Noise-augmented directional clustering of genetic association data identifies distinct mechanisms underlying obesity.

ABSTRACT: Clustering genetic variants based on their associations with different traits can provide insight into their underlying biological mechanisms. Existing clustering approaches typically group variants based on the similarity of their association estimates for various traits. We present a new procedure for clustering variants based on their proportional associations with different traits, which is more reflective of the underlying mechanisms to which they relate. The method is based on a mixture model approach for directional clustering and includes a noise cluster that provides robustness to outliers. The procedure performs well across a range of simulation scenarios. In an applied setting, clustering genetic variants associated with body mass index generates groups reflective of distinct bi

SUBMITTER: Grant AJ 

PROVIDER: S-EPMC8794082 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature