Gene-Interaction-Sensitive enrichment analysis in congenital heart disease

BioData Mining

Elizabeth Goldmuntz¹  Deanne M. Taylor¹  Alexa A. Woodward²  Jason H. Moore³  Ryan J. Urbanowicz³  A.J. Agopian⁴  Laura E. Mitchell⁴ 

[1] Children’s Hospital of Philadelphia, Philadelphia, PA, USA;Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA;Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA;Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, UTHealth School of Public Health, Houston, TX, USA;

关键词: Gene set enrichment analysis;    GWAS;    Epistasis;    Congenital heart disease;   

DOI  :  10.1186/s13040-022-00287-w

来源: Springer

【 摘 要 】

BackgroundGene 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).ResultsIn the simulation study and for both CTD datasets, both Relief-based approaches to GSEA captured more relevant and significant gene ontology terms compared to the univariate GSEA. Key terms and themes of interest include cell adhesion, migration, and signaling. A leading edge analysis highlighted semaphorins and their receptors, the Slit-Robo pathway, and other genes with roles in the secondary heart field and outflow tract development.ConclusionsOur results indicate that interaction-sensitive approaches to enrichment analysis can improve upon traditional univariate GSEA. This approach replicated univariate findings and identified additional and more robust support for the role of the secondary heart field and cardiac neural crest cell migration in the development of CTDs.

【 授权许可】

CC BY