Efficient Variant Set Mixed Model Association Tests for Continuous and Binary Traits in Large-Scale Whole-Genome...

Area of Science:

• Genetics
• Statistical Genetics
• Bioinformatics

Background:

• Whole-genome sequencing (WGS) generates vast amounts of data, necessitating advanced statistical methods for genetic association studies.
• Existing variant-set tests (e.g., burden test, SKAT) have limitations in handling complex samples with population structure and relatedness, common in large-scale WGS projects.
• There is a need for computationally efficient and powerful methods to analyze rare variants in large, complex datasets.

Purpose of the Study:

• To propose novel variant-set mixed model association tests (SMMAT) for analyzing rare variants in large-scale WGS studies.
• To develop methods applicable to both continuous and binary traits within a generalized linear mixed model framework.
• To address the challenges posed by population structure and relatedness in complex study samples.

Main Methods:

• Developed SMMAT using the generalized linear mixed model framework.
• Implemented a shared null model that requires fitting only once per genome-wide analysis, enhancing computational efficiency.
• Applied the tests to simulated data and a real dataset from the National Heart, Lung, and Blood Institute's Trans-Omics for Precision Medicine (TOPMed) program.

Main Results:

• SMMAT correctly controls type I error rates for both continuous and binary traits in the presence of population structure and relatedness.
• The proposed methods are computationally efficient and suitable for large-scale WGS studies.
• Demonstrated the utility of SMMAT in analyzing plasma fibrinogen levels in a large TOPMed cohort.

Conclusions:

• SMMAT provides a powerful and efficient approach for rare variant association testing in large-scale WGS studies with complex sample structures.
• The methods are robust to population structure and relatedness, making them suitable for diverse genomic datasets.
• SMMAT facilitates genetic discovery in precision medicine initiatives like TOPMed.