A Novel Framework for Analysis of the Shared Genetic Background of Correlated Traits.

ABSTRACT: We propose a novel effective framework for the analysis of the shared genetic background for a set of genetically correlated traits using SNP-level GWAS summary statistics. This framework called SHAHER is based on the construction of a linear combination of traits by maximizing the proportion of its genetic variance explained by the shared genetic factors. SHAHER requires only full GWAS summary statistics and matrices of genetic and phenotypic correlations between traits as inputs. Our framework allows both shared and unshared genetic factors to be effectively analyzed. We tested our framework using simulation studies, compared it with previous developments, and assessed its performance using three real datasets: anthropometric traits, psychiatric conditions and lipid concentrations. SHAHE

SUBMITTER: Svishcheva GR 

PROVIDER: S-EPMC9602050 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature