Trade-offs of Linear Mixed Models in Genome-Wide Association Studies.

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  • Author(s): Wang H;Wang H; Aragam B; Aragam B; Xing EP; Xing EP
  • Source:
    Journal of computational biology : a journal of computational molecular cell biology [J Comput Biol] 2022 Mar; Vol. 29 (3), pp. 233-242. Date of Electronic Publication: 2022 Feb 25.
  • Publication Type:
    Journal Article; Research Support, U.S. Gov't, Non-P.H.S.; Research Support, N.I.H., Extramural
  • Language:
    English
  • Additional Information
    • Source:
      Publisher: Mary Ann Liebert, Inc Country of Publication: United States NLM ID: 9433358 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1557-8666 (Electronic) Linking ISSN: 10665277 NLM ISO Abbreviation: J Comput Biol Subsets: MEDLINE
    • Publication Information:
      Original Publication: New York, NY : Mary Ann Liebert, Inc., c1994-
    • Subject Terms:
      Genome-Wide Association Study*/methods ; Models, Genetic*; Linear Models ; Polymorphism, Single Nucleotide
    • Abstract:
      Motivated by empirical arguments that are well known from the genome-wide association studies (GWAS) literature, we study the statistical properties of linear mixed models (LMMs) applied to GWAS. First, we study the sensitivity of LMMs to the inclusion of a candidate single nucleotide polymorphism (SNP) in the kinship matrix, which is often done in practice to speed up computations. Our results shed light on the size of the error incurred by including a candidate SNP, providing a justification to this technique to trade off velocity against veracity. Second, we investigate how mixed models can correct confounders in GWAS, which is widely accepted as an advantage of LMMs over traditional methods. We consider two sources of confounding factors-population stratification and environmental confounding factors-and study how different methods that are commonly used in practice trade off these two confounding factors differently.
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    • Grant Information:
      R01 GM093156 United States GM NIGMS NIH HHS; P30 DA035778 United States DA NIDA NIH HHS; U01 AG024904 United States AG NIA NIH HHS
    • Contributed Indexing:
      Keywords: GWAS; kinship matrix; linear mixed model
    • Publication Date:
      Date Created: 20220301 Date Completed: 20220405 Latest Revision: 20230302
    • Publication Date:
      20240104
    • Accession Number:
      PMC8968846
    • Accession Number:
      10.1089/cmb.2021.0157
    • Accession Number:
      35230156