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Joint genetic analysis using variant sets reveals polygenic gene-context interactions.

Francesco Paolo Casale1, Danilo Horta1, Barbara Rakitsch1

  • 1European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, CB10 1SD Hinxton, Cambridge, United Kingdom.

Plos Genetics
|April 21, 2017
PubMed
Summary
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We introduce iSet, a novel method for analyzing genetic interactions across different contexts. iSet enhances the detection of gene-environment interactions and subgroup-specific genetic effects, improving association analyses.

Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Multi-trait genetic models enhance association analyses but often overlook interaction analysis.
  • Existing methods primarily focus on increasing detection power, with less attention to interactions.

Purpose of the Study:

  • Propose iSet, a linear mixed model-based method to test for interactions between variant sets and environmental states or contexts.
  • Generalize existing interaction tests and enable the detection of local differences in genetic architecture across contexts.

Main Methods:

  • Developed iSet, a method utilizing linear mixed models for interaction analysis.
  • Validated iSet through simulations.
  • Applied iSet to genotype-environment interaction analysis in an eQTL study.

Related Experiment Videos

  • Applied iSet to identify subgroup-specific genetic effects in human lipid levels.

    • Main Results:

    • iSet successfully identified a larger number of interactions compared to alternative methods.
    • Up to 20% of cases exhibited context-specific causal variant configurations.
    • Identified a significant gene-sex interaction for C-reactive protein in a human cohort, which was missed by other methods.

    Conclusions:

    • iSet is a powerful tool for detecting complex genetic interactions, including genotype-environment interactions and subgroup-specific effects.
    • The method reveals context-specific genetic architectures and identifies novel interactions, such as gene-sex interactions affecting lipid levels.