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Family-based gene-environment interaction using sequence kernel association test (FGE-SKAT) for complex quantitative

Chao-Yu Guo1,2, Reng-Hong Wang3,4, Hsin-Chou Yang5

  • 1Division of Biostatistics, Department of Medicine, Institute of Public Health, School of Medicine, National Yang-Ming University, Taipei, Taiwan. cyguo@ym.edu.tw.

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This study introduces a new method, Fast Gene-Environment Sequence Kernel Association Test (FGE-SKAT), to find gene-environment interactions in family studies. FGE-SKAT improves statistical power for complex trait association analysis using whole-genome sequencing data.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Whole-genome sequencing (WGS) is crucial for identifying rare variants associated with complex traits.
  • Existing methods often analyze genetic and environmental factors separately, potentially missing complex interactions.
  • Familial dependencies and covariates require careful adjustment in genetic association studies.

Purpose of the Study:

  • To propose a novel statistical method for detecting gene-environment interactions in family-based association studies.
  • To develop an R function, Fast Gene-Environment Sequence Kernel Association Test (FGE-SKAT), for practical implementation.
  • To assess the performance and utility of FGE-SKAT in analyzing complex trait etiology.

Main Methods:

  • Development of a score-based variance-component test incorporating gene-environment interactions.
  • Utilizing kernel association tests to handle familial dependencies and correlated structures.
  • Implementation of the FGE-SKAT method as an R function for user-friendly application.
  • Validation through simulation studies and application to real whole-genome sequence data (GAW18).

Main Results:

  • Simulation studies demonstrated the validity and superior statistical power of the proposed FGE-SKAT strategy.
  • Application to GAW18 whole-genome sequence data identified significant genetic regions.
  • Comparison with methods ignoring gene-environment interactions revealed both concordant and discordant findings, highlighting the impact of interaction analysis.

Conclusions:

  • The novel FGE-SKAT method effectively detects gene-environment interactions in family-based studies.
  • FGE-SKAT offers enhanced statistical power for uncovering complex trait associations.
  • This approach provides a valuable tool for genome-wide association studies (GWAS) to explore joint genetic and environmental effects.