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Petty LE, Chen HH, Frankel EG, Zhu W, Downie CG, Graff M, Lin P, Sharma P, Zhang X, Scartozzi AC, Roshani R, Landman JM, Boehnke M, Bowden DW, Chambers JC, Mahajan A, McCarthy MI, Ng MCY, Sim X, Spracklen CN, Zhang W, Preuss M, Bottinger EP, Nadkarni GN, Loos RJF, Chen YDI, Tan J, Ipp E, Genter P, Emery LS, Louie T, Sofer T, Stilp AM, Taylor KD, Xiang AH, Buchanan TA, Roll K, Gao C, Palmer ND, Norris JM, Wagenknecht LE, Nousome D, Varma R, McKean-Cowdin R, Guo X, Hai Y, Hsueh W, Sandow K, Parra EJ, Cruz M, Valladares-Salgado A, Wacher-Rodarte N, Rotter JI, Goodarzi MO, Rich SS, Bertoni A, Raffel LJ, Nadler JL, Kandeel FR, Duggirala R, Blangero J, Lehman DM, DeFronzo RA, Thameem F, Wang Y, Gahagan S, Blanco E, Burrows R, Huerta-Chagoya A, Florez JC, Tusie-Luna T, González-Villalpando C, Orozco L, Haiman CA, Hanis CL, Rohde R, Whitsel EA, Reiner AP, Kooperberg C, Li Y, Duan Q, Lee M, Correa-Burrows P, Fried SK, North KE, McCormick JB, Fisher-Hoch SP, Gamazon ER, Morris AP, Mercader JM, Highland HM, Below JE. Large-scale multi-omics analyses in Hispanic/Latino populations identify genes for cardiometabolic traits. Nat Commun 2025; 16:3438. [PMID: 40210677 PMCID: PMC11985957 DOI: 10.1038/s41467-025-58574-z] [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: 03/28/2024] [Accepted: 03/21/2025] [Indexed: 04/12/2025] Open
Abstract
Here, we present a multi-omics study of type 2 diabetes and quantitative blood lipid and lipoprotein traits conducted to date in Hispanic/Latino populations (nmax = 63,184). We conduct a meta-analysis of 16 type 2 diabetes and 19 lipid trait GWAS, identifying 20 genome-wide significant loci for type 2 diabetes, including one novel locus and novel signals at two known loci, based on fine-mapping. We also identify sixty-one genome-wide significant loci across the lipid/lipoprotein traits, including nine novel loci, and novel signals at 19 known loci through fine-mapping. Next, we analyze genetically regulated expression, perform Mendelian randomization, and analyze association with transcriptomic and proteomic measure using multi-omics data from a Hispanic/Latino population. Using this approach, we identify genes linked to type 2 diabetes and lipid/lipoprotein traits, including TMEM205 and NEDD9 for HDL cholesterol, TREH for triglycerides, and ANXA4 for type 2 diabetes.
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Affiliation(s)
- Lauren E Petty
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Hung-Hsin Chen
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan, ROC
| | - Elizabeth G Frankel
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Wanying Zhu
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Carolina G Downie
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Mariaelisa Graff
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Phillip Lin
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Priya Sharma
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Xinruo Zhang
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Alyssa C Scartozzi
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Rashedeh Roshani
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Joshua M Landman
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Donald W Bowden
- Center for Precision Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - John C Chambers
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- Department of Cardiology, Ealing Hospital, London North West Healthcare NHS Trust, London, UK
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
- MRC-PHE Centre for Environment and Health, Imperial College London, London, UK
- Imperial College Healthcare NHS Trust, Imperial College London, London, UK
| | - Anubha Mahajan
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Mark I McCarthy
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Oxford NIHR Biomedical Research Centre, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Maggie C Y Ng
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Center for Precision Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Xueling Sim
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Cassandra N Spracklen
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Epidemiology and Biostatistics, University of Massachusetts-Amherst, Amherst, MA, USA
| | - Weihua Zhang
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- Department of Cardiology, Ealing Hospital, London North West Healthcare NHS Trust, London, UK
| | - Michael Preuss
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Erwin P Bottinger
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Girish N Nadkarni
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Digital Health Center, Hasso-Plattner-Institut, Potsdam, Germany
| | - Ruth J F Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Mindich Child Health Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 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
| | - Jingyi Tan
- 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
| | - Eli Ipp
- Division of Endocrinology & Metabolism, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Pauline Genter
- Division of Endocrinology & Metabolism, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Leslie S Emery
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Tin Louie
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Tamar Sofer
- Departments of Medicine and Biostatistics, Harvard University, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
| | - Adrienne M Stilp
- Department of Biostatistics, University of Washington, Seattle, WA, 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
| | - Anny H Xiang
- Department of Research & Evaluation, Division of Biostatistics Research, Kaiser Permanente of Southern California, Pasadena, CA, USA
| | - Thomas A Buchanan
- Department of Medicine, Division of Endocrine, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Kathryn Roll
- 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
| | - Chuan Gao
- Molecular Genetics and Genomics Program, Wake Forest School of Medicine, Winston-, Salem, NC, USA
| | - Nicholette D Palmer
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Jill M Norris
- Department of Epidemiology, University of Colorado Denver, Aurora, CO, USA
| | - Lynne E Wagenknecht
- Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Darryl Nousome
- Department of Preventative Medicine, Keck School of Medicine of the University of Southern California, Los Angeles, CA, USA
| | - Rohit Varma
- Southern California Eye Institute, CHA Hollywood Presbyterian Medical Center, Los Angeles, CA, USA
| | - Roberta McKean-Cowdin
- Department of Preventative Medicine, Keck School of Medicine of the University of Southern California, Los Angeles, CA, USA
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Yang Hai
- 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
| | - Willa Hsueh
- Department of Internal Medicine, Diabetes and Metabolism Research Center, Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Kevin Sandow
- 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
| | - Esteban J Parra
- Department of Anthropology, University of Toronto at Mississauga, Mississauga, ON, Canada
| | - Miguel Cruz
- Unidad de Investigación Médica en Bioquimica, Hospital de Especialidades, Centro Medico Nacional Siglo XXI, IMSS, Mexico City, Mexico
| | - Adan Valladares-Salgado
- Unidad de Investigación Médica en Bioquimica, Hospital de Especialidades, Centro Medico Nacional Siglo XXI, IMSS, Mexico City, Mexico
| | - Niels Wacher-Rodarte
- Unidad de Investigación Médica en Epidemiologia Clinica, Hospital de Especialidades, Centro Medico Nacional Siglo XXI, IMSS, Mexico City, Mexico
| | - 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
| | - Mark O Goodarzi
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Alain Bertoni
- Department of Epidemiology and Prevention, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Leslie J Raffel
- Department of Pediatrics, Division of Medical Genetics, UCI Irvine School of Medicine, Irvine, CA, USA
| | - Jerry L Nadler
- Department of Medicine and Pharmacology, New York Medical College, Valhalla, NY, USA
| | - Fouad R Kandeel
- Departments of Clinical Diabetes, Endocrinology & Metabolism and Translational Research and Cellular Therapeutics, City of Hope, Duarte, CA, USA
| | | | - John Blangero
- Human Genetics and STDOI, University of Texas Rio Grande Valley, Edinburg, TX, USA
| | - Donna M Lehman
- Department of Medicine, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Ralph A DeFronzo
- Department of Medicine, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Farook Thameem
- Department of Biochemistry, Faculty of Medicine, Health Science Center, Kuwait University, Safat, Kuwait
| | - Yujie Wang
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Sheila Gahagan
- Division of Academic General Pediatrics, Child Development and Community Health, University of California at San Diego, San Diego, CA, USA
| | - Estela Blanco
- College y Escuela de Salud Pública, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Raquel Burrows
- Department of Public Health Nutrition, Institute of Nutrition and Food Technology, University of Chile, Santiago, Chile
| | - Alicia Huerta-Chagoya
- Consejo Nacional de Ciencia y Tecnología (CONACYT), Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Jose C Florez
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Teresa Tusie-Luna
- Unidad de Biología Molecular y Medicina Genómica, Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México/Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | - Clicerio González-Villalpando
- Centro de Estudios en Diabetes, Unidad de Investigacion en Diabetes y Riesgo Cardiovascular, Centro de Investigacion en Salud Poblacional, Instituto Nacional de Salud Pública, Mexico City, Mexico
| | - Lorena Orozco
- Instituto Nacional de Medicina Genómica, Mexico City, Mexico
| | - Christopher A Haiman
- Department of Population and Public Health Sciences, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Craig L Hanis
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Rebecca Rohde
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Eric A Whitsel
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Medicine, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Alexander P Reiner
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | | | - Yun Li
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Qing Duan
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Miryoung Lee
- Department of Epidemiology, Human Genetics & Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Brownsville Regional Campus, Brownsville, TX, USA
| | - Paulina Correa-Burrows
- Department of Public Health Nutrition, Institute of Nutrition and Food Technology, University of Chile, Santiago, Chile
| | - Susan K Fried
- Diabetes, Obesity Metabolism Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kari E North
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Joseph B McCormick
- Department of Epidemiology, Human Genetics & Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Brownsville Regional Campus, Brownsville, TX, USA
| | - Susan P Fisher-Hoch
- Department of Epidemiology, Human Genetics & Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Brownsville Regional Campus, Brownsville, TX, USA
| | - Eric R Gamazon
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Clare Hall & MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Andrew P Morris
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Division of Musculoskeletal and Dermatological Sciences, University of Manchester, Manchester, UK
| | - Josep M Mercader
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Heather M Highland
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jennifer E Below
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA.
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2
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Xu X, Wang S, Zhou H, Tan Q, Lang Z, Zhu Y, Yuan H, Wu Z, Zhu L, Hu K, Li W, Zhou D, Wu M, Wu X. Transcriptome-wide association study of alternative polyadenylation identifies susceptibility genes in non-small cell lung cancer. Oncogene 2025:10.1038/s41388-025-03338-8. [PMID: 40205015 DOI: 10.1038/s41388-025-03338-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2024] [Revised: 02/09/2025] [Accepted: 02/28/2025] [Indexed: 04/11/2025]
Abstract
Alternative polyadenylation (APA) plays a crucial role in cancer development and prognosis. However, the molecular characteristics of APA related to non-small cell lung cancer (NSCLC) susceptibility remain understudied, especially in East Asian populations. In this study, we constructed an atlas of APA-regulated 3' untranslated region (3'UTR) and profiled its genetic regulation in 747 lung tissue samples (including tumors and paired normal tissues) from 417 NSCLC Chinese patients. We verified a significant global shortening of 3'UTRs in tumor samples compared to normal samples and underscored the value of APA-regulation as a prognostic marker. The 3'UTR APA quantitative trait loci (3'aQTL) was identified by regressing the percentage of distal poly(A) site usage index (PDUI) value on genetic variants. We found that a significant proportion 3'aQTLs are independent of genetic regulation of expression and are specific in Chinese. We also conducted a 3'UTR APA transcriptome-wide association study (3'aTWAS) by integrating the APA regulation atlas with a genome-wide association study (GWAS) for NSCLC involving 7035 cases and 185,413 cancer-free controls. We identified NSCLC-associated genes, highlighting TUBB, TEAD3, and PPP1R10. Combining the consistent results from colocalization analysis, differential APA analysis, and survival analysis, we provide novel evidence for the role TUBB APA regulation in NSCLC and identified potential upstream regulators. Overall, our study profiled the APA regulation and highlighted the substantial role of APA in NSCLC carcinogenesis and prognosis in East Asian populations.
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Affiliation(s)
- Xiaohang Xu
- Center of Clinical Big Data and Analytics of the Second Affiliated Hospital and School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
- Zhejiang Key Laboratory of Intelligent Preventive Medicine, Hangzhou, China
| | - Sicong Wang
- Center of Clinical Big Data and Analytics of the Second Affiliated Hospital and School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
- Zhejiang Key Laboratory of Intelligent Preventive Medicine, Hangzhou, China
| | - Hanyi Zhou
- Center of Clinical Big Data and Analytics of the Second Affiliated Hospital and School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
| | - Qilong Tan
- Center of Clinical Big Data and Analytics of the Second Affiliated Hospital and School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
- Zhejiang Key Laboratory of Intelligent Preventive Medicine, Hangzhou, China
| | - Zeyong Lang
- Center of Clinical Big Data and Analytics of the Second Affiliated Hospital and School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
| | - Yun Zhu
- Center of Clinical Big Data and Analytics of the Second Affiliated Hospital and School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
| | - Huadi Yuan
- Department of Thoracic Surgery, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Zixiang Wu
- Department of Thoracic Surgery, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Ling Zhu
- Department of Thoracic Surgery, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Kejia Hu
- Center of Clinical Big Data and Analytics of the Second Affiliated Hospital and School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
- National Institute for Data Science in Health and Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Wenyuan Li
- Center of Clinical Big Data and Analytics of the Second Affiliated Hospital and School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
- Zhejiang Key Laboratory of Intelligent Preventive Medicine, Hangzhou, China
| | - Dan Zhou
- Center of Clinical Big Data and Analytics of the Second Affiliated Hospital and School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
- Zhejiang Key Laboratory of Intelligent Preventive Medicine, Hangzhou, China
| | - Ming Wu
- Department of Thoracic Surgery, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xifeng Wu
- Center of Clinical Big Data and Analytics of the Second Affiliated Hospital and School of Public Health, Zhejiang University School of Medicine, Hangzhou, China.
- National Institute for Data Science in Health and Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
- School of Medicine and Health Science, George Washington University, Washington, DC, USA.
- Zhejiang Cancer Hospital, Hangzhou, China.
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Yang H, Huang H, Pu K. A cross-tissue transcriptome-wide association study identified susceptibility genes for age-related macular degeneration. Sci Rep 2025; 15:4788. [PMID: 39922885 PMCID: PMC11807202 DOI: 10.1038/s41598-025-89246-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2024] [Accepted: 02/04/2025] [Indexed: 02/10/2025] Open
Abstract
Age-related macular degeneration (AMD) is a complex multifactorial disease with a significant genetic component. Despite extensive research efforts, the underlying molecular mechanisms remain elusive, necessitating innovative approaches to identify specific genes involved in the pathogenesis of AMD and to elucidate their functional mechanisms. A transcriptome-wide association study (TWAS) was conducted by integrating eQTL data from 49 tissues of the Genotype-Tissue Expression Project (GTEx) v8 and AMD data from FinnGen R10. The Unified Test for Molecular Signatures (UTMOST) and Functional Summary-based Imputation (FUSION) were used to evaluate gene associations with AMD across tissues and within individual tissues, respectively. Multi-marker Analysis of Genomic Annotation (MAGMA) was employed to validate results and identify reliable susceptibility genes, followed by summary data-based Mendelian randomization (SMR) and colocalization analyses to explore causal associations. The cross-tissue and single-tissue TWAS analyses identified 12 reliable AMD-associated genes. MAGMA analysis confirmed 6 of these as reliable susceptibility genes. SMR analysis provided further validation of these genes, although colocalization results were not significant. This study identified six susceptibility genes associated with the risk of AMD through a cross-tissue TWAS, providing new insights into the potential systemic regulatory mechanisms underlying AMD.
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Affiliation(s)
- Hongfan Yang
- Department of ophthalmology, Pengzhou Hospital of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Haofei Huang
- Department of ophthalmology, Pengzhou Hospital of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Kunlin Pu
- Department of ophthalmology, Pengzhou Hospital of Traditional Chinese Medicine, Chengdu, Sichuan, China.
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4
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Slack SD, Esquinca E, Arehart CH, Boorgula MP, Szczesny B, Romero A, Campbell M, Chavan S, Rafaels N, Watson H, Landis RC, Hansel NN, Rotimi CN, Olopade CO, Figueiredo CA, Ober C, Liu AH, Kenny EE, Kammers K, Ruczinski I, Taub MA, Daya M, Gignoux CR, Kechris K, Barnes KC, Mathias RA, Johnson RK. Prediction and Characterization of Genetically Regulated Expression of Target Tissues in Asthma. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2025:2025.02.06.25321273. [PMID: 39974046 PMCID: PMC11838648 DOI: 10.1101/2025.02.06.25321273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 02/21/2025]
Abstract
Background Genetic control of gene expression in asthma-related tissues is not well-characterized, particularly for African-ancestry populations, limiting advancement in our understanding of the increased prevalence and severity of asthma in those populations. Objective To create novel transcriptome prediction models for asthma tissues (nasal epithelium and CD4+ T cells) and apply them in transcriptome-wide association study (TWAS) to discover candidate asthma genes. Methods We developed and validated gene expression prediction databases for unstimulated CD4+ T cells (CD4+T) and nasal epithelium using an elastic net framework. Combining these with existing prediction databases (N=51), we performed TWAS of 9,284 individuals of African-ancestry to identify tissue-specific and cross-tissue candidate genes for asthma. For detailed Methods, please see the Supplemental Methods. Results Novel databases for CD4+T and nasal epithelial gene expression prediction contain 8,351 and 10,296 genes, respectively, including four asthma loci (SCGB1A1, MUC5AC, ZNF366, LTC4S) not predictable with existing public databases. Prediction performance was comparable to existing databases and was most accurate for populations sharing ancestry with the training set (e.g. African ancestry). From TWAS, we identified 17 candidate causal asthma genes (adjusted P<0.1), including genes with tissue-specific (IL33 in nasal epithelium) and cross-tissue (CCNC and FBXW7) effects. Conclusions Expression of IL33, CCNC, and FBXW7 may affect asthma risk in African ancestry populations by mediating inflammatory responses. The addition of CD4+T and nasal epithelium prediction databases to the public sphere will improve ancestry representation and power to detect novel gene-trait associations from TWAS.
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Affiliation(s)
- Sarah D. Slack
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Erika Esquinca
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
- Department of Biostatistics & Informatics, Colorado School of Public Health, Aurora, CO, USA
| | - Christopher H. Arehart
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA
- Department of Ecology & Evolutionary Biology, University of Colorado Boulder, Boulder, CO, USA
| | - Meher Preethi Boorgula
- Department of Medicine, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO, USA
| | - Brooke Szczesny
- Genomics and Precision Health Section, Laboratory of Allergic Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Alex Romero
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Monica Campbell
- Department of Medicine, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO, USA
| | - Sameer Chavan
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Nicholas Rafaels
- Colorado Center for Personalized Medicine, University of Colorado School of Medicine, Aurora, CO, USA
| | - Harold Watson
- Faculty of Medical Sciences, The University of the West Indies, Queen Elizabeth Hospital, St. Michael, Bridgetown, Barbados
| | - R. Clive Landis
- Edmund Cohen Laboratory for Vascular Research, George Alleyne Chronic Disease Research Centre, Caribbean Institute for Health Research, The University of the West Indies, Cave Hill Campus, Wanstead, Barbados
| | - Nadia N. Hansel
- Department of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Charles N. Rotimi
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | | | - Camila A. Figueiredo
- Instituto de Ciências de Saúde, Universidade Federal da Bahia, Salvador, Brazil
- Program for Control of Asthma in Bahia (ProAR), Salvador, Brazil
| | - Carole Ober
- Departments of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Andrew H. Liu
- Department of Pediatrics, Childrens Hospital Colorado and University of Colorado Denver, Anschutz Medical Campus, Aurora, CO, USA
| | - Eimear E. Kenny
- Center for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kai Kammers
- Department of Oncology, Johns Hopkins University, Baltimore, MD, USA
| | - Ingo Ruczinski
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Margaret A. Taub
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Michelle Daya
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Christopher R. Gignoux
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Katerina Kechris
- Department of Biostatistics & Informatics, Colorado School of Public Health, Aurora, CO, USA
| | - Kathleen C. Barnes
- Department of Medicine, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO, USA
| | - Rasika A. Mathias
- Genomics and Precision Health Section, Laboratory of Allergic Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Randi K. Johnson
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
- Department of Epidemiology, Colorado School of Public Health, Aurora, CO, USA
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5
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Pagnuco I, Eyre S, Rattray M, Morris AP. Transferability of Single- and Cross-Tissue Transcriptome Imputation Models Across Ancestry Groups. Genet Epidemiol 2025; 49:e22611. [PMID: 39812501 PMCID: PMC11734644 DOI: 10.1002/gepi.22611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2024] [Revised: 10/02/2024] [Accepted: 12/19/2024] [Indexed: 01/16/2025]
Abstract
Transcriptome-wide association studies (TWAS) investigate the links between genetically regulated gene expression and complex traits. TWAS involves imputing gene expression using expression quantitative trait loci (eQTL) as predictors and testing the association between the imputed expression and the trait. The effectiveness of TWAS depends on the accuracy of these imputation models, which require genotype and gene expression data from the same samples. However, publicly accessible resources, such as the Genotype Tissue Expression (GTEx) Project, are biased toward individuals of European ancestry, potentially reducing prediction accuracy into other ancestry groups. This study explored eQTL transferability across ancestry groups by comparing two imputation models: PrediXcan (tissue-specific) and UTMOST (cross-tissue). Both models were trained on tissues from the GTEx Project using European ancestry individuals and then tested on data sets of European ancestry and African American individuals. Results showed that both models performed best when the training and testing data sets were from the same ancestry group, with the cross-tissue approach generally outperforming the tissue-specific approach. This study underscores that eQTL detection is influenced by ancestry and tissue context. Developing ancestry-specific reference panels across tissues can improve prediction accuracy, enhancing TWAS analysis and our understanding of the biological processes contributing to complex traits.
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Affiliation(s)
- Inti Pagnuco
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Division of Musculoskeletal and Dermatological SciencesThe University of ManchesterManchesterUK
| | - Stephen Eyre
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Division of Musculoskeletal and Dermatological SciencesThe University of ManchesterManchesterUK
| | - Magnus Rattray
- Division of Informatics, Imaging and Data SciencesThe University of ManchesterManchesterUK
| | - Andrew P. Morris
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Division of Musculoskeletal and Dermatological SciencesThe University of ManchesterManchesterUK
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6
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Evans P, Nagai T, Konkashbaev A, Zhou D, Knapik EW, Gamazon ER. Transcriptome-Wide Association Studies (TWAS): Methodologies, Applications, and Challenges. Curr Protoc 2024; 4:e981. [PMID: 38314955 PMCID: PMC10846672 DOI: 10.1002/cpz1.981] [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] [Indexed: 02/07/2024]
Abstract
Transcriptome-wide association study (TWAS) methodologies aim to identify genetic effects on phenotypes through the mediation of gene transcription. In TWAS, in silico models of gene expression are trained as functions of genetic variants and then applied to genome-wide association study (GWAS) data. This post-GWAS analysis identifies gene-trait associations with high interpretability, enabling follow-up functional genomics studies and the development of genetics-anchored resources. We provide an overview of commonly used TWAS approaches, their advantages and limitations, and some widely used applications. © 2024 Wiley Periodicals LLC.
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Affiliation(s)
- Patrick Evans
- Division of Genetic Medicine and Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Taylor Nagai
- Division of Genetic Medicine and Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Anuar Konkashbaev
- Division of Genetic Medicine and Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Dan Zhou
- Division of Genetic Medicine and Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Ela W Knapik
- Division of Genetic Medicine and Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Eric R Gamazon
- Division of Genetic Medicine and Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee
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7
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Araujo DS, Nguyen C, Hu X, Mikhaylova AV, Gignoux C, Ardlie K, Taylor KD, Durda P, Liu Y, Papanicolaou G, Cho MH, Rich SS, Rotter JI, NHLBI TOPMed Consortium, Im HK, Manichaikul A, Wheeler HE. Multivariate adaptive shrinkage improves cross-population transcriptome prediction and association studies in underrepresented populations. HGG ADVANCES 2023; 4:100216. [PMID: 37869564 PMCID: PMC10589725 DOI: 10.1016/j.xhgg.2023.100216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 06/27/2023] [Indexed: 10/24/2023] Open
Abstract
Transcriptome prediction models built with data from European-descent individuals are less accurate when applied to different populations because of differences in linkage disequilibrium patterns and allele frequencies. We hypothesized that methods that leverage shared regulatory effects across different conditions, in this case, across different populations, may improve cross-population transcriptome prediction. To test this hypothesis, we made transcriptome prediction models for use in transcriptome-wide association studies (TWASs) using different methods (elastic net, joint-tissue imputation [JTI], matrix expression quantitative trait loci [Matrix eQTL], multivariate adaptive shrinkage in R [MASHR], and transcriptome-integrated genetic association resource [TIGAR]) and tested their out-of-sample transcriptome prediction accuracy in population-matched and cross-population scenarios. Additionally, to evaluate model applicability in TWASs, we integrated publicly available multiethnic genome-wide association study (GWAS) summary statistics from the Population Architecture using Genomics and Epidemiology (PAGE) study and Pan-ancestry genetic analysis of the UK Biobank (PanUKBB) with our developed transcriptome prediction models. In regard to transcriptome prediction accuracy, MASHR models performed better or the same as other methods in both population-matched and cross-population transcriptome predictions. Furthermore, in multiethnic TWASs, MASHR models yielded more discoveries that replicate in both PAGE and PanUKBB across all methods analyzed, including loci previously mapped in GWASs and loci previously not found in GWASs. Overall, our study demonstrates the importance of using methods that benefit from different populations' effect size estimates in order to improve TWASs for multiethnic or underrepresented populations.
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Affiliation(s)
- Daniel S. Araujo
- Program in Bioinformatics, Loyola University Chicago, Chicago, IL 60660, USA
| | - Chris Nguyen
- Department of Biology, Loyola University Chicago, Chicago, IL 60660, USA
| | - Xiaowei Hu
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22908, USA
| | - Anna V. Mikhaylova
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
| | - Chris Gignoux
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, UC Denver Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Kristin Ardlie
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, 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 90502, USA
| | - Peter Durda
- Laboratory for Clinical Biochemistry Research, University of Vermont, Colchester, VT 05446, USA
| | - Yongmei Liu
- Department of Medicine, Duke University School of Medicine, Durham, NC 27710, USA
| | - George Papanicolaou
- Epidemiology Branch, Division of Cardiovascular Sciences, National Heart, Lung and Blood Institute, Bethesda, MD 20892, USA
| | - Michael H. Cho
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
| | - Stephen S. Rich
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22908, 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 90502, USA
| | - NHLBI TOPMed Consortium
- Program in Bioinformatics, Loyola University Chicago, Chicago, IL 60660, USA
- Department of Biology, Loyola University Chicago, Chicago, IL 60660, USA
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22908, USA
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, UC Denver Anschutz Medical Campus, Aurora, CO 80045, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, the Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
- Laboratory for Clinical Biochemistry Research, University of Vermont, Colchester, VT 05446, USA
- Department of Medicine, Duke University School of Medicine, Durham, NC 27710, USA
- Epidemiology Branch, Division of Cardiovascular Sciences, National Heart, Lung and Blood Institute, Bethesda, MD 20892, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
- Section of Genetic Medicine, University of Chicago, Chicago, IL 60637, USA
| | - Hae Kyung Im
- Section of Genetic Medicine, University of Chicago, Chicago, IL 60637, USA
| | - Ani Manichaikul
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22908, USA
| | - Heather E. Wheeler
- Program in Bioinformatics, Loyola University Chicago, Chicago, IL 60660, USA
- Department of Biology, Loyola University Chicago, Chicago, IL 60660, USA
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8
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Ghaffar A, Nyholt DR. Integrating eQTL and GWAS data characterises established and identifies novel migraine risk loci. Hum Genet 2023; 142:1113-1137. [PMID: 37245199 PMCID: PMC10449685 DOI: 10.1007/s00439-023-02568-8] [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: 03/02/2023] [Accepted: 05/02/2023] [Indexed: 05/29/2023]
Abstract
Migraine-a painful, throbbing headache disorder-is the most common complex brain disorder, yet its molecular mechanisms remain unclear. Genome-wide association studies (GWAS) have proven successful in identifying migraine risk loci; however, much work remains to identify the causal variants and genes. In this paper, we compared three transcriptome-wide association study (TWAS) imputation models-MASHR, elastic net, and SMultiXcan-to characterise established genome-wide significant (GWS) migraine GWAS risk loci, and to identify putative novel migraine risk gene loci. We compared the standard TWAS approach of analysing 49 GTEx tissues with Bonferroni correction for testing all genes present across all tissues (Bonferroni), to TWAS in five tissues estimated to be relevant to migraine, and TWAS with Bonferroni correction that took into account the correlation between eQTLs within each tissue (Bonferroni-matSpD). Elastic net models performed in all 49 GTEx tissues using Bonferroni-matSpD characterised the highest number of established migraine GWAS risk loci (n = 20) with GWS TWAS genes having colocalisation (PP4 > 0.5) with an eQTL. SMultiXcan in all 49 GTEx tissues identified the highest number of putative novel migraine risk genes (n = 28) with GWS differential expression at 20 non-GWS GWAS loci. Nine of these putative novel migraine risk genes were later found to be at and in linkage disequilibrium with true (GWS) migraine risk loci in a recent, more powerful migraine GWAS. Across all TWAS approaches, a total of 62 putative novel migraine risk genes were identified at 32 independent genomic loci. Of these 32 loci, 21 were true risk loci in the recent, more powerful migraine GWAS. Our results provide important guidance on the selection, use, and utility of imputation-based TWAS approaches to characterise established GWAS risk loci and identify novel risk gene loci.
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Affiliation(s)
- Ammarah Ghaffar
- Statistical and Genomic Epidemiology Laboratory, School of Biomedical Sciences, Faculty of Health, Centre for Genomics and Personalised Health, Queensland University of Technology, Brisbane, QLD, 4059, Australia.
| | - Dale R Nyholt
- Statistical and Genomic Epidemiology Laboratory, School of Biomedical Sciences, Faculty of Health, Centre for Genomics and Personalised Health, Queensland University of Technology, Brisbane, QLD, 4059, Australia.
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9
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Kachuri L, Mak ACY, Hu D, Eng C, Huntsman S, Elhawary JR, Gupta N, Gabriel S, Xiao S, Keys KL, Oni-Orisan A, Rodríguez-Santana JR, LeNoir MA, Borrell LN, Zaitlen NA, Williams LK, Gignoux CR, Burchard EG, Ziv E. Gene expression in African Americans, Puerto Ricans and Mexican Americans reveals ancestry-specific patterns of genetic architecture. Nat Genet 2023; 55:952-963. [PMID: 37231098 PMCID: PMC10260401 DOI: 10.1038/s41588-023-01377-z] [Citation(s) in RCA: 44] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 03/21/2023] [Indexed: 05/27/2023]
Abstract
We explored ancestry-related differences in the genetic architecture of whole-blood gene expression using whole-genome and RNA sequencing data from 2,733 African Americans, Puerto Ricans and Mexican Americans. We found that heritability of gene expression significantly increased with greater proportions of African genetic ancestry and decreased with higher proportions of Indigenous American ancestry, reflecting the relationship between heterozygosity and genetic variance. Among heritable protein-coding genes, the prevalence of ancestry-specific expression quantitative trait loci (anc-eQTLs) was 30% in African ancestry and 8% for Indigenous American ancestry segments. Most anc-eQTLs (89%) were driven by population differences in allele frequency. Transcriptome-wide association analyses of multi-ancestry summary statistics for 28 traits identified 79% more gene-trait associations using transcriptome prediction models trained in our admixed population than models trained using data from the Genotype-Tissue Expression project. Our study highlights the importance of measuring gene expression across large and ancestrally diverse populations for enabling new discoveries and reducing disparities.
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Affiliation(s)
- Linda Kachuri
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
- Department of Epidemiology and Population Health, Stanford University, Stanford, CA, USA
| | - Angel C Y Mak
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Donglei Hu
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Celeste Eng
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Scott Huntsman
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Jennifer R Elhawary
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Namrata Gupta
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Shujie Xiao
- Center for Individualized and Genomic Medicine Research, Henry Ford Health System, Detroit, MI, USA
| | - Kevin L Keys
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
- Berkeley Institute for Data Science, University of California, Berkeley, Berkeley, CA, USA
| | - Akinyemi Oni-Orisan
- Department of Clinical Pharmacy, University of California, San Francisco, San Francisco, CA, USA
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA
| | | | | | - Luisa N Borrell
- Department of Epidemiology and Biostatistics, Graduate School of Public Health and Health Policy, City University of New York, New York, NY, USA
| | - Noah A Zaitlen
- Department of Neurology, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - L Keoki Williams
- Center for Individualized and Genomic Medicine Research, Henry Ford Health System, Detroit, MI, USA
- Department of Internal Medicine, Henry Ford Health System, Detroit, MI, USA
| | - Christopher R Gignoux
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
| | - Esteban González 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.
| | - Elad Ziv
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA.
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA.
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA.
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10
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Araujo DS, Nguyen C, Hu X, Mikhaylova AV, Gignoux C, Ardlie K, Taylor KD, Durda P, Liu Y, Papanicolaou G, Cho MH, Rich SS, Rotter JI, NHLBI TOPMed Consortium, Im HK, Manichaikul A, Wheeler HE. Multivariate adaptive shrinkage improves cross-population transcriptome prediction for transcriptome-wide association studies in underrepresented populations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.09.527747. [PMID: 36798214 PMCID: PMC9934635 DOI: 10.1101/2023.02.09.527747] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/12/2023]
Abstract
Transcriptome prediction models built with data from European-descent individuals are less accurate when applied to different populations because of differences in linkage disequilibrium patterns and allele frequencies. We hypothesized methods that leverage shared regulatory effects across different conditions, in this case, across different populations may improve cross-population transcriptome prediction. To test this hypothesis, we made transcriptome prediction models for use in transcriptome-wide association studies (TWAS) using different methods (Elastic Net, Joint-Tissue Imputation (JTI), Matrix eQTL, Multivariate Adaptive Shrinkage in R (MASHR), and Transcriptome-Integrated Genetic Association Resource (TIGAR)) and tested their out-of-sample transcriptome prediction accuracy in population-matched and cross-population scenarios. Additionally, to evaluate model applicability in TWAS, we integrated publicly available multi-ethnic genome-wide association study (GWAS) summary statistics from the Population Architecture using Genomics and Epidemiology Study (PAGE) and Pan-UK Biobank with our developed transcriptome prediction models. In regard to transcriptome prediction accuracy, MASHR models performed better or the same as other methods in both population-matched and cross-population transcriptome predictions. Furthermore, in multi-ethnic TWAS, MASHR models yielded more discoveries that replicate in both PAGE and PanUKBB across all methods analyzed, including loci previously mapped in GWAS and new loci previously not found in GWAS. Overall, our study demonstrates the importance of using methods that benefit from different populations' effect size estimates in order to improve TWAS for multi-ethnic or underrepresented populations.
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Affiliation(s)
- Daniel S. Araujo
- Program in Bioinformatics, Loyola University Chicago, Chicago, IL, 60660, USA
| | - Chris Nguyen
- Department of Biology, Loyola University Chicago, Chicago, IL, 60660, USA
| | - Xiaowei Hu
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, 22908, USA
| | - Anna V. Mikhaylova
- Department of Biostatistics, University of Washington, Seattle, WA, 98195, USA
| | - Chris Gignoux
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, UC Denver Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Kristin Ardlie
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, 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, 90502, USA
| | - Peter Durda
- Laboratory for Clinical Biochemistry Research, University of Vermont, Colchester, VT, 05446, USA
| | - Yongmei Liu
- Department of Medicine, Duke University School of Medicine, Durham, NC, 27710, USA
| | - George Papanicolaou
- Epidemiology Branch, Division of Cardiovascular Sciences, National Heart, Lung and Blood Institute, Bethesda, MD, 20892, USA
| | - Michael H. Cho
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, 02115, USA
| | - Stephen S. Rich
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, 22908, 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, 90502, USA
| | | | - Hae Kyung Im
- Section of Genetic Medicine, The University of Chicago, Chicago, IL, 60637, USA
| | - Ani Manichaikul
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, 22908, USA
| | - Heather E. Wheeler
- Program in Bioinformatics, Loyola University Chicago, Chicago, IL, 60660, USA
- Department of Biology, Loyola University Chicago, Chicago, IL, 60660, USA
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11
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Bhattacharya A, Hirbo JB, Zhou D, Zhou W, Zheng J, Kanai M, the Global Biobank Meta-analysis Initiative, Pasaniuc B, Gamazon ER, Cox NJ. Best practices for multi-ancestry, meta-analytic transcriptome-wide association studies: Lessons from the Global Biobank Meta-analysis Initiative. CELL GENOMICS 2022; 2:100180. [PMID: 36341024 PMCID: PMC9631681 DOI: 10.1016/j.xgen.2022.100180] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Revised: 08/09/2022] [Accepted: 09/01/2022] [Indexed: 12/13/2022]
Abstract
The Global Biobank Meta-analysis Initiative (GBMI), through its diversity, provides a valuable opportunity to study population-wide and ancestry-specific genetic associations. However, with multiple ascertainment strategies and multi-ancestry study populations across biobanks, GBMI presents unique challenges in implementing statistical genetics methods. Transcriptome-wide association studies (TWASs) boost detection power for and provide biological context to genetic associations by integrating genetic variant-to-trait associations from genome-wide association studies (GWASs) with predictive models of gene expression. TWASs present unique challenges beyond GWASs, especially in a multi-biobank, meta-analytic setting. Here, we present the GBMI TWAS pipeline, outlining practical considerations for ancestry and tissue specificity, meta-analytic strategies, and open challenges at every step of the framework. We advise conducting ancestry-stratified TWASs using ancestry-specific expression models and meta-analyzing results using inverse-variance weighting, showing the least test statistic inflation. Our work provides a foundation for adding transcriptomic context to biobank-linked GWASs, allowing for ancestry-aware discovery to accelerate genomic medicine.
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Affiliation(s)
- Arjun Bhattacharya
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Institute of Quantitative and Computational Biosciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Jibril B. Hirbo
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Dan Zhou
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Wei Zhou
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Jie Zheng
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK
| | - Masahiro Kanai
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita 565-0871, Japan
| | - the Global Biobank Meta-analysis Initiative
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Institute of Quantitative and Computational Biosciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita 565-0871, Japan
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Bogdan Pasaniuc
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Eric R. Gamazon
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Nancy J. Cox
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
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12
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Yin X, Kim K, Suetsugu H, Bang SY, Wen L, Koido M, Ha E, Liu L, Sakamoto Y, Jo S, Leng RX, Otomo N, Kwon YC, Sheng Y, Sugano N, Hwang MY, Li W, Mukai M, Yoon K, Cai M, Ishigaki K, Chung WT, Huang H, Takahashi D, Lee SS, Wang M, Karino K, Shim SC, Zheng X, Miyamura T, Kang YM, Ye D, Nakamura J, Suh CH, Tang Y, Motomura G, Park YB, Ding H, Kuroda T, Choe JY, Li C, Niiro H, Park Y, Shen C, Miyamoto T, Ahn GY, Fei W, Takeuchi T, Shin JM, Li K, Kawaguchi Y, Lee YK, Wang YF, Amano K, Park DJ, Yang W, Tada Y, Lau YL, Yamaji K, Zhu Z, Shimizu M, Atsumi T, Suzuki A, Sumida T, Okada Y, Matsuda K, Matsuo K, Kochi Y, Yamamoto K, Ohmura K, Kim TH, Yang S, Yamamoto T, Kim BJ, Shen N, Ikegawa S, Lee HS, Zhang X, Terao C, Cui Y, Bae SC. Biological insights into systemic lupus erythematosus through an immune cell-specific transcriptome-wide association study. Ann Rheum Dis 2022; 81:1273-1280. [PMID: 35609976 PMCID: PMC9380500 DOI: 10.1136/annrheumdis-2022-222345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Accepted: 05/11/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVE Genome-wide association studies (GWAS) have identified >100 risk loci for systemic lupus erythematosus (SLE), but the disease genes at most loci remain unclear, hampering translation of these genetic discoveries. We aimed to prioritise genes underlying the 110 SLE loci that were identified in the latest East Asian GWAS meta-analysis. METHODS We built gene expression predictive models in blood B cells, CD4+ and CD8+ T cells, monocytes, natural killer cells and peripheral blood cells of 105 Japanese individuals. We performed a transcriptome-wide association study (TWAS) using data from the latest genome-wide association meta-analysis of 208 370 East Asians and searched for candidate genes using TWAS and three data-driven computational approaches. RESULTS TWAS identified 171 genes for SLE (p<1.0×10-5); 114 (66.7%) showed significance only in a single cell type; 127 (74.3%) were in SLE GWAS loci. TWAS identified a strong association between CD83 and SLE (p<7.7×10-8). Meta-analysis of genetic associations in the existing 208 370 East Asian and additional 1498 cases and 3330 controls found a novel single-variant association at rs72836542 (OR=1.11, p=4.5×10-9) around CD83. For the 110 SLE loci, we identified 276 gene candidates, including 104 genes at recently-identified SLE novel loci. We demonstrated in vitro that putative causal variant rs61759532 exhibited an allele-specific regulatory effect on ACAP1, and that presence of the SLE risk allele decreased ACAP1 expression. CONCLUSIONS Cell-level TWAS in six types of immune cells complemented SLE gene discovery and guided the identification of novel genetic associations. The gene findings shed biological insights into SLE genetic associations.
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Affiliation(s)
- Xianyong Yin
- Department of Dermatology and Institute of Dermatology, First Affiliated Hospital, Anhui Medical University, Hefei, Anhui, People's Republic of China
- Key Lab of Dermatology, Ministry of Education (Anhui Medical University), Hefei, Anhui, People's Republic of China
- Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, People's Republic of China
- Department of Dermatology, China-Japan Friendship Hospital, Beijing, People's Republic of China
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
- Human Phenome Institute, Fudan University, Shanghai, People's Republic of China
| | - Kwangwoo Kim
- Department of Biology and Department of Life and Nanopharmaceutical Sciences, Kyung Hee University, Seoul, Korea
| | - Hiroyuki Suetsugu
- Laboratory for Bone and Joint Diseases, RIKEN Center for Medical Sciences, Tokyo, Japan
- Laboratory for Statistical and Translational Genetics Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Department of Orthopaedic Surgery, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - So-Young Bang
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, Seoul, South Korea
- Hanyang University Institute for Rheumatology Research, Seoul, South Korea
| | - Leilei Wen
- Department of Dermatology and Institute of Dermatology, First Affiliated Hospital, Anhui Medical University, Hefei, Anhui, People's Republic of China
- Key Lab of Dermatology, Ministry of Education (Anhui Medical University), Hefei, Anhui, People's Republic of China
| | - Masaru Koido
- Laboratory for Statistical and Translational Genetics Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Division of Molecular Pathology, Department of Cancer Biology, Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Eunji Ha
- Department of Biology and Department of Life and Nanopharmaceutical Sciences, Kyung Hee University, Seoul, Korea
| | - Lu Liu
- Department of Dermatology and Institute of Dermatology, First Affiliated Hospital, Anhui Medical University, Hefei, Anhui, People's Republic of China
- Key Lab of Dermatology, Ministry of Education (Anhui Medical University), Hefei, Anhui, People's Republic of China
| | - Yuma Sakamoto
- Laboratory for Bone and Joint Diseases, RIKEN Center for Medical Sciences, Tokyo, Japan
- Koga Hospital 21, Kurume, Japan
| | - Sungsin Jo
- Hanyang University Institute for Rheumatology Research, Seoul, South Korea
| | - Rui-Xue Leng
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, People's Republic of China
| | - Nao Otomo
- Laboratory for Bone and Joint Diseases, RIKEN Center for Medical Sciences, Tokyo, Japan
- Laboratory for Statistical and Translational Genetics Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Department of Orthopedic Surgery, Keio University School of Medicine, Tokyo, Japan
| | - Young-Chang Kwon
- Hanyang University Institute for Rheumatology Research, Seoul, South Korea
| | - Yujun Sheng
- Department of Dermatology and Institute of Dermatology, First Affiliated Hospital, Anhui Medical University, Hefei, Anhui, People's Republic of China
- Key Lab of Dermatology, Ministry of Education (Anhui Medical University), Hefei, Anhui, People's Republic of China
| | - Nobuhiko Sugano
- Department of Orthopaedic Medical Engineering, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Mi Yeong Hwang
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, South Korea
| | - Weiran Li
- Department of Dermatology and Institute of Dermatology, First Affiliated Hospital, Anhui Medical University, Hefei, Anhui, People's Republic of China
- Key Lab of Dermatology, Ministry of Education (Anhui Medical University), Hefei, Anhui, People's Republic of China
| | - Masaya Mukai
- Department of Rheumatology & Clinical Immunology, Sapporo City General Hospital, Hokkaido, Japan
| | - Kyungheon Yoon
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, South Korea
| | - Minglong Cai
- Department of Dermatology and Institute of Dermatology, First Affiliated Hospital, Anhui Medical University, Hefei, Anhui, People's Republic of China
- Key Lab of Dermatology, Ministry of Education (Anhui Medical University), Hefei, Anhui, People's Republic of China
| | - Kazuyoshi Ishigaki
- Laboratory for Statistical and Translational Genetics Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Center for Data Sciences, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Won Tae Chung
- Department of Internal Medicine, Dong-A University Hospital, Busan, South Korea
| | - He Huang
- Department of Dermatology and Institute of Dermatology, First Affiliated Hospital, Anhui Medical University, Hefei, Anhui, People's Republic of China
- Key Lab of Dermatology, Ministry of Education (Anhui Medical University), Hefei, Anhui, People's Republic of China
| | - Daisuke Takahashi
- Department of Orthopaedic Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Hokkaido, Japan
| | - Shin-Seok Lee
- Division of Rheumatology, Department of Internal Medicine, Chonnam National University Medical School and Hospital, Gwangju, South Korea
| | - Mengwei Wang
- Department of Dermatology and Institute of Dermatology, First Affiliated Hospital, Anhui Medical University, Hefei, Anhui, People's Republic of China
- Key Lab of Dermatology, Ministry of Education (Anhui Medical University), Hefei, Anhui, People's Republic of China
| | - Kohei Karino
- Department of Rheumatology, Endocrinology and Nephrology, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Hokkaido, Japan
| | - Seung-Cheol Shim
- Division of Rheumatology, Department of Internal Medicine, Chungnam National University Hospital, Daejeon, South Korea
| | - Xiaodong Zheng
- Department of Dermatology and Institute of Dermatology, First Affiliated Hospital, Anhui Medical University, Hefei, Anhui, People's Republic of China
- Key Lab of Dermatology, Ministry of Education (Anhui Medical University), Hefei, Anhui, People's Republic of China
| | - Tomoya Miyamura
- Department of Internal Medicine and Rheumatology, National Hospital Organization, Kyushu Medical Center, Fukuoka, Japan
| | - Young Mo Kang
- Division of Rheumatology, Department of Internal Medicine, Kyungpook National University Hospital, Daegu, South Korea
| | - Dongqing Ye
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, People's Republic of China
| | - Junichi Nakamura
- Department of Orthopaedic Surgery, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Chang-Hee Suh
- Department of Rheumatology, Ajou University School of Medicine, Suwon, South Korea
| | - Yuanjia Tang
- Shanghai Institute of Rheumatology, Renji Hospital, Shanghai Jiao Tong University, School of Medicine (SJTUSM), Shanghai, People's Republic of China
| | - Goro Motomura
- Department of Orthopaedic Surgery, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Yong-Beom Park
- Department of Internal Medicine, Yonsei University College of Medicine, Seoul, South Korea
| | - Huihua Ding
- Shanghai Institute of Rheumatology, Renji Hospital, Shanghai Jiao Tong University, School of Medicine (SJTUSM), Shanghai, People's Republic of China
| | - Takeshi Kuroda
- Niigata University Health Administration Center, Niigata, Japan
| | - Jung-Yoon Choe
- Department of Rheumatology, Catholic University of Daegu School of Medicine, Daegu, South Korea
| | - Chengxu Li
- Department of Dermatology, China-Japan Friendship Hospital, Beijing, People's Republic of China
| | - Hiroaki Niiro
- Department of Medical Education, Kyushu University Graduate School of Medical Sciences, Fukuoka, Japan
| | - Youngho Park
- Hanyang University Institute for Rheumatology Research, Seoul, South Korea
| | - Changbing Shen
- Department of Dermatology, Peking University Shenzhen Hospital, Shenzhen, Guangdong, People's Republic of China
- Shenzhen Key Laboratory for Translational Medicine of Dermatology, Shenzhen Peking University-The Hong Kong University of Science and Technology Medical Center, Shenzhen, Guangdong, People's Republic of China
| | - Takeshi Miyamoto
- Department of Orthopaedic Surgery, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan
| | - Ga-Young Ahn
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, Seoul, South Korea
| | - Wenmin Fei
- Department of Dermatology, China-Japan Friendship Hospital, Beijing, People's Republic of China
| | - Tsutomu Takeuchi
- Division of Rheumatology, Department of Internal Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Jung-Min Shin
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, Seoul, South Korea
| | - Keke Li
- Department of Dermatology, China-Japan Friendship Hospital, Beijing, People's Republic of China
| | - Yasushi Kawaguchi
- Institute of Rheumatology, Tokyo Women's Medical University, Tokyo, Japan
| | - Yeon-Kyung Lee
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, Seoul, South Korea
| | - Yong-Fei Wang
- Department of Paediatrics and Adolescent Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Koichi Amano
- Department of Rheumatology & Clinical Immunology, Saitama Medical Center, Saitama Medical University, Saitama, Japan
| | - Dae Jin Park
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, Seoul, South Korea
| | - Wanling Yang
- Department of Paediatrics and Adolescent Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Yoshifumi Tada
- Department of Rheumatology, Faculty of Medicine, Saga University, Saga, Japan
| | - Yu Lung Lau
- Department of Paediatrics and Adolescent Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Ken Yamaji
- Department of Internal Medicine and Rheumatology, Juntendo University School of Medicine, Tokyo, Japan
| | - Zhengwei Zhu
- Department of Dermatology and Institute of Dermatology, First Affiliated Hospital, Anhui Medical University, Hefei, Anhui, People's Republic of China
- Key Lab of Dermatology, Ministry of Education (Anhui Medical University), Hefei, Anhui, People's Republic of China
| | - Masato Shimizu
- Hokkaido Medical Center for Rheumatic Diseases, Sapporo, Japan
| | - Takashi Atsumi
- Department of Orthopaedic Surgery, Showa University School of Medicine, Tokyo, Japan
| | - Akari Suzuki
- Laboratory for Autoimmune Diseases, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan
| | - Takayuki Sumida
- Department of Internal Medicine, Faculty of Medicine, University of Tsukuba, Ibaraki, Japan
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan
- Department of Genome Informatics, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan
| | - Koichi Matsuda
- Laboratory of Genome Technology, Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan
- Laboratory of Clinical Genome Sequencing, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo, Tokyo, Japan
| | - Keitaro Matsuo
- Division of Cancer Epidemiology and Prevention, Aichi Cancer Center Research Institute, Nagoya, Japan
- Department of Epidemiology, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Yuta Kochi
- Department of Genomic Function and Diversity, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan
| | - Kazuhiko Yamamoto
- Laboratory for Autoimmune Diseases, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan
| | - Koichiro Ohmura
- Department of Rheumatology and Clinical Immunology, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Tae-Hwan Kim
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, Seoul, South Korea
- Hanyang University Institute for Rheumatology Research, Seoul, South Korea
| | - Sen Yang
- Department of Dermatology and Institute of Dermatology, First Affiliated Hospital, Anhui Medical University, Hefei, Anhui, People's Republic of China
- Key Lab of Dermatology, Ministry of Education (Anhui Medical University), Hefei, Anhui, People's Republic of China
| | - Takuaki Yamamoto
- Department of Orthopaedic Surgery, Faculty of Medicine, Fukuoka University, Fukuoka, Japan
| | - Bong-Jo Kim
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, South Korea
| | - Nan Shen
- Shanghai Institute of Rheumatology, Renji Hospital, Shanghai Jiao Tong University, School of Medicine (SJTUSM), Shanghai, People's Republic of China
- State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine (SJTUSM), Shanghai, People's Republic of China
- Center for Autoimmune Genomics and Etiology (CAGE), Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Shiro Ikegawa
- Laboratory for Bone and Joint Diseases, RIKEN Center for Medical Sciences, Tokyo, Japan
| | - Hye-Soon Lee
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, Seoul, South Korea
- Hanyang University Institute for Rheumatology Research, Seoul, South Korea
| | - Xuejun Zhang
- Department of Dermatology and Institute of Dermatology, First Affiliated Hospital, Anhui Medical University, Hefei, Anhui, People's Republic of China
- Key Lab of Dermatology, Ministry of Education (Anhui Medical University), Hefei, Anhui, People's Republic of China
- Department of Dermatology, Institute of Dermatology, Huashan Hospital, Fudan University, Shanghai, People's Republic of China
| | - Chikashi Terao
- Laboratory for Statistical and Translational Genetics Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Clinical Research Center, Shizuoka General Hospital, Shizuoka, Japan
- Department of Applied Genetics, School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan
| | - Yong Cui
- Department of Dermatology, China-Japan Friendship Hospital, Beijing, People's Republic of China
| | - Sang-Cheol Bae
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, Seoul, South Korea
- Hanyang University Institute for Rheumatology Research, Seoul, South Korea
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13
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Dai Y, Jia P, Zhao Z, Gottlieb A. A Method for Bridging Population-Specific Genotypes to Detect Gene Modules Associated with Alzheimer's Disease. Cells 2022; 11:2219. [PMID: 35883662 PMCID: PMC9319087 DOI: 10.3390/cells11142219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Revised: 07/12/2022] [Accepted: 07/14/2022] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND Genome-wide association studies have successfully identified variants associated with multiple conditions. However, generalizing discoveries across diverse populations remains challenging due to large variations in genetic composition. Methods that perform gene expression imputation have attempted to address the transferability of gene discoveries across populations, but with limited success. METHODS Here, we introduce a pipeline that combines gene expression imputation with gene module discovery, including a dense gene module search and a gene set variation analysis, to address the transferability issue. Our method feeds association probabilities of imputed gene expression with a selected phenotype into tissue-specific gene-module discovery over protein interaction networks to create higher-level gene modules. RESULTS We demonstrate our method's utility in three case-control studies of Alzheimer's disease (AD) for three different race/ethnic populations (Whites, African descent and Hispanics). We discovered 182 AD-associated genes from gene modules shared between these populations, highlighting new gene modules associated with AD. CONCLUSIONS Our innovative framework has the potential to identify robust discoveries across populations based on gene modules, as demonstrated in AD.
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Affiliation(s)
- Yulin Dai
- Center for Precision Health, School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Peilin Jia
- Center for Precision Health, School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Zhongming Zhao
- Center for Precision Health, School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Assaf Gottlieb
- Center for Precision Health, School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX 77030, USA
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14
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Highland HM, Wojcik GL, Graff M, Nishimura KK, Hodonsky CJ, Baldassari AR, Cote AC, Cheng I, Gignoux CR, Tao R, Li Y, Boerwinkle E, Fornage M, Haessler J, Hindorff LA, Hu Y, Justice AE, Lin BM, Lin D, Stram DO, Haiman CA, Kooperberg C, Le Marchand L, Matise TC, Kenny EE, Carlson CS, Stahl EA, Avery CL, North KE, Ambite JL, Buyske S, Loos RJ, Peters U, Young KL, Bien SA, Huckins LM. Predicted gene expression in ancestrally diverse populations leads to discovery of susceptibility loci for lifestyle and cardiometabolic traits. Am J Hum Genet 2022; 109:669-679. [PMID: 35263625 PMCID: PMC9069067 DOI: 10.1016/j.ajhg.2022.02.013] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Accepted: 02/15/2022] [Indexed: 02/06/2023] Open
Abstract
One mechanism by which genetic factors influence complex traits and diseases is altering gene expression. Direct measurement of gene expression in relevant tissues is rarely tenable; however, genetically regulated gene expression (GReX) can be estimated using prediction models derived from large multi-omic datasets. These approaches have led to the discovery of many gene-trait associations, but whether models derived from predominantly European ancestry (EA) reference panels can map novel associations in ancestrally diverse populations remains unclear. We applied PrediXcan to impute GReX in 51,520 ancestrally diverse Population Architecture using Genomics and Epidemiology (PAGE) participants (35% African American, 45% Hispanic/Latino, 10% Asian, and 7% Hawaiian) across 25 key cardiometabolic traits and relevant tissues to identify 102 novel associations. We then compared associations in PAGE to those in a random subset of 50,000 White British participants from UK Biobank (UKBB50k) for height and body mass index (BMI). We identified 517 associations across 47 tissues in PAGE but not UKBB50k, demonstrating the importance of diverse samples in identifying trait-associated GReX. We observed that variants used in PrediXcan models were either more or less differentiated across continental-level populations than matched-control variants depending on the specific population reflecting sampling bias. Additionally, variants from identified genes specific to either PAGE or UKBB50k analyses were more ancestrally differentiated than those in genes detected in both analyses, underlining the value of population-specific discoveries. This suggests that while EA-derived transcriptome imputation models can identify new associations in non-EA populations, models derived from closely matched reference panels may yield further insights. Our findings call for more diversity in reference datasets of tissue-specific gene expression.
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Affiliation(s)
- Heather M Highland
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA.
| | - Genevieve L Wojcik
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Mariaelisa Graff
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Katherine K Nishimura
- Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Chani J Hodonsky
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA; Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA
| | - Antoine R Baldassari
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Alanna C Cote
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Iona Cheng
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Christopher R Gignoux
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Ran Tao
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Yuqing Li
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Eric Boerwinkle
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center, Houston, TX 77030, USA
| | - Myriam Fornage
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center, Houston, TX 77030, USA; Brown Foundation Institute for Molecular Medicine, McGovern Medical School, The University of Texas Health Science Center, Houston, TX 77030, USA
| | - Jeffrey Haessler
- Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Lucia A Hindorff
- Division of Genomic Medicine, NIH National Human Genome Research Institute, Bethesda, MD 20892, USA
| | - Yao Hu
- Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Anne E Justice
- Department of Population Health Sciences, Geisinger Health System, Danville, PA 17822, USA
| | - Bridget M Lin
- Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Danyu Lin
- Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Daniel O Stram
- Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Christopher A Haiman
- Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Charles Kooperberg
- Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA; School of Public Health, University of Washington, Seattle, WA 98195, USA
| | | | - Tara C Matise
- Genetics, Rutgers University, New Brunswick, NJ 08901-8554, USA
| | - Eimear E Kenny
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Christopher S Carlson
- Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Eli A Stahl
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Christy L Avery
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Kari E North
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Jose Luis Ambite
- Information Sciences Institute, University of Southern California, Marina del Rey, CA 90292, USA
| | - Steven Buyske
- Statistics, Rutgers University, New Brunswick, NJ 08901-8554, USA
| | - Ruth J Loos
- Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Ulrike Peters
- Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA; School of Public Health, University of Washington, Seattle, WA 98195, USA
| | - Kristin L Young
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Stephanie A Bien
- Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Laura M Huckins
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Mental Illness Research, Education and Clinical Centers, James J. Peters Department of Veterans Affairs Medical Center, Bronx, NY 14068, USA.
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15
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Schubert R, Geoffroy E, Gregga I, Mulford AJ, Aguet F, Ardlie K, Gerszten R, Clish C, Van Den Berg D, Taylor KD, Durda P, Johnson WC, Cornell E, Guo X, Liu Y, Tracy R, Conomos M, Blackwell T, Papanicolaou G, Lappalainen T, Mikhaylova AV, Thornton TA, Cho MH, Gignoux CR, Lange L, Lange E, Rich SS, Rotter JI, NHLBI TOPMed Consortium, Manichaikul A, Im HK, Wheeler HE. Protein prediction for trait mapping in diverse populations. PLoS One 2022; 17:e0264341. [PMID: 35202437 PMCID: PMC8870552 DOI: 10.1371/journal.pone.0264341] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 02/08/2022] [Indexed: 11/18/2022] Open
Abstract
Genetically regulated gene expression has helped elucidate the biological mechanisms underlying complex traits. Improved high-throughput technology allows similar interrogation of the genetically regulated proteome for understanding complex trait mechanisms. Here, we used the Trans-omics for Precision Medicine (TOPMed) Multi-omics pilot study, which comprises data from Multi-Ethnic Study of Atherosclerosis (MESA), to optimize genetic predictors of the plasma proteome for genetically regulated proteome-wide association studies (PWAS) in diverse populations. We built predictive models for protein abundances using data collected in TOPMed MESA, for which we have measured 1,305 proteins by a SOMAscan assay. We compared predictive models built via elastic net regression to models integrating posterior inclusion probabilities estimated by fine-mapping SNPs prior to elastic net. In order to investigate the transferability of predictive models across ancestries, we built protein prediction models in all four of the TOPMed MESA populations, African American (n = 183), Chinese (n = 71), European (n = 416), and Hispanic/Latino (n = 301), as well as in all populations combined. As expected, fine-mapping produced more significant protein prediction models, especially in African ancestries populations, potentially increasing opportunity for discovery. When we tested our TOPMed MESA models in the independent European INTERVAL study, fine-mapping improved cross-ancestries prediction for some proteins. Using GWAS summary statistics from the Population Architecture using Genomics and Epidemiology (PAGE) study, which comprises ∼50,000 Hispanic/Latinos, African Americans, Asians, Native Hawaiians, and Native Americans, we applied S-PrediXcan to perform PWAS for 28 complex traits. The most protein-trait associations were discovered, colocalized, and replicated in large independent GWAS using proteome prediction model training populations with similar ancestries to PAGE. At current training population sample sizes, performance between baseline and fine-mapped protein prediction models in PWAS was similar, highlighting the utility of elastic net. Our predictive models in diverse populations are publicly available for use in proteome mapping methods at https://doi.org/10.5281/zenodo.4837327.
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Affiliation(s)
- Ryan Schubert
- Department of Mathematics and Statistics, Loyola University Chicago, Chicago, IL, United States of America
- Department of Biology, Loyola University Chicago, Chicago, IL, United States of America
- Program in Bioinformatics, Loyola University Chicago, Chicago, IL, United States of America
| | - Elyse Geoffroy
- Program in Bioinformatics, Loyola University Chicago, Chicago, IL, United States of America
| | - Isabelle Gregga
- Department of Biology, Loyola University Chicago, Chicago, IL, United States of America
| | - Ashley J. Mulford
- Department of Biology, Loyola University Chicago, Chicago, IL, United States of America
- Program in Bioinformatics, Loyola University Chicago, Chicago, IL, United States of America
| | - Francois Aguet
- Broad Institute, Cambridge, MA, United States of America
| | - Kristin Ardlie
- Broad Institute, Cambridge, MA, United States of America
| | - Robert Gerszten
- Beth Israel Deaconess Medical Center, Boston, MA, United States of America
| | - Clary Clish
- Broad Institute, Cambridge, MA, United States of America
| | - David Van Den Berg
- University of Southern California, Los Angeles, CA, United States of America
| | - 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, United States of America
| | - Peter Durda
- Laboratory for Clinical Biochemistry Research, University of Vermont, Burlington, VT, United States of America
| | - W. Craig Johnson
- Collaborative Health Studies Coordinating Center, University of Washington, Seattle, WA, United States of America
| | - Elaine Cornell
- Laboratory for Clinical Biochemistry Research, University of Vermont, Burlington, VT, United States of America
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, United States of America
| | - Yongmei Liu
- Department of Medicine, Duke University School of Medicine, Durham, NC, United States of America
| | - Russell Tracy
- Laboratory for Clinical Biochemistry Research, University of Vermont, Burlington, VT, United States of America
| | - Matthew Conomos
- Department of Biostatistics, University of Washington, Seattle, WA, United States of America
| | - Tom Blackwell
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, United States of America
| | - George Papanicolaou
- Epidemiology Branch, National Heart, Lung and Blood Institute, Bethesda, MD, United States of America
| | - Tuuli Lappalainen
- New York Genome Center and Department of Systems Biology, Columbia University, New York, NY United States of America
| | - Anna V. Mikhaylova
- Department of Biostatistics, University of Washington, Seattle, WA, United States of America
| | - Timothy A. Thornton
- Department of Biostatistics, University of Washington, Seattle, WA, United States of America
| | - Michael H. Cho
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, United States of America
| | - Christopher R. Gignoux
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States of America
| | - Leslie Lange
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States of America
| | - Ethan Lange
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States of America
| | - Stephen S. Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, United States of America
| | - 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, United States of America
| | | | - Ani Manichaikul
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, United States of America
| | - Hae Kyung Im
- Section of Genetic Medicine, The University of Chicago, Chicago, IL, United States of America
| | - Heather E. Wheeler
- Department of Biology, Loyola University Chicago, Chicago, IL, United States of America
- Program in Bioinformatics, Loyola University Chicago, Chicago, IL, United States of America
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Soumboundou M, Dossou J, Kalaga Y, Nkengurutse I, Faye I, Guingani A, Gadji M, Yameogo KJ, Zongo H, Mbaye G, Dem A, Diarra M, Adjibade R, Djebou C, Junker S, Oudrhiri N, Hempel WM, Dieterlen A, Jeandidier E, Carde P, El Maalouf E, Colicchio B, Bennaceur-Griscelli A, Fenech M, Voisin P, Rodriguez-Lafrasse C, M'Kacher R. Is Response to Genotoxic Stress Similar in Populations of African and European Ancestry? A Study of Dose-Response After in vitro Irradiation. Front Genet 2021; 12:657999. [PMID: 34868192 PMCID: PMC8632650 DOI: 10.3389/fgene.2021.657999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Accepted: 05/13/2021] [Indexed: 11/17/2022] Open
Abstract
Background: Exposure to genotoxic stress such as radiation is an important public health issue affecting a large population. The necessity of analyzing cytogenetic effects of such exposure is related to the need to estimate the associated risk. Cytogenetic biological dosimetry is based on the relationship between the absorbed dose and the frequency of scored chromosomal aberrations. The influence of confounding factors on radiation response is a topical issue. The role of ethnicity is unclear. Here, we compared the dose-response curves obtained after irradiation of circulating lymphocytes from healthy donors of African and European ancestry. Materials and Methods: Blood samples from six Africans living in Africa, five Africans living in Europe, and five Caucasians living in Europe were exposed to various doses (0–4 Gy) of X-rays at a dose-rate of 0.1 Gy/min using an X-RAD320 irradiator. A validated cohort composed of 14 healthy Africans living in three African countries was included and blood samples were irradiated using the same protocols. Blood lymphocytes were cultured for 48 h and chromosomal aberrations scored during the first mitosis by telomere and centromere staining. The distribution of dicentric chromosomes was determined and the Kruskal-Wallis test was used to compare the dose-response curves of the two populations. Results: No spontaneous dicentric chromosomes were detected in African donors, thus establishing a very low background of unstable chromosomal aberrations relative to the European population. There was a significant difference in the dose response curves between native African and European donors. At 4 Gy, African donors showed a significantly lower frequency of dicentric chromosomes (p = 8.65 10–17), centric rings (p = 4.0310–14), and resulting double-strand-breaks (DSB) (p = 1.32 10–18) than European donors. In addition, a significant difference was found between African donors living in Europe and Africans living in Africa. Conclusion: This is the first study to demonstrate the important role of ethnic and environmental factors that may epigenetically influence the response to irradiation. It will be necessary to establish country-of-origen-specific dose response curves to practice precise and adequate biological dosimetry. This work opens new perspective for the comparison of treatments based on genotoxic agents, such as irradiation.
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Affiliation(s)
| | - Julien Dossou
- Département du Génie d'Imagerie Médicale et Radiobiologie, Cotonou, Benin
| | - Yossef Kalaga
- Centre Hospitalier Yalgado Radioprotection-Radiobiologie, Ouagadougou, Burkina Faso
| | | | | | - Albert Guingani
- Centre Hospitalier Yalgado Radioprotection-Radiobiologie, Ouagadougou, Burkina Faso
| | | | - Koudbi J Yameogo
- Centre Hospitalier Yalgado Radioprotection-Radiobiologie, Ouagadougou, Burkina Faso
| | - Henri Zongo
- Centre Hospitalier Yalgado Radioprotection-Radiobiologie, Ouagadougou, Burkina Faso
| | - Gora Mbaye
- Laboratoire Biophysique UFR-Santé, Dakar, Senegal
| | | | | | - Rached Adjibade
- Département du Génie d'Imagerie Médicale et Radiobiologie, Cotonou, Benin
| | - Catherine Djebou
- Département du Génie d'Imagerie Médicale et Radiobiologie, Cotonou, Benin
| | - Steffen Junker
- Institute of Biomedicine, University of Aarhus, Aarhus, Denmark
| | - Noufissa Oudrhiri
- APHP-Service d'Hématologie - Oncohématologie Moléculaire et Cytogénétique Hôpital Paul Brousse Université Paris Saclay/Inserm UMR 935, Villejuif, France
| | | | - Alain Dieterlen
- IRIMAS, Institut de Recherche en Informatique, Mathématiques, Automatique et Signal, Université de Haute-Alsace, Mulhouse, France
| | - Eric Jeandidier
- Service de Génétique Groupe Hospitalier de la Région de Mulhouse et Sud Alsace, Mulhouse, France
| | - Patrice Carde
- Department of Hematology, Gustave Roussy Cancer Campus, Villejuif, France
| | | | - Bruno Colicchio
- IRIMAS, Institut de Recherche en Informatique, Mathématiques, Automatique et Signal, Université de Haute-Alsace, Mulhouse, France
| | - Annelise Bennaceur-Griscelli
- APHP-Service d'Hématologie - Oncohématologie Moléculaire et Cytogénétique Hôpital Paul Brousse Université Paris Saclay/Inserm UMR 935, Villejuif, France
| | - Michael Fenech
- School of Pharmacy and Medical Sciences, University of South Australia, Adelaide, SA, Australia.,Genome Health Foundation, North Brighton, SA, Australia.,Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
| | | | - Claire Rodriguez-Lafrasse
- Laboratoire de Radiobiologie Cellulaire et Moléculaire, Faculté de Médecine Lyon-Sud, UMR CNRS5822/IN2P3, IPNL, PRISME, Oullins, France
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17
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Wen J, Xie M, Rowland B, Rosen JD, Sun Q, Chen J, Tapia AL, Qian H, Kowalski MH, Shan Y, Young KL, Graff M, Argos M, Avery CL, Bien SA, Buyske S, Yin J, Choquet H, Fornage M, Hodonsky CJ, Jorgenson E, Kooperberg C, Loos RJF, Liu Y, Moon JY, North KE, Rich SS, Rotter JI, Smith JA, Zhao W, Shang L, Wang T, Zhou X, Reiner AP, Raffield LM, Li Y. Transcriptome-Wide Association Study of Blood Cell Traits in African Ancestry and Hispanic/Latino Populations. Genes (Basel) 2021; 12:1049. [PMID: 34356065 PMCID: PMC8307403 DOI: 10.3390/genes12071049] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 06/29/2021] [Accepted: 07/02/2021] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND Thousands of genetic variants have been associated with hematological traits, though target genes remain unknown at most loci. Moreover, limited analyses have been conducted in African ancestry and Hispanic/Latino populations; hematological trait associated variants more common in these populations have likely been missed. METHODS To derive gene expression prediction models, we used ancestry-stratified datasets from the Multi-Ethnic Study of Atherosclerosis (MESA, including n = 229 African American and n = 381 Hispanic/Latino participants, monocytes) and the Depression Genes and Networks study (DGN, n = 922 European ancestry participants, whole blood). We then performed a transcriptome-wide association study (TWAS) for platelet count, hemoglobin, hematocrit, and white blood cell count in African (n = 27,955) and Hispanic/Latino (n = 28,324) ancestry participants. RESULTS Our results revealed 24 suggestive signals (p < 1 × 10-4) that were conditionally distinct from known GWAS identified variants and successfully replicated these signals in European ancestry subjects from UK Biobank. We found modestly improved correlation of predicted and measured gene expression in an independent African American cohort (the Genetic Epidemiology Network of Arteriopathy (GENOA) study (n = 802), lymphoblastoid cell lines) using the larger DGN reference panel; however, some genes were well predicted using MESA but not DGN. CONCLUSIONS These analyses demonstrate the importance of performing TWAS and other genetic analyses across diverse populations and of balancing sample size and ancestry background matching when selecting a TWAS reference panel.
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Affiliation(s)
- Jia Wen
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA; (J.W.); (M.X.); (L.M.R.)
| | - Munan Xie
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA; (J.W.); (M.X.); (L.M.R.)
| | - Bryce Rowland
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; (B.R.); (J.D.R.); (Q.S.); (J.C.); (A.L.T.); (M.H.K.); (Y.S.)
| | - Jonathan D. Rosen
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; (B.R.); (J.D.R.); (Q.S.); (J.C.); (A.L.T.); (M.H.K.); (Y.S.)
| | - Quan Sun
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; (B.R.); (J.D.R.); (Q.S.); (J.C.); (A.L.T.); (M.H.K.); (Y.S.)
| | - Jiawen Chen
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; (B.R.); (J.D.R.); (Q.S.); (J.C.); (A.L.T.); (M.H.K.); (Y.S.)
| | - Amanda L. Tapia
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; (B.R.); (J.D.R.); (Q.S.); (J.C.); (A.L.T.); (M.H.K.); (Y.S.)
| | - Huijun Qian
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA;
| | - Madeline H. Kowalski
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; (B.R.); (J.D.R.); (Q.S.); (J.C.); (A.L.T.); (M.H.K.); (Y.S.)
| | - Yue Shan
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; (B.R.); (J.D.R.); (Q.S.); (J.C.); (A.L.T.); (M.H.K.); (Y.S.)
| | - Kristin L. Young
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27516, USA; (K.L.Y.); (M.G.); (C.L.A.); (K.E.N.)
| | - Marielisa Graff
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27516, USA; (K.L.Y.); (M.G.); (C.L.A.); (K.E.N.)
| | - Maria Argos
- Division of Epidemiology and Biostatistics, University of Illinois at Chicago, Chicago, IL 60612, USA;
| | - Christy L. Avery
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27516, USA; (K.L.Y.); (M.G.); (C.L.A.); (K.E.N.)
| | - Stephanie A. Bien
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA; (S.A.B.); (C.K.)
| | - Steve Buyske
- Department of Statistics, Rutgers University, Piscataway, NJ 08854, USA;
| | - Jie Yin
- Division of Research, Kaiser Permanente Northern California, Oakland, CA 94612, USA; (J.Y.); (H.C.)
| | - Hélène Choquet
- Division of Research, Kaiser Permanente Northern California, Oakland, CA 94612, USA; (J.Y.); (H.C.)
| | - Myriam Fornage
- Institute of Molecular Medicine, McGovern Medical School, The University of Texas Health Science Center, Houston, TX 77030, USA;
| | - Chani J. Hodonsky
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA; (C.J.H.); (S.S.R.)
| | | | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA; (S.A.B.); (C.K.)
| | - Ruth J. F. Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA;
| | - Yongmei Liu
- Molecular Physiology Institute, Duke University, Durham, NC 27701, USA;
| | - Jee-Young Moon
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY 10461, USA; (J.-Y.M.); (T.W.)
| | - Kari E. North
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27516, USA; (K.L.Y.); (M.G.); (C.L.A.); (K.E.N.)
| | - Stephen S. Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA; (C.J.H.); (S.S.R.)
| | - 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 90502, USA;
| | - Jennifer A. Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA; (J.A.S.); (W.Z.)
| | - Wei Zhao
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA; (J.A.S.); (W.Z.)
| | - Lulu Shang
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA; (L.S.); (X.Z.)
| | - Tao Wang
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY 10461, USA; (J.-Y.M.); (T.W.)
| | - Xiang Zhou
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA; (L.S.); (X.Z.)
| | - Alexander P. Reiner
- Department of Epidemiology, University of Washington, Seattle, WA 98195, USA;
| | - Laura M. Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA; (J.W.); (M.X.); (L.M.R.)
| | - Yun Li
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA; (J.W.); (M.X.); (L.M.R.)
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; (B.R.); (J.D.R.); (Q.S.); (J.C.); (A.L.T.); (M.H.K.); (Y.S.)
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18
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Feng H, Mancuso N, Pasaniuc B, Kraft P. Multitrait transcriptome-wide association study (TWAS) tests. Genet Epidemiol 2021; 45:563-576. [PMID: 34082479 DOI: 10.1002/gepi.22391] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Revised: 03/26/2021] [Accepted: 04/05/2021] [Indexed: 12/19/2022]
Abstract
Multitrait tests can improve power to detect associations between individual single-nucleotide polymorphisms (SNPs) and several related traits. Here, we develop methods for multi-SNP transcriptome-wide association (TWAS) tests to test the association between predicted gene expression levels and multiple phenotypes. We show that the correlation in TWAS test statistics for multiple phenotypes has the same form as multitrait statistics for the single-SNP setting. Thus, established methods for combining single-SNP test statistics across multiple traits can be extended directly to the TWAS setting. We performed an extensive evaluation across eight multitrait methods in simulations that varied gene-phenotype effect sizes in addition to the underlying covariance structure among the phenotypes. We found that all multitrait TWAS tests have well-calibrated Type I error (except ASSET, which can have a slightly elevated or depressed Type I error rate). Our results show that multitrait TWAS can improve statistical power compared with multiple single-trait TWAS followed by Bonferroni correction. To illustrate our approach to real data, we conducted a multitrait TWAS of four circulating lipid traits from the Global Lipids Genetics Consortium. We found that our multitrait Wald TWAS approach identified 506 genes associated with lipid levels compared with 87 identified through Bonferroni-corrected single-trait TWAS. Overall, we find that our proposed multitrait TWAS framework outperforms single-trait approaches to identify new genetic associations, especially for functionally correlated phenotypes and phenotypes with overlapping genome-wide association studies samples, leading to insights into the genetic architecture of multiple phenotypes.
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Affiliation(s)
- Helian Feng
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Nicholas Mancuso
- Department of Preventive Medicine, Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
- Division of Biostatistics, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Bogdan Pasaniuc
- Department of Human Genetics, University of California Los Angeles, Los Angeles, California, USA
- Department of Pathology and Laboratory Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Peter Kraft
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
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