Sequence Kernel Association Test of Multiple Continuous Phenotypes.

Area of Science:

• Human genetics
• Statistical genetics
• Complex disease etiology

Background:

• Joint analysis of multiple correlated traits can enhance statistical power in genetic studies.
• Current methods for multiple trait association tests predominantly focus on common genetic variants.
• A significant gap exists in methods for testing associations between rare variants and multiple traits.

Purpose of the Study:

• To extend the Sequence Kernel Association Test (SKAT) for single-trait analysis to jointly test rare variant sets with multiple correlated traits.
• To address the lack of methods for rare variant association testing in multi-trait genetic studies.

Main Methods:

• Developed a novel multi-trait association test by extending the existing SKAT framework.
• Conducted extensive simulation studies to evaluate the performance of the proposed method.
• Applied the method to analyze diabetes-related traits in the Atherosclerosis Risk in Communities (ARIC) Study.

Main Results:

• The proposed method demonstrated effective joint association testing for rare variants across multiple traits.
• Simulation studies confirmed the method's performance in various scenarios.
• An exome-wide significant rare variant set within the YAP1 gene was identified in relation to diabetes traits.

Conclusions:

• The extended SKAT method provides a powerful tool for rare variant association studies involving multiple traits.
• The findings highlight the potential of analyzing rare variants jointly across correlated traits to uncover disease associations.
• The identified YAP1 variant set warrants further investigation for its role in diabetes etiology.