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The sequence kernel association test for multicategorical outcomes.

Zhiwen Jiang1, Haoyu Zhang2, Thomas U Ahearn2

  • 1Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.

Genetic Epidemiology
|April 20, 2023
PubMed
Summary
This summary is machine-generated.

We developed a new method, the sequence kernel association test for multicategorical outcomes (SKAT-MC), to analyze genetic associations with disease subtypes. SKAT-MC improves statistical power for identifying genetic variants linked to distinct disease categories.

Keywords:
SKATmulticategorical datathe generalized logit modelthe proportional odds model

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Area of Science:

  • Genetics
  • Statistical genetics
  • Bioinformatics

Background:

  • Disease heterogeneity presents challenges in genetic studies.
  • Existing genome-wide association study methods struggle with multicategorical disease outcomes.
  • Understanding genetic underpinnings of disease subtypes is crucial.

Purpose of the Study:

  • To introduce a novel set-based association analysis method, SKAT-MC, for multicategorical outcomes.
  • To evaluate the performance of SKAT-MC in identifying genetic associations with disease subtypes.
  • To provide an efficient tool for genetic association studies with complex disease classifications.

Main Methods:

  • Developed the sequence kernel association test for multicategorical outcomes (SKAT-MC).
  • Conducted comprehensive simulation studies to assess type I error rate and statistical power.
  • Applied SKAT-MC to real-world datasets, including the Polish Breast Cancer Study (PBCS) and UK Biobank data.

Main Results:

  • SKAT-MC effectively preserves nominal type I error rates.
  • SKAT-MC significantly increases statistical power compared to existing methods.
  • Identified FGFR2 association with ER+ and ER- breast cancer subtypes in PBCS.
  • Discovered 21 significant genes associated with educational attainment in UK Biobank data.

Conclusions:

  • SKAT-MC is a powerful and efficient tool for genetic association studies with multicategorical outcomes.
  • The method enhances the ability to detect genetic associations with distinct disease subtypes.
  • SKAT-MC facilitates deeper understanding of genetic contributions to disease heterogeneity.