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Area of Science:

  • Genetics
  • Statistical Genomics
  • Bioinformatics

Background:

  • Genome-wide association studies (GWAS) aim to identify associations between genetic variants and traits.
  • Testing for gene-environment (G x E) and gene-gene (G x G) interactions is crucial but computationally challenging, especially for latent variables.
  • Existing methods for indirect interaction testing in GWAS are limited to quantitative traits, not binary ones.

Purpose of the Study:

  • To develop a novel approach for indirectly testing gene-environment interactions in GWAS for binary traits.
  • To propose a joint statistical test that incorporates both main and interaction effects of single-nucleotide polymorphisms (SNPs).
  • To provide a practical and computationally feasible method for identifying SNPs and genes with latent interaction effects.

Main Methods:

  • Proposed an indirect testing approach for G x E interactions in GWAS for binary traits.
  • Introduced a joint test by adding a non-additive (dominance) term to standard additive GWAS models.
  • Evaluated the method's statistical properties, including type-I error control and power, through extensive simulations.

Main Results:

  • The proposed method demonstrates effective type-I error control and robust statistical power in numerical studies.
  • Application to the UK Biobank dataset successfully identified SNPs and genes potentially involved in latent interaction effects.
  • The method proved straightforward to implement within existing GWAS frameworks.

Conclusions:

  • The developed method offers a practical solution for indirectly testing gene-environment interactions for binary traits in GWAS.
  • This approach enhances the ability to detect complex genetic architectures underlying diseases.
  • The findings highlight the utility of incorporating non-additive terms for a more comprehensive analysis of genetic associations.