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• W2321889479 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 pathophysiologies, in which disease-associated variants have different effect sizes in the 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 maximising power in comparison to a 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 datasets where expected results are already known. We investigate subgroups of type 1 diabetes (T1D) cases defined by autoantibody positivity, establishing evidence for differential genetic basis with thyroid peroxidase antibody positivity, driven generally by variants in known T1D associated regions." @default.
• W2321889479 created "2016-06-24" @default.
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• W2321889479 date "2016-10-04" @default.
• W2321889479 modified "2023-09-25" @default.
• W2321889479 title "A genetic test for differential causative pathology in disease subgroups" @default.
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• W2321889479 doi "https://doi.org/10.6084/m9.figshare.3980253.v1" @default.
• W2321889479 hasPublicationYear "2016" @default.
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