Proceeding talk – Theme: Genes.
Abstract
While many statistical methods for pathway-based analyses using rare variants have been proposed to analyze pathways individually, neglecting correlations between multiple pathways can result in misleading solutions. We propose a novel statistical approach for the analysis of rare variants by constructing a single hierarchical model that consists of collapsed gene-level summaries and pathways and analyzes entire pathways simultaneously by imposing ridge-type penalties on both gene and pathway coefficient estimates. Through simulation studies, we showed that the proposed method had higher statistical power than the existing pathway-based methods. In addition, our method was applied to the large-scale whole-exome sequencing data with levels of a liver enzyme using two well-known pathway databases Biocarta and KEGG. This application demonstrated that our method not only identified associated pathways but also successfully detected biologically plausible pathways for a phenotype of interest. These findings were successfully replicated by an independent large-scale exome chip study.
Authors
Sungyoung Lee, Seoul National University Republic of Korea
Sungkyoung Choi, Seoul National University, Republic of Korea
Young Jin Kim, Center for Genome Science, National Institute of Health, Republic of Korea
Bong-Jo Kim, Center for Genome Science, National Institute of Health, Republic of Korea
Tiidgenes Consortium, Seoul National University, Republic of Korea
Heungsun Hwang, McGill University, Canada
Taesung Park, Seoul National University, Republic of Korea