A method for identifying genetic heterogeneity within phenotypically defined disease subgroups

Nat Genet. 2017 Feb;49(2):310-316. doi: 10.1038/ng.3751. Epub 2016 Dec 26.

Abstract

Many common diseases show wide phenotypic variation. We present a statistical method for determining whether phenotypically defined subgroups of disease cases represent different genetic architectures, in which disease-associated variants have different effect sizes in two subgroups. Our method models the genome-wide distributions of genetic association statistics with mixture Gaussians. We apply a global test without requiring explicit identification of disease-associated variants, thus maximizing power in comparison to standard variant-by-variant subgroup analysis. Where evidence for genetic subgrouping is found, we present methods for post hoc identification of the contributing genetic variants. We demonstrate the method on a range of simulated and test data sets, for which expected results are already known. We investigate subgroups of individuals with type 1 diabetes (T1D) defined by autoantibody positivity, establishing evidence for differential genetic architecture with positivity for thyroid-peroxidase-specific antibody, driven generally by variants in known T1D-associated genomic regions.

MeSH terms

  • Diabetes Mellitus, Type 1 / genetics*
  • Genetic Heterogeneity
  • Genetic Predisposition to Disease / genetics
  • Genetic Variation / genetics*
  • Genome-Wide Association Study / methods
  • Genomics / methods
  • Humans
  • Models, Genetic
  • Phenotype