| Title: | Divide and conquer approach for genome-wide association studies |
| Journal: | Genetics |
| Published: | 13 Mar 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/40080676/ |
| DOI: | https://doi.org/10.1093/genetics/iyaf019 |
| URL: | https://academic.oup.com/genetics/advance-article-pdf/doi/10.1093/genetics/iyaf019/62402710/iyaf019.pdf |
Publication 15601
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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.</p>
5 Keywords
- Computational Biology
- Computer Simulation
- Genome-Wide Association Study
- Humans
- Polymorphism, Single Nucleotide
4 Authors
- Mustafa İsmail Özkaraca
- Mulya Agung
- Pau Navarro
- Albert Tenesa
1 Application
| Application ID | Title |
|---|---|
| 788 | Heritability of disease frequency |
Enabling scientific discoveries that improve human health