Gene-Interaction-Sensitive enrichment analysis in congenital heart disease.

ABSTRACT:

Background

Gene set enrichment analysis (GSEA) uses gene-level univariate associations to identify gene set-phenotype associations for hypothesis generation and interpretation. We propose that GSEA can be adapted to incorporate SNP and gene-level interactions. To this end, gene scores are derived by Relief-based feature importance algorithms that efficiently detect both univariate and interaction effects (MultiSURF) or exclusively interaction effects (MultiSURF*). We compare these interaction-sensitive GSEA approaches to traditional χ² rankings in simulated genome-wide array data, and in a target and replication cohort of congenital heart disease patients with conotruncal defects (CTDs).

Results

In the simulation study and for both CTD datasets, both Relief-based

SUBMITTER: Woodward AA 

PROVIDER: S-EPMC8841104 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature