights into novel loci and biological processes plausibly implicated in these two common, complex, biofilm-mediated diseases. This paper presents a summary of protocols, methods, tools, and pipelines for the conduct of GWAS of dental caries, periodontal disease, and related traits. The protocol begins with the consideration of different traits for both diseases and outlines procedures for genotyping, quality control, adjustment for population stratification, heritability and association analyses, annotation, reporting, and interpretation. Methods and tools available for GWAS are being constantly updated and improved; with this in mind, the presented approaches have been successfully applied in numerous GWAS and meta-analyses among tens of thousands of individuals, including dental traits such as dental caries and periodontal disease. As such, they can serve as a guide or template for future genomic investigations of these and other traits.
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Affiliation(s)
- Cary S Agler
- Oral and Craniofacial Health Sciences, UNC School of Dentistry, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA
| | - Dmitry Shungin
- Department of Odontology, Umeå University, Umeå, Sweden
- Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
| | - Andrea G Ferreira Zandoná
- Department of Comprehensive Dentistry, Tufts University School of Dental Medicine, Tufts University, Boston, MA, USA
| | - Paige Schmadeke
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA
- Biospecimen Core Processing Facility, Gillings School of Global Public Health, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA
| | - Patricia V Basta
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA
- Biospecimen Core Processing Facility, Gillings School of Global Public Health, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA
| | - Jason Luo
- Lineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina, Chapel Hill, NC, USA
- Mammalian Genotyping Core, University of North Carolina, Chapel Hill, NC, USA
| | - John Cantrell
- Oral and Craniofacial Health Sciences, UNC School of Dentistry, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA
| | - Thomas D Pahel
- Oral and Craniofacial Health Sciences, UNC School of Dentistry, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA
| | - Beau D Meyer
- Department of Pediatric Dentistry, UNC School of Dentistry, CB#7450, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA
| | - John R Shaffer
- Center for Craniofacial and Dental Genetics, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Oral Biology, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Arne S Schaefer
- Department of Periodontology, Institute of Dental, Oral and Maxillary Medicine, Charité-University Medicine Berlin, Berlin, Germany
| | - Kari E North
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA
- Carolina Center for Genome Sciences, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA
| | - Kimon Divaris
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA.
- Department of Pediatric Dentistry, UNC School of Dentistry, CB#7450, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA.
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Meta-analysis of genome-wide association studies of aggressive and chronic periodontitis identifies two novel risk loci. Eur J Hum Genet 2018; 27:102-113. [PMID: 30218097 DOI: 10.1038/s41431-018-0265-5] [Citation(s) in RCA: 57] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2017] [Revised: 07/06/2018] [Accepted: 08/09/2018] [Indexed: 01/08/2023] Open
Abstract
Periodontitis is one of the most common inflammatory diseases, with a prevalence of 11% worldwide for the severe forms and an estimated heritability of 50%. It is classified into the widespread moderate form chronic periodontitis (CP) and the rare early-onset and severe phenotype aggressive periodontitis (AgP). These different disease manifestations are thought to share risk alleles and predisposing environmental factors. To obtain novel insights into the shared genetic etiology and the underlying molecular mechanisms of both forms, we performed a two step-wise meta-analysis approach using genome-wide association studies of both phenotypes. Genotypes from imputed genome-wide association studies (GWAS) of AgP and CP comprising 5,095 cases and 9,908 controls of North-West European genetic background were included. Two loci were associated with periodontitis at a genome-wide significance level. They located within the pseudogene MTND1P5 on chromosome 8 (rs16870060-G, P = 3.69 × 10-9, OR = 1.36, 95% CI = [1.23-1.51]) and intronic of the long intergenic non-coding RNA LOC107984137 on chromosome 16, downstream of the gene SHISA9 (rs729876-T, P = 9.77 × 10-9, OR = 1.24, 95% CI = [1.15-1.34]). This study identified novel risk loci of periodontitis, adding to the genetic basis of AgP and CP.
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24
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Genome-wide association meta-analysis of coronary artery disease and periodontitis reveals a novel shared risk locus. Sci Rep 2018; 8:13678. [PMID: 30209331 PMCID: PMC6135769 DOI: 10.1038/s41598-018-31980-8] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2017] [Accepted: 08/31/2018] [Indexed: 02/07/2023] Open
Abstract
Evidence for a shared genetic basis of association between coronary artery disease (CAD) and periodontitis (PD) exists. To explore the joint genetic basis, we performed a GWAS meta-analysis. In the discovery stage, we used a German aggressive periodontitis sample (AgP-Ger; 680 cases vs 3,973 controls) and the CARDIoGRAMplusC4D CAD meta-analysis dataset (60,801 cases vs 123,504 controls). Two SNPs at the known CAD risk loci ADAMTS7 (rs11634042) and VAMP8 (rs1561198) passed the pre-assigned selection criteria (PAgP-Ger < 0.05; PCAD < 5 × 10−8; concordant effect direction) and were replicated in an independent GWAS meta-analysis dataset of PD (4,415 cases vs 5,935 controls). SNP rs1561198 showed significant association (PD[Replication]: P = 0.008 OR = 1.09, 95% CI = [1.02–1.16]; PD [Discovery + Replication]: P = 0.0002, OR = 1.11, 95% CI = [1.05–1.17]). For the associated haplotype block, allele specific cis-effects on VAMP8 expression were reported. Our data adds to the shared genetic basis of CAD and PD and indicate that the observed association of the two disease conditions cannot be solely explained by shared environmental risk factors. We conclude that the molecular pathway shared by CAD and PD involves VAMP8 function, which has a role in membrane vesicular trafficking, and is manipulated by pathogens to corrupt host immune defense.
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25
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O'Connell KS, McGregor NW, Malhotra A, Lencz T, Emsley R, Warnich L. Variation within voltage-gated calcium channel genes and antipsychotic treatment response in a South African first episode schizophrenia cohort. THE PHARMACOGENOMICS JOURNAL 2018; 19:109-114. [PMID: 30032160 DOI: 10.1038/s41397-018-0033-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2017] [Revised: 02/16/2018] [Accepted: 05/14/2018] [Indexed: 02/07/2023]
Abstract
Voltage-gated calcium channels have been implicated in schizophrenia aetiology; however, little is known about their involvement in antipsychotic treatment response. This study investigated variants within the calcium channel subunit genes for association with antipsychotic treatment response in a first episode schizophrenia cohort. Twelve regulatory variants within seven genes were shown to be significantly associated with treatment outcome. Most notably, the CACNA1B rs2229949 CC genotype was associated with improved negative symptomology, where the C allele was predicted to abolish a miRNA-binding site (has-mir-5002-3p), suggesting a possible mechanism of action through which this variant may have an effect. These results implicate the calcium channel subunits in antipsychotic treatment response and suggest that increased activation of these channels may be explored to enhance or predict antipsychotic treatment outcome.
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Affiliation(s)
- Kevin S O'Connell
- System Genetics Working Group, Department of Genetics, Faculty of AgriSciences, Stellenbosch University, Stellenbosch, South Africa.,Department of Genetics, Faculty of AgriSciences, Stellenbosch University, Stellenbosch, South Africa
| | - Nathaniel W McGregor
- System Genetics Working Group, Department of Genetics, Faculty of AgriSciences, Stellenbosch University, Stellenbosch, South Africa.,Department of Genetics, Faculty of AgriSciences, Stellenbosch University, Stellenbosch, South Africa
| | - Anil Malhotra
- Department of Psychiatry, Zucker Hillside Hospital, North Shore-Long Island Jewish Health System, New York, NY, USA
| | - Todd Lencz
- Department of Psychiatry, Zucker Hillside Hospital, North Shore-Long Island Jewish Health System, New York, NY, USA
| | - Robin Emsley
- Department of Psychiatry, Faculty of Medicine and Health Sciences, Tygerberg Medical Campus, Tygerberg, Stellenbosch University, Stellenbosch, South Africa
| | - Louise Warnich
- Department of Genetics, Faculty of AgriSciences, Stellenbosch University, Stellenbosch, South Africa.
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26
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Genetic variant for behavioral regulation factor of executive function and its possible brain mechanism in attention deficit hyperactivity disorder. Sci Rep 2018; 8:7620. [PMID: 29769613 PMCID: PMC5956073 DOI: 10.1038/s41598-018-26042-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2018] [Accepted: 04/30/2018] [Indexed: 12/18/2022] Open
Abstract
As a childhood-onset psychiatric disorder, attention deficit hyperactivity disorder (ADHD) is complicated by phenotypic and genetic heterogeneity. Lifelong executive function deficits in ADHD are described in many literatures and have been proposed as endophenotypes of ADHD. However, its genetic basis is still elusive. In this study, we performed a genome-wide association study of executive function, rated with Behavioral Rating Inventory of Executive Function (BRIEF), in ADHD children. We identified one significant variant (rs852004, P = 2.51e-08) for the overall score of BRIEF. The association analyses for each component of executive function found this locus was more associated with inhibit and monitor components. Further principle component analysis and confirmatory factor analysis provided an ADHD-specific executive function pattern including inhibit and monitor factors. SNP rs852004 was mainly associated with the Behavioral Regulation factor. Meanwhile, we found the significant locus was associated with ADHD symptom. The Behavioral Regulation factor mediated its effect on ADHD symptom. Functional magnetic resonance imaging (fMRI) analyses further showed evidence that this variant affected the activity of inhibition control related brain regions. It provided new insights for the genetic basis of executive function in ADHD.
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27
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Vawter MP, Philibert R, Rollins B, Ruppel PL, Osborn TW. Exon Array Biomarkers for the Differential Diagnosis of Schizophrenia and Bipolar Disorder. MOLECULAR NEUROPSYCHIATRY 2018; 3:197-213. [PMID: 29888231 DOI: 10.1159/000485800] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2017] [Accepted: 11/16/2017] [Indexed: 12/26/2022]
Abstract
This study developed potential blood-based biomarker tests for diagnosing and differentiating schizophrenia (SZ), bipolar disorder type I (BD), and normal control (NC) subjects using mRNA gene expression signatures. A total of 90 subjects (n = 30 each for the three groups of subjects) provided blood samples at two visits. The Affymetrix exon microarray was used to profile the expression of over 1.4 million probesets. We selected potential biomarker panels using the temporal stability of the probesets and also back-tested them at two different visits for each subject. The 18-gene biomarker panels, using logistic regression modeling, correctly differentiated the three groups of subjects with high accuracy across the two different clinical visits (83-88% accuracy). The results are also consistent with the actual data and the "leave-one-out" analyses, indicating that the models should be predictive when applied to independent data cohorts. Many of the SZ and BD subjects were taking antipsychotic and mood stabilizer medications at the time of blood draw, raising the possibility that these drugs could have affected some of the differential transcription signatures. Using an independent Illumina data set of gene expression data from antipsychotic medication-free SZ subjects, the 18-gene biomarker panels produced a receiver operating characteristic curve accuracy greater than 0.866 in patients that were less than 30 years of age and medication free. We confirmed select transcripts by quantitative PCR and the nCounter® System. The episodic nature of psychiatric disorders might lead to highly variable results depending on when blood is collected in relation to the severity of the disease/symptoms. We have found stable trait gene panel markers for lifelong psychiatric disorders that may have diagnostic utility in younger undiagnosed subjects where there is a critical unmet need. The study requires replication in subjects for ultimate proof of the utility of the differential diagnosis.
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Affiliation(s)
- Marquis Philip Vawter
- Functional Genomics Laboratory, Department of Psychiatry, University of California, Irvine, California, USA
| | - Robert Philibert
- Department of Psychiatry, University of Iowa, Iowa City, Iowa, USA
| | - Brandi Rollins
- Functional Genomics Laboratory, Department of Psychiatry, University of California, Irvine, California, USA
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28
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Genetic Polymorphisms in Cytokine Genes in Colombian Patients with Ocular Toxoplasmosis. Infect Immun 2018; 86:IAI.00597-17. [PMID: 29426041 DOI: 10.1128/iai.00597-17] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2017] [Accepted: 01/22/2018] [Indexed: 01/23/2023] Open
Abstract
Toxoplasmosis is caused by infection with the protozoan parasite Toxoplasma gondii, which has the capacity to infect all warm-blooded animals worldwide. Toxoplasmosis is a major cause of visual defects in the Colombian population; however, the association between genetic polymorphisms in cytokine genes and susceptibility to ocular toxoplasmosis has not been studied in this population. This work evaluates the associations between polymorphisms in genes coding for the cytokines tumor necrosis factor alpha (TNF-α) (rs1799964, rs1800629, rs1799724, rs1800630, and rs361525), interleukin 1β (IL-1β) (rs16944, rs1143634, and rs1143627), IL-1α (rs1800587), gamma interferon (IFN-γ) (rs2430561), and IL-10 (rs1800896 and rs1800871) and the presence of ocular toxoplasmosis (OT) in a sample of a Colombian population (61 patients with OT and 116 healthy controls). Genotyping was performed with the "dideoxynucleotide (ddNTP) primer extension" technique. Functional-effect predictions of single nucleotide polymorphisms (SNPs) were done by using FuncPred. A polymorphism in the IL-10 gene promoter (-1082G/A) was significantly more prevalent in OT patients than in controls (P = 1.93e-08; odds ratio [OR] = 5.27e+03; 95% confidence interval [CI] = 3.18 to 8.739; Bonferroni correction [BONF] = 3.48e-07). In contrast, haplotype "AG" of the IL-10 gene promoter polymorphisms (rs1800896 and rs1800871) was present at a lower frequency in OT patients (P = 7e-04; OR = 0.10; 95% CI = 0.03 to 0.35). The +874A/T polymorphism of IFN-γ was associated with OT (P = 3.37e-05; OR = 4.2; 95% CI = 2.478 to 7.12; BONF = 6.07e-04). Haplotype "GAG" of the IL-1β gene promoter polymorphisms (rs1143634, rs1143627, and rs16944) appeared to be significantly associated with OT (P = 0.0494). The IL-10, IFN-γ, and IL-1β polymorphisms influence the development of OT in the Colombian population.
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29
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Visit-to-visit lipid variability: Clinical significance, effects of lipid-lowering treatment, and (pharmaco) genetics. J Clin Lipidol 2018; 12:266-276.e3. [DOI: 10.1016/j.jacl.2018.01.001] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2017] [Revised: 12/30/2017] [Accepted: 01/03/2018] [Indexed: 12/24/2022]
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30
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Zhou D, Zhang D, Sun X, Li Z, Ni Y, Shan Z, Li H, Liu C, Zhang S, Liu Y, Zheng R, Pan F, Zhu Y, Shi Y, Lai M. A novel variant associated with HDL-C levels by modifying DAGLB expression levels: An annotation-based genome-wide association study. Eur J Hum Genet 2018; 26:838-847. [PMID: 29476167 DOI: 10.1038/s41431-018-0108-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2017] [Revised: 12/10/2017] [Accepted: 01/23/2018] [Indexed: 11/09/2022] Open
Abstract
Although numbers of genome-wide association studies (GWAS) have been performed for serum lipid levels, limited heritability has been explained. Studies showed that combining data from GWAS and expression quantitative trait loci (eQTLs) signals can both enhance the discovery of trait-associated SNPs and gain a better understanding of the mechanism. We performed an annotation-based, multistage genome-wide screening for serum-lipid-level-associated loci in totally 6863 Han Chinese. A serum high-density lipoprotein cholesterol (HDL-C) associated variant rs1880118 (hg19 chr7:g. 6435220G>C) was replicated (Pcombined = 1.4E-10). rs1880118 was associated with DAGLB (diacylglycerol lipase, beta) expression levels in subcutaneous adipose tissue (P = 5.9E-42) and explained 47.7% of the expression variance. After the replication, an active segment covering variants tagged by rs1880118 near 5' of DAGLB was annotated using histone modification and transcription factor binding signals. The luciferase report assay revealed that the segment containing the minor alleles showed increased transcriptional activity compared with segment contains the major alleles, which was consistent with the eQTL analyses. The expression-trait association tests indicated the association between the DAGLB and serum HDL-C levels using gene-based approaches called "TWAS" (P = 3.0E-8), "SMR" (P = 1.1E-4), and "Sherlock" (P = 1.6E-6). To summarize, we identified a novel HDL-C-associated variant which explained nearly half of the expression variance of DAGLB. Integrated analyses established a genotype-gene-phenotype three-way association and expanded our knowledge of DAGLB in lipid metabolism.
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Affiliation(s)
- Dan Zhou
- Department of Epidemiology & Biostatistics, Zhejiang University School of Public Health, Hangzhou, Zhejiang, 310058, China.,Department of Pathology, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310058, China.,Key Laboratory of Disease Proteomics of Zhejiang Province, Hangzhou, Zhejiang, 310058, China
| | - Dandan Zhang
- Department of Pathology, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310058, China.,Key Laboratory of Disease Proteomics of Zhejiang Province, Hangzhou, Zhejiang, 310058, China
| | - Xiaohui Sun
- Department of Epidemiology & Biostatistics, Zhejiang University School of Public Health, Hangzhou, Zhejiang, 310058, China
| | - Zhiqiang Li
- The Affiliated Hospital of Qingdao University & The Biomedical Sciences Institute of Qingdao University (Qingdao Branch of SJTU Bio-X Institutes), Qingdao University, Qingdao, 266000, China.,Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education) Collaborative Innovation Center for Brain Science, Shanghai Jiao Tong University, Shanghai, 200030, China
| | - Yaqin Ni
- Department of Epidemiology & Biostatistics, Zhejiang University School of Public Health, Hangzhou, Zhejiang, 310058, China
| | - Zhongyan Shan
- The Endocrine Institute and Liaoning Provincial Key Laboratory of Endocrine Diseases, Department of Endocrinology and Metabolism, The First Hospital of China Medical University, Shenyang, Liaoning, 110001, China
| | - Hong Li
- Department of Endocrinology, Sir Run Run Shaw Hospital Affiliated to School of Medicine, Zhejiang University, Hangzhou, Zhejiang, 310020, China
| | - Chengguo Liu
- Putuo District People's Hospital, Zhoushan, Zhejiang, 316100, China
| | - Shuai Zhang
- Department of Pathology, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310058, China.,Key Laboratory of Disease Proteomics of Zhejiang Province, Hangzhou, Zhejiang, 310058, China
| | - Yi Liu
- Department of Epidemiology & Biostatistics, Zhejiang University School of Public Health, Hangzhou, Zhejiang, 310058, China
| | - Ruizhi Zheng
- Department of Epidemiology & Biostatistics, Zhejiang University School of Public Health, Hangzhou, Zhejiang, 310058, China
| | - Feixia Pan
- Department of Epidemiology & Biostatistics, Zhejiang University School of Public Health, Hangzhou, Zhejiang, 310058, China
| | - Yimin Zhu
- Department of Epidemiology & Biostatistics, Zhejiang University School of Public Health, Hangzhou, Zhejiang, 310058, China.
| | - Yongyong Shi
- The Affiliated Hospital of Qingdao University & The Biomedical Sciences Institute of Qingdao University (Qingdao Branch of SJTU Bio-X Institutes), Qingdao University, Qingdao, 266000, China. .,Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education) Collaborative Innovation Center for Brain Science, Shanghai Jiao Tong University, Shanghai, 200030, China. .,Department of Psychiatry, The First Teaching Hospital of Xinjiang Medical University, Urumqi, 830000, China.
| | - Maode Lai
- Department of Pathology, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310058, China. .,Key Laboratory of Disease Proteomics of Zhejiang Province, Hangzhou, Zhejiang, 310058, China.
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Han J, Li J, Achour I, Pesce L, Foster I, Li H, Lussier YA. Convergent downstream candidate mechanisms of independent intergenic polymorphisms between co-classified diseases implicate epistasis among noncoding elements. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2018; 23:524-535. [PMID: 29218911 PMCID: PMC5730078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Eighty percent of DNA outside protein coding regions was shown biochemically functional by the ENCODE project, enabling studies of their interactions. Studies have since explored how convergent downstream mechanisms arise from independent genetic risks of one complex disease. However, the cross-talk and epistasis between intergenic risks associated with distinct complex diseases have not been comprehensively characterized. Our recent integrative genomic analysis unveiled downstream biological effectors of disease-specific polymorphisms buried in intergenic regions, and we then validated their genetic synergy and antagonism in distinct GWAS. We extend this approach to characterize convergent downstream candidate mechanisms of distinct intergenic SNPs across distinct diseases within the same clinical classification. We construct a multipartite network consisting of 467 diseases organized in 15 classes, 2,358 disease-associated SNPs, 6,301 SNPassociated mRNAs by eQTL, and mRNA annotations to 4,538 Gene Ontology mechanisms. Functional similarity between two SNPs (similar SNP pairs) is imputed using a nested information theoretic distance model for which p-values are assigned by conservative scale-free permutation of network edges without replacement (node degrees constant). At FDR≤5%, we prioritized 3,870 intergenic SNP pairs associated, among which 755 are associated with distinct diseases sharing the same disease class, implicating 167 intergenic SNPs, 14 classes, 230 mRNAs, and 134 GO terms. Co-classified SNP pairs were more likely to be prioritized as compared to those of distinct classes confirming a noncoding genetic underpinning to clinical classification (odds ratio ∼3.8; p≤10-25). The prioritized pairs were also enriched in regions bound to the same/interacting transcription factors and/or interacting in long-range chromatin interactions suggestive of epistasis (odds ratio ∼ 2,500; p≤10-25). This prioritized network implicates complex epistasis between intergenic polymorphisms of co-classified diseases and offers a roadmap for a novel therapeutic paradigm: repositioning medications that target proteins within downstream mechanisms of intergenic disease-associated SNPs. Supplementary information and software: http://lussiergroup.org/publications/disease_class.
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Affiliation(s)
- Jiali Han
- Center for Biomedical Informatics and Biostatistics (CB2) and Departments of Medicine and of Systems and Industrial Engineering, The University of Arizona, Tucson, AZ 85721, USA,
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An overview of posttraumatic stress disorder genetic studies by analyzing and integrating genetic data into genetic database PTSDgene. Neurosci Biobehav Rev 2017; 83:647-656. [DOI: 10.1016/j.neubiorev.2017.08.021] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2016] [Revised: 07/08/2017] [Accepted: 08/30/2017] [Indexed: 01/08/2023]
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Functional mapping and annotation of genetic associations with FUMA. Nat Commun 2017; 8:1826. [PMID: 29184056 PMCID: PMC5705698 DOI: 10.1038/s41467-017-01261-5] [Citation(s) in RCA: 2349] [Impact Index Per Article: 293.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2017] [Accepted: 08/30/2017] [Indexed: 02/06/2023] Open
Abstract
A main challenge in genome-wide association studies (GWAS) is to pinpoint possible causal variants. Results from GWAS typically do not directly translate into causal variants because the majority of hits are in non-coding or intergenic regions, and the presence of linkage disequilibrium leads to effects being statistically spread out across multiple variants. Post-GWAS annotation facilitates the selection of most likely causal variant(s). Multiple resources are available for post-GWAS annotation, yet these can be time consuming and do not provide integrated visual aids for data interpretation. We, therefore, develop FUMA: an integrative web-based platform using information from multiple biological resources to facilitate functional annotation of GWAS results, gene prioritization and interactive visualization. FUMA accommodates positional, expression quantitative trait loci (eQTL) and chromatin interaction mappings, and provides gene-based, pathway and tissue enrichment results. FUMA results directly aid in generating hypotheses that are testable in functional experiments aimed at proving causal relations. Prioritizing genetic variants is a major challenge in genome-wide association studies. Here, the authors develop FUMA, a web-based bioinformatics tool that uses a combination of positional, eQTL and chromatin interaction mapping to prioritize likely causal variants and genes.
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Sasayama D, Hattori K, Ogawa S, Yokota Y, Matsumura R, Teraishi T, Hori H, Ota M, Yoshida S, Kunugi H. Genome-wide quantitative trait loci mapping of the human cerebrospinal fluid proteome. Hum Mol Genet 2017; 26:44-51. [PMID: 28031287 DOI: 10.1093/hmg/ddw366] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2016] [Accepted: 10/21/2016] [Indexed: 11/12/2022] Open
Abstract
Cerebrospinal fluid (CSF) is virtually the only one accessible source of proteins derived from the central nervous system (CNS) of living humans and possibly reflects the pathophysiology of a variety of neuropsychiatric diseases. However, little is known regarding the genetic basis of variation in protein levels of human CSF. We examined CSF levels of 1,126 proteins in 133 subjects and performed a genome-wide association analysis of 514,227 single nucleotide polymorphisms (SNPs) to detect protein quantitative trait loci (pQTLs). To be conservative, Spearman's correlation was used to identify an association between genotypes of SNPs and protein levels. A total of 421 cis and 25 trans SNP-protein pairs were significantly correlated at a false discovery rate (FDR) of less than 0.01 (nominal P < 7.66 × 10-9). Cis-only analysis revealed additional 580 SNP-protein pairs with FDR < 0.01 (nominal P < 2.13 × 10-5). pQTL SNPs were more likely, compared to non-pQTL SNPs, to be a disease/trait-associated variants identified by previous genome-wide association studies. The present findings suggest that genetic variations play an important role in the regulation of protein expression in the CNS. The obtained database may serve as a valuable resource to understand the genetic bases for CNS protein expression pattern in humans.
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Affiliation(s)
- Daimei Sasayama
- Department of Mental Disorder Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Ogawahigashi, Kodaira, Tokyo, Japan.,Department of Psychiatry, Shinshu University School of Medicine, Matsumoto, Nagano, Japan
| | - Kotaro Hattori
- Department of Mental Disorder Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Ogawahigashi, Kodaira, Tokyo, Japan.,Translational Medical Center, National Center of Neurology and Psychiatry, Kodaira, Tokyo, Japan
| | - Shintaro Ogawa
- Department of Mental Disorder Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Ogawahigashi, Kodaira, Tokyo, Japan
| | - Yuuki Yokota
- Department of Mental Disorder Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Ogawahigashi, Kodaira, Tokyo, Japan.,Translational Medical Center, National Center of Neurology and Psychiatry, Kodaira, Tokyo, Japan
| | - Ryo Matsumura
- Department of Mental Disorder Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Ogawahigashi, Kodaira, Tokyo, Japan.,Translational Medical Center, National Center of Neurology and Psychiatry, Kodaira, Tokyo, Japan
| | - Toshiya Teraishi
- Department of Mental Disorder Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Ogawahigashi, Kodaira, Tokyo, Japan
| | - Hiroaki Hori
- Department of Mental Disorder Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Ogawahigashi, Kodaira, Tokyo, Japan
| | - Miho Ota
- Department of Mental Disorder Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Ogawahigashi, Kodaira, Tokyo, Japan
| | - Sumiko Yoshida
- Department of Psychiatry, National Center Hospital, National Center of Neurology and Psychiatry, Kodaira, Tokyo, Japan
| | - Hiroshi Kunugi
- Department of Mental Disorder Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Ogawahigashi, Kodaira, Tokyo, Japan
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Chatterjee P, Roy D, Bhattacharyya M, Bandyopadhyay S. Biological networks in Parkinson's disease: an insight into the epigenetic mechanisms associated with this disease. BMC Genomics 2017; 18:721. [PMID: 28899360 PMCID: PMC5596942 DOI: 10.1186/s12864-017-4098-3] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2016] [Accepted: 08/30/2017] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND Parkinson's disease (PD) is the second most prevalent neurodegenerative disorders in the world. Studying PD from systems biology perspective involving genes and their regulators might provide deeper insights into the complex molecular interactions associated with this disease. RESULT We have studied gene co-expression network obtained from a PD-specific microarray data. The co-expression network identified 11 hub genes, of which eight genes are not previously known to be associated with PD. Further study on the functionality of these eight novel hub genes revealed that these genes play important roles in several neurodegenerative diseases. Furthermore, we have studied the tissue-specific expression and histone modification patterns of the novel hub genes. Most of these genes possess several histone modification sites those are already known to be associated with neurodegenerative diseases. Regulatory network namely mTF-miRNA-gene-gTF involves microRNA Transcription Factor (mTF), microRNA (miRNA), gene and gene Transcription Factor (gTF). Whereas long noncoding RNA (lncRNA) mediated regulatory network involves miRNA, gene, mTF and lncRNA. mTF-miRNA-gene-gTF regulatory network identified a novel feed-forward loop. lncRNA-mediated regulatory network identified novel lncRNAs of PD and revealed the two-way regulatory pattern of PD-specific miRNAs where miRNAs can be regulated by both the TFs and lncRNAs. SNP analysis of the most significant genes of the co-expression network identified 20 SNPs. These SNPs are present in the 3' UTR of known PD genes and are controlled by those miRNAs which are also involved in PD. CONCLUSION Our study identified eight novel hub genes which can be considered as possible candidates for future biomarker identification studies for PD. The two regulatory networks studied in our work provide a detailed overview of the cellular regulatory mechanisms where the non-coding RNAs namely miRNA and lncRNA, can act as epigenetic regulators of PD. SNPs identified in our study can be helpful for identifying PD at an earlier stage. Overall, this study may impart a better comprehension of the complex molecular interactions associated with PD from systems biology perspective.
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Affiliation(s)
- Paulami Chatterjee
- Department of Biophysics, Bose Institute, Acharya J.C. Bose Centenary Building, P-1/12 C.I.T. Scheme VII M, Kolkata, 700054 India
| | - Debjani Roy
- Department of Biophysics, Bose Institute, Acharya J.C. Bose Centenary Building, P-1/12 C.I.T. Scheme VII M, Kolkata, 700054 India
| | - Malay Bhattacharyya
- Department of Information Technology, Indian Institute of Engineering Science and Technology, Shibpur, Botanic Garden, Howrah, PO 711103 India
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Liu Z, Lin X. Multiple phenotype association tests using summary statistics in genome-wide association studies. Biometrics 2017; 74:165-175. [PMID: 28653391 DOI: 10.1111/biom.12735] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2016] [Revised: 05/01/2017] [Accepted: 05/01/2017] [Indexed: 12/13/2022]
Abstract
We study in this article jointly testing the associations of a genetic variant with correlated multiple phenotypes using the summary statistics of individual phenotype analysis from Genome-Wide Association Studies (GWASs). We estimated the between-phenotype correlation matrix using the summary statistics of individual phenotype GWAS analyses, and developed genetic association tests for multiple phenotypes by accounting for between-phenotype correlation without the need to access individual-level data. Since genetic variants often affect multiple phenotypes differently across the genome and the between-phenotype correlation can be arbitrary, we proposed robust and powerful multiple phenotype testing procedures by jointly testing a common mean and a variance component in linear mixed models for summary statistics. We computed the p-values of the proposed tests analytically. This computational advantage makes our methods practically appealing in large-scale GWASs. We performed simulation studies to show that the proposed tests maintained correct type I error rates, and to compare their powers in various settings with the existing methods. We applied the proposed tests to a GWAS Global Lipids Genetics Consortium summary statistics data set and identified additional genetic variants that were missed by the original single-trait analysis.
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Affiliation(s)
- Zhonghua Liu
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston 02115, U.S.A
| | - Xihong Lin
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston 02115, U.S.A
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39
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Ju JH, Shenoy SA, Crystal RG, Mezey JG. An independent component analysis confounding factor correction framework for identifying broad impact expression quantitative trait loci. PLoS Comput Biol 2017; 13:e1005537. [PMID: 28505156 PMCID: PMC5448815 DOI: 10.1371/journal.pcbi.1005537] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2016] [Revised: 05/30/2017] [Accepted: 04/28/2017] [Indexed: 11/19/2022] Open
Abstract
Genome-wide expression Quantitative Trait Loci (eQTL) studies in humans have provided numerous insights into the genetics of both gene expression and complex diseases. While the majority of eQTL identified in genome-wide analyses impact a single gene, eQTL that impact many genes are particularly valuable for network modeling and disease analysis. To enable the identification of such broad impact eQTL, we introduce CONFETI: Confounding Factor Estimation Through Independent component analysis. CONFETI is designed to address two conflicting issues when searching for broad impact eQTL: the need to account for non-genetic confounding factors that can lower the power of the analysis or produce broad impact eQTL false positives, and the tendency of methods that account for confounding factors to model broad impact eQTL as non-genetic variation. The key advance of the CONFETI framework is the use of Independent Component Analysis (ICA) to identify variation likely caused by broad impact eQTL when constructing the sample covariance matrix used for the random effect in a mixed model. We show that CONFETI has better performance than other mixed model confounding factor methods when considering broad impact eQTL recovery from synthetic data. We also used the CONFETI framework and these same confounding factor methods to identify eQTL that replicate between matched twin pair datasets in the Multiple Tissue Human Expression Resource (MuTHER), the Depression Genes Networks study (DGN), the Netherlands Study of Depression and Anxiety (NESDA), and multiple tissue types in the Genotype-Tissue Expression (GTEx) consortium. These analyses identified both cis-eQTL and trans-eQTL impacting individual genes, and CONFETI had better or comparable performance to other mixed model confounding factor analysis methods when identifying such eQTL. In these analyses, we were able to identify and replicate a few broad impact eQTL although the overall number was small even when applying CONFETI. In light of these results, we discuss the broad impact eQTL that have been previously reported from the analysis of human data and suggest that considerable caution should be exercised when making biological inferences based on these reported eQTL.
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Affiliation(s)
- Jin Hyun Ju
- Department of Genetic Medicine, Weill Cornell Medical College, New York, NY, United States of America
- Institute for Computational Biomedicine, Weill Cornell Medical College, New York, NY, United States of America
| | - Sushila A. Shenoy
- Department of Genetic Medicine, Weill Cornell Medical College, New York, NY, United States of America
| | - Ronald G. Crystal
- Department of Genetic Medicine, Weill Cornell Medical College, New York, NY, United States of America
| | - Jason G. Mezey
- Department of Genetic Medicine, Weill Cornell Medical College, New York, NY, United States of America
- Institute for Computational Biomedicine, Weill Cornell Medical College, New York, NY, United States of America
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY, United States of America
- * E-mail:
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40
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Kim SK, Roos TR, Roos AK, Kleimeyer JP, Ahmed MA, Goodlin GT, Fredericson M, Ioannidis JPA, Avins AL, Dragoo JL. Genome-wide association screens for Achilles tendon and ACL tears and tendinopathy. PLoS One 2017; 12:e0170422. [PMID: 28358823 PMCID: PMC5373512 DOI: 10.1371/journal.pone.0170422] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2016] [Accepted: 01/04/2017] [Indexed: 11/18/2022] Open
Abstract
Achilles tendinopathy or rupture and anterior cruciate ligament (ACL) rupture are substantial injuries affecting athletes, associated with delayed recovery or inability to return to competition. To identify genetic markers that might be used to predict risk for these injuries, we performed genome-wide association screens for these injuries using data from the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort consisting of 102,979 individuals. We did not find any single nucleotide polymorphisms (SNPs) associated with either of these injuries with a p-value that was genome-wide significant (p<5x10-8). We found, however, four and three polymorphisms with p-values that were borderline significant (p<10-6) for Achilles tendon injury and ACL rupture, respectively. We then tested SNPs previously reported to be associated with either Achilles tendon injury or ACL rupture. None showed an association in our cohort with a false discovery rate of less than 5%. We obtained, however, moderate to weak evidence for replication in one case; specifically, rs4919510 in MIR608 had a p-value of 5.1x10-3 for association with Achilles tendon injury, corresponding to a 7% chance of false replication. Finally, we tested 2855 SNPs in 90 candidate genes for musculoskeletal injury, but did not find any that showed a significant association below a false discovery rate of 5%. We provide data containing summary statistics for the entire genome, which will be useful for future genetic studies on these injuries.
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Affiliation(s)
- Stuart K. Kim
- Department Developmental Biology, Stanford University Medical Center, Stanford CA, United States of America
| | - Thomas R. Roos
- Department Developmental Biology, Stanford University Medical Center, Stanford CA, United States of America
- Department Health Research and Policy, Division of Epidemiology, Stanford University Medical Center, Stanford CA, United States of America
| | - Andrew K. Roos
- Department Developmental Biology, Stanford University Medical Center, Stanford CA, United States of America
- Department Health Research and Policy, Division of Epidemiology, Stanford University Medical Center, Stanford CA, United States of America
| | - John P. Kleimeyer
- Department Orthopaedic Surgery, Stanford University Medical Center, Stanford CA, United States of America
| | - Marwa A. Ahmed
- Department Orthopaedic Surgery, Stanford University Medical Center, Stanford CA, United States of America
| | - Gabrielle T. Goodlin
- College of Medicine, California Northstate University, Elk Grove CA, United States of America
| | - Michael Fredericson
- Department Orthopaedic Surgery, Stanford University Medical Center, Stanford CA, United States of America
| | - John P. A. Ioannidis
- Department of Medicine, Stanford Prevention Research Center, Stanford University School of Medicine, Stanford CA, United States of America
- Department of Health Research and Policy, Division of Epidemiology, Stanford University School of Medicine, Stanford CA, United States of America
- Department of Statistics, Stanford University School of Humanities and Sciences, Stanford CA, United States of America
| | - Andrew L. Avins
- Kaiser Permanente Northern California, Division of Research, Oakland, California, United States of America
| | - Jason L. Dragoo
- Department Orthopaedic Surgery, Stanford University Medical Center, Stanford CA, United States of America
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Suzuki J. A novel Chow–Liu algorithm and its application to gene differential analysis. Int J Approx Reason 2017. [DOI: 10.1016/j.ijar.2016.08.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Vathipadiekal V, Farrell JJ, Wang S, Edward HL, Shappell H, Al-Rubaish A, Al-Muhanna F, Naserullah Z, Alsuliman A, Qutub HO, Simkin I, Farrer LA, Jiang Z, Luo HY, Huang S, Mostoslavsky G, Murphy GJ, Patra PK, Chui DH, Alsultan A, Al-Ali AK, Sebastiani P, Steinberg MH. A candidate transacting modulator of fetal hemoglobin gene expression in the Arab-Indian haplotype of sickle cell anemia. Am J Hematol 2016; 91:1118-1122. [PMID: 27501013 DOI: 10.1002/ajh.24527] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2016] [Revised: 08/02/2016] [Accepted: 08/03/2016] [Indexed: 12/30/2022]
Abstract
Fetal hemoglobin (HbF) levels are higher in the Arab-Indian (AI) β-globin gene haplotype of sickle cell anemia compared with African-origin haplotypes. To study genetic elements that effect HbF expression in the AI haplotype we completed whole genome sequencing in 14 Saudi AI haplotype sickle hemoglobin homozygotes-seven selected for low HbF (8.2% ± 1.3%) and seven selected for high HbF (23.5% ± 2.6%). An intronic single nucleotide polymorphism (SNP) in ANTXR1, an anthrax toxin receptor (chromosome 2p13), was associated with HbF. These results were replicated in two independent Saudi AI haplotype cohorts of 120 and 139 patients, but not in 76 Saudi Benin haplotype, 894 African origin haplotype and 44 AI haplotype patients of Indian origin, suggesting that this association is effective only in the Saudi AI haplotype background. ANTXR1 variants explained 10% of the HbF variability compared with 8% for BCL11A. These two genes had independent, additive effects on HbF and together explained about 15% of HbF variability in Saudi AI sickle cell anemia patients. ANTXR1 was expressed at mRNA and protein levels in erythroid progenitors derived from induced pluripotent stem cells (iPSCs) and CD34+ cells. As CD34+ cells matured and their HbF decreased ANTXR1 expression increased; as iPSCs differentiated and their HbF increased, ANTXR1 expression decreased. Along with elements in cis to the HbF genes, ANTXR1 contributes to the variation in HbF in Saudi AI haplotype sickle cell anemia and is the first gene in trans to HBB that is associated with HbF only in carriers of the Saudi AI haplotype. Am. J. Hematol. 91:1118-1122, 2016. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Vinod Vathipadiekal
- Department of Medicine; Boston University School of Medicine; Boston Massachusetts
| | - John J. Farrell
- Department of Medicine; Boston University School of Medicine; Boston Massachusetts
| | - Shuai Wang
- Department of Biostatistics; Boston University School of Public Health; Boston Massachusetts
| | - Heather L. Edward
- Department of Medicine; Boston University School of Medicine; Boston Massachusetts
| | - Heather Shappell
- Department of Biostatistics; Boston University School of Public Health; Boston Massachusetts
| | - A.M. Al-Rubaish
- Department of Internal Medicine; College of Medicine, University of Dammam; Dammam Kingdom of Saudi Arabia
| | - Fahad Al-Muhanna
- Department of Internal Medicine; College of Medicine, University of Dammam; Dammam Kingdom of Saudi Arabia
| | - Z. Naserullah
- Al-Omran Scientific Chair for Hematological Diseases; King Faisal University; Al-Ahsa Kingdom of Saudi Arabia
- Department of Pediatrics; Maternity and Child Hospital; Dammam Kingdom of Saudi Arabia
| | - A. Alsuliman
- Alomran Scientific Chair; King Faisal University, King Fahd Hospital; Hafof Al-Ahsa Kingdom of Saudi Arabia
| | - Hatem Othman Qutub
- Alomran Scientific Chair; King Faisal University; Al-Ahsa Kingdom of Saudi Arabia
| | - Irene Simkin
- Department of Medicine; Boston University School of Medicine; Boston Massachusetts
| | - Lindsay A. Farrer
- Department of Medicine; Boston University School of Medicine; Boston Massachusetts
| | - Zhihua Jiang
- Department of Medicine; Boston University School of Medicine; Boston Massachusetts
| | - Hong-Yuan Luo
- Department of Medicine; Boston University School of Medicine; Boston Massachusetts
| | - Shengwen Huang
- Department of Medicine; Boston University School of Medicine; Boston Massachusetts
| | - Gustavo Mostoslavsky
- Department of Medicine; Boston University School of Medicine; Boston Massachusetts
| | - George J. Murphy
- Department of Medicine; Boston University School of Medicine; Boston Massachusetts
| | - Pradeep K. Patra
- Department of Biochemistry; Pt. J. N. M. Medical College; Raipur Chattisgarh India
| | - David H.K. Chui
- Department of Medicine; Boston University School of Medicine; Boston Massachusetts
| | - Abdulrahman Alsultan
- Sickle Cell Disease Research Center and Department of Pediatrics; College of Medicine, King Saud University; Riyadh Saudi Arabia
| | - Amein K. Al-Ali
- Center for Research and Medical Consultation; University of Dammam; Dammam Kingdom of Saudi Arabia
| | - Paola Sebastiani
- Department of Biostatistics; Boston University School of Public Health; Boston Massachusetts
| | - Martin H. Steinberg
- Department of Medicine; Boston University School of Medicine; Boston Massachusetts
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Wang J, Qu S, Wang W, Guo L, Zhang K, Chang S, Wang J. A combined analysis of genome-wide expression profiling of bipolar disorder in human prefrontal cortex. J Psychiatr Res 2016; 82:23-9. [PMID: 27459029 DOI: 10.1016/j.jpsychires.2016.07.016] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/30/2016] [Revised: 07/12/2016] [Accepted: 07/15/2016] [Indexed: 01/29/2023]
Abstract
Numbers of gene expression profiling studies of bipolar disorder have been published. Besides different array chips and tissues, variety of the data processes in different cohorts aggravated the inconsistency of results of these genome-wide gene expression profiling studies. By searching the gene expression databases, we obtained six data sets for prefrontal cortex (PFC) of bipolar disorder with raw data and combinable platforms. We used standardized pre-processing and quality control procedures to analyze each data set separately and then combined them into a large gene expression matrix with 101 bipolar disorder subjects and 106 controls. A standard linear mixed-effects model was used to calculate the differentially expressed genes (DEGs). Multiple levels of sensitivity analyses and cross validation with genetic data were conducted. Functional and network analyses were carried out on basis of the DEGs. In the result, we identified 198 unique differentially expressed genes in the PFC of bipolar disorder and control. Among them, 115 DEGs were robust to at least three leave-one-out tests or different pre-processing methods; 51 DEGs were validated with genetic association signals. Pathway enrichment analysis showed these DEGs were related with regulation of neurological system, cell death and apoptosis, and several basic binding processes. Protein-protein interaction network further identified one key hub gene. We have contributed the most comprehensive integrated analysis of bipolar disorder expression profiling studies in PFC to date. The DEGs, especially those with multiple validations, may denote a common signature of bipolar disorder and contribute to the pathogenesis of disease.
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Affiliation(s)
- Jinglu Wang
- The Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China
| | - Susu Qu
- The Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China
| | - Weixiao Wang
- The Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China
| | - Liyuan Guo
- The Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Kunlin Zhang
- The Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Suhua Chang
- The Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.
| | - Jing Wang
- The Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.
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Singh SK, Lupo PJ, Scheurer ME, Saxena A, Kennedy AE, Ibrahimou B, Barbieri MA, Mills KI, McCauley JL, Okcu MF, Dorak MT. A childhood acute lymphoblastic leukemia genome-wide association study identifies novel sex-specific risk variants. Medicine (Baltimore) 2016; 95:e5300. [PMID: 27861356 PMCID: PMC5120913 DOI: 10.1097/md.0000000000005300] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Childhood acute lymphoblastic leukemia (ALL) occurs more frequently in males. Reasons behind sex differences in childhood ALL risk are unknown. In the present genome-wide association study (GWAS), we explored the genetic basis of sex differences by comparing genotype frequencies between male and female cases in a case-only study to assess effect-modification by sex.The case-only design included 236 incident cases of childhood ALL consecutively recruited at the Texas Children's Cancer Center in Houston, Texas from 2007 to 2012. All cases were non-Hispanic whites, aged 1 to 10 years, and diagnosed with confirmed B-cell precursor ALL. Genotyping was performed using the Illumina HumanCoreExome BeadChip on the Illumina Infinium platform. Besides the top 100 statistically most significant results, results were also analyzed by the top 100 highest effect size with a nominal statistical significance (P <0.05).The statistically most significant sex-specific association (P = 4 × 10) was with the single nucleotide polymorphism (SNP) rs4813720 (RASSF2), an expression quantitative trait locus (eQTL) for RASSF2 in peripheral blood. rs4813720 is also a strong methylation QTL (meQTL) for a CpG site (cg22485289) within RASSF2 in pregnancy, at birth, childhood, and adolescence. cg22485289 is one of the hypomethylated CpG sites in ALL compared with pre-B cells. Two missense SNPs, rs12722042 and 12722039, in the HLA-DQA1 gene yielded the highest effect sizes (odds ratio [OR] ∼ 14; P <0.01) for sex-specific results. The HLA-DQA1 SNPs belong to DQA1*01 and confirmed the previously reported male-specific association with DQA1*01. This finding supports the proposed infection-related etiology in childhood ALL risk for males. Further analyses revealed that most SNPs (either direct effect or through linkage disequilibrium) were within active enhancers or active promoter regions and had regulatory effects on gene expression levels.Cumulative data suggested that RASSF2 rs4813720, which correlates with increased RASSF2 expression, may counteract the suppressor effect of estrogen-regulated miR-17-92 on RASSF2 resulting in protection in males. Given the amount of sex hormone-related mechanisms suggested by our findings, future studies should examine prenatal or early postnatal programming by sex hormones when hormone levels show a large variation.
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Affiliation(s)
- Sandeep K. Singh
- Department of Environmental and Occupational Health, Robert Stempel College of Public Health and Social Work, Florida International University, Miami, FL
- Department of Biological Sciences, Florida International University, Miami, FL
| | - Philip J. Lupo
- Department of Pediatrics, Section of Hematology-Oncology, Texas Children's Cancer Center
| | - Michael E. Scheurer
- Department of Pediatrics, Section of Hematology-Oncology, Baylor College of Medicine, Houston, TX
| | - Anshul Saxena
- Department of Health Promotion and Disease Prevention, Robert Stempel College of Public Health and Social Work, Florida International University, Miami, FL
| | - Amy E. Kennedy
- Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, MD
| | - Boubakari Ibrahimou
- Department of Biostatistics, Robert Stempel College of Public Health and Social Work, Florida International University, Miami, FL
| | | | - Ken I. Mills
- Centre for Cancer Research and Cell Biology (CCRCB), Queen's University Belfast, Belfast, UK
| | - Jacob L. McCauley
- Dr. John T. Macdonald Foundation, Department of Human Genetics, John P. Hussman Institute for Human Genomics, Biorepository Facility, Center for Genome Technology University of Miami, Miller School of Medicine
| | - Mehmet Fatih Okcu
- Department of Pediatrics, Section of Hematology-Oncology, Baylor College of Medicine, Houston, TX
| | - Mehmet Tevfik Dorak
- Department of Epidemiology, Robert Stempel College of Public Health and Social Work, Florida International University, Miami, FL
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Zhou D, Li Z, Yu D, Wan L, Zhu Y, Lai M, Zhang D. Polymorphisms involving gain or loss of CpG sites are significantly enriched in trait-associated SNPs. Oncotarget 2016; 6:39995-40004. [PMID: 26503467 PMCID: PMC4741875 DOI: 10.18632/oncotarget.5650] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2015] [Accepted: 10/02/2015] [Indexed: 12/30/2022] Open
Abstract
Some single nucleotide polymorphisms (SNPs) influence the existence of CpG sites, the basis of DNA modification such as methylation and hydroxymethylation. These polymorphisms can lead to gain or loss of CpG sites and were defined as CpG site related SNPs (cgSNPs) in this study. The cgSNPs change DNA sequence and might potentially affect DNA modification such as methylation. However, the functional consequence of cgSNPs is poorly understood. We observed that a considerable proportion (23.0%) of common variants were cgSNPs in human genome. Mutations involving loss of CpG sites were associated with reduced levels of methylation (~20.2%) using The Cancer Genome Atlas (TCGA) data. Using public databases (SCAN and seeQTL) of expression quantitative trait loci (eQTLs), we found that the cgSNPs were significantly enriched in eQTLs via logistic regression and simulation test. Furthermore, we observed that cgSNPs were more likely to be trait-associated loci especially cancers using a catalog of published genome-wide association studies (GWAS) recorded by National Human Genome Research Institute (NHGRI). Our results indicated that cgSNP might be meaningful as annotation either in SNP functional prediction or in screening for trait-associated SNPs.
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Affiliation(s)
- Dan Zhou
- Department of Pathology, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310058, China.,Key Laboratory of Disease Proteomics of Zhejiang Province, Hangzhou, Zhejiang, 310058, China.,Department of Epidemiology & Biostatistics, Zhejiang University School of Public Health, Hangzhou, Zhejiang, 310058, China
| | - Zhenli Li
- Department of Pathology, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310058, China.,Key Laboratory of Disease Proteomics of Zhejiang Province, Hangzhou, Zhejiang, 310058, China
| | - Dan Yu
- Department of Pathology, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310058, China.,Key Laboratory of Disease Proteomics of Zhejiang Province, Hangzhou, Zhejiang, 310058, China
| | - Ledong Wan
- Department of Pathology, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310058, China.,Key Laboratory of Disease Proteomics of Zhejiang Province, Hangzhou, Zhejiang, 310058, China
| | - Yimin Zhu
- Department of Epidemiology & Biostatistics, Zhejiang University School of Public Health, Hangzhou, Zhejiang, 310058, China
| | - Maode Lai
- Department of Pathology, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310058, China.,Key Laboratory of Disease Proteomics of Zhejiang Province, Hangzhou, Zhejiang, 310058, China
| | - Dandan Zhang
- Department of Pathology, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310058, China.,Key Laboratory of Disease Proteomics of Zhejiang Province, Hangzhou, Zhejiang, 310058, China
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Hill-Burns EM, Ross OA, Wissemann WT, Soto-Ortolaza AI, Zareparsi S, Siuda J, Lynch T, Wszolek ZK, Silburn PA, Mellick GD, Ritz B, Scherzer CR, Zabetian CP, Factor SA, Breheny PJ, Payami H. Identification of genetic modifiers of age-at-onset for familial Parkinson's disease. Hum Mol Genet 2016; 25:3849-3862. [PMID: 27402877 PMCID: PMC5216611 DOI: 10.1093/hmg/ddw206] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2016] [Revised: 06/15/2016] [Accepted: 06/23/2016] [Indexed: 01/27/2023] Open
Abstract
Parkinson's disease (PD) is the most common cause of neurodegenerative movement disorder and the second most common cause of dementia. Genes are thought to have a stronger effect on age-at-onset of PD than on risk, yet there has been a phenomenal success in identifying risk loci but not age-at-onset modifiers. We conducted a genome-wide study for age-at-onset. We analysed familial and non-familial PD separately, per prior evidence for strong genetic effect on age-at-onset in familial PD. GWAS was conducted in 431 unrelated PD individuals with at least one affected relative (familial PD) and 1544 non-familial PD from the NeuroGenetics Research Consortium (NGRC); an additional 737 familial PD and 2363 non-familial PD were used for replication. In familial PD, two signals were detected and replicated robustly: one mapped to LHFPL2 on 5q14.1 (PNGRC = 3E-8, PReplication = 2E-5, PNGRC + Replication = 1E-11), the second mapped to TPM1 on 15q22.2 (PNGRC = 8E-9, PReplication = 2E-4, PNGRC + Replication = 9E-11). The variants that were associated with accelerated onset had low frequencies (<0.02). The LHFPL2 variant was associated with earlier onset by 12.33 [95% CI: 6.2; 18.45] years in NGRC, 8.03 [2.95; 13.11] years in replication, and 9.79 [5.88; 13.70] years in the combined data. The TPM1 variant was associated with earlier onset by 15.30 [8.10; 22.49] years in NGRC, 9.29 [1.79; 16.79] years in replication, and 12.42 [7.23; 17.61] years in the combined data. Neither LHFPL2 nor TPM1 was associated with age-at-onset in non-familial PD. LHFPL2 (function unknown) is overexpressed in brain tumours. TPM1 encodes a highly conserved protein that regulates muscle contraction, and is a tumour-suppressor gene.
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Affiliation(s)
- Erin M Hill-Burns
- Department of Neurology, University of Alabama at Birmingham, AL, USA
| | - Owen A Ross
- Department of Neuroscience, Mayo Clinic Jacksonville, FL, USA
| | | | | | - Sepideh Zareparsi
- Department of Molecular and Medical Genetics, Oregon Health & Sciences University, Portland, OR, USA
| | - Joanna Siuda
- Department of Neurology, Medical University of Silesia, Katowice, Poland
| | - Timothy Lynch
- Dublin Neurological Institute at the Mater Misericordiae University Hospital, Conway Institute of Biomolecular & Biomedical Research, University College Dublin, Ireland
| | | | - Peter A Silburn
- Eskitis Institute for Drug Discovery, Griffith University, Queensland, Australia
| | - George D Mellick
- Eskitis Institute for Drug Discovery, Griffith University, Queensland, Australia
| | - Beate Ritz
- Department of Epidemiology, Fielding School of Public Health and Neurology, Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Clemens R Scherzer
- The Neurogenomics Laboratory, Harvard Medical School and Brigham & Women's Hospital, Cambridge, MA, USA
| | - Cyrus P Zabetian
- VA Puget Sound Health Care System and Department of Neurology, University of Washington, Seattle, WA, USA
| | - Stewart A Factor
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
| | | | - Haydeh Payami
- Department of Neurology, University of Alabama at Birmingham, AL, USA
- Center for Genomic Medicine, HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA
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Phenome-Wide Association Study to Explore Relationships between Immune System Related Genetic Loci and Complex Traits and Diseases. PLoS One 2016; 11:e0160573. [PMID: 27508393 PMCID: PMC4980020 DOI: 10.1371/journal.pone.0160573] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2016] [Accepted: 07/16/2016] [Indexed: 12/21/2022] Open
Abstract
We performed a Phenome-Wide Association Study (PheWAS) to identify interrelationships between the immune system genetic architecture and a wide array of phenotypes from two de-identified electronic health record (EHR) biorepositories. We selected variants within genes encoding critical factors in the immune system and variants with known associations with autoimmunity. To define case/control status for EHR diagnoses, we used International Classification of Diseases, Ninth Revision (ICD-9) diagnosis codes from 3,024 Geisinger Clinic MyCode® subjects (470 diagnoses) and 2,899 Vanderbilt University Medical Center BioVU biorepository subjects (380 diagnoses). A pooled-analysis was also carried out for the replicating results of the two data sets. We identified new associations with potential biological relevance including SNPs in tumor necrosis factor (TNF) and ankyrin-related genes associated with acute and chronic sinusitis and acute respiratory tract infection. The two most significant associations identified were for the C6orf10 SNP rs6910071 and “rheumatoid arthritis” (ICD-9 code category 714) (pMETAL = 2.58 x 10−9) and the ATN1 SNP rs2239167 and “diabetes mellitus, type 2” (ICD-9 code category 250) (pMETAL = 6.39 x 10−9). This study highlights the utility of using PheWAS in conjunction with EHRs to discover new genotypic-phenotypic associations for immune-system related genetic loci.
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Abstract
There are thousands of known associations between genetic variants and complex human phenotypes, and the rate of novel discoveries is rapidly increasing. Translating those associations into knowledge of disease mechanisms remains a fundamental challenge because the associated variants are overwhelmingly in noncoding regions of the genome where we have few guiding principles to predict their function. Intersecting the compendium of identified genetic associations with maps of regulatory activity across the human genome has revealed that phenotype-associated variants are highly enriched in candidate regulatory elements. Allele-specific analyses of gene regulation can further prioritize variants that likely have a functional effect on disease mechanisms; and emerging high-throughput assays to quantify the activity of candidate regulatory elements are a promising next step in that direction. Together, these technologies have created the ability to systematically and empirically test hypotheses about the function of noncoding variants and haplotypes at the scale needed for comprehensive and systematic follow-up of genetic association studies. Major coordinated efforts to quantify regulatory mechanisms across genetically diverse populations in increasingly realistic cell models would be highly beneficial to realize that potential.
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Affiliation(s)
- William L Lowe
- Division of Endocrinology, Metabolism and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois 60611, USA
| | - Timothy E Reddy
- Department of Biostatistics and Bioinformatics, Duke University Medical School, Durham, North Carolina 27708, USA; Center for Genomic and Computational Biology, Duke University Medical School, Durham, North Carolina 27708, USA
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Francis SM, Kistner-Griffin E, Yan Z, Guter S, Cook EH, Jacob S. Variants in Adjacent Oxytocin/Vasopressin Gene Region and Associations with ASD Diagnosis and Other Autism Related Endophenotypes. Front Neurosci 2016; 10:195. [PMID: 27242401 PMCID: PMC4863894 DOI: 10.3389/fnins.2016.00195] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2016] [Accepted: 04/20/2016] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND There has been increasing interest in oxytocin (peptide: OT, gene: OXT) as a treatment pathway for neurodevelopmental disorders such as Autism Spectrum Disorder (ASD). Neurodevelopmental disorders affect functional, social, and intellectual abilities. With advances in molecular biology, research has connected multiple gene regions to the clinical presentation of ASD. Studies have also shown that the neuropeptide hormones OT and arginine vasopressin (AVP) influence mammalian social and territorial behaviors and may have treatment potential for neurodevelopmental disorders. Published data examining molecular and phenotypic variation in ASD, such as cognitive abilities, are limited. Since most studies have focused on the receptors in the OT-AVP system, we investigated genetic variation within peptide genes for association with phenotypic ASD features that help identify subgroups within the spectrum. METHODS In this study, TDT analysis was carried out utilizing FBAT in 207 probands (156 trios) and a European Ancestry (EA) subsample (108 trios).The evolutionarily related and adjacent genes of OXT and AVP were studied for associations between the tagged single nucleotide polymorphisms and ASD diagnosis, social abilities, restrictive and repetitive behaviors, and IQ for cognitive abilities. Additionally, relationships with whole blood serotonin (WB5HT) were explored because of the developmental relationships connecting plasma levels of OT and WB5HT within ASD. RESULTS RESULTS indicate significant association between OXT rs6084258 (p = 0.001) and ASD. Associations with several endophenotypes were also noted: OXT rs6133010 was associated with IQ (full scale IQ, p = 0.008; nonverbal IQ, p = 0.010, verbal IQ, p = 0.006); and OXT rs4813625 and OXT rs877172 were associated with WB5HT levels (EA, p = 0.027 and p = 0.033, respectively). Additionally, we measured plasma OT (pOT) levels in a subsample (N = 54). RESULTS show the three polymorphisms, OXT rs6084258, OXT rs11697250, and OXT rs877172, have significant association with pOT (EA, p = 0.011, p = 0.010, and p = 0.002, respectively). CONCLUSIONS These findings suggest that SNPs near OXT and AVP are associated with diagnosis of ASD, social behaviors, restricted and repetitive behaviors, IQ, pOT, and WB5HT. Future studies need to replicate these findings and examine gene-interactions in other neurodevelopmental disorders. Mechanisms of action may influence early social and cognitive development that may or may not be limited to ASD diagnosis.
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Affiliation(s)
- Sunday M. Francis
- Department of Psychiatry, University of MinnesotaMinneapolis, MN, USA
| | - Emily Kistner-Griffin
- Biostatistics Shared Resource, Hollings Cancer Center, Medical University of South CarolinaCharleston, SC, USA
| | | | - Stephen Guter
- Department of Psychiatry, Institute of Juvenile Research, University of Illinois at ChicagoChicago, IL, USA
| | - Edwin H. Cook
- Department of Psychiatry, Institute of Juvenile Research, University of Illinois at ChicagoChicago, IL, USA
| | - Suma Jacob
- Department of Psychiatry, University of MinnesotaMinneapolis, MN, USA
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Integrative genomics analyses unveil downstream biological effectors of disease-specific polymorphisms buried in intergenic regions. NPJ Genom Med 2016; 1. [PMID: 27482468 PMCID: PMC4966659 DOI: 10.1038/npjgenmed.2016.6] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Functionally altered biological mechanisms arising from disease-associated polymorphisms, remain difficult to characterise when those variants are intergenic, or, fall between genes. We sought to identify shared downstream mechanisms by which inter- and intragenic single-nucleotide polymorphisms (SNPs) contribute to a specific physiopathology. Using computational modelling of 2 million pairs of disease-associated SNPs drawn from genome-wide association studies (GWAS), integrated with expression Quantitative Trait Loci (eQTL) and Gene Ontology functional annotations, we predicted 3,870 inter–intra and inter–intra SNP pairs with convergent biological mechanisms (FDR<0.05). These prioritised SNP pairs with overlapping messenger RNA targets or similar functional annotations were more likely to be associated with the same disease than unrelated pathologies (OR>12). We additionally confirmed synergistic and antagonistic genetic interactions for a subset of prioritised SNP pairs in independent studies of Alzheimer’s disease (entropy P=0.046), bladder cancer (entropy P=0.039), and rheumatoid arthritis (PheWAS case–control P<10−4). Using ENCODE data sets, we further statistically validated that the biological mechanisms shared within prioritised SNP pairs are frequently governed by matching transcription factor binding sites and long-range chromatin interactions. These results provide a ‘roadmap’ of disease mechanisms emerging from GWAS and further identify candidate therapeutic targets among downstream effectors of intergenic SNPs.
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