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Asymptotically exact fit for linear mixed model in genetic association studies.

The linear mixed model (LMM) has become a standard in genetic association studies to account for population stratification and relatedness in the samples to reduce false positives. Much recent progresses in LMM focused on approximate computations. Exact methods remained computationally demanding and...

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Published in:Genetics Vol. 228; no. 2; pp. 1 - 13
Main Authors: Guan, Yongtao, Levy, Daniel
Format: computer program equations & formulas research tables/charts Journal Article
Published: Oxford University Press / USA Oct2024
Online Access:View this record in EBSCOhost
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      dt: Oct2024
      vid: 228
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      pub: Oxford University Press / USA
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        10.1093/genetics/iyae143
        180152627
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        atl: Asymptotically exact fit for linear mixed model in genetic association studies.
      aug:
        au:
          Guan, Yongtao
          Levy, Daniel
        affil: Framingham Heart Study , 73 Mt. Wayte, Framingham, MA 01702 , USA
      sug:
        subj:
          Genetic Techniques
          Models, Biological
          Linear Regression
          Probability
          Software
          Algorithms
          Multiomics
          Phenotype
          Funding Source
          Human
          Genome Wide Association Study
          Genotype
          Maximum Likelihood
          P-Value
      ab: The linear mixed model (LMM) has become a standard in genetic association studies to account for population stratification and relatedness in the samples to reduce false positives. Much recent progresses in LMM focused on approximate computations. Exact methods remained computationally demanding and without theoretical assurance. The computation is particularly challenging for multiomics studies where tens of thousands of phenotypes are tested for association with millions of genetic markers. We present IDUL and IDUL † that use iterative dispersion updates to fit LMMs, where IDUL † is a modified version of IDUL that guarantees likelihood increase between updates. Practically, IDUL and IDUL † produced identical results, both are markedly more efficient than the state-of-the-art Newton–Raphson method, and in particular, both are highly efficient for additional phenotypes, making them ideal to study genetic determinants of multiomics phenotypes. Theoretically, the LMM likelihood is asymptotically unimodal, and therefore the gradient ascent algorithm IDUL † is asymptotically exact. A software package implementing IDUL and IDUL † for genetic association studies is freely available at https://github.com/haplotype/IDUL.
      pubtype: Academic Journal
      doctype:
        computer program
        equations & formulas
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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