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Multi-SKAT: General framework to test for rare-variant association with multiple phenotypes.

Diptavo Dutta1,2, Laura Scott1,2, Michael Boehnke1,2

  • 1Department of Biostatistics, University of Michigan, Ann Arbor, Michigan.

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Summary
This summary is machine-generated.

This study introduces Multi-SKAT, a novel framework for analyzing rare variants associated with multiple traits. Multi-SKAT enhances power in genetic association studies by using multivariate kernel regression, outperforming existing methods.

Keywords:
CopulaMETSIM studySKATgene-based testmultiple phenotypesphenotype kernelpleiotropyrare variantsrelated individuals

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Area of Science:

  • Statistical Genetics
  • Genomic Association Studies
  • Bioinformatics

Background:

  • Joint testing of multiple phenotypes can improve power in genetic association analysis for identifying trait-associated variants.
  • Existing multiphenotype tests for rare variants often rely on specific assumptions that, if violated, can decrease detection power.
  • There is a need for flexible and powerful methods to test pleiotropic effects of rare variants across multiple continuous phenotypes.

Purpose of the Study:

  • To develop a general and powerful framework, Multivariate SKAT (Multi-SKAT), for testing pleiotropic effects of rare variants on multiple continuous phenotypes.
  • To demonstrate the flexibility of Multi-SKAT by showing its equivalence to existing tests with specific kernel choices.
  • To improve the power of detecting associations across various tests by developing an approximation for the minimum P-value significance.

Main Methods:

  • Developed a multivariate kernel regression framework (Multi-SKAT) to model variant effects on multiple phenotypes.
  • Utilized a kernel matrix to represent variant effects and performed a variance component test for association.
  • Incorporated random effects for the kinship matrix to account for related individuals and developed an approximation for minimum P-value significance.

Main Results:

  • Multi-SKAT demonstrated improved power for detecting associations compared to single-phenotype SKAT-O and existing multiple-phenotype tests.
  • The framework showed flexibility, encompassing many existing tests through specific kernel matrix selections.
  • Simulations and analysis of METSIM study data confirmed Multi-SKAT's enhanced power while maintaining the Type I error rate.

Conclusions:

  • Multi-SKAT provides a powerful and flexible general framework for testing rare variant associations with multiple continuous phenotypes.
  • The method effectively addresses the limitations of existing tests by relaxing assumptions and improving power.
  • Multi-SKAT is a valuable tool for genetic association studies aiming to uncover pleiotropic effects of rare variants.