ML-MAGES enables multivariate genetic association analyses with genes and effect size shrinkage.

ABSTRACT: A fundamental goal of genetics is to identify which and how genetic variants are associated with a trait, often using the regression results from genome-wide association (GWA) studies. Important methodological challenges account for inflation in GWA effect estimates as well as in investigating more than one trait simultaneously. We leverage machine learning approaches for these two challenges, developing a computationally efficient method called ML-MAGES. First, we shrink the inflation in GWA effect sizes caused by nonindependence among variants using neural networks. We then cluster variant associations among multiple traits via variational inference. We compare the performance of shrinkage via neural networks to regularized regression and fine-mapping, two approaches used for addressing

SUBMITTER: Liu X 

PROVIDER: S-EPMC12667714 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature