A method for identifying genetic heterogeneity within phenotypically defined disease subgroups

Journal article

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.

Authors: Liley, J; Todd, J; Wallace, C

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Springer Nature
• Journal: Nature Genetics
• Volume: 49
• Issue: 2
• Pages: 310-316
• Publication date: 2016-12-26
• Acceptance date: 2016-11-23
• DOI: 10.1038/ng.3751
• EISSN: 1546-1718
• ISSN: 1061-4036
• Pmid: 28024155
• Language: English
• Keywords: FFR