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A copula-based set-variant association test for bivariate continuous, binary or mixed phenotypes.

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  • 1Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada.

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|October 24, 2022
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Summary

A new copula-based multivariate association test (CBMAT) effectively analyzes genetic associations with non-normal traits in genome-wide association studies (GWAS). CBMAT offers improved power and controlled error rates for complex trait analysis.

Keywords:
copulasgene-based testsgeneralized linear mixed modelsmixed binary-continuous phenotypesstatistical geneticsvariance component score test

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Genome-wide association studies (GWAS) often involve dichotomous, non-normally distributed, or mixed traits.
  • Existing region-based methods using multivariate linear mixed models (mvLMMs) assume multivariate normality, limiting their application to non-normal phenotypes.
  • There is a need for unified, flexible methods to assess associations between genetic variants and non-normal multivariate phenotypes.

Purpose of the Study:

  • To develop a novel, unified, and flexible copula-based multivariate association test (CBMAT).
  • To enable the discovery of associations between genetic regions and bivariate phenotypes that are continuous, binary, or mixed.
  • To provide a data-driven analytic p-value procedure for the region-based score-type test.

Main Methods:

  • Utilized copulas, which are multivariate distribution functions with uniform margins, to model non-normality in multivariate association studies.
  • Developed a copula-based multivariate association test (CBMAT) for region-based association analysis.
  • Derived an analytic p-value procedure for the CBMAT score-type test.

Main Results:

  • Simulation studies demonstrated that CBMAT maintains well-controlled type I error rates.
  • CBMAT exhibited higher power in detecting associations compared to existing methods, particularly for discrete and non-normally distributed traits.
  • The CBMAT was successfully applied to identify associations between genes on chromosome 11 and lipid levels in the ASLPAC study.

Conclusions:

  • CBMAT is a powerful and flexible method for genetic association studies involving non-normal multivariate phenotypes.
  • The proposed method addresses limitations of existing approaches that assume normality.
  • CBMAT facilitates the discovery of genetic associations with complex traits in large-scale studies.