A family-based robust multivariate association test using maximum statistic.

Area of Science:

• Genetics
• Biostatistics
• Complex Disease Research

Background:

• Familial data with multiple quantitative traits are crucial for understanding complex disease genetic mechanisms.
• Existing multivariate association tests risk reduced power due to genetic model misspecification.

Purpose of the Study:

• To develop a family-based robust multivariate association test for complex diseases.
• To enhance statistical power in genetic studies involving multiple correlated quantitative traits.

Main Methods:

• Established optimal multivariate score tests for recessive, additive, and dominant genetic models.
• Developed a maximum-type robust multivariate association test based on optimal score tests.
• Conducted simulations to compare power against existing multivariate methods.

Main Results:

• The proposed robust multivariate test demonstrated consistent power across all plausible genetic models.
• Simulations confirmed the robust test's superior performance compared to other multivariate methods.
• The robust multivariate test showed greater power than the robust univariate test for multiple quantitative traits.

Conclusions:

• The developed family-based robust multivariate association test effectively addresses power loss from genetic model misspecification.
• This robust approach offers improved power for analyzing genetic associations with multiple quantitative traits in complex diseases.
• The method's applicability is validated through practical data set analysis.