GAWMerge expands GWAS sample size and diversity by combining array-based genotyping and whole-genome sequencing

Genome-wide association studies (GWAS) have made impactful discoveries for complex diseases, often by amassing very large sample sizes. Yet, GWAS of many diseases remain underpowered, especially for non-European ancestries. One cost-effective approach to increase sample size is to combine existing c...

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Bibliographic Details
Published in:Communications biology Vol. 5; no. 1; pp. 806 - 9
Main Authors: Mathur, Ravi, Fang, Fang, Gaddis, Nathan, Hancock, Dana B., Cho, Michael H., Hokanson, John E., Bierut, Laura J., Lutz, Sharon M., Young, Kendra, Smith, Albert V., Silverman, Edwin K., Page, Grier P., Johnson, Eric O.
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 11.08.2022
Nature Publishing Group
Nature Portfolio
Subjects:
45/43
631/114
631/208/205/2138
Biomedical and Life Sciences
Computer applications
Genome-wide association studies
Genome-Wide Association Study - methods
Genomes
Genotype
Genotypes
Genotyping
Life Sciences
Phenotype
Precision medicine
Sample Size
Single-nucleotide polymorphism
Whole genome sequencing
Whole Genome Sequencing - methods
ISSN:2399-3642, 2399-3642
Online Access:Get full text
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Summary:Genome-wide association studies (GWAS) have made impactful discoveries for complex diseases, often by amassing very large sample sizes. Yet, GWAS of many diseases remain underpowered, especially for non-European ancestries. One cost-effective approach to increase sample size is to combine existing cohorts, which may have limited sample size or be case-only, with public controls, but this approach is limited by the need for a large overlap in variants across genotyping arrays and the scarcity of non-European controls. We developed and validated a protocol, Genotyping Array-WGS Merge (GAWMerge), for combining genotypes from arrays and whole-genome sequencing, ensuring complete variant overlap, and allowing for diverse samples like Trans-Omics for Precision Medicine to be used. Our protocol involves phasing, imputation, and filtering. We illustrated its ability to control technology driven artifacts and type-I error, as well as recover known disease-associated signals across technologies, independent datasets, and ancestries in smoking-related cohorts. GAWMerge enables genetic studies to leverage existing cohorts to validly increase sample size and enhance discovery for understudied traits and ancestries. GAWMerge is a computational tool that allows users to integrate SNP genotyping data from array techniques or whole-genome sequencing, providing a feasible method to leverage existing cohorts to increase sample size in genetic studies.
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ISSN:2399-3642
2399-3642
DOI:10.1038/s42003-022-03738-6