Trans-ethnic genome-wide association studies: advantages and challenges of mapping in diverse populations
Open accessAbstract
Genome-wide association studies (GWASs) are the method most often used by geneticists to interrogate the human genome, and they provide a cost-effective way to identify the genetic variants underpinning complex traits and diseases. Most initial GWASs have focused on genetically homogeneous cohorts from European populations given the limited availability of ethnic minority samples and so as to limit population stratification effects. Transethnic studies have been invaluable in explaining the heritability of common quantitative traits, such as height, and in examining the genetic architecture of complex diseases, such as type 2 diabetes. They provide an opportunity for large-scale signal replication in independent populations and for cross-population meta-analyses to boost statistical power. In addition, transethnic GWASs enable prioritization of candidate genes, fine-mapping of functional variants, and potentially identification of SNPs associated with disease risk in admixed populations, by taking advantage of natural differences in genomic linkage disequilibrium across ethnically diverse populations. Recent efforts to assess the biological function of variants identified by GWAS have highlighted the need for large-scale replication, meta-analyses and fine-mapping across worldwide populations of ethnically diverse genetic ancestries. Here, we review recent advances and new approaches that are important to consider when performing, designing or interpreting transethnic GWASs, and we highlight existing challenges, such as the limited ability to handle heterogeneity in linkage disequilibrium across populations and limitations in dissecting complex architectures, such as those found in recently admixed populations.
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A large transethnic GWAS utilizing samples from Caucasians, African Americans, Japanese, and Israeli-Arabs identified several loci contributing to disease risk in all populations.
“a large transethnic GWAS utilizing samples from Caucasians, African Americans, Japanese, and Israeli-Arabs identified several loci contributing to disease risk in all populations [101].”
From Genetic architecture of Alzheimer's disease (2020) · cites this paper as [101]
Utilizing multiple ethnic groups in GWAS enhances the ability of researchers to identify putative causal variants in associated loci due to variations in LD structure across populations.
“Utilizing multiple ethnic groups in GWAS also enhances the ability of researchers to identify putative causal variants in associated loci due to variations in LD structure across populations [101].”
From Genetic architecture of Alzheimer's disease (2020) · cites this paper as [101]
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- Authors
- Li, Y. R., & Keating, B. J.
- Affiliations
- Children's Hospital of Philadelphia (US)
- Corresponding author
- Li, Yun R
- Keating, Brendan J
- ORCID
- Keating, Brendan J
- Venue
- Genome Medicine
- Published
- 2014-10-31
- Volume
- 6
- Issue
- 10
- Pages
- 91-91
- ISSN
- 1756-994X (ISSN-L)
- PMC
- PMC4254423
- OpenAlex
- W2136967659
- Cited by
- 214
- References
- 117
- Citations by year
- 2026: 9 · 2025: 14 · 2024: 24 · 2023: 27 · 2022: 25 · 2021: 24 · 2020: 23 · 2019: 21 · 2018: 15 · 2017: 14 · 2016: 14 · 2015: 3
- FWCI
- 6.79
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Genome Medicine (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Forensic and Genetic Research
- Keywords
- population stratification, admixed populations, fine mapping, replication, complex traits, complex diseases, genetic ancestry, candidate genes, type 2 diabetes, height
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