Divide and conquer approach for genome-wide association studies

被引:0
|
作者
Ozkaraca, Mustafa Ismail [1 ,2 ]
Agung, Mulya [2 ]
Navarro, Pau [1 ]
Tenesa, Albert [1 ,2 ]
机构
[1] Univ Edinburgh, Roslin Inst, Edinburgh EH25 9RG, Scotland
[2] Univ Edinburgh, Inst Genet & Canc, Edinburgh EH4 2XU, Scotland
基金
英国生物技术与生命科学研究理事会;
关键词
Genome-wide association studies (GWAS); meta-analysis; population structure; winner's curse; HERITABILITY; TOOL; METAANALYSIS; RESOURCE;
D O I
10.1093/genetics/iyaf019
中图分类号
Q3 [遗传学];
学科分类号
071007 ; 090102 ;
摘要
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.
引用
收藏
页数:10
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