Divide and conquer approach for genome-wide association studies

TL;DR

This paper introduces a faster and more efficient method for genome-wide association studies by splitting data into smaller parts and combining results.

Contribution

A novel divide-and-conquer GWAS pipeline that reduces computational costs while maintaining accuracy and handling population structure.

Findings

The pipeline achieves same discovery levels as standard GWAS with reduced computational costs.

Effectively handles related individuals and controls inflated effect sizes in real datasets.

Supports incremental analysis as new samples are added and improves reproducibility.

Abstract

Genome-wide association studies (GWAS) are computationally intensive, requiring significant time and resources with computational complexity scaling at least linearly with sample size. Here, we present an accurate and resource-efficient pipeline for GWAS that mitigates the impact of sample size on computational demands. Our approach involves (1) randomly partitioning the cohort into equally sized sub-cohorts, (2) conducting independent GWAS within each sub-cohort, and (3) integrating the results using a novel meta-analysis technique that accounts for population structure and other confounders between sub-cohorts. Importantly, we demonstrate through simulations and real-data examples in humans that our approach effectively manages analyzing related individuals, a critical factor in real datasets, while controlling for inflated effect sizes, a phenomenon known as winner's curse. We show that our method achieves the same discovery levels as standard approaches but with significantly reduced computational costs. Additionally, it is well-suited for incremental GWAS as new samples are added over time. Our implementation within a bioinformatics workflow management system enhances reproducibility and scalability.

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