A genome-first approach to aggregating rare genetic variants in LMNA for association with electronic health record phenotypes.

ABSTRACT:

Purpose

"Genome-first" approaches, in which genetic sequencing is agnostically linked to associated phenotypes, can enhance our understanding of rare variants' contributions to disease. Loss-of-function variants in LMNA cause a range of rare diseases, including cardiomyopathy.

Methods

We leveraged exome sequencing from 11,451 unselected individuals in the Penn Medicine Biobank to associate rare variants in LMNA with diverse electronic health record (EHR)-derived phenotypes. We used Rare Exome Variant Ensemble Learner (REVEL) to annotate rare missense variants, clustered predicted deleterious and loss-of-function variants into a "gene burden" (N = 72 individuals), and performed a phenome-wide association study (PheWAS). Major findings were replicated in DiscovEHR.

Results<

SUBMITTER: Park J 

PROVIDER: S-EPMC7719049 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature