Fast set-based association analysis using summary data from GWAS identifies novel gene loci for human complex traits

Area of Science:

• Genetics and Genomics
• Statistical Genetics
• Bioinformatics

Background:

• Genome-wide association studies (GWAS) are crucial for understanding complex human traits.
• Existing methods for analyzing GWAS summary data can be computationally intensive and less accurate.
• Set-based association analysis offers a powerful framework for detecting genetic influences on complex traits.

Purpose of the Study:

• To introduce fastBAT, a novel, fast, and accurate method for set-based association analysis using GWAS summary data.
• To enhance the power of detecting genetic associations for complex traits by leveraging linkage disequilibrium (LD) data.
• To identify novel genetic loci associated with complex traits such as height, body mass index (BMI), and schizophrenia.

Main Methods:

• Developed fastBAT, a computationally efficient algorithm for set-based association analysis.
• Utilized summary-level GWAS data and reference panel LD data with individual-level genotypes.
• Validated the method through simulations and analyses of large-scale real-world GWAS datasets.

Main Results:

• fastBAT demonstrated superior accuracy and was orders of magnitude faster than existing methods.
• Analysis of large GWAS datasets (150,064-339,224 individuals) identified significant novel gene loci.
• Discovered 6 novel loci for height, 2 for BMI, and 3 for schizophrenia at P < 5 × 10⁻⁸.
• Identified multiple small, independent association signals contributing to the power gain at these loci.

Conclusions:

• fastBAT provides a powerful and efficient tool for genetic association analysis of complex traits.
• The method successfully identified novel genetic associations for height, BMI, and schizophrenia.
• fastBAT is broadly applicable to GWAS data across various complex traits and diseases in humans and other species.