HisCoM-G×E: Hierarchical Structural Component Analysis of Gene-Based Gene-Environment Interactions

Area of Science:

• Genetics
• Statistical Genomics
• Bioinformatics

Background:

• Genome-wide association studies (GWAS) face the "missing heritability" problem.
• Existing gene-environment interaction (G×E) methods often rely on single nucleotide polymorphism (SNP)-level analysis.
• A more comprehensive approach is needed to capture complex G×E effects.

Purpose of the Study:

• To propose a novel statistical method, Hierarchical structural CoMponent analysis of gene-based Gene-Environment interactions (HisCoM-G×E).
• To improve the detection and understanding of G×E.
• To investigate gene-alcohol intake interactions on systolic blood pressure (SBP).

Main Methods:

• Developed HisCoM-G×E, a method utilizing hierarchical structural relationships among SNPs within a gene.
• Incorporates all SNP-level effects into a single latent variable using a ridge penalty.
• Evaluated performance through simulation studies and applied to real-world data from the Korea Associated REsource (KARE) consortium.

Main Results:

• HisCoM-G×E efficiently accounts for latent G×E terms.
• The method demonstrated effectiveness in simulation studies.
• Applied to KARE data, it investigated gene-alcohol intake interactions impacting SBP.

Conclusions:

• HisCoM-G×E offers a more efficient approach to G×E analysis by considering gene-wide effects.
• This method can help address the missing heritability in GWAS.
• Further application of HisCoM-G×E can enhance our understanding of gene-environment interactions in complex diseases.