A method for analyzing multiple continuous phenotypes in rare variant association studies allowing for flexible...

Area of Science:

• Genetics
• Statistical Genetics
• Bioinformatics

Background:

• Analyzing region-based sequencing data requires methods that enhance the power to detect genetic associations.
• Multiple related phenotypes can improve the power of genetic association studies.

Purpose of the Study:

• To propose a novel statistical test for detecting simultaneous associations between rare variants in a genomic region and multiple continuous phenotypes.
• To develop a computationally efficient and powerful method for genetic association analysis.

Main Methods:

• A multivariate test is developed based on a linear mixed model, assuming variant effects follow a multivariate normal distribution.
• A data-adaptive variance component test using score-type statistics is derived to handle unknown correlation parameters.
• Analytical P-value calculation ensures computational efficiency.

Main Results:

• The proposed multivariate test demonstrates superior power compared to univariate tests in simulations.
• The test is particularly effective when dealing with pleiotropic effects or highly correlated phenotypes.
• Application to the UK10K project data validated the method's performance.

Conclusions:

• The novel multivariate test provides a powerful approach for genetic association studies involving rare variants and multiple phenotypes.
• This method enhances the ability to detect genetic associations in complex trait analyses.
• The computational efficiency makes it suitable for large-scale genomic studies.