title: A method for identifying genetic heterogeneity within phenotypically defined disease subgroups.
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.
authors.affiliation: JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Department of Medical Genetics, NIHR Cambridge Biomedical Research Centre, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK.|Department of Medicine, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK.
authors.forename: James
authors.initials: J
authors.lastname: Liley
authors.affiliation: JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Department of Medical Genetics, NIHR Cambridge Biomedical Research Centre, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK.|Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
authors.forename: John A
authors.initials: JA
authors.lastname: Todd
authors.affiliation: JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Department of Medical Genetics, NIHR Cambridge Biomedical Research Centre, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK.|Department of Medicine, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK.
authors.forename: Chris
authors.identifier: http://orcid.org/0000-0001-9755-1703
authors.initials: C
authors.lastname: Wallace
mesh_terms: D003922:Diabetes Mellitus, Type 1 / Q000235:genetics*; D018740:Genetic Heterogeneity; D020022:Genetic Predisposition to Disease / Q000235:genetics; D014644:Genetic Variation / Q000235:genetics*; D055106:Genome-Wide Association Study / Q000379:methods; D023281:Genomics / Q000379:methods; D006801:Humans; D008957:Models, Genetic; D010641:Phenotype