A versatile omnibus test for detecting mean and variance heterogeneity

Area of Science:

• Genetics and Genomics
• Statistical Genetics
• Quantitative Trait Loci (QTL) Analysis

Background:

• Genetic loci can influence trait variability through mechanisms like biological disruption, linkage disequilibrium (LD), gene-by-gene (G × G), or gene-environment interactions.
• Identifying loci with variance heterogeneity (vQTL) is crucial for understanding complex trait architecture and interactions.

Purpose of the Study:

• To propose a versatile likelihood ratio test (LRT) for joint testing of mean and variance heterogeneity, or either effect alone, in the presence of covariates.
• To evaluate the performance of the proposed LRT(MV) method against existing approaches using simulations and empirical data.
• To explore the utility of vQTL detection for identifying G × G interactions and understanding trait relationships.

Main Methods:

• Development of a likelihood ratio test (LRT(MV)) for joint testing of mean and variance heterogeneity.
• Extensive simulations were conducted to compare the proposed method with existing statistical tests.
• Parametric bootstrap was employed to address sensitivity to nonnormality observed in parametric tests.

Main Results:

• The proposed LRT(MV) method, when coupled with a parametric bootstrap, effectively handles nonnormality issues that affect other parametric tests.
• Linkage disequilibrium (LD) can predictably generate vQTL based on allele frequencies and LD measures (D', r²).
• A joint test for mean and variance heterogeneity (LRT(MV)) demonstrates higher power for detecting vQTL compared to variance-only tests.

Conclusions:

• The LRT(MV) offers a powerful and versatile approach for detecting genetic loci influencing trait variance, with improved performance over variance-only tests.
• vQTL detection can serve as a valuable strategy for uncovering G × G interactions and elucidating complex relationships between traits.
• The proposed method enhances the ability to identify genetic factors contributing to trait variability and their interactions.