A method for identifying genetic heterogeneity within phenotypically defined disease subgroups View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-02

AUTHORS

James Liley, John A Todd, Chris Wallace

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. More... »

PAGES

310-316

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/ng.3751

DOI

http://dx.doi.org/10.1038/ng.3751

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1052664349

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/28024155


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