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Verma A, Huffman JE, Rodriguez A, Conery M, Liu M, Ho YL, Kim Y, Heise DA, Guare L, Panickan VA, Garcon H, Linares F, Costa L, Goethert I, Tipton R, Honerlaw J, Davies L, Whitbourne S, Cohen J, Posner DC, Sangar R, Murray M, Wang X, Dochtermann DR, Devineni P, Shi Y, Nandi TN, Assimes TL, Brunette CA, Carroll RJ, Clifford R, Duvall S, Gelernter J, Hung A, Iyengar SK, Joseph J, Kember R, Kranzler H, Kripke CM, Levey D, Luoh SW, Merritt VC, Overstreet C, Deak JD, Grant SFA, Polimanti R, Roussos P, Shakt G, Sun YV, Tsao N, Venkatesh S, Voloudakis G, Justice A, Begoli E, Ramoni R, Tourassi G, Pyarajan S, Tsao P, O'Donnell CJ, Muralidhar S, Moser J, Casas JP, Bick AG, Zhou W, Cai T, Voight BF, Cho K, Gaziano JM, Madduri RK, Damrauer S, Liao KP. Diversity and scale: Genetic architecture of 2068 traits in the VA Million Veteran Program. Science 2024; 385:eadj1182. [PMID: 39024449 DOI: 10.1126/science.adj1182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 05/10/2024] [Indexed: 07/20/2024]
Abstract
One of the justifiable criticisms of human genetic studies is the underrepresentation of participants from diverse populations. Lack of inclusion must be addressed at-scale to identify causal disease factors and understand the genetic causes of health disparities. We present genome-wide associations for 2068 traits from 635,969 participants in the Department of Veterans Affairs Million Veteran Program, a longitudinal study of diverse United States Veterans. Systematic analysis revealed 13,672 genomic risk loci; 1608 were only significant after including non-European populations. Fine-mapping identified causal variants at 6318 signals across 613 traits. One-third (n = 2069) were identified in participants from non-European populations. This reveals a broadly similar genetic architecture across populations, highlights genetic insights gained from underrepresented groups, and presents an extensive atlas of genetic associations.
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Affiliation(s)
- Anurag Verma
- Corporal Michael Crescenz VA Medical Center, Philadelphia, PA 19104, USA
- Department of Medicine, Division of Translational Medicine and Human Genetics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
- Institute for Biomedical Informatics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Jennifer E Huffman
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA 02130, USA
- Palo Alto Veterans Institute for Research (PAVIR), Palo Alto Health Care System, Palo Alto, CA 94304, USA
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
| | - Alex Rodriguez
- Data Science and Learning, Argonne National Laboratory, Lemont, IL 60439, USA
| | - Mitchell Conery
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Molei Liu
- Department of Biostatistics, Columbia University's Mailman School of Public Health, New York, NY 10032, USA
| | - Yuk-Lam Ho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA 02130, USA
| | - Youngdae Kim
- Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL 60439, USA
| | - David A Heise
- National Security Sciences Directorate, Cyber Resilience and Intelligence Division, Oak Ridge National Laboratory, Dept of Energy, Oak Ridge, TN 37831, USA
| | - Lindsay Guare
- Department of Medicine, Division of Translational Medicine and Human Genetics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
| | | | - Helene Garcon
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA 02130, USA
| | - Franciel Linares
- R&D Systems Engineering, Information Technology Services Directorate, Oak Ridge National Laboratory, Dept of Energy, Oak Ridge, TN 37831, USA
| | - Lauren Costa
- MVP Boston Coordinating Center, VA Boston Healthcare System, Boston, MA 02111, USA
| | - Ian Goethert
- Data Management and Engineering, Information Technology Services Division, Oak Ridge National Laboratory, Dept of Energy, Oak Ridge, TN 37831, USA
| | - Ryan Tipton
- Knowledge Discovery Infrastructure, Information Technology Services Division, Oak Ridge National Laboratory, Dept of Energy, Oak Ridge, TN 37831, USA
| | - Jacqueline Honerlaw
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA 02130, USA
| | - Laura Davies
- Computing and Computational Sciences Dir PMO, PMO, Oak Ridge National Laboratory, Dept of Energy, Oak Ridge, TN 37831, USA
| | - Stacey Whitbourne
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
- MVP Boston Coordinating Center, VA Boston Healthcare System, Boston, MA 02111, USA
- Department of Medicine, Division of Aging, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Jeremy Cohen
- National Security Sciences Directorate, Cyber Resilience and Intelligence Division, Oak Ridge National Laboratory, Dept of Energy, Oak Ridge, TN 37831, USA
| | - Daniel C Posner
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA 02130, USA
| | - Rahul Sangar
- MVP Boston Coordinating Center, VA Boston Healthcare System, Boston, MA 02111, USA
| | - Michael Murray
- MVP Boston Coordinating Center, VA Boston Healthcare System, Boston, MA 02111, USA
| | - Xuan Wang
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
- Department of Population Health Sciences, University of Utah, Salt Lake City, UT 84112, USA
| | - Daniel R Dochtermann
- VA Cooperative Studies Program, VA Boston Healthcare System, Boston, MA 02130, USA
| | - Poornima Devineni
- VA Cooperative Studies Program, VA Boston Healthcare System, Boston, MA 02130, USA
| | - Yunling Shi
- VA Cooperative Studies Program, VA Boston Healthcare System, Boston, MA 02130, USA
| | - Tarak Nath Nandi
- Data Science and Learning, Argonne National Laboratory, Lemont, IL 60439, USA
| | | | - Charles A Brunette
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
- Research Service, VA Boston Healthcare System, Boston, MA 02130, USA
| | - Robert J Carroll
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37211, USA
| | - Royce Clifford
- Research Department, VA San Diego Healthcare System, San Diego, CA 92161, USA
- Department of Otolaryngology, UCSD San Diego, La Jolla, CA 92093, USA
| | - Scott Duvall
- VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, UT 84148, USA
- Internal Medicine, Epidemiology, University of Utah School of Medicine, Salt Lake City, UT 84132, USA
| | - Joel Gelernter
- Psychiatry, Human Genetics, Yale University, New Haven, CT, 06520, USA
- VA Connecticut Healthcare System West Haven, West Haven, CT, 06516, USA
| | - Adriana Hung
- Medicine, Nephrology & Hypertension, VA Tennessee Valley Healthcare System & Vanderbilt University, Nashville, TN 37232, USA
| | - Sudha K Iyengar
- Departments of Population and Quantitative Health Sciences, Genetics and Genome Sciences, and Ophthalmology and Visual Sciences and the Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Jacob Joseph
- Medicine, Cardiology Section, VA Providence Healthcare System, Providence, RI 02908, USA
- Department of Medicine, Brown University, Providence, RI, 02908, USA
| | - Rachel Kember
- Mental Illness Research, Education and Clinical Center, Corporal Michael Crescenz VA Medical Center, Philadelphia, PA 19104, USA
- Department of Psychiatry, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Henry Kranzler
- Mental Illness Research, Education and Clinical Center, Corporal Michael Crescenz VA Medical Center, Philadelphia, PA 19104, USA
- Department of Psychiatry, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Colleen M Kripke
- Department of Medicine, Division of Translational Medicine and Human Genetics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Daniel Levey
- Psychiatry, Human Genetics, Yale University, New Haven, CT, 06520, USA
- Medicine, VA Connecticut Healthcare System West Haven, West Haven, CT 06516, USA
| | - Shiuh-Wen Luoh
- VA Portland Health Care System, Portland, OR 97239, USA
- Division of Hematology and Medical Oncology, Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97239, USA
| | - Victoria C Merritt
- Research Department, VA San Diego Healthcare System, San Diego, CA 92161, USA
| | - Cassie Overstreet
- Psychiatry, Human Genetics, Yale University, New Haven, CT, 06520, USA
| | - Joseph D Deak
- Psychiatry, Yale University, New Haven, CT 06520, USA
- Psychiatry, VA Connecticut Healthcare System West Haven, West Haven, CT 06516, USA
| | - Struan F A Grant
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Pediatrics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
- Divisions of Human Genetics and Endocrinology and Diabetes, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Genetics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
| | | | - Panos Roussos
- Psychiatry, Mental Illness Research, Education and Clinical Center, James J. Peters VA Medical Center; Icahn School of Medicine at Mount Sinai, Bronx, NY 10468, USA
| | - Gabrielle Shakt
- Corporal Michael Crescenz VA Medical Center, Philadelphia, PA 19104, USA
- Department of Surgery, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Yan V Sun
- Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA 30322, USA
| | - Noah Tsao
- Corporal Michael Crescenz VA Medical Center, Philadelphia, PA 19104, USA
- Department of Surgery, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Sanan Venkatesh
- Psychiatry, Mental Illness Research, Education and Clinical Center, James J. Peters VA Medical Center; Icahn School of Medicine at Mount Sinai, Bronx, NY 10468, USA
| | - Georgios Voloudakis
- Psychiatry, Mental Illness Research, Education and Clinical Center, James J. Peters VA Medical Center; Icahn School of Medicine at Mount Sinai, Bronx, NY 10468, USA
| | - Amy Justice
- Medicine, VA Connecticut Healthcare System West Haven, West Haven, CT 06516, USA
- Internal Medicine, General Medicine, Yale University, New Haven, CT 06520, USA
- Health Policy, Yale School of Public Health, New Haven, CT 06520, USA
| | - Edmon Begoli
- Oak Ridge National Laboratory, Dept of Energy, Oak Ridge, TN, 37831, USA
| | - Rachel Ramoni
- Office of Research and Development, Department of Veterans Affairs, Washington, DC, 20420, USA
| | - Georgia Tourassi
- National Center for Computational Sciences, Oak Ridge National Laboratory, Dept of Energy, Oak Ridge, TN, 37831, USA
| | - Saiju Pyarajan
- VA Cooperative Studies Program, VA Boston Healthcare System, Boston, MA 02130, USA
| | - Philip Tsao
- Medicine, Cardiology, VA Palo Alto Healthcare System, Palo Alto, CA 94304, USA
- Department of Medicine, Stanford University, Palo Alto, CA, 94304, USA
| | | | - Sumitra Muralidhar
- Office of Research and Development, Department of Veterans Affairs, Washington, DC, 20420, USA
| | - Jennifer Moser
- Office of Research and Development, Department of Veterans Affairs, Washington, DC, 20420, USA
| | - Juan P Casas
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA 02130, USA
| | - Alexander G Bick
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University, Nashville, TN, 37325, USA
| | - Wei Zhou
- Department of Medicine, Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research, Cambridge, MA 02142, USA
- Program in Medical and Population Genetics, Cambridge, MA 02142, USA
| | - Tianxi Cai
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
| | - Benjamin F Voight
- Corporal Michael Crescenz VA Medical Center, Philadelphia, PA 19104, USA
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
- Department of Genetics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
- Institute of Translational Medicine and Therapeutics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Kelly Cho
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
- MVP Boston Coordinating Center, VA Boston Healthcare System, Boston, MA 02111, USA
- Department of Medicine, Division of Aging, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - J Michael Gaziano
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
- MVP Boston Coordinating Center, VA Boston Healthcare System, Boston, MA 02111, USA
- Department of Medicine, Division of Aging, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Ravi K Madduri
- Data Science and Learning, Argonne National Laboratory, Lemont, IL 60439, USA
| | - Scott Damrauer
- Corporal Michael Crescenz VA Medical Center, Philadelphia, PA 19104, USA
- Department of Genetics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
- Department of Surgery, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
- Cardiovascular Institute, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Katherine P Liao
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA 02130, USA
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
- Medicine, Rheumatology, VA Boston Healthcare System, Boston, MA 02130, USA
- Department of Medicine, Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital, Boston, MA 02115, USA
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Lin D, Wu S, Li W, Ye P, Pan X, Zheng T, Gao F. A cross-tissue transcriptome-wide association study identifies new susceptibility genes for frailty. Front Genet 2024; 15:1404456. [PMID: 39071775 PMCID: PMC11272547 DOI: 10.3389/fgene.2024.1404456] [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: 04/08/2024] [Accepted: 06/25/2024] [Indexed: 07/30/2024] Open
Abstract
Background: Although genome-wide association studies (GWAS) have identified 14 loci associated with frailty index (FI) susceptibility, the underlying causative genes and biological mechanisms remain elusive. Methods: A cross-tissue transcriptome-wide association study (TWAS) was conducted utilizing the Unified Test for Molecular Markers (UTMOST), which integrates GWAS summary statistics from 164,610 individuals of European ancestry and 10,616 Swedish participants, alongside gene expression matrices from the Genotype-Tissue Expression (GTEx) Project. Validation of the significant genes was performed through three distinct methods: FUSION, FOCUS, and Multiple Marker Analysis of Genome-wide Annotation (MAGMA). Exploration of tissue and functional enrichment for FI-associated SNPs was conducted using MAGMA. Conditional and joint analyses, along with fine mapping, were employed to enhance our understanding of FI's genetic architecture. Mendelian randomization was employed to ascertain causal relationships between significant genes and FI, and co-localization analysis was utilized to investigate shared SNPs between significant genes and FI. Results: In this study, two novel susceptibility genes associated with the risk of FI were identified through the application of four TWAS methods. Mendelian randomization demonstrated that HTT may elevate the risk of developing frailty, whereas LRPPRC could offer protection against the onset of frailty. Additionally, co-localization analysis identified a shared SNP between LRPPRC and FI. Tissue enrichment analyses revealed that genomic regions linked to SNPs associated with frailty were predominantly enriched in various brain regions, including the frontal cortex, cerebral cortex, and cerebellar hemispheres. Conditional, combined analyses, and fine mapping collectively identified two genetic regions associated with frailty: 2p21 and 4q16.3. Functional enrichment analyses revealed that the pathways associated with frailty were primarily related to the MHC complex, PD-1 signaling, cognition, inflammatory response to antigenic stimuli, and the production of second messenger molecules. Conclusion: This investigation uncovers two newly identified genes with forecasted expression levels associated with the risk of FI, offering new perspectives on the genetic architecture underlying FI.
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Affiliation(s)
- Daoyi Lin
- Department of Anesthesiology, Fujian Provincial Hospital, Shengli Clinical Medical College of Fujian Medical University, Fuzhou University Affiliated Provincial Hospital, Fuzhou, China
- Department of Anesthesia, China-Japan Friendship Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Shuyan Wu
- Department of Anesthesiology, Fujian Provincial Hospital, Shengli Clinical Medical College of Fujian Medical University, Fuzhou University Affiliated Provincial Hospital, Fuzhou, China
| | - Wangyu Li
- Department of Pain Management, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, Fuzhou, China
| | - Peng Ye
- Department of Anesthesiology, Fujian Provincial Hospital, Shengli Clinical Medical College of Fujian Medical University, Fuzhou University Affiliated Provincial Hospital, Fuzhou, China
- Fujian Provincial Key Laboratory of Emergency Medicine, Fujian Emergency Medical Center, Fuzhou, China
| | - Xuan Pan
- Department of Anesthesiology, Fujian Provincial Hospital, Shengli Clinical Medical College of Fujian Medical University, Fuzhou University Affiliated Provincial Hospital, Fuzhou, China
| | - Ting Zheng
- Department of Anesthesiology, Fujian Provincial Hospital, Shengli Clinical Medical College of Fujian Medical University, Fuzhou University Affiliated Provincial Hospital, Fuzhou, China
- Fujian Provincial Key Laboratory of Emergency Medicine, Fujian Emergency Medical Center, Fuzhou, China
- Fujian Provincial Co-Constructed Laboratory of “Belt and Road”, Fujian Emergency Medical Center, Fuzhou, China
| | - Fei Gao
- Department of Anesthesiology, Fujian Provincial Hospital, Shengli Clinical Medical College of Fujian Medical University, Fuzhou University Affiliated Provincial Hospital, Fuzhou, China
- Fujian Provincial Key Laboratory of Emergency Medicine, Fujian Emergency Medical Center, Fuzhou, China
- Fujian Provincial Co-Constructed Laboratory of “Belt and Road”, Fujian Emergency Medical Center, Fuzhou, China
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Chang KJ, Wu HY, Chiang PH, Hsu YT, Weng PY, Yu TH, Li CY, Chen YH, Dai HJ, Tsai HY, Chang YJ, Wu YR, Yang YP, Li CT, Hsu CC, Chen SJ, Chen YC, Cheng CY, Hsieh AR, Chiou SH. Decoding and reconstructing disease relations between dry eye and depression: a multimodal investigation comprising meta-analysis, genetic pathways and Mendelian randomization. J Adv Res 2024:S2090-1232(24)00115-2. [PMID: 38548265 DOI: 10.1016/j.jare.2024.03.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Revised: 03/07/2024] [Accepted: 03/23/2024] [Indexed: 04/05/2024] Open
Abstract
INTRODUCTION The clinical presentations of dry eye disease (DED) and depression (DEP) often comanifest. However, the robustness and the mechanisms underlying this association were undetermined. OBJECTIVES To this end, we set up a three-segment study that employed multimodality results (meta-analysis, genome-wide association study [GWAS] and Mendelian randomization [MR]) to elucidate the association, common pathways and causality between DED and DEP. METHODS A meta-analysis comprising 26 case-control studies was first conducted to confirm the DED-DEP association. Next, we performed a linkage disequilibrium (LD)-adjusted GWAS and targeted phenotype association study (PheWAS) in East Asian TW Biobank (TWB) and European UK Biobank (UKB) populations. Single-nucleotide polymorphisms (SNPs) were further screened for molecular interactions and common pathways at the functional gene level. To further elucidate the activated pathways in DED and DEP, a systemic transcriptome review was conducted on RNA sequencing samples from the Gene Expression Omnibus. Finally, 48 MR experiments were implemented to examine the bidirectional causation between DED and DEP. RESULTS Our meta-analysis showed that DED patients are associated with an increased DEP prevalence (OR = 1.83), while DEP patients have a concurrent higher risk of DED (OR = 2.34). Notably, cross-disease GWAS analysis revealed that similar genetic architecture (rG = 0.19) and pleiotropic functional genes contributed to phenotypes in both diseases. Through protein-protein interaction and ontology convergence, we summarized the pleiotropic functional genes under the ontology of immune activation, which was further validated by a transcriptome systemic review. Importantly, the inverse variance-weighted (IVW)-MR experiments in both TWB and UKB populations (p value <0.001) supported the bidirectional exposure-outcome causation for DED-to-DEP and DEP-to-DED. Despite stringent LD-corrected instrumental variable re-selection, the bidirectional causation between DED and DEP remained. CONCLUSION With the multi-modal evidence combined, we consolidated the association and causation between DED and DEP.
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Affiliation(s)
- Kao-Jung Chang
- Department of Medical Research, Taipei Veterans General Hospital, 112201 No.201, Sec. 2, Shipai Rd., Beitou District, Taipei, Taiwan; Institute of Clinical Medicine, National Yang Ming Chiao Tung University, 112304 No. 155, Sec. 2, Linong St. Beitou District, Taipei, Taiwan; Department of Ophthalmology, Taipei Veterans General Hospital, 112201 No.201, Sec. 2, Shipai Rd., Beitou District, Taipei, Taiwan; Department of Medical Education, Taipei Veterans General Hospital, 112201 No.201, Sec. 2, Shipai Rd., Beitou District, Taipei, Taiwan
| | - Hsin-Yu Wu
- Department of Medical Research, Taipei Veterans General Hospital, 112201 No.201, Sec. 2, Shipai Rd., Beitou District, Taipei, Taiwan; School of Medicine, National Yang Ming Chiao Tung University, 112304 No. 155, Sec. 2, Linong St. Beitou District, Taipei, Taiwan
| | - Pin-Hsuan Chiang
- Department of Medical Research, Taipei Veterans General Hospital, 112201 No.201, Sec. 2, Shipai Rd., Beitou District, Taipei, Taiwan; Big Data Center, Taipei Veterans General Hospital, 112201 No.201, Sec. 2, Shipai Rd., Beitou District, Taipei, Taiwan; Department of Statistics, Tamkang University, 251301 No.151, Yingzhuan Rd., Tamsui District, New Taipei, Taiwan
| | - Yu-Tien Hsu
- Department of Social & Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, 02115 No.677 Huntington Avenue, MA, USA
| | - Pei-Yu Weng
- Department of Medical Research, Taipei Veterans General Hospital, 112201 No.201, Sec. 2, Shipai Rd., Beitou District, Taipei, Taiwan
| | - Ting-Han Yu
- Department of Medical Research, Taipei Veterans General Hospital, 112201 No.201, Sec. 2, Shipai Rd., Beitou District, Taipei, Taiwan; School of Medicine, National Yang Ming Chiao Tung University, 112304 No. 155, Sec. 2, Linong St. Beitou District, Taipei, Taiwan
| | - Cheng-Yi Li
- Department of Medical Research, Taipei Veterans General Hospital, 112201 No.201, Sec. 2, Shipai Rd., Beitou District, Taipei, Taiwan; School of Medicine, National Yang Ming Chiao Tung University, 112304 No. 155, Sec. 2, Linong St. Beitou District, Taipei, Taiwan
| | - Yu-Hsiang Chen
- Department of Medical Research, Taipei Veterans General Hospital, 112201 No.201, Sec. 2, Shipai Rd., Beitou District, Taipei, Taiwan; School of Medicine, National Yang Ming Chiao Tung University, 112304 No. 155, Sec. 2, Linong St. Beitou District, Taipei, Taiwan
| | - He-Jhen Dai
- Department of Medical Research, Taipei Veterans General Hospital, 112201 No.201, Sec. 2, Shipai Rd., Beitou District, Taipei, Taiwan; School of Medicine, National Yang Ming Chiao Tung University, 112304 No. 155, Sec. 2, Linong St. Beitou District, Taipei, Taiwan
| | - Han-Ying Tsai
- Department of Medical Research, Taipei Veterans General Hospital, 112201 No.201, Sec. 2, Shipai Rd., Beitou District, Taipei, Taiwan; Big Data Center, Taipei Veterans General Hospital, 112201 No.201, Sec. 2, Shipai Rd., Beitou District, Taipei, Taiwan; Department of Statistics, Tamkang University, 251301 No.151, Yingzhuan Rd., Tamsui District, New Taipei, Taiwan
| | - Yu-Jung Chang
- Department of Statistics, Tamkang University, 251301 No.151, Yingzhuan Rd., Tamsui District, New Taipei, Taiwan
| | - You-Ren Wu
- Department of Medical Research, Taipei Veterans General Hospital, 112201 No.201, Sec. 2, Shipai Rd., Beitou District, Taipei, Taiwan; Institute of Pharmacology, National Yang Ming Chiao Tung University, 112304 No. 155, Sec. 2, Linong St. Beitou District, Taipei, Taiwan
| | - Yi-Ping Yang
- Department of Medical Research, Taipei Veterans General Hospital, 112201 No.201, Sec. 2, Shipai Rd., Beitou District, Taipei, Taiwan; School of Medicine, National Yang Ming Chiao Tung University, 112304 No. 155, Sec. 2, Linong St. Beitou District, Taipei, Taiwan; Institute of Pharmacology, National Yang Ming Chiao Tung University, 112304 No. 155, Sec. 2, Linong St. Beitou District, Taipei, Taiwan
| | - Cheng-Ta Li
- Department of Psychiatry, Taipei Veterans General Hospital, 112201 No.201, Sec. 2, Shipai Rd., Beitou District, Taipei, Taiwan; Division of Psychiatry, School of Medicine, National Yang Ming Chiao Tung University, 112304 No. 155, Sec. 2, Linong St. Beitou District, Taipei, Taiwan; Institute of Brain Science and Brain Research Center, School of Medicine, National Yang Ming Chiao Tung University, 112304 No. 155, Sec. 2, Linong St. Beitou District, Taipei, Taiwan; Institute of Cognitive Neuroscience, National Central University, 320317 No. 300, Zhongda Rd., Zhongli District, Jhongli, Taiwan
| | - Chih-Chien Hsu
- Department of Medical Research, Taipei Veterans General Hospital, 112201 No.201, Sec. 2, Shipai Rd., Beitou District, Taipei, Taiwan; Department of Ophthalmology, Taipei Veterans General Hospital, 112201 No.201, Sec. 2, Shipai Rd., Beitou District, Taipei, Taiwan; School of Medicine, National Yang Ming Chiao Tung University, 112304 No. 155, Sec. 2, Linong St. Beitou District, Taipei, Taiwan
| | - Shih-Jen Chen
- Big Data Center, Taipei Veterans General Hospital, 112201 No.201, Sec. 2, Shipai Rd., Beitou District, Taipei, Taiwan
| | - Yu-Chun Chen
- School of Medicine, National Yang Ming Chiao Tung University, 112304 No. 155, Sec. 2, Linong St. Beitou District, Taipei, Taiwan; Big Data Center, Taipei Veterans General Hospital, 112201 No.201, Sec. 2, Shipai Rd., Beitou District, Taipei, Taiwan; Institute of Hospital and Health Care Administration, National Yang Ming Chiao Tung University, 112304 No. 155, Sec. 2, Linong St. Beitou District, Taipei, Taiwan; Department of Family Medicine, Taipei Veterans General Hospital, 112201 No.201, Sec. 2, Shipai Rd., Beitou District, Taipei, Taiwan
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, 168751 No.11 Third Hospital Ave, Singapore; Department of Ophthalmology, Yong Loo Lin school of Medicine, National University of Singapore, 119228 No.21 Lower Kent Ridge Road, Singapore
| | - Ai-Ru Hsieh
- Department of Statistics, Tamkang University, 251301 No.151, Yingzhuan Rd., Tamsui District, New Taipei, Taiwan.
| | - Shih-Hwa Chiou
- Department of Medical Research, Taipei Veterans General Hospital, 112201 No.201, Sec. 2, Shipai Rd., Beitou District, Taipei, Taiwan; Department of Ophthalmology, Taipei Veterans General Hospital, 112201 No.201, Sec. 2, Shipai Rd., Beitou District, Taipei, Taiwan; School of Medicine, National Yang Ming Chiao Tung University, 112304 No. 155, Sec. 2, Linong St. Beitou District, Taipei, Taiwan; Institute of Pharmacology, National Yang Ming Chiao Tung University, 112304 No. 155, Sec. 2, Linong St. Beitou District, Taipei, Taiwan.
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Zilinskas R, Li C, Shen X, Pan W, Yang T. Inferring a directed acyclic graph of phenotypes from GWAS summary statistics. Biometrics 2024; 80:ujad039. [PMID: 38470257 PMCID: PMC10928990 DOI: 10.1093/biomtc/ujad039] [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: 03/01/2023] [Revised: 11/24/2023] [Accepted: 01/04/2024] [Indexed: 03/13/2024]
Abstract
Estimating phenotype networks is a growing field in computational biology. It deepens the understanding of disease etiology and is useful in many applications. In this study, we present a method that constructs a phenotype network by assuming a Gaussian linear structure model embedding a directed acyclic graph (DAG). We utilize genetic variants as instrumental variables and show how our method only requires access to summary statistics from a genome-wide association study (GWAS) and a reference panel of genotype data. Besides estimation, a distinct feature of the method is its summary statistics-based likelihood ratio test on directed edges. We applied our method to estimate a causal network of 29 cardiovascular-related proteins and linked the estimated network to Alzheimer's disease (AD). A simulation study was conducted to demonstrate the effectiveness of this method. An R package sumdag implementing the proposed method, all relevant code, and a Shiny application are available.
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Affiliation(s)
| | - Chunlin Li
- Department of Statistics, Iowa State University, Ames, IA 50011, United States
| | - Xiaotong Shen
- School of Statistics, University of Minnesota, Minneapolis, MN 55455, United States
| | - Wei Pan
- Division of Biostatistics and Health Data Science, University of Minnesota, Minneapolis, MN 55455, United States
| | - Tianzhong Yang
- Division of Biostatistics and Health Data Science, University of Minnesota, Minneapolis, MN 55455, United States
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Zilinskas R, Li C, Shen X, Pan W, Yang T. Inferring a directed acyclic graph of phenotypes from GWAS summary statistics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.10.528092. [PMID: 38045347 PMCID: PMC10690198 DOI: 10.1101/2023.02.10.528092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
Estimating phenotype networks is a growing field in computational biology. It deepens the understanding of disease etiology and is useful in many applications. In this study, we present a method that constructs a phenotype network by assuming a Gaussian linear structure model embedding a directed acyclic graph (DAG). We utilize genetic variants as instrumental variables and show how our method only requires access to summary statistics from a genome-wide association study (GWAS) and a reference panel of genotype data. Besides estimation, a distinct feature of the method is its summary statistics-based likelihood ratio test on directed edges. We applied our method to estimate a causal network of 29 cardiovascular-related proteins and linked the estimated network to Alzheimer's disease (AD). A simulation study was conducted to demonstrate the effectiveness of this method. An R package sumdag implementing the proposed method, all relevant code, and a Shiny application are available at https://github.com/chunlinli/sumdag.
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Affiliation(s)
| | - Chunlin Li
- Department of Statistics, Iowa State University, Ames, Iowa 50011, U.S.A
| | - Xiaotong Shen
- School of Statistics, University of Minnesota, Minneapolis, Minnesota 55455, U.S.A
| | - Wei Pan
- Division of Biostatistics and Health Data Science, University of Minnesota, Minneapolis, Minnesota 55455, U.S.A
| | - Tianzhong Yang
- Division of Biostatistics and Health Data Science, University of Minnesota, Minneapolis, Minnesota 55455, U.S.A
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Zhang J, Liang X, Gonzales S, Liu J, Gao XR, Wang X. A gene based combination test using GWAS summary data. BMC Bioinformatics 2023; 24:2. [PMID: 36597047 PMCID: PMC9811798 DOI: 10.1186/s12859-022-05114-x] [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: 06/02/2022] [Accepted: 12/13/2022] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND Gene-based association tests provide a useful alternative and complement to the usual single marker association tests, especially in genome-wide association studies (GWAS). The way of weighting for variants in a gene plays an important role in boosting the power of a gene-based association test. Appropriate weights can boost statistical power, especially when detecting genetic variants with weak effects on a trait. One major limitation of existing gene-based association tests lies in using weights that are predetermined biologically or empirically. This limitation often attenuates the power of a test. On another hand, effect sizes or directions of causal genetic variants in real data are usually unknown, driving a need for a flexible yet robust methodology of gene based association tests. Furthermore, access to individual-level data is often limited, while thousands of GWAS summary data are publicly and freely available. RESULTS To resolve these limitations, we propose a combination test named as OWC which is based on summary statistics from GWAS data. Several traditional methods including burden test, weighted sum of squared score test [SSU], weighted sum statistic [WSS], SNP-set Kernel Association Test [SKAT], and the score test are special cases of OWC. To evaluate the performance of OWC, we perform extensive simulation studies. Results of simulation studies demonstrate that OWC outperforms several existing popular methods. We further show that OWC outperforms comparison methods in real-world data analyses using schizophrenia GWAS summary data and a fasting glucose GWAS meta-analysis data. The proposed method is implemented in an R package available at https://github.com/Xuexia-Wang/OWC-R-package CONCLUSIONS: We propose a novel gene-based association test that incorporates four different weighting schemes (two constant weights and two weights proportional to normal statistic Z) and includes several popular methods as its special cases. Results of the simulation studies and real data analyses illustrate that the proposed test, OWC, outperforms comparable methods in most scenarios. These results demonstrate that OWC is a useful tool that adapts to the underlying biological model for a disease by weighting appropriately genetic variants and combination of well-known gene-based tests.
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Affiliation(s)
- Jianjun Zhang
- grid.266869.50000 0001 1008 957XDepartment of Mathematics, University of North Texas, 225 Avenue E, Denton, TX 76201 USA
| | - Xiaoyu Liang
- grid.17088.360000 0001 2150 1785Department of Epidemiology and Biostatistics, Michigan State University, 909 Wilson Rd Room B601, East Lansing, MI 48824 USA
| | - Samantha Gonzales
- grid.266869.50000 0001 1008 957XDepartment of Mathematics, University of North Texas, 225 Avenue E, Denton, TX 76201 USA
| | - Jianguo Liu
- grid.266869.50000 0001 1008 957XDepartment of Mathematics, University of North Texas, 225 Avenue E, Denton, TX 76201 USA
| | - Xiaoyi Raymond Gao
- grid.261331.40000 0001 2285 7943Department of Ophthalmology and Visual Science, Department of Biomedical informatics, Division of Human Genetics, Ohio State University, 915 Olentangy River Road, Columbus, OH 43212 USA
| | - Xuexia Wang
- grid.65456.340000 0001 2110 1845Department of Biostatistics, Robert Stempel College of Public Health and Social Work, Florida International University, 11200 SW 8th street, Miami, FL 33174 USA
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Škrabišová M, Dietz N, Zeng S, Chan YO, Wang J, Liu Y, Biová J, Joshi T, Bilyeu KD. A novel Synthetic phenotype association study approach reveals the landscape of association for genomic variants and phenotypes. J Adv Res 2022; 42:117-133. [PMID: 36513408 PMCID: PMC9788956 DOI: 10.1016/j.jare.2022.04.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 02/14/2022] [Accepted: 04/08/2022] [Indexed: 12/27/2022] Open
Abstract
INTRODUCTION Genome-Wide Association Studies (GWAS) identify tagging variants in the genome that are statistically associated with the phenotype because of their linkage disequilibrium (LD) relationship with the causative mutation (CM). When both low-density genotyped accession panels with phenotypes and resequenced data accession panels are available, tagging variants can assist with post-GWAS challenges in CM discovery. OBJECTIVES Our objective was to identify additional GWAS evaluation criteria to assess correspondence between genomic variants and phenotypes, as well as enable deeper analysis of the localized landscape of association. METHODS We used genomic variant positions as Synthetic phenotypes in GWAS that we named "Synthetic phenotype association study" (SPAS). The extreme case of SPAS is what we call an "Inverse GWAS" where we used CM positions of cloned soybean genes. We developed and validated the Accuracy concept as a measure of the correspondence between variant positions and phenotypes. RESULTS The SPAS approach demonstrated that the genotype status of an associated variant used as a Synthetic phenotype enabled us to explore the relationships between tagging variants and CMs, and further, that utilizing CMs as Synthetic phenotypes in Inverse GWAS illuminated the landscape of association. We implemented the Accuracy calculation for a curated accession panel to an online Accuracy calculation tool (AccuTool) as a resource for gene identification in soybean. We demonstrated our concepts on three examples of soybean cloned genes. As a result of our findings, we devised an enhanced "GWAS to Genes" analysis (Synthetic phenotype to CM strategy, SP2CM). Using SP2CM, we identified a CM for a novel gene. CONCLUSION The SP2CM strategy utilizing Synthetic phenotypes and the Accuracy calculation of correspondence provides crucial information to assist researchers in CM discovery. The impact of this work is a more effective evaluation of landscapes of GWAS associations.
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Affiliation(s)
- Mária Škrabišová
- Department of Biochemistry, Faculty of Science, Palacky University Olomouc, Olomouc 78371, Czech Republic
| | - Nicholas Dietz
- Division of Plant Sciences, University of Missouri, Columbia, MO 65201, USA
| | - Shuai Zeng
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65212, USA,Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65212, USA
| | - Yen On Chan
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65212, USA,MU Data Science and Informatics Institute, University of Missouri, Columbia, MO 65212, USA
| | - Juexin Wang
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65212, USA,Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65212, USA
| | - Yang Liu
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65212, USA,MU Data Science and Informatics Institute, University of Missouri, Columbia, MO 65212, USA
| | - Jana Biová
- Department of Biochemistry, Faculty of Science, Palacky University Olomouc, Olomouc 78371, Czech Republic
| | - Trupti Joshi
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65212, USA,Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65212, USA,MU Data Science and Informatics Institute, University of Missouri, Columbia, MO 65212, USA,Department of Health Management and Informatics, School of Medicine, University of Missouri, Columbia, MO 65212, USA,Corresponding authors at: Department of Health Management and Informatics, School of Medicine, 1201 E Rollins St, 271B Life Science Center, Columbia, MO 65201, USA (T. Joshi). Plant Genetics Research Unit, United States Department of Agriculture-Agricultural Research Service, 110 Waters Hall, University of Missouri, Columbia, MO 65211, USA (K.D. Bilyeu).
| | - Kristin D. Bilyeu
- Plant Genetics Research Unit, United States Department of Agriculture-Agricultural Research Service, University of Missouri, Columbia, MO 65211, USA,Corresponding authors at: Department of Health Management and Informatics, School of Medicine, 1201 E Rollins St, 271B Life Science Center, Columbia, MO 65201, USA (T. Joshi). Plant Genetics Research Unit, United States Department of Agriculture-Agricultural Research Service, 110 Waters Hall, University of Missouri, Columbia, MO 65211, USA (K.D. Bilyeu).
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Cao X, Wang X, Zhang S, Sha Q. Gene-based association tests using GWAS summary statistics and incorporating eQTL. Sci Rep 2022; 12:3553. [PMID: 35241742 PMCID: PMC8894384 DOI: 10.1038/s41598-022-07465-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 02/11/2022] [Indexed: 01/29/2023] Open
Abstract
Although genome-wide association studies (GWAS) have been successfully applied to a variety of complex diseases and identified many genetic variants underlying complex diseases via single marker tests, there is still a considerable heritability of complex diseases that could not be explained by GWAS. One alternative approach to overcome the missing heritability caused by genetic heterogeneity is gene-based analysis, which considers the aggregate effects of multiple genetic variants in a single test. Another alternative approach is transcriptome-wide association study (TWAS). TWAS aggregates genomic information into functionally relevant units that map to genes and their expression. TWAS is not only powerful, but can also increase the interpretability in biological mechanisms of identified trait associated genes. In this study, we propose a powerful and computationally efficient gene-based association test, called Overall. Using extended Simes procedure, Overall aggregates information from three types of traditional gene-based association tests and also incorporates expression quantitative trait locus (eQTL) information into a gene-based association test using GWAS summary statistics. We show that after a small number of replications to estimate the correlation among the integrated gene-based tests, the p values of Overall can be calculated analytically. Simulation studies show that Overall can control type I error rates very well and has higher power than the tests that we compared with. We also apply Overall to two schizophrenia GWAS summary datasets and two lipids GWAS summary datasets. The results show that this newly developed method can identify more significant genes than other methods we compared with.
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Affiliation(s)
- Xuewei Cao
- Department of Mathematical Sciences, Michigan Technological University, Houghton, MI, 49931, USA
| | - Xuexia Wang
- Department of Mathematics, University of North Texas, Denton, TX, USA
| | - Shuanglin Zhang
- Department of Mathematical Sciences, Michigan Technological University, Houghton, MI, 49931, USA
| | - Qiuying Sha
- Department of Mathematical Sciences, Michigan Technological University, Houghton, MI, 49931, USA.
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Shashkova TI, Gorev DD, Pakhomov ED, Shadrina AS, Sharapov SZ, Tsepilov YA, Karssen LC, Aulchenko YS. The GWAS-MAP platform for aggregation of results of genome-wide association studies and the GWAS-MAP|homo database of 70 billion genetic associations of human traits. Vavilovskii Zhurnal Genet Selektsii 2020; 24:876-884. [PMID: 35088001 PMCID: PMC8763720 DOI: 10.18699/vj20.686] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Revised: 09/30/2020] [Accepted: 10/01/2020] [Indexed: 11/23/2022] Open
Abstract
Hundreds of genome-wide association studies (GWAS) of human traits are performed each year. The
results of GWAS are often published in the form of summary statistics. Information from summary statistics can
be used for multiple purposes – from fundamental research in biology and genetics to the search for potential
biomarkers and therapeutic targets. While the amount of GWAS summary statistics collected by the scientific community is rapidly increasing, the use of this data is limited by the lack of generally accepted standards. In particular,
the researchers who would like to use GWAS summary statistics in their studies have to become aware that the data
are scattered across multiple websites, are presented in a variety of formats, and, often, were not quality controlled.
Moreover, each available summary statistics analysis tools will ask for data to be presented in their own internal
format. To address these issues, we developed GWAS-MAP, a high-throughput platform for aggregating, storing,
analyzing, visualizing and providing access to a database of big data that result from region- and genome-wide
association studies. The database currently contains information on more than 70 billion associations between
genetic variants and human diseases, quantitative traits, and “omics” traits. The GWAS-MAP platform and database
can be used for studying the etiology of human diseases, building predictive risk models and finding potential biomarkers and therapeutic interventions. In order to demonstrate a typical application of the platform as an approach
for extracting new biological knowledge and establishing mechanistic hypotheses, we analyzed varicose veins, a
disease affecting on average every third adult in Russia. The results of analysis confirmed known epidemiologic associations for this disease and led us to propose a hypothesis that increased levels of MICB and CD209 proteins in
human plasma may increase susceptibility to varicose veins.
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Affiliation(s)
- T. I. Shashkova
- Laboratory of Theoretical and Applied Functional Genomics, Novosibirsk State University
| | - D. D. Gorev
- Laboratory of Theoretical and Applied Functional Genomics, Novosibirsk State University
| | - E. D. Pakhomov
- Laboratory of Theoretical and Applied Functional Genomics, Novosibirsk State University;
PolyKnomics BV
| | - A. S. Shadrina
- Laboratory of Theoretical and Applied Functional Genomics, Novosibirsk State University
| | - S. Zh. Sharapov
- Laboratory of Theoretical and Applied Functional Genomics, Novosibirsk State University
| | - Y. A. Tsepilov
- Laboratory of Theoretical and Applied Functional Genomics, Novosibirsk State University
| | | | - Y. S. Aulchenko
- Laboratory of Theoretical and Applied Functional Genomics, Novosibirsk State University
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Knutson KA, Deng Y, Pan W. Implicating causal brain imaging endophenotypes in Alzheimer's disease using multivariable IWAS and GWAS summary data. Neuroimage 2020; 223:117347. [PMID: 32898681 PMCID: PMC7778364 DOI: 10.1016/j.neuroimage.2020.117347] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2020] [Revised: 08/24/2020] [Accepted: 08/28/2020] [Indexed: 02/06/2023] Open
Abstract
Recent evidence suggests the existence of many undiscovered heritable brain phenotypes involved in Alzheimer's Disease (AD) pathogenesis. This finding necessitates methods for the discovery of causal brain changes in AD that integrate Magnetic Resonance Imaging measures and genotypic data. However, existing approaches for causal inference in this setting, such as the univariate Imaging Wide Association Study (UV-IWAS), suffer from inconsistent effect estimation and inflated Type I errors in the presence of genetic pleiotropy, the phenomenon in which a variant affects multiple causal intermediate risk phenotypes. In this study, we implement a multivariate extension to the IWAS model, namely MV-IWAS, to consistently estimate and test for the causal effects of multiple brain imaging endophenotypes from the Alzheimer's Disease Neuroimaging Initiative (ADNI) in the presence of pleiotropic and possibly correlated SNPs. We further extend MV-IWAS to incorporate variant-specific direct effects on AD, analogous to the existing Egger regression Mendelian Randomization approach, which allows for testing of remaining pleiotropy after adjusting for multiple intermediate pathways. We propose a convenient approach for implementing MV-IWAS that solely relies on publicly available GWAS summary data and a reference panel. Through simulations with either individual-level or summary data, we demonstrate the well controlled Type I errors and superior power of MV-IWAS over UV-IWAS in the presence of pleiotropic SNPs. We apply the summary statistic based tests to 1578 heritable imaging derived phenotypes (IDPs) from the UK Biobank. MV-IWAS detected numerous IDPs as possible false positives by UV-IWAS while uncovering many additional causal neuroimaging phenotypes in AD which are strongly supported by the existing literature.
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Affiliation(s)
- Katherine A Knutson
- Division of Biostatistics, University of Minnesota, Minneapolis, Minnesota United States
| | - Yangqing Deng
- Division of Biostatistics, University of Minnesota, Minneapolis, Minnesota United States
| | - Wei Pan
- Division of Biostatistics, University of Minnesota, Minneapolis, Minnesota United States.
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Svishcheva GR, Belonogova NM, Zorkoltseva IV, Kirichenko AV, Axenovich TI. Gene-based association tests using GWAS summary statistics. Bioinformatics 2020; 35:3701-3708. [PMID: 30860568 DOI: 10.1093/bioinformatics/btz172] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2018] [Revised: 02/12/2019] [Accepted: 03/11/2019] [Indexed: 01/09/2023] Open
Abstract
MOTIVATION A huge number of genome-wide association studies (GWAS) summary statistics freely available in databases provide a new material for gene-based association analysis aimed at identifying rare genetic variants. Only a few of the many popular gene-based methods developed for individual genotype and phenotype data are adapted for the practical use of the GWAS summary statistics as input. RESULTS We analytically prove and numerically illustrate that all popular powerful methods developed for gene-based association analysis of individual phenotype and genotype data can be modified to utilize GWAS summary statistics. We have modified and implemented all of the popular methods, including burden and kernel machine-based tests, multiple and functional linear regression, principal components analysis and others, in the R package sumFREGAT. Using real summary statistics for coronary artery disease, we show that the new package is able to detect genes not found by the existing packages. AVAILABILITY AND IMPLEMENTATION The R package sumFREGAT is freely and publicly available at: https://CRAN.R-project.org/package=sumFREGAT. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Gulnara R Svishcheva
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia.,Vavilov Institute of General Genetics, the Russian Academy of Sciences, Moscow, Russia
| | - Nadezhda M Belonogova
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
| | - Irina V Zorkoltseva
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
| | - Anatoly V Kirichenko
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
| | - Tatiana I Axenovich
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia.,Department of Natural Sciences, Novosibirsk State University, Novosibirsk, Russia.,Department of Biotechnology, L.K. Ernst Federal Center for Animal Husbandry, Dubrovitsy, Russia
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12
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Deng Y, Pan W. A powerful and versatile colocalization test. PLoS Comput Biol 2020; 16:e1007778. [PMID: 32275709 PMCID: PMC7176287 DOI: 10.1371/journal.pcbi.1007778] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2019] [Revised: 04/22/2020] [Accepted: 03/08/2020] [Indexed: 12/17/2022] Open
Abstract
Transcriptome-wide association studies (TWAS and PrediXcan) have been increasingly applied to detect associations between genetically predicted gene expressions and GWAS traits, which may suggest, however do not completely determine, causal genes for GWAS traits, due to the likely violation of their imposed strong assumptions for causal inference. Testing colocalization moves it closer to establishing causal relationships: if a GWAS trait and a gene's expression share the same associated SNP, it may suggest a regulatory (and thus putative causal) role of the SNP mediated through the gene on the GWAS trait. Accordingly, it is of interest to develop and apply various colocalization testing approaches. The existing approaches may each have some severe limitations. For instance, some methods test the null hypothesis that there is colocalization, which is not ideal because often the null hypothesis cannot be rejected simply due to limited statistical power (with too small sample sizes). Some other methods arbitrarily restrict the maximum number of causal SNPs in a locus, which may lead to loss of power in the presence of wide-spread allelic heterogeneity. Importantly, most methods cannot be applied to either GWAS/eQTL summary statistics or cases with more than two possibly correlated traits. Here we present a simple and general approach based on conditional analysis of a locus on multiple traits, overcoming the above and other shortcomings of the existing methods. We demonstrate that, compared with other methods, our new method can be applied to a wider range of scenarios and often perform better. We showcase its applications to both simulated and real data, including a large-scale Alzheimer's disease GWAS summary dataset and a gene expression dataset, and a large-scale blood lipid GWAS summary association dataset. An R package "jointsum" implementing the proposed method is publicly available at github.
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Affiliation(s)
- Yangqing Deng
- Division of Biostatistics, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Wei Pan
- Division of Biostatistics, University of Minnesota, Minneapolis, Minnesota, United States of America
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Shared genetic etiology underlying Alzheimer's disease and major depressive disorder. Transl Psychiatry 2020; 10:88. [PMID: 32152295 PMCID: PMC7062839 DOI: 10.1038/s41398-020-0769-y] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Revised: 02/14/2020] [Accepted: 02/25/2020] [Indexed: 01/22/2023] Open
Abstract
Patients with late-onset Alzheimer's disease (LOAD) frequently manifest comorbid neuropsychiatric symptoms with depression and anxiety being most frequent, and individuals with major depressive disorder (MDD) have an increased prevalence of LOAD. This suggests shared etiologies and intersecting pathways between LOAD and MDD. We performed pleiotropy analyses using LOAD and MDD GWAS data sets from the International Genomics of Alzheimer's Project (IGAP) and the Psychiatric Genomics Consortium (PGC), respectively. We found a moderate enrichment for SNPs associated with LOAD across increasingly stringent levels of significance with the MDD GWAS association (LOAD|MDD), of maximum four and eightfolds, including and excluding the APOE-region, respectively. Association analysis excluding the APOE-region identified numerous SNPs corresponding to 40 genes, 9 of which are known LOAD-risk loci primarily in chromosome 11 regions that contain the SPI1 gene and MS4A genes cluster, and others were novel pleiotropic risk-loci for LOAD conditional with MDD. The most significant associated SNPs on chromosome 11 overlapped with eQTLs found in whole-blood and monocytes, suggesting functional roles in gene regulation. The reverse conditional association analysis (MDD|LOAD) showed a moderate level, ~sevenfold, of polygenic overlap, however, no SNP showed significant association. Pathway analyses replicated previously reported LOAD biological pathways related to immune response and regulation of endocytosis. In conclusion, we provide insights into the overlapping genetic signatures underpinning the common phenotypic manifestations and inter-relationship between LOAD and MDD. This knowledge is crucial to the development of actionable targets for novel therapies to treat depression preceding dementia, in an effort to delay or ultimately prevent the onset of LOAD.
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