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Leveraging phenotypic variability to identify genetic interactions in human phenotypes.

Andrew R Marderstein1, Emily R Davenport2, Scott Kulm3

  • 1Tri-Institutional Program in Computational Biology & Medicine, Weill Cornell Medicine, New York, NY 10021, USA; Institute of Computational Biomedicine, Weill Cornell Medicine, New York, NY 10021, USA; Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA; Department of Computational Biology, Cornell University, Ithaca, NY 14850, USA.

American Journal of Human Genetics
|December 16, 2020
PubMed
Summary

Gene-environment interactions significantly impact disease risk. Prioritizing genetic variants affecting trait variance (vQTLs) improves the discovery of these gene-environment interactions for complex traits like body mass index (BMI) and diabetes.

Keywords:
GWASGxEbody mass indexcomplex traitsdiabetesgene-environment interactionsphenotypic variancevQTL

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Area of Science:

  • Genetics
  • Biostatistics
  • Human Phenotypes

Background:

  • Thousands of genetic loci are linked to human traits, but the role of gene-environment (GxE) interactions in disease risk is poorly understood.
  • Detecting GxE interactions is challenging due to statistical power limitations from numerous tests.

Purpose of the Study:

  • To develop a statistical framework for assessing quantitative trait loci (QTLs) associated with trait means and variances to enhance GxE interaction detection.
  • To investigate the utility of variance QTLs (vQTLs) in discovering GxE effects.

Main Methods:

  • Developed a statistical framework to analyze genetic associations with both trait means and variances.
  • Applied the framework to body mass index (BMI) data to identify GxE interactions.
  • Assessed vQTLs for enrichment of associations with other environmentally influenced phenotypes.

Main Results:

  • Prioritizing genetic variants associated with phenotype variance (vQTLs) significantly increased GxE discovery and replication rates for BMI compared to assessing all variants.
  • vQTLs showed enrichment for associations with other phenotypes influenced by environment, such as diabetes and ulcerative colitis.
  • GxE effects identified in quantitative traits like BMI were transferable for GxE discovery in disease phenotypes like diabetes.

Conclusions:

  • Gene-environment interactions play a crucial role in the genetic contribution to body weight and diabetes risk.
  • The developed statistical framework and focus on vQTLs offer a powerful approach for uncovering GxE interactions in complex human diseases.