Use of >100,000 NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium whole genome sequences improves imputation quality and detection of rare variant associations in admixed African and Hispanic/Latino populations
Most genome-wide association and fine-mapping studies to date have been conducted in individuals of European descent, and genetic studies of populations of Hispanic/Latino and African ancestry are limited. In addition, these populations have more complex linkage disequilibrium structure. In order to...
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| Vydáno v: | PLoS genetics Ročník 15; číslo 12; s. e1008500 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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United States
Public Library of Science
01.12.2019
Public Library of Science (PLoS) |
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| ISSN: | 1553-7404, 1553-7390, 1553-7404 |
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| Abstract | Most genome-wide association and fine-mapping studies to date have been conducted in individuals of European descent, and genetic studies of populations of Hispanic/Latino and African ancestry are limited. In addition, these populations have more complex linkage disequilibrium structure. In order to better define the genetic architecture of these understudied populations, we leveraged >100,000 phased sequences available from deep-coverage whole genome sequencing through the multi-ethnic NHLBI Trans-Omics for Precision Medicine (TOPMed) program to impute genotypes into admixed African and Hispanic/Latino samples with genome-wide genotyping array data. We demonstrated that using TOPMed sequencing data as the imputation reference panel improves genotype imputation quality in these populations, which subsequently enhanced gene-mapping power for complex traits. For rare variants with minor allele frequency (MAF) < 0.5%, we observed a 2.3- to 6.1-fold increase in the number of well-imputed variants, with 11-34% improvement in average imputation quality, compared to the state-of-the-art 1000 Genomes Project Phase 3 and Haplotype Reference Consortium reference panels. Impressively, even for extremely rare variants with minor allele count <10 (including singletons) in the imputation target samples, average information content rescued was >86%. Subsequent association analyses of TOPMed reference panel-imputed genotype data with hematological traits (hemoglobin (HGB), hematocrit (HCT), and white blood cell count (WBC)) in ~21,600 African-ancestry and ~21,700 Hispanic/Latino individuals identified associations with two rare variants in the HBB gene (rs33930165 with higher WBC [p = 8.8x10-15] in African populations, rs11549407 with lower HGB [p = 1.5x10-12] and HCT [p = 8.8x10-10] in Hispanics/Latinos). By comparison, neither variant would have been genome-wide significant if either 1000 Genomes Project Phase 3 or Haplotype Reference Consortium reference panels had been used for imputation. Our findings highlight the utility of the TOPMed imputation reference panel for identification of novel rare variant associations not previously detected in similarly sized genome-wide studies of under-represented African and Hispanic/Latino populations. |
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| AbstractList | Most genome-wide association and fine-mapping studies to date have been conducted in individuals of European descent, and genetic studies of populations of Hispanic/Latino and African ancestry are limited. In addition, these populations have more complex linkage disequilibrium structure. In order to better define the genetic architecture of these understudied populations, we leveraged >100,000 phased sequences available from deep-coverage whole genome sequencing through the multi-ethnic NHLBI Trans-Omics for Precision Medicine (TOPMed) program to impute genotypes into admixed African and Hispanic/Latino samples with genome-wide genotyping array data. We demonstrated that using TOPMed sequencing data as the imputation reference panel improves genotype imputation quality in these populations, which subsequently enhanced gene-mapping power for complex traits. For rare variants with minor allele frequency (MAF) < 0.5%, we observed a 2.3- to 6.1-fold increase in the number of well-imputed variants, with 11–34% improvement in average imputation quality, compared to the state-of-the-art 1000 Genomes Project Phase 3 and Haplotype Reference Consortium reference panels. Impressively, even for extremely rare variants with minor allele count <10 (including singletons) in the imputation target samples, average information content rescued was >86%. Subsequent association analyses of TOPMed reference panel-imputed genotype data with hematological traits (hemoglobin (HGB), hematocrit (HCT), and white blood cell count (WBC)) in ~21,600 African-ancestry and ~21,700 Hispanic/Latino individuals identified associations with two rare variants in the HBB gene (rs33930165 with higher WBC [p = 8.8x10-15] in African populations, rs11549407 with lower HGB [p = 1.5x10-12] and HCT [p = 8.8x10-10] in Hispanics/Latinos). By comparison, neither variant would have been genome-wide significant if either 1000 Genomes Project Phase 3 or Haplotype Reference Consortium reference panels had been used for imputation. Our findings highlight the utility of the TOPMed imputation reference panel for identification of novel rare variant associations not previously detected in similarly sized genome-wide studies of under-represented African and Hispanic/Latino populations. Most genome-wide association and fine-mapping studies to date have been conducted in individuals of European descent, and genetic studies of populations of Hispanic/Latino and African ancestry are limited. In addition, these populations have more complex linkage disequilibrium structure. In order to better define the genetic architecture of these understudied populations, we leveraged >100,000 phased sequences available from deep-coverage whole genome sequencing through the multi-ethnic NHLBI Trans-Omics for Precision Medicine (TOPMed) program to impute genotypes into admixed African and Hispanic/Latino samples with genome-wide genotyping array data. We demonstrated that using TOPMed sequencing data as the imputation reference panel improves genotype imputation quality in these populations, which subsequently enhanced gene-mapping power for complex traits. For rare variants with minor allele frequency (MAF) < 0.5%, we observed a 2.3- to 6.1-fold increase in the number of well-imputed variants, with 11–34% improvement in average imputation quality, compared to the state-of-the-art 1000 Genomes Project Phase 3 and Haplotype Reference Consortium reference panels. Impressively, even for extremely rare variants with minor allele count <10 (including singletons) in the imputation target samples, average information content rescued was >86%. Subsequent association analyses of TOPMed reference panel-imputed genotype data with hematological traits (hemoglobin (HGB), hematocrit (HCT), and white blood cell count (WBC)) in ~21,600 African-ancestry and ~21,700 Hispanic/Latino individuals identified associations with two rare variants in the HBB gene (rs33930165 with higher WBC [p = 8.8x10-15] in African populations, rs11549407 with lower HGB [p = 1.5x10-12] and HCT [p = 8.8x10-10] in Hispanics/Latinos). By comparison, neither variant would have been genome-wide significant if either 1000 Genomes Project Phase 3 or Haplotype Reference Consortium reference panels had been used for imputation. Our findings highlight the utility of the TOPMed imputation reference panel for identification of novel rare variant associations not previously detected in similarly sized genome-wide studies of under-represented African and Hispanic/Latino populations. Admixed African and Hispanic/Latino populations remain understudied in genetic studies of complex diseases. These populations have more complex linkage disequilibrium (LD) structure that can impair mapping of variants. Genotype imputation represents an approach to improve genome coverage, especially for rare or ancestry-specific variation; however, these understudied populations also have smaller relevant imputation reference panels. In this study, we leveraged >100,000 phased sequences generated from the multi-ethnic NHLBI TOPMed project for imputation in ~21,600 individuals of African ancestry (AAs) and ~21,700 Hispanics/Latinos. We demonstrated substantially higher imputation quality for low frequency and rare variants in comparison to the 1000 Genomes Project and Haplotype Reference Consortium reference panels. Analysis of quantitative hematological traits led to the discovery of associations with two rare variants in the HBB gene; one of these variants was replicated in an independent sample, and the other is known to cause anemia in the homozygous state. By comparison, the same HBB variants would not have been genome-wide significant using current reference panels due to lower imputation quality. Our findings demonstrate the power of TOPMed whole genome sequencing data for imputation and subsequent association analysis in admixed African and Hispanic/Latino populations. Most genome-wide association and fine-mapping studies to date have been conducted in individuals of European descent, and genetic studies of populations of Hispanic/Latino and African ancestry are limited. In addition, these populations have more complex linkage disequilibrium structure. In order to better define the genetic architecture of these understudied populations, we leveraged >100,000 phased sequences available from deep-coverage whole genome sequencing through the multi-ethnic NHLBI Trans-Omics for Precision Medicine (TOPMed) program to impute genotypes into admixed African and Hispanic/Latino samples with genome-wide genotyping array data. We demonstrated that using TOPMed sequencing data as the imputation reference panel improves genotype imputation quality in these populations, which subsequently enhanced gene-mapping power for complex traits. For rare variants with minor allele frequency (MAF) < 0.5%, we observed a 2.3- to 6.1-fold increase in the number of well-imputed variants, with 11-34% improvement in average imputation quality, compared to the state-of-the-art 1000 Genomes Project Phase 3 and Haplotype Reference Consortium reference panels. Impressively, even for extremely rare variants with minor allele count <10 (including singletons) in the imputation target samples, average information content rescued was >86%. Subsequent association analyses of TOPMed reference panel-imputed genotype data with hematological traits (hemoglobin (HGB), hematocrit (HCT), and white blood cell count (WBC)) in ~21,600 African-ancestry and ~21,700 Hispanic/Latino individuals identified associations with two rare variants in the HBB gene (rs33930165 with higher WBC [p = 8.8x10-15] in African populations, rs11549407 with lower HGB [p = 1.5x10-12] and HCT [p = 8.8x10-10] in Hispanics/Latinos). By comparison, neither variant would have been genome-wide significant if either 1000 Genomes Project Phase 3 or Haplotype Reference Consortium reference panels had been used for imputation. Our findings highlight the utility of the TOPMed imputation reference panel for identification of novel rare variant associations not previously detected in similarly sized genome-wide studies of under-represented African and Hispanic/Latino populations.Most genome-wide association and fine-mapping studies to date have been conducted in individuals of European descent, and genetic studies of populations of Hispanic/Latino and African ancestry are limited. In addition, these populations have more complex linkage disequilibrium structure. In order to better define the genetic architecture of these understudied populations, we leveraged >100,000 phased sequences available from deep-coverage whole genome sequencing through the multi-ethnic NHLBI Trans-Omics for Precision Medicine (TOPMed) program to impute genotypes into admixed African and Hispanic/Latino samples with genome-wide genotyping array data. We demonstrated that using TOPMed sequencing data as the imputation reference panel improves genotype imputation quality in these populations, which subsequently enhanced gene-mapping power for complex traits. For rare variants with minor allele frequency (MAF) < 0.5%, we observed a 2.3- to 6.1-fold increase in the number of well-imputed variants, with 11-34% improvement in average imputation quality, compared to the state-of-the-art 1000 Genomes Project Phase 3 and Haplotype Reference Consortium reference panels. Impressively, even for extremely rare variants with minor allele count <10 (including singletons) in the imputation target samples, average information content rescued was >86%. Subsequent association analyses of TOPMed reference panel-imputed genotype data with hematological traits (hemoglobin (HGB), hematocrit (HCT), and white blood cell count (WBC)) in ~21,600 African-ancestry and ~21,700 Hispanic/Latino individuals identified associations with two rare variants in the HBB gene (rs33930165 with higher WBC [p = 8.8x10-15] in African populations, rs11549407 with lower HGB [p = 1.5x10-12] and HCT [p = 8.8x10-10] in Hispanics/Latinos). By comparison, neither variant would have been genome-wide significant if either 1000 Genomes Project Phase 3 or Haplotype Reference Consortium reference panels had been used for imputation. Our findings highlight the utility of the TOPMed imputation reference panel for identification of novel rare variant associations not previously detected in similarly sized genome-wide studies of under-represented African and Hispanic/Latino populations. |
| Author | Faraday, Nauder Mathias, Rasika A. Moon, Jee-Young Smith, Jennifer A. Shan, Yue Kelly, Tanika N. Choquet, Hélène Cupples, L. Adrienne Wang, Tao Buyske, Steve Fornage, Myriam Bowden, Donald W. Cho, Michael H. Irvin, Marguerite R. Loos, Ruth J. F. McHugh, Caitlin P. Auer, Paul L. Yanek, Lisa R. Becker, Lewis C. de Vries, Paul S. Weng, Lu-Chen Morrison, Alanna C. Cai, Jianwen Papanicolaou, George J. Montgomery, Courtney Choi, Seung Hoan Tracy, Russell P. Cushman, Mary Peyser, Patricia A. Tapia, Amanda L. Qian, Huijun Ellinor, Patrick T. Peralta, Juan M. Smith, Nicholas L. Wilson, James G. Zöllner, Sebastian Avery, Christy Palmer, Nicholette D. Silverman, Edwin K. Li, Yun Daya, Michelle Kardia, Sharon L. R. Taylor, Kent D. Boerwinkle, Eric Lasky-Su, Jessica A. Arnett, Donna K. Wiggins, Kerri L. Raffield, Laura M. Hou, Ziyi Argos, Maria Thornton, Timothy A. Bis, Joshua C. Gabriel, Stacey Kooperberg, Charles Rotter, Jerome I. Bien, Stephanie A. Ganesh, Santhi K. He, Jiang North, Kari E. Gupta, Namrata Hodonsky, Chani J. Kowalski, Madeline H. Heckbert, |
| AuthorAffiliation | 52 Carolina Center of Genome Sciences, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America 53 Zilber School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin, United States of America 54 Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, United States of America 37 The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America 3 Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America 18 Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America 41 National Heart, Lung, and Blood Institute, Division of Cardiovascular Sciences, PPSP/EB, NIH, Bethesda, Maryland, United States of America 55 Department of Computer Science, University of North Carolina, Chapel Hill, North Carolina, United States of America 16 |
| AuthorAffiliation_xml | – name: 36 Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America – name: Stanford University School of Medicine, UNITED STATES – name: 1 Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, United States of America – name: 40 Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, Minnesota, United States of America – name: 3 Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America – name: 16 Department of Statistics, Rutgers University, Piscataway, New Jersey, United States of America – name: 24 Departments of Medicine & Pathology, Larner College of Medicine, University of Vermont, Colchester, Vermont, United States of America – name: 33 Department of Epidemiology, Ryals School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama, United States of America – name: 13 Human Genome Sequencing Center, University of Texas Health Science Center at Houston; Baylor College of Medicine, Houston, Texas, United States of America – name: 27 Genomics Platform, Broad Institute, Cambridge, Massachusetts, United States of America – name: 28 Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, United States of America – name: 14 Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America – name: 30 Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, Los Angeles, United States of America – name: 15 Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America – name: 9 GeneSTAR Research Program, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland, United States of America – name: 46 Department of Biostatistics, Ryals School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama, United States of America – name: 18 Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America – name: 4 Department of Biostatistics, University of Washington, Seattle, Washington, United States of America – name: 6 College of Public Health, University of Kentucky, Lexington, Kentucky, United States of America – name: 42 Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia, United States of America – name: 44 Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America – name: 50 Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America – name: 20 Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America – name: 39 Department of Genes and Human Disease, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma, United States of America – name: 22 Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, United States of America – name: 45 Seattle Epidemiologic Research and Information Center, Department of Veterans Affairs Office of Research and Development, Seattle, Washington, United States of America – name: 8 Department of Medicine, Anschutz Medical Campus, University of Colorado Denver, Aurora, Colorado, United States of America – name: 21 Division of Research, Kaiser Permanente Northern California, Oakland, California, United States of America – name: 43 The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, California, United States of America – name: 12 Department of Human Genetics and South Texas Diabetes Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, Texas, United States of America – name: 35 Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, New York, United States of America – name: 53 Zilber School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin, United States of America – name: 49 Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, Mississippi, United States of America – name: 55 Department of Computer Science, University of North Carolina, Chapel Hill, North Carolina, United States of America – name: 2 Department of Statistics and Operation Research, University of North Carolina, Chapel Hill, North Carolina, United States of America – name: 34 Population Sciences Branch, Division of Intramural Research, National Heart, Lung and Blood Institute, Framingham, Massachusetts, United States of America – name: 23 Framingham Heart Study, Framingham, Massachusetts, United States of America – name: 54 Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, United States of America – name: 51 Department of Psychiatry, University of Michigan, Ann Arbor, Michigan, United States of America – name: 38 The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America – name: 31 Department of Epidemiology, University of Washington, Seattle, Washington, United States of America – name: 37 The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America – name: 7 Department of Epidemiology, University of North Carolina, Chapel Hill, North Carolina, United States of America – name: 47 Departments of Pathology & Laboratory Medicine and Biochemistry, Larrner College of Medicine, University of Vermont, Colchester, Vermont, United States of America – name: 52 Carolina Center of Genome Sciences, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America – name: 19 Department of Medicine, Harvard Medical School, Boston, Massachusetts, United States of America – name: 41 National Heart, Lung, and Blood Institute, Division of Cardiovascular Sciences, PPSP/EB, NIH, Bethesda, Maryland, United States of America – name: 17 Collaborative Studies Coordinating Center, Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, United States of America – name: 29 Department of Human Genetics, University of Michigan, Ann Arbor, Michigan, United States of America – name: 32 Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, Washington, United States of America – name: 48 Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York, United States of America – name: 11 Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, Washington, United States of America – name: 5 Division of Epidemiology and Biostatistics, University of Illinois at Chicago, Chicago, Illinois, United States of America – name: 10 Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America – name: 25 Cardiac Arrhythmia Service and Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, United States of America – name: 26 School of Public Health, The University of Texas Health Science Center, Houston, Texas, United States of America |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31869403$$D View this record in MEDLINE/PubMed |
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| Notes | new_version ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 LMR, APR and YL also contributed equally to this work. Edwin K Silverman and Michael H Cho have received grant support from GSK, MHC has received consulting fees from Genentech. Scott T. Weiss and Kathleen C. Barnes received royalties from UpToDate. Patrick T. Ellinor is supported by a grant from Bayer AG to the Broad Institute focused on the genetics and therapeutics of cardiovascular diseases, and has also served on advisory boards or consulted for Bayer AG, Quest Diagnostics and Novartis. Steven A Lubitz receives sponsored research support from Bristol Myers Squibb / Pfizer, Bayer HealthCare, and Boehringer Ingelheim, and has consulted for Abbott, Quest Diagnostics, Bristol Myers Squibb / Pfizer. Other authors declared no conflicts of interest. Membership in Trans-Omics for Precision Medicine Consortium and the Hematology & Hemostasis Working Group are listed in supplemental files. |
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| Title | Use of >100,000 NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium whole genome sequences improves imputation quality and detection of rare variant associations in admixed African and Hispanic/Latino populations |
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