Leveraging phenotypic variability to identify genetic interactions in human phenotypes

Andrew R. Marderstein, Emily R. Davenport, Scott Kulm, Cristopher V. Van Hout, Olivier Elemento, Andrew G. Clark

Research output: Contribution to journalArticlepeer-review

Abstract

Although thousands of loci have been associated with human phenotypes, the role of gene-environment (GxE) interactions in determining individual risk of human diseases remains unclear. This is partly because of the severe erosion of statistical power resulting from the massive number of statistical tests required to detect such interactions. Here, we focus on improving the power of GxE tests by developing a statistical framework for assessing quantitative trait loci (QTLs) associated with the trait means and/or trait variances. When applying this framework to body mass index (BMI), we find that GxE discovery and replication rates are significantly higher when prioritizing genetic variants associated with the variance of the phenotype (vQTLs) compared to when assessing all genetic variants. Moreover, we find that vQTLs are enriched for associations with other non-BMI phenotypes having strong environmental influences, such as diabetes or ulcerative colitis. We show that GxE effects first identified in quantitative traits such as BMI can be used for GxE discovery in disease phenotypes such as diabetes. A clear conclusion is that strong GxE interactions mediate the genetic contribution to body weight and diabetes risk.

Original languageEnglish (US)
Pages (from-to)49-67
Number of pages19
JournalAmerican Journal of Human Genetics
Volume108
Issue number1
DOIs
StatePublished - Jan 7 2021

All Science Journal Classification (ASJC) codes

  • Genetics
  • Genetics(clinical)

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