Divide and conquer approach for genome-wide association studies

Area of Science:

• Genetics and Genomics
• Computational Biology
• Statistical Genetics

Background:

• Genome-wide association studies (GWAS) are crucial for identifying genetic variants associated with traits and diseases.
• Traditional GWAS methods face significant computational challenges, with complexity increasing linearly with sample size.
• Analyzing related individuals and controlling for the winner's curse are critical issues in real-world GWAS datasets.

Purpose of the Study:

• To develop an accurate and resource-efficient pipeline for conducting genome-wide association studies (GWAS).
• To mitigate the impact of large sample sizes on computational demands in GWAS.
• To provide a scalable and reproducible solution for genetic association analyses.

Main Methods:

• A novel GWAS pipeline involving cohort partitioning into sub-cohorts.
• Independent GWAS conducted within each sub-cohort.
• A meta-analysis technique integrating sub-cohort results, accounting for population structure and confounders.

Main Results:

• The proposed method significantly reduces computational costs compared to standard GWAS approaches.
• The pipeline effectively analyzes related individuals and controls for inflated effect sizes (winner's curse).
• Achieves comparable discovery levels to standard methods with substantially lower resource requirements.

Conclusions:

• The developed GWAS pipeline offers a computationally efficient and accurate alternative for genetic association studies.
• The approach is well-suited for large cohorts, incremental data additions, and analyses involving related individuals.
• Implementation within a bioinformatics workflow management system ensures reproducibility and scalability.