Leveraging correlations between variants in polygenic risk scores to detect heterogeneity in GWAS cohorts.

ABSTRACT: Evidence from both GWAS and clinical observation has suggested that certain psychiatric, metabolic, and autoimmune diseases are heterogeneous, comprising multiple subtypes with distinct genomic etiologies and Polygenic Risk Scores (PRS). However, the presence of subtypes within many phenotypes is frequently unknown. We present CLiP (Correlated Liability Predictors), a method to detect heterogeneity in single GWAS cohorts. CLiP calculates a weighted sum of correlations between SNPs contributing to a PRS on the case/control liability scale. We demonstrate mathematically and through simulation that among i.i.d. homogeneous cases generated by a liability threshold model, significant anti-correlations are expected between otherwise independent predictors due to ascertainment on the hidden liabi

SUBMITTER: Yuan J 

PROVIDER: S-EPMC7529195 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature