Abstract
Genome-wide association studies (GWAS) have been very successful at identifying genetic variants influencing a large number of traits. Although the great majority of these studies have been performed in European-descent individuals, it has been recognised that including populations with differing ancestries enhances the potential for identifying causal SNPs due to their differing patterns of linkage disequilibrium. However, when individuals from distinct ethnicities are included in a GWAS, it is necessary to implement a number of control steps to ensure that the identified associations are real genotype-phenotype relationships. In this Review, we discuss the analyses that are required when performing multi-ethnic studies, including methods for determining ancestry at the global and local level for sample exclusion, controlling for ancestry in association testing, and post-GWAS interrogation methods such as genomic control and meta-analysis. We hope that this overview provides a primer for those researchers interested in including distinct populations in their studies.
Highlights
Genome-wide association studies (GWAS) aim to identify genetic variants that are associated with a phenotype of interest
The purpose of this review is to discuss the main approaches that are used in order to account for population structure in admixed individuals in a GWAS to select data to include, control for its influence on findings, and compare or aggregate results across populations
The second set of criteria we can evaluate with genotype data can elucidate the ancestry of the individuals in the study. For this set we can use the methods we described above: admixture analysis and principal component analysis
Summary
Genome-wide association studies (GWAS) aim to identify genetic variants (usually single-nucleotide polymorphisms or SNPs) that are associated with a phenotype of interest. Driven by the need to identify SNPs with even more modest effect sizes to further elucidate genetic architecture, GWAS sample sizes have necessarily increased; studies of a wider range of populations are warranted. The proportion of studies including individuals of non-European descent has increased in recent years (Gurdasani et al, 2019). Such adaptations of study design require re-assessment of analytical approaches; when individuals from multiple distinct genetic ancestries are included in a study, it is necessary to implement a number of control steps to ensure that the associations identified are not detecting ancestry-driven rather than trait-related genetic effects
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