Harmonizing Genetic Ancestry and Self-identified Race/Ethnicity in Genome-wide Association Studies.

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  • Additional Information
    • Corporate Authors:
      VA Million Veteran Program
    • Source:
      Publisher: Cell Press Country of Publication: United States NLM ID: 0370475 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1537-6605 (Electronic) Linking ISSN: 00029297 NLM ISO Abbreviation: Am J Hum Genet Subsets: MEDLINE
    • Publication Information:
      Publication: 2008- : [Cambridge, MA] : Cell Press
      Original Publication: Baltimore, American Society of Human Genetics.
    • Subject Terms:
      Genome-Wide Association Study*; Ethnicity/*genetics ; Racial Groups/*genetics; Algorithms ; Humans ; Machine Learning ; Support Vector Machine
    • Abstract:
      Large-scale multi-ethnic cohorts offer unprecedented opportunities to elucidate the genetic factors influencing complex traits related to health and disease among minority populations. At the same time, the genetic diversity in these cohorts presents new challenges for analysis and interpretation. We consider the utility of race and/or ethnicity categories in genome-wide association studies (GWASs) of multi-ethnic cohorts. We demonstrate that race/ethnicity information enhances the ability to understand population-specific genetic architecture. To address the practical issue that self-identified racial/ethnic information may be incomplete, we propose a machine learning algorithm that produces a surrogate variable, termed HARE. We use height as a model trait to demonstrate the utility of HARE and ethnicity-specific GWASs.
      (Copyright © 2019 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.)
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    • Grant Information:
      R01 NR013520 United States NR NINR NIH HHS; R01 GM073059 United States GM NIGMS NIH HHS; I01 BX003340 United States BX BLRD VA; I01 BX003362 United States BX BLRD VA; I01 BX002641 United States BX BLRD VA; R35 GM127063 United States GM NIGMS NIH HHS
    • Contributed Indexing:
      Investigator: JM Gaziano; R Ramoni; J Breeling; KM Chang; G Huang; S Muralidhar; CJ O'Donnell; PS Tsao; S Muralidhar; J Moser; SB Whitbourne; JV Brewer; J Concato; S Warren; DP Argyres; B Stephens; MT Brophy; DE Humphries; N Do; S Shayan; XT Nguyen; S Pyarajan; K Cho; E Hauser; Y Sun; H Zhao; P Wilson; R McArdle; L Dellitalia; J Harley; J Whittle; J Beckham; J Wells; S Gutierrez; G Gibson; L Kaminsky; G Villareal; S Kinlay; J Xu; M Hamner; KS Haddock; S Bhushan; P Iruvanti; M Godschalk; Z Ballas; M Buford; S Mastorides; J Klein; N Ratcliffe; H Florez; A Swann; M Murdoch; P Sriram; SS Yeh; R Washburn; D Jhala; S Aguayo; D Cohen; S Sharma; J Callaghan; KA Oursler; M Whooley; S Ahuja; A Gutierrez; R Schifman; J Greco; M Rauchman; R Servatius; M Oehlert; A Wallbom; R Fernando; T Morgan; T Stapley; S Sherman; G Anderson; E Sonel; E Boyko; L Meyer; S Gupta; J Fayad; A Hung; J Lichy; R Hurley; B Robey; R Striker
      Keywords: biobank; ethnicity-specific trait loci; genetic ancestry; multi-ethnic cohort; self-reported race/ethnicity; stratified analysis; trans-ethnic GWAS
    • Publication Date:
      Date Created: 20191001 Date Completed: 20200402 Latest Revision: 20211204
    • Publication Date:
      20231215
    • Accession Number:
      PMC6817526
    • Accession Number:
      10.1016/j.ajhg.2019.08.012
    • Accession Number:
      31564439