Journal
NATURE GENETICS
Volume 49, Issue 2, Pages 310-316Publisher
NATURE PUBLISHING GROUP
DOI: 10.1038/ng.3751
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Funding
- Wellcome Trust [107212, 089989, 107881, 100140]
- JDRF [5-SRA-2015-130-A-N]
- NIHR Cambridge Biomedical Research Centre
- European Union [241447]
- MRC [MC_UP_1302/5]
- MRC [MC_UU_00002/4, MR/N01104X/1, G1001799] Funding Source: UKRI
- Medical Research Council [MC_UP_1302/5, MR/N01104X/1, G1001799, MC_UU_00002/4] Funding Source: researchfish
- National Institute for Health Research [NF-SI-0513-10143] Funding Source: researchfish
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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 thyroidperoxidase-specific antibody, driven generally by variants in known T1 D-associated genomic regions.
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