Pathways-driven sparse regression identifies pathways and genes associated with high-density lipoprotein cholesterol...

Area of Science:

• Genetics
• Bioinformatics
• Statistical Genomics

Background:

• Standard genome-wide association studies (GWAS) often overlook functional gene relationships.
• Gene pathway analysis incorporates prior functional information to identify trait-associated pathways and genes.
• Existing pathway methods typically analyze single nucleotide polymorphisms (SNPs) individually, missing multi-SNP interaction benefits.

Purpose of the Study:

• To develop a novel dual-level, sparse regression model for simultaneous identification of gene pathways and genes associated with quantitative traits.
• To address challenges in joint modeling of genome-wide data, including correlated predictors and overlapping pathways.
• To identify pathways and genes associated with serum high-density lipoprotein cholesterol levels using a new method.

Main Methods:

• A dual-level, sparse regression model was developed for simultaneous pathway and gene identification.
• The model accounts for correlated genetic predictors and variants overlapping multiple pathways.
• A resampling strategy was employed for robust pathway and gene ranking.

Main Results:

• The method was validated through simulation studies.
• Application to GWAS cohorts identified candidate pathways including cardiomyopathy, T cell receptor, and PPAR signaling.
• Key genes associated with L-type calcium channel, adenylate cyclase, integrin, laminin, MAPK signaling, and immune function were highlighted.

Conclusions:

• The proposed dual-level sparse regression model offers a powerful approach for pathways-driven gene selection in GWAS.
• This method enhances the discovery of genetic architecture by considering gene interactions within pathways.
• The findings provide insights into the genetic basis of serum high-density lipoprotein cholesterol levels.