An expanded analysis framework for multivariate GWAS connects inflammatory biomarkers to functional variants and disease.

ABSTRACT: Multivariate methods are known to increase the statistical power to detect associations in the case of shared genetic basis between phenotypes. They have, however, lacked essential analytic tools to follow-up and understand the biology underlying these associations. We developed a novel computational workflow for multivariate GWAS follow-up analyses, including fine-mapping and identification of the subset of traits driving associations (driver traits). Many follow-up tools require univariate regression coefficients which are lacking from multivariate results. Our method overcomes this problem by using Canonical Correlation Analysis to turn each multivariate association into its optimal univariate Linear Combination Phenotype (LCP). This enables an LCP-GWAS, which in turn generates the stat

SUBMITTER: Ruotsalainen SE 

PROVIDER: S-EPMC7868371 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature