Subset-Based Analysis Using Gene-Environment Interactions for Discovery of Genetic Associations across Multiple...

Area of Science:

• Genetics
• Statistical genetics
• Bioinformatics

Background:

• Genome-wide association studies (GWAS) traditionally use marginal genetic effects, limiting power with heterogeneous genetic effects.
• Environmental factors can influence genetic associations, a complexity often overlooked by classical methods.

Purpose of the Study:

• To develop a novel statistical framework, pvalue-assisted subset testing for associations (pASTA), to enhance the discovery of genetic loci.
• To account for heterogeneity in genetic effects, particularly in relation to environmental factors and gene-environment interactions.

Main Methods:

• Introduced the pASTA framework, an extension of the association analysis based on subsets (ASSET) method.
• Incorporated gene-environment (G-E) interactions into the statistical testing procedure.
• Validated the method through simulation studies and analysis of two real-world genetic datasets.

Main Results:

• Simulation studies demonstrated that pASTA is more powerful than methods relying solely on marginal associations when G-E interactions are present.
• pASTA maintained comparable statistical power even in the absence of G-E interactions.
• Data examples showed pASTA's ability to increase power for detecting overall genetic associations and pinpoint novel contributing studies or phenotypes.

Conclusions:

• pASTA serves as a valuable screening tool for identifying candidate single nucleotide polymorphisms (SNPs) for further validation.
• The method aids researchers in identifying specific subsets of traits exhibiting genetic associations.
• pASTA enhances statistical power and provides insights into complex genetic architectures influenced by environmental factors.