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Chen DM, Dong R, Kachuri L, Hoffmann TJ, Jiang Y, Berndt SI, Shelley JP, Schaffer KR, Machiela MJ, Freedman ND, Huang WY, Li SA, Lilja H, Justice AC, Madduri RK, Rodriguez AA, Van Den Eeden SK, Chanock SJ, Haiman CA, Conti DV, Klein RJ, Mosley JD, Witte JS, Graff RE. Transcriptome-wide association analysis identifies candidate susceptibility genes for prostate-specific antigen levels in men without prostate cancer. HGG ADVANCES 2024; 5:100315. [PMID: 38845201 DOI: 10.1016/j.xhgg.2024.100315] [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: 01/17/2024] [Revised: 05/31/2024] [Accepted: 06/03/2024] [Indexed: 06/18/2024] Open
Abstract
Deciphering the genetic basis of prostate-specific antigen (PSA) levels may improve their utility for prostate cancer (PCa) screening. Using genome-wide association study (GWAS) summary statistics from 95,768 PCa-free men, we conducted a transcriptome-wide association study (TWAS) to examine impacts of genetically predicted gene expression on PSA. Analyses identified 41 statistically significant (p < 0.05/12,192 = 4.10 × 10-6) associations in whole blood and 39 statistically significant (p < 0.05/13,844 = 3.61 × 10-6) associations in prostate tissue, with 18 genes associated in both tissues. Cross-tissue analyses identified 155 statistically significantly (p < 0.05/22,249 = 2.25 × 10-6) genes. Out of 173 unique PSA-associated genes across analyses, we replicated 151 (87.3%) in a TWAS of 209,318 PCa-free individuals from the Million Veteran Program. Based on conditional analyses, we found 20 genes (11 single tissue, nine cross-tissue) that were associated with PSA levels in the discovery TWAS that were not attributable to a lead variant from a GWAS. Ten of these 20 genes replicated, and two of the replicated genes had colocalization probability of >0.5: CCNA2 and HIST1H2BN. Six of the 20 identified genes are not known to impact PCa risk. Fine-mapping based on whole blood and prostate tissue revealed five protein-coding genes with evidence of causal relationships with PSA levels. Of these five genes, four exhibited evidence of colocalization and one was conditionally independent of previous GWAS findings. These results yield hypotheses that should be further explored to improve understanding of genetic factors underlying PSA levels.
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Affiliation(s)
- Dorothy M Chen
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Ruocheng Dong
- Department of Epidemiology and Population Health, Stanford University, Stanford, CA 94305, USA
| | - Linda Kachuri
- Department of Epidemiology and Population Health, Stanford University, Stanford, CA 94305, USA; Stanford Cancer Institute, Stanford University, Stanford, CA 94305, USA
| | - Thomas J Hoffmann
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA 94158, USA; Institute for Human Genetics, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Yu Jiang
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Sonja I Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD 20814, USA
| | - John P Shelley
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Kerry R Schaffer
- Department of Internal Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Mitchell J Machiela
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD 20814, USA
| | - Neal D Freedman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD 20814, USA
| | - Wen-Yi Huang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD 20814, USA
| | - Shengchao A Li
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD 20814, USA
| | - Hans Lilja
- Departments of Pathology and Laboratory Medicine, Surgery, Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Department of Translational Medicine, Lund University, 21428 Malmö, Sweden
| | | | | | | | | | - Stephen J Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD 20814, USA
| | - Christopher A Haiman
- Center for Genetic Epidemiology, Department of Population and Preventive Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90032, USA; Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - David V Conti
- Center for Genetic Epidemiology, Department of Population and Preventive Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90032, USA; Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Robert J Klein
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Jonathan D Mosley
- Departments of Internal Medicine and Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - John S Witte
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA 94158, USA; Department of Epidemiology and Population Health, Stanford University, Stanford, CA 94305, USA; Departments of Biomedical Data Science and Genetics (by courtesy), Stanford University, Stanford, CA 94305, USA.
| | - Rebecca E Graff
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA 94158, USA.
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2
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Keener R, Chhetri SB, Connelly CJ, Taub MA, Conomos MP, Weinstock J, Ni B, Strober B, Aslibekyan S, Auer PL, Barwick L, Becker LC, Blangero J, Bleecker ER, Brody JA, Cade BE, Celedon JC, Chang YC, Cupples LA, Custer B, Freedman BI, Gladwin MT, Heckbert SR, Hou L, Irvin MR, Isasi CR, Johnsen JM, Kenny EE, Kooperberg C, Minster RL, Naseri T, Viali S, Nekhai S, Pankratz N, Peyser PA, Taylor KD, Telen MJ, Wu B, Yanek LR, Yang IV, Albert C, Arnett DK, Ashley-Koch AE, Barnes KC, Bis JC, Blackwell TW, Boerwinkle E, Burchard EG, Carson AP, Chen Z, Chen YDI, Darbar D, de Andrade M, Ellinor PT, Fornage M, Gelb BD, Gilliland FD, He J, Islam T, Kaab S, Kardia SLR, Kelly S, Konkle BA, Kumar R, Loos RJF, Martinez FD, McGarvey ST, Meyers DA, Mitchell BD, Montgomery CG, North KE, Palmer ND, Peralta JM, Raby BA, Redline S, Rich SS, Roden D, Rotter JI, Ruczinski I, Schwartz D, Sciurba F, Shoemaker MB, Silverman EK, Sinner MF, Smith NL, Smith AV, Tiwari HK, Vasan RS, Weiss ST, Williams LK, Zhang Y, Ziv E, Raffield LM, Reiner AP, Arvanitis M, Greider CW, Mathias RA, Battle A. Validation of human telomere length multi-ancestry meta-analysis association signals identifies POP5 and KBTBD6 as human telomere length regulation genes. Nat Commun 2024; 15:4417. [PMID: 38789417 PMCID: PMC11126610 DOI: 10.1038/s41467-024-48394-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 04/26/2024] [Indexed: 05/26/2024] Open
Abstract
Genome-wide association studies (GWAS) have become well-powered to detect loci associated with telomere length. However, no prior work has validated genes nominated by GWAS to examine their role in telomere length regulation. We conducted a multi-ancestry meta-analysis of 211,369 individuals and identified five novel association signals. Enrichment analyses of chromatin state and cell-type heritability suggested that blood/immune cells are the most relevant cell type to examine telomere length association signals. We validated specific GWAS associations by overexpressing KBTBD6 or POP5 and demonstrated that both lengthened telomeres. CRISPR/Cas9 deletion of the predicted causal regions in K562 blood cells reduced expression of these genes, demonstrating that these loci are related to transcriptional regulation of KBTBD6 and POP5. Our results demonstrate the utility of telomere length GWAS in the identification of telomere length regulation mechanisms and validate KBTBD6 and POP5 as genes affecting telomere length regulation.
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Grants
- 5K12GM123914 U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS)
- R01AG069120 U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS)
- R35GM139580 U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS)
- R01 DK071891 NIDDK NIH HHS
- R01HL153805 U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS)
- R01AG081244 U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS)
- R35CA209974 U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS)
- R01HL105756 U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (NHLBI)
- R01HL68959 U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (NHLBI)
- R01HL079915 U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (NHLBI)
- R01HL87681 U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (NHLBI)
- R01 HL153805 NHLBI NIH HHS
- R01HL-120393 U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS)
- U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS)
- U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (NHLBI)
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Affiliation(s)
- Rebecca Keener
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Surya B Chhetri
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Carla J Connelly
- Department of Molecular Biology and Genetics, Johns Hopkins University, Baltimore, MD, USA
| | - Margaret A Taub
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Matthew P Conomos
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA, USA
| | - Joshua Weinstock
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Bohan Ni
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Benjamin Strober
- Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA
| | | | - Paul L Auer
- Division of Biostatistics, Institute for Health & Equity, and Cancer Center, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Lucas Barwick
- LTRC Data Coordinating Center, The Emmes Company, LLC, Rockville, MD, USA
| | - Lewis C Becker
- GeneSTAR Research Program, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - John Blangero
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA
| | - Eugene R Bleecker
- Department of Medicine, Division of Genetics, Genomics and Precision Medicine, University of Arizona, Tucson, AZ, USA
- Division of Pharmacogenomics, University of Arizona, Tucson, AZ, USA
| | - Jennifer A Brody
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Brian E Cade
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA
| | - Juan C Celedon
- Division of Pediatric Pulmonary Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Yi-Cheng Chang
- Department of Internal Medicine, National Taiwan University, Taipei, Taiwan
- Graduate Institute of Medical Genomics and Proteomics, National Taiwan University, Taipei, Taiwan
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - L Adrienne Cupples
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- The National Heart, Lung, and Blood Institute, Boston University's Framingham Heart Study, Framingham, MA, USA
| | - Brian Custer
- Vitalant Research Institute, San Francisco, CA, USA
- Department of Laboratory Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Barry I Freedman
- Internal Medicine - Nephrology, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Mark T Gladwin
- School of Medicine, University of Maryland, Baltimore, MD, USA
| | - Susan R Heckbert
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Lifang Hou
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Evanston, IL, USA
| | - Marguerite R Irvin
- Department of Epidemiology, University of Alabama Birmingham, Birmingham, AL, USA
| | - Carmen R Isasi
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Jill M Johnsen
- Department of Medicine and Institute for Stem Cell & Regenerative Medicine, University of Washington, Seattle, WA, USA
| | - Eimear E Kenny
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Center for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Ryan L Minster
- Department of Human Genetics, University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA, USA
| | - Take Naseri
- Naseri & Associates Public Health Consultancy Firm and Family Health Clinic, Apia, Samoa
- International Health Institute, School of Public Health, Brown University, Providence, RI, USA
| | - Satupa'itea Viali
- Oceania University of Medicine, Apia, Samoa
- School of Medicine, National University of Samoa, Apia, Samoa
- Department of Chronic Disease Epidemiology, Yale University School of Public Health, New Haven, CT, USA
| | - Sergei Nekhai
- Center for Sickle Cell Disease and Department of Medicine, College of Medicine, Howard University, Washington DC, USA
| | - Nathan Pankratz
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA
| | - Patricia A Peyser
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Kent D Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Marilyn J Telen
- Department of Medicine, Duke University Medical Center, Durham, NC, USA
| | - Baojun Wu
- Center for Individualized and Genomic Medicine Research (CIGMA), Department of Internal Medicine, Henry Ford Health System, Detroit, MI, USA
| | - Lisa R Yanek
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Ivana V Yang
- Departments of Biomedical Informatics, Medicine, and Epidemiology, University of Colorado, Boulder, CO, USA
| | - Christine Albert
- Harvard Medical School, Boston, MA, USA
- Division of Cardiovascular, Brigham and Women's Hospital, Boston, MA, USA
| | - Donna K Arnett
- Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC, USA
| | | | - Kathleen C Barnes
- Department of Medicine, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO, USA
| | - Joshua C Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Thomas W Blackwell
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Eric Boerwinkle
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Esteban G Burchard
- Department of Medicine, University of California San Francisco, San Francisco, CA, USA
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - April P Carson
- Department of Medicine, University of Mississippi Medical Center, Jackson, MI, USA
| | - Zhanghua Chen
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA
| | - Yii-Der Ida Chen
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Dawood Darbar
- Division of Cardiology, University of Illinois at Chicago, Chicago, IL, USA
| | - Mariza de Andrade
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA
| | - Patrick T Ellinor
- Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Myriam Fornage
- Institute of Molecular Medicine, McGovern Medical School, the University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Bruce D Gelb
- Mindich Child Health and Development Institute and Departments of Pediatrics and Genetics and Genomic Sciences, Icahn School of Medicine, New York, NY, USA
| | - Frank D Gilliland
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA
| | - Jiang He
- Department of Medicine, Tulane University School of Medicine, New Orleans, LA, USA
| | - Talat Islam
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA
| | - Stefan Kaab
- Department of Cardiology, University Hospital, LMU Munich, Munich, Germany
| | - Sharon L R Kardia
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Shannon Kelly
- Vitalant Research Institute, San Francisco, CA, USA
- University of California San Francisco Benioff Children's Hospital, Oakland, CA, USA
| | - Barbara A Konkle
- Department of Medicine, University of Washington, Seattle, WA, USA
| | - Rajesh Kumar
- Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- The Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA
| | - Ruth J F Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Fernando D Martinez
- Asthma & Airway Disease Research Center, University of Arizona, Tucson, AZ, USA
| | - Stephen T McGarvey
- Department of Epidemiology & International Health Institute, Brown University School of Public Health, Providence, RI, USA
| | - Deborah A Meyers
- Department of Medicine, Division of Genetics, Genomics and Precision Medicine, University of Arizona, Tucson, AZ, USA
- Division of Pharmacogenomics, University of Arizona, Tucson, AZ, USA
| | - Braxton D Mitchell
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Courtney G Montgomery
- Genes and Human Disease, Oklahoma Medical Research Foundation, Oklahoma City, OK, USA
| | - Kari E North
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Nicholette D Palmer
- Department of Biochemistry, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Juan M Peralta
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA
| | - Benjamin A Raby
- Division of Pulmonary and Critical Care, Brigham and Women's Hospital, Boston, MA, USA
- Division of Pulmonary Medicine, Boston Children's Hospital, Boston, MA, USA
| | - Susan Redline
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA
| | - Stephen S Rich
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - Dan Roden
- Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Ingo Ruczinski
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - David Schwartz
- Departments of Medicine and Immunology, University of Colorado, Boulder, CO, USA
| | - Frank Sciurba
- Division of Pulmonary, Allergy and Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - M Benjamin Shoemaker
- Departments of Medicine, Pharmacology, and Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Edwin K Silverman
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Moritz F Sinner
- Department of Cardiology, University Hospital, LMU Munich, Munich, Germany
| | - Nicholas L Smith
- Department of Medicine, Tulane University School of Medicine, New Orleans, LA, USA
| | - Albert V Smith
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Hemant K Tiwari
- Department of Biostatistics, University of Alabama Birmingham, Birmingham, AL, USA
| | | | - Scott T Weiss
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - L Keoki Williams
- Center for Individualized and Genomic Medicine Research (CIGMA), Department of Internal Medicine, Henry Ford Health System, Detroit, MI, USA
| | - Yingze Zhang
- Division of Pulmonary Allergy and Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Elad Ziv
- Department of Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Laura M Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Alexander P Reiner
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Marios Arvanitis
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD, USA
| | - Carol W Greider
- Department of Molecular Cell and Developmental Biology, University of California Santa Cruz, Santa Cruz, CA, USA
- University Professor Johns Hopkins University, Baltimore, MD, USA
| | - Rasika A Mathias
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - Alexis Battle
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
- Department of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA.
- Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, USA.
- Data Science and AI Institute, Johns Hopkins University, Baltimore, MD, USA.
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3
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Eulalio T, Sun MW, Gevaert O, Greicius MD, Montine TJ, Nachun D, Montgomery SB. regionalpcs: improved discovery of DNA methylation associations with complex traits. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.01.590171. [PMID: 38746367 PMCID: PMC11092597 DOI: 10.1101/2024.05.01.590171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
We have developed the regional principal components (rPCs) method, a novel approach for summarizing gene-level methylation. rPCs address the challenge of deciphering complex epigenetic mechanisms in diseases like Alzheimer's disease (AD). In contrast to traditional averaging, rPCs leverage principal components analysis to capture complex methylation patterns across gene regions. Our method demonstrated a 54% improvement in sensitivity over averaging in simulations, offering a robust framework for identifying subtle epigenetic variations. Applying rPCs to the AD brain methylation data in ROSMAP, combined with cell type deconvolution, we uncovered 838 differentially methylated genes associated with neuritic plaque burden-significantly outperforming conventional methods. Integrating methylation quantitative trait loci (meQTL) with genome-wide association studies (GWAS) identified 17 genes with potential causal roles in AD, including MS4A4A and PICALM. Our approach is available in the Bioconductor package regionalpcs, opening avenues for research and facilitating a deeper understanding of the epigenetic landscape in complex diseases.
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Affiliation(s)
- Tiffany Eulalio
- Department of Biomedical Data Science, Stanford University, Stanford, CA, 94305, USA
| | - Min Woo Sun
- Department of Biomedical Data Science, Stanford University, Stanford, CA, 94305, USA
| | - Olivier Gevaert
- Department of Biomedical Data Science, Stanford University, Stanford, CA, 94305, USA
| | - Michael D Greicius
- Department of Neurology & Neurological Sciences, Stanford University, Stanford, CA, 94305, USA
| | - Thomas J Montine
- Department of Pathology, Stanford University, Stanford, CA, 94305, USA
| | - Daniel Nachun
- Department of Pathology, Stanford University, Stanford, CA, 94305, USA
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4
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De Almeida SD, Richter GM, de Coo A, Jepsen S, Kapferer-Seebacher I, Dommisch H, Berger K, Laudes M, Lieb W, Loos BG, van der Velde N, van Schoor N, de Groot L, Blanco J, Carracedo A, Cruz R, Schaefer AS. A genome-wide association study meta-analysis in a European sample of stage III/IV grade C periodontitis patients ≤35 years of age identifies new risk loci. J Clin Periodontol 2024; 51:431-440. [PMID: 38140892 DOI: 10.1111/jcpe.13922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 11/07/2023] [Accepted: 12/02/2023] [Indexed: 12/24/2023]
Abstract
AIM Few genome-wide association studies (GWAS) have been conducted for severe forms of periodontitis (stage III/IV grade C), and the number of known risk genes is scarce. To identify further genetic risk variants to improve the understanding of the disease aetiology, a GWAS meta-analysis in cases with a diagnosis at ≤35 years of age was performed. MATERIALS AND METHODS Genotypes from German, Dutch and Spanish GWAS studies of III/IV-C periodontitis diagnosed at age ≤35 years were imputed using TopMed. After quality control, a meta-analysis was conducted on 8,666,460 variants in 1306 cases and 7817 controls with METAL. Variants were prioritized using FUMA for gene-based tests, functional annotation and a transcriptome-wide association study integrating eQTL data. RESULTS The study identified a novel genome-wide significant association in the FCER1G gene (p = 1.0 × 10-9 ), which was previously suggestively associated with III/IV-C periodontitis. Six additional genes showed suggestive association with p < 10-5 , including the known risk gene SIGLEC5. HMCN2 showed the second strongest association in this study (p = 6.1 × 10-8 ). CONCLUSIONS This study expands the set of known genetic loci for severe periodontitis with an age of onset ≤35 years. The putative functions ascribed to the associated genes highlight the significance of oral barrier tissue stability, wound healing and tissue regeneration in the aetiology of these periodontitis forms and suggest the importance of tissue regeneration in maintaining oral health.
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Affiliation(s)
- Silvia Diz De Almeida
- Centro Singular de Investigación en Medicina Molecular y Enfermedades Crónicas (CIMUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER-ISCIII), Instituto de Salud Carlos III, Madrid, Spain
| | - Gesa M Richter
- Department of Periodontology, Oral Medicine and Oral Surgery, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Alicia de Coo
- Centro Singular de Investigación en Medicina Molecular y Enfermedades Crónicas (CIMUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain
| | - Søren Jepsen
- Department of Periodontology, Operative and Preventive Dentistry, University of Bonn, Bonn, Germany
| | - Ines Kapferer-Seebacher
- Department of Dental and Oral Medicine and Cranio-Maxillofacial and Oral Surgery, University Hospital for Conservative Dentistry and Periodontology, Medical University Innsbruck, Innsbruck, Austria
| | - Henrik Dommisch
- Department of Periodontology, Oral Medicine and Oral Surgery, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Klaus Berger
- Institute of Epidemiology and Social Medicine, University Münster, Münster, Germany
| | - Matthias Laudes
- Institute of Diabetes and Clinical Metabolic Research, University Medical Center Schleswig-Holstein, Campus Kiel, Kiel, Germany
| | - Wolfgang Lieb
- Institute of Epidemiology, Christian-Albrechts-University, Kiel, Germany
| | - Bruno G Loos
- Department of Periodontology and Oral Biochemistry, Academic Centre for Dentistry Amsterdam (ACTA), University of Amsterdam and Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Nathalie van der Velde
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
- Department of Internal Medicine Section of Geriatrics, Amsterdam Medical Center, Amsterdam, The Netherlands
| | - Natasja van Schoor
- Department of Epidemiology and Data Science, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Lisette de Groot
- Division of Human Nutrition, Wageningen University, Wageningen, The Netherlands
| | - Juan Blanco
- Research Group of Medical-Surgery Dentistry (OMEQUI), Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain
| | - Angel Carracedo
- Centro Singular de Investigación en Medicina Molecular y Enfermedades Crónicas (CIMUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER-ISCIII), Instituto de Salud Carlos III, Madrid, Spain
- Fundación Pública Galega de Medicina Xenómica, Sistema Galego de Saúde (SERGAS) Santiago de Compostela, Santiago de Compostela, Spain
- Genetics Research Group, Instituto de Investigación Sanitaria de Santiago (IDIS), Santiago de Compostela, Spain
| | - Raquel Cruz
- Centro Singular de Investigación en Medicina Molecular y Enfermedades Crónicas (CIMUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER-ISCIII), Instituto de Salud Carlos III, Madrid, Spain
| | - Arne S Schaefer
- Department of Periodontology, Oral Medicine and Oral Surgery, Charité - Universitätsmedizin Berlin, Berlin, Germany
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5
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Lappalainen T, Li YI, Ramachandran S, Gusev A. Genetic and molecular architecture of complex traits. Cell 2024; 187:1059-1075. [PMID: 38428388 DOI: 10.1016/j.cell.2024.01.023] [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: 10/03/2023] [Revised: 12/20/2023] [Accepted: 01/16/2024] [Indexed: 03/03/2024]
Abstract
Human genetics has emerged as one of the most dynamic areas of biology, with a broadening societal impact. In this review, we discuss recent achievements, ongoing efforts, and future challenges in the field. Advances in technology, statistical methods, and the growing scale of research efforts have all provided many insights into the processes that have given rise to the current patterns of genetic variation. Vast maps of genetic associations with human traits and diseases have allowed characterization of their genetic architecture. Finally, studies of molecular and cellular effects of genetic variants have provided insights into biological processes underlying disease. Many outstanding questions remain, but the field is well poised for groundbreaking discoveries as it increases the use of genetic data to understand both the history of our species and its applications to improve human health.
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Affiliation(s)
- Tuuli Lappalainen
- New York Genome Center, New York, NY, USA; Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden.
| | - Yang I Li
- Section of Genetic Medicine, University of Chicago, Chicago, IL, USA; Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Sohini Ramachandran
- Ecology, Evolution and Organismal Biology, Center for Computational Molecular Biology, and the Data Science Institute, Brown University, Providence, RI 029129, USA
| | - Alexander Gusev
- Harvard Medical School and Dana-Farber Cancer Institute, Boston, MA, USA
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6
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Jia K, Shen J. Transcriptome-wide association studies associated with Crohn's disease: challenges and perspectives. Cell Biosci 2024; 14:29. [PMID: 38403629 PMCID: PMC10895848 DOI: 10.1186/s13578-024-01204-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 02/04/2024] [Indexed: 02/27/2024] Open
Abstract
Crohn's disease (CD) is regarded as a lifelong progressive disease affecting all segments of the intestinal tract and multiple organs. Based on genome-wide association studies (GWAS) and gene expression data, transcriptome-wide association studies (TWAS) can help identify susceptibility genes associated with pathogenesis and disease behavior. In this review, we overview seven reported TWASs of CD, summarize their study designs, and discuss the key methods and steps used in TWAS, which affect the prioritization of susceptibility genes. This article summarized the screening of tissue-specific susceptibility genes for CD, and discussed the reported potential pathological mechanisms of overlapping susceptibility genes related to CD in a certain tissue type. We observed that ileal lipid-related metabolism and colonic extracellular vesicles may be involved in the pathogenesis of CD by performing GO pathway enrichment analysis for susceptibility genes. We further pointed the low reproducibility of TWAS associated with CD and discussed the reasons for these issues, strategies for solving them. In the future, more TWAS are needed to be designed into large-scale, unified cohorts, unified analysis pipelines, and fully classified databases of expression trait loci.
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Affiliation(s)
- Keyu Jia
- Laboratory of Medicine, Baoshan Branch, Ren Ji Hospital, School of Medicine, Nephrology department, Shanghai Jiao Tong University, 1058 Huanzhen Northroad, Shanghai, 200444, China
| | - Jun Shen
- Laboratory of Medicine, Baoshan Branch, Ren Ji Hospital, School of Medicine, Nephrology department, Shanghai Jiao Tong University, 1058 Huanzhen Northroad, Shanghai, 200444, China.
- Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Inflammatory Bowel Research Center, Ren Ji Hospital, School of Medicine, Shanghai Institute of Digestive Disease, Shanghai Jiao Tong University, Shanghai, China.
- NHC Key Laboratory of Digestive Diseases, Renji Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
- Division of Gastroenterology and Hepatology, Baoshan Branch, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
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7
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Zhao S, Crouse W, Qian S, Luo K, Stephens M, He X. Adjusting for genetic confounders in transcriptome-wide association studies improves discovery of risk genes of complex traits. Nat Genet 2024; 56:336-347. [PMID: 38279041 PMCID: PMC10864181 DOI: 10.1038/s41588-023-01648-9] [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: 12/20/2022] [Accepted: 12/14/2023] [Indexed: 01/28/2024]
Abstract
Many methods have been developed to leverage expression quantitative trait loci (eQTL) data to nominate candidate genes from genome-wide association studies. These methods, including colocalization, transcriptome-wide association studies (TWAS) and Mendelian randomization-based methods; however, all suffer from a key problem-when assessing the role of a gene in a trait using its eQTLs, nearby variants and genetic components of other genes' expression may be correlated with these eQTLs and have direct effects on the trait, acting as potential confounders. Our extensive simulations showed that existing methods fail to account for these 'genetic confounders', resulting in severe inflation of false positives. Our new method, causal-TWAS (cTWAS), borrows ideas from statistical fine-mapping and allows us to adjust all genetic confounders. cTWAS showed calibrated false discovery rates in simulations, and its application on several common traits discovered new candidate genes. In conclusion, cTWAS provides a robust statistical framework for gene discovery.
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Affiliation(s)
- Siming Zhao
- Department of Biomedical Data Science, Dartmouth College, Hanover, NH, USA.
- Department of Human Genetics, University of Chicago, Chicago, IL, USA.
- Dartmouth Cancer Center, Lebanon, NH, USA.
| | - Wesley Crouse
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Sheng Qian
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Kaixuan Luo
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Matthew Stephens
- Department of Human Genetics, University of Chicago, Chicago, IL, USA.
- Department of Statistics, University of Chicago, Chicago, IL, USA.
| | - Xin He
- Department of Human Genetics, University of Chicago, Chicago, IL, USA.
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8
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Cabana-Domínguez J, Llonga N, Arribas L, Alemany S, Vilar-Ribó L, Demontis D, Fadeuilhe C, Corrales M, Richarte V, Børglum AD, Ramos-Quiroga JA, Soler Artigas M, Ribasés M. Transcriptomic risk scores for attention deficit/hyperactivity disorder. Mol Psychiatry 2023; 28:3493-3502. [PMID: 37537283 PMCID: PMC10618083 DOI: 10.1038/s41380-023-02200-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 07/17/2023] [Accepted: 07/21/2023] [Indexed: 08/05/2023]
Abstract
Attention deficit/hyperactivity disorder (ADHD) is a highly heritable neurodevelopmental disorder. We performed a transcriptome-wide association study (TWAS) using the latest genome-wide association study (GWAS) meta-analysis, in 38,691 individuals with ADHD and 186,843 controls, and 14 gene-expression reference panels across multiple brain tissues and whole blood. Based on TWAS results, we selected subsets of genes and constructed transcriptomic risk scores (TRSs) for the disorder in peripheral blood mononuclear cells of individuals with ADHD and controls. We found evidence of association between ADHD and TRSs constructed using expression profiles from multiple brain areas, with individuals with ADHD carrying a higher burden of TRSs than controls. TRSs were uncorrelated with the polygenic risk score (PRS) for ADHD and, in combination with PRS, improved significantly the proportion of variance explained over the PRS-only model. These results support the complementary predictive potential of genetic and transcriptomic profiles in blood and underscore the potential utility of gene expression for risk prediction and deeper insight in molecular mechanisms underlying ADHD.
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Affiliation(s)
- Judit Cabana-Domínguez
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain.
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain.
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Madrid, Spain.
- Department of Genetics, Microbiology, and Statistics, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain.
| | - Natalia Llonga
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain
| | - Lorena Arribas
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain
| | - Silvia Alemany
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain
| | - Laura Vilar-Ribó
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Madrid, Spain
| | - Ditte Demontis
- Department of Biomedicine/Human Genetics, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Christian Fadeuilhe
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Madrid, Spain
- Department of Psychiatry and Forensic Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Montse Corrales
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Madrid, Spain
- Department of Psychiatry and Forensic Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Vanesa Richarte
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Madrid, Spain
- Department of Psychiatry and Forensic Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Anders D Børglum
- Department of Biomedicine/Human Genetics, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Josep Antoni Ramos-Quiroga
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Madrid, Spain
- Department of Psychiatry and Forensic Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - María Soler Artigas
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain.
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain.
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Madrid, Spain.
- Department of Genetics, Microbiology, and Statistics, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain.
| | - Marta Ribasés
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain.
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain.
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Madrid, Spain.
- Department of Genetics, Microbiology, and Statistics, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain.
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9
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Ursini G, Di Carlo P, Mukherjee S, Chen Q, Han S, Kim J, Deyssenroth M, Marsit CJ, Chen J, Hao K, Punzi G, Weinberger DR. Prioritization of potential causative genes for schizophrenia in placenta. Nat Commun 2023; 14:2613. [PMID: 37188697 PMCID: PMC10185564 DOI: 10.1038/s41467-023-38140-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 04/17/2023] [Indexed: 05/17/2023] Open
Abstract
Our earlier work has shown that genomic risk for schizophrenia converges with early life complications in affecting risk for the disorder and sex-biased neurodevelopmental trajectories. Here, we identify specific genes and potential mechanisms that, in placenta, may mediate such outcomes. We performed TWAS in healthy term placentae (N = 147) to derive candidate placental causal genes that we confirmed with SMR; to search for placenta and schizophrenia-specific associations, we performed an analogous analysis in fetal brain (N = 166) and additional placenta TWAS for other disorders/traits. The analyses in the whole sample and stratifying by sex ultimately highlight 139 placenta and schizophrenia-specific risk genes, many being sex-biased; the candidate molecular mechanisms converge on the nutrient-sensing capabilities of placenta and trophoblast invasiveness. These genes also implicate the Coronavirus-pathogenesis pathway and showed increased expression in placentae from a small sample of SARS-CoV-2-positive pregnancies. Investigating placental risk genes for schizophrenia and candidate mechanisms may lead to opportunities for prevention that would not be suggested by study of the brain alone.
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Affiliation(s)
- Gianluca Ursini
- Lieber Institute for Brain Development, Johns Hopkins University Medical Campus, Baltimore, MD, USA.
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - Pasquale Di Carlo
- Lieber Institute for Brain Development, Johns Hopkins University Medical Campus, Baltimore, MD, USA
- Group of Psychiatric Neuroscience, Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Sreya Mukherjee
- Lieber Institute for Brain Development, Johns Hopkins University Medical Campus, Baltimore, MD, USA
| | - Qiang Chen
- Lieber Institute for Brain Development, Johns Hopkins University Medical Campus, Baltimore, MD, USA
| | - Shizhong Han
- Lieber Institute for Brain Development, Johns Hopkins University Medical Campus, Baltimore, MD, USA
| | - Jiyoung Kim
- Lieber Institute for Brain Development, Johns Hopkins University Medical Campus, Baltimore, MD, USA
| | - Maya Deyssenroth
- Departments of Environmental Medicine and Public Health, Icahn School of Public Health at Mount Sinai, New York, NY, USA
| | - Carmen J Marsit
- Departments of Environmental Health and Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Jia Chen
- Departments of Environmental Medicine and Public Health, Icahn School of Public Health at Mount Sinai, New York, NY, USA
| | - Ke Hao
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Giovanna Punzi
- Lieber Institute for Brain Development, Johns Hopkins University Medical Campus, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Daniel R Weinberger
- Lieber Institute for Brain Development, Johns Hopkins University Medical Campus, Baltimore, MD, USA.
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- McKusick-Nathans Institute, Department of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA.
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA.
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA.
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10
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Chen DM, Dong R, Kachuri L, Hoffmann T, Jiang Y, Berndt SI, Shelley JP, Schaffer KR, Machiela MJ, Freedman ND, Huang WY, Li SA, Lilja H, Van Den Eeden SK, Chanock S, Haiman CA, Conti DV, Klein RJ, Mosley JD, Witte JS, Graff RE. Transcriptome-Wide Association Analysis Identifies Novel Candidate Susceptibility Genes for Prostate-Specific Antigen Levels in Men Without Prostate Cancer. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.05.04.23289526. [PMID: 37205487 PMCID: PMC10187439 DOI: 10.1101/2023.05.04.23289526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Deciphering the genetic basis of prostate-specific antigen (PSA) levels may improve their utility to screen for prostate cancer (PCa). We thus conducted a transcriptome-wide association study (TWAS) of PSA levels using genome-wide summary statistics from 95,768 PCa-free men, the MetaXcan framework, and gene prediction models trained in Genotype-Tissue Expression (GTEx) project data. Tissue-specific analyses identified 41 statistically significant (p < 0.05/12,192 = 4.10e-6) associations in whole blood and 39 statistically significant (p < 0.05/13,844 = 3.61e-6) associations in prostate tissue, with 18 genes associated in both tissues. Cross-tissue analyses that combined associations across 45 tissues identified 155 genes that were statistically significantly (p < 0.05/22,249 = 2.25e-6) associated with PSA levels. Based on conditional analyses that assessed whether TWAS associations were attributable to a lead GWAS variant, we found 20 novel genes (11 single-tissue, 9 cross-tissue) that were associated with PSA levels in the TWAS. Of these novel genes, five showed evidence of colocalization (colocalization probability > 0.5): EXOSC9, CCNA2, HIST1H2BN, RP11-182L21.6, and RP11-327J17.2. Six of the 20 novel genes are not known to impact PCa risk. These findings yield new hypotheses for genetic factors underlying PSA levels that should be further explored toward improving our understanding of PSA biology.
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Affiliation(s)
- Dorothy M. Chen
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, 94158, USA
| | - Ruocheng Dong
- Department of Epidemiology and Population Health, Stanford University, Stanford, CA, 94305, USA
| | - Linda Kachuri
- Department of Epidemiology and Population Health, Stanford University, Stanford, CA, 94305, USA
- Stanford Cancer Institute, Stanford University, Stanford, CA, 94305, USA
| | - Thomas Hoffmann
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, 94158, USA
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA, 94143, USA
| | - Yu Jiang
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, 94158, USA
| | - Sonja I. Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, 20814, USA
| | - John P. Shelley
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Kerry R. Schaffer
- Division of Hematology and Oncology, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
- Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
| | - Mitchell J. Machiela
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, 20814, USA
| | - Neal D. Freedman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, 20814, USA
| | - Wen-Yi Huang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, 20814, USA
| | - Shengchao A. Li
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, 20814, USA
| | - Hans Lilja
- Departments of Pathology and Laboratory Medicine, Surgery, Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
- Department of Translational Medicine, Lund University, Malmö, 21428, Sweden
| | | | - Stephen Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, 20814, USA
| | - Christopher A. Haiman
- Center for Genetic Epidemiology, Department of Population and Preventive Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90032, USA
- Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
| | - David V. Conti
- Center for Genetic Epidemiology, Department of Population and Preventive Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90032, USA
- Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
| | - Robert J. Klein
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Jonathan D. Mosley
- Departments of Internal Medicine and Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - John S. Witte
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, 94158, USA
- Department of Epidemiology and Population Health, Stanford University, Stanford, CA, 94305, USA
- Departments of Biomedical Data Science and Genetics (by courtesy), Stanford University, Stanford, CA, 94305, USA
| | - Rebecca E. Graff
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, 94158, USA
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11
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Okamoto J, Wang L, Yin X, Luca F, Pique-Regi R, Helms A, Im HK, Morrison J, Wen X. Probabilistic integration of transcriptome-wide association studies and colocalization analysis identifies key molecular pathways of complex traits. Am J Hum Genet 2023; 110:44-57. [PMID: 36608684 PMCID: PMC9892769 DOI: 10.1016/j.ajhg.2022.12.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 12/06/2022] [Indexed: 01/07/2023] Open
Abstract
Integrative genetic association methods have shown great promise in post-GWAS (genome-wide association study) analyses, in which one of the most challenging tasks is identifying putative causal genes and uncovering molecular mechanisms of complex traits. Recent studies suggest that prevailing computational approaches, including transcriptome-wide association studies (TWASs) and colocalization analysis, are individually imperfect, but their joint usage can yield robust and powerful inference results. This paper presents INTACT, a computational framework to integrate probabilistic evidence from these distinct types of analyses and implicate putative causal genes. This procedure is flexible and can work with a wide range of existing integrative analysis approaches. It has the unique ability to quantify the uncertainty of implicated genes, enabling rigorous control of false-positive discoveries. Taking advantage of this highly desirable feature, we further propose an efficient algorithm, INTACT-GSE, for gene set enrichment analysis based on the integrated probabilistic evidence. We examine the proposed computational methods and illustrate their improved performance over the existing approaches through simulation studies. We apply the proposed methods to analyze the multi-tissue eQTL data from the GTEx project and eight large-scale complex- and molecular-trait GWAS datasets from multiple consortia and the UK Biobank. Overall, we find that the proposed methods markedly improve the existing putative gene implication methods and are particularly advantageous in evaluating and identifying key gene sets and biological pathways underlying complex traits.
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Affiliation(s)
- Jeffrey Okamoto
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.
| | - Lijia Wang
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Xianyong Yin
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Francesca Luca
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI 48201, USA
| | - Roger Pique-Regi
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI 48201, USA
| | - Adam Helms
- University of Michigan School of Medicine, Ann Arbor, MI 48109, USA
| | - Hae Kyung Im
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL 60637, USA
| | - Jean Morrison
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Xiaoquan Wen
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.
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12
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Yin X, Bose D, Kwon A, Hanks SC, Jackson AU, Stringham HM, Welch R, Oravilahti A, Fernandes Silva L, Locke AE, Fuchsberger C, Service SK, Erdos MR, Bonnycastle LL, Kuusisto J, Stitziel NO, Hall IM, Morrison J, Ripatti S, Palotie A, Freimer NB, Collins FS, Mohlke KL, Scott LJ, Fauman EB, Burant C, Boehnke M, Laakso M, Wen X. Integrating transcriptomics, metabolomics, and GWAS helps reveal molecular mechanisms for metabolite levels and disease risk. Am J Hum Genet 2022; 109:1727-1741. [PMID: 36055244 PMCID: PMC9606383 DOI: 10.1016/j.ajhg.2022.08.007] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 08/09/2022] [Indexed: 01/25/2023] Open
Abstract
Transcriptomics data have been integrated with genome-wide association studies (GWASs) to help understand disease/trait molecular mechanisms. The utility of metabolomics, integrated with transcriptomics and disease GWASs, to understand molecular mechanisms for metabolite levels or diseases has not been thoroughly evaluated. We performed probabilistic transcriptome-wide association and locus-level colocalization analyses to integrate transcriptomics results for 49 tissues in 706 individuals from the GTEx project, metabolomics results for 1,391 plasma metabolites in 6,136 Finnish men from the METSIM study, and GWAS results for 2,861 disease traits in 260,405 Finnish individuals from the FinnGen study. We found that genetic variants that regulate metabolite levels were more likely to influence gene expression and disease risk compared to the ones that do not. Integrating transcriptomics with metabolomics results prioritized 397 genes for 521 metabolites, including 496 previously identified gene-metabolite pairs with strong functional connections and suggested 33.3% of such gene-metabolite pairs shared the same causal variants with genetic associations of gene expression. Integrating transcriptomics and metabolomics individually with FinnGen GWAS results identified 1,597 genes for 790 disease traits. Integrating transcriptomics and metabolomics jointly with FinnGen GWAS results helped pinpoint metabolic pathways from genes to diseases. We identified putative causal effects of UGT1A1/UGT1A4 expression on gallbladder disorders through regulating plasma (E,E)-bilirubin levels, of SLC22A5 expression on nasal polyps and plasma carnitine levels through distinct pathways, and of LIPC expression on age-related macular degeneration through glycerophospholipid metabolic pathways. Our study highlights the power of integrating multiple sets of molecular traits and GWAS results to deepen understanding of disease pathophysiology.
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Affiliation(s)
- Xianyong Yin
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Debraj Bose
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Annie Kwon
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Sarah C Hanks
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Anne U Jackson
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Heather M Stringham
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Ryan Welch
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Anniina Oravilahti
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio 70210, Finland
| | - Lilian Fernandes Silva
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio 70210, Finland
| | - Adam E Locke
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO 63108, USA
| | - Christian Fuchsberger
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Institute for Biomedicine, Eurac Research, Bolzano 39100, Italy
| | - Susan K Service
- Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA 90024, USA
| | - Michael R Erdos
- Molecular Genetics Section, Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Lori L Bonnycastle
- Molecular Genetics Section, Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Johanna Kuusisto
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio 70210, Finland; Center for Medicine and Clinical Research, Kuopio University Hospital, Kuopio 70210, Finland
| | - Nathan O Stitziel
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO 63108, USA; Cardiovascular Division, Department of Medicine, Washington University School of Medicine, St Louis, MO 63110, USA; Department of Genetics, Washington University School of Medicine, St Louis, MO 63110, USA
| | - Ira M Hall
- Center for Genomic Health, Department of Genetics, Yale University, New Haven, CT 06510, USA
| | - Jean Morrison
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki 00290, Finland; Department of Public Health, University of Helsinki, Helsinki 00014, Finland; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Aarno Palotie
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki 00290, Finland; Department of Public Health, University of Helsinki, Helsinki 00014, Finland; Analytic and Translational Genetics Unit, Department of Medicine, Department of Neurology, and Department of Psychiatry, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Nelson B Freimer
- Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA 90024, USA
| | - Francis S Collins
- Molecular Genetics Section, Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Laura J Scott
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Eric B Fauman
- Internal Medicine Research Unit, Pfizer Worldwide Research, Development and Medical, Cambridge, MA 02139, USA
| | - Charles Burant
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Markku Laakso
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio 70210, Finland.
| | - Xiaoquan Wen
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA.
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