Powerful rare variant association testing in a copula-based joint analysis of multiple phenotypes

Area of Science:

• Genetics
• Statistical genetics
• Bioinformatics

Background:

• Genetic association studies of rare variants face challenges due to low statistical power.
• Existing methods often analyze single phenotypes, potentially missing complex genetic signals.
• Developing powerful and robust methods is crucial for identifying disease-related genetic variants.

Purpose of the Study:

• To introduce C-JAMP (Copula-based Joint Analysis of Multiple Phenotypes), a novel single-marker association test.
• To evaluate C-JAMP's performance, including type I error and power, against existing rare-variant association tests.
• To apply C-JAMP to real genetic sequencing data for association studies.

Main Methods:

• C-JAMP utilizes a joint model of multiple phenotypes, genetic markers, and covariates.
• Performance was assessed through extensive simulation studies comparing C-JAMP with univariate, multivariate, and multi-marker tests.
• The method was applied to genome-wide association studies of adiponectin plasma concentrations.

Main Results:

• C-JAMP demonstrated unbiased genetic effect estimates and valid type I errors.
• It achieved the highest power when analyzing strongly dependent traits.
• In a real-data application, C-JAMP identified 20 rare variants associated with adiponectin concentrations, outperforming other tests.

Conclusions:

• C-JAMP is a powerful, flexible, and robust method for rare-variant association studies.
• The method successfully identified novel candidate genetic markers for adiponectin.
• C-JAMP is available as an R package for broader research use.