Genetic interactions drive heterogeneity in causal variant effect sizes for gene expression and complex traits

Area of Science:

• Genetics
• Human Complex Traits
• Population Genetics

Background:

• Genome-wide association studies (GWASs) have limitations in quantifying gene-by-gene and gene-by-environment interactions for complex traits.
• Detecting individual genetic interactions is challenging due to the underpowered nature of standard GWASs.

Purpose of the Study:

• To develop and apply a novel method for testing genetic interactions by aggregating information across trait-associated loci.
• To investigate the influence of genetic interactions on gene expression and low-density lipoprotein cholesterol (LDL-C) levels in diverse populations.

Main Methods:

• Developed a method to test for genetic interactions by comparing SNP effect sizes in shared European ancestry regions between European American and admixed African American individuals.
• Applied the method to gene expression data from the Multi-Ethnic Study of Atherosclerosis (MESA) and LDL-C data from the Million Veteran Program (MVP).

Main Results:

• Found significant evidence of genetic interactions influencing gene expression in the MESA cohort.
• Observed a similar trend for LDL-C in the MVP cohort, though not statistically significant, likely due to power limitations.
• Hypothesized that genetic interactions alter causal variant effect sizes in complex human traits.

Conclusions:

• Genetic interactions play a significant role in modifying the effect sizes of causal variants for complex human traits.
• The developed method provides a powerful approach to detect genetic interactions across diverse populations.
• Further research with larger datasets is warranted to fully elucidate the role of genetic interactions in complex traits like LDL-C.