Multivariate Gene-Based Association Test on Family Data in MGAS

Area of Science:

• Genetics
• Statistical genetics
• Bioinformatics

Background:

• Multivariate gene-based association testing enhances the detection of complex genetic associations.
• Previous methods like multivariate gene-based association test by extended Simes (MGAS) were validated for unrelated individuals.
• The applicability of MGAS to family data, which includes genetically related subjects, remained to be demonstrated.

Purpose of the Study:

• To evaluate the performance of MGAS when applied to family data.
• To confirm the Type I error rate and statistical power of MGAS in the presence of genetic relatedness.
• To assess the utility of MGAS for identifying novel genetic associations in complex traits.

Main Methods:

• Simulations were conducted to test MGAS on datasets with varying degrees of genetic relatedness.
• P-value data from Plink and generalized estimating equations, incorporating family structure via sandwich correction, were utilized.
• The Type I error rate and statistical power were assessed under simulated conditions.

Main Results:

• MGAS demonstrated a correct Type I error rate when applied to simulated family data.
• The method exhibited adequate statistical power for detecting genetic associations in related individuals.
• Application to seven eye measurement phenotypes identified two statistically significant gene associations missed by univariate analyses.

Conclusions:

• MGAS is a robust and versatile tool for multivariate gene-based genome-wide association analysis.
• The method is suitable for both unrelated and genetically related individuals, including family data.
• MGAS facilitates the discovery of genetic associations that may be missed by traditional univariate approaches.