Trans-ethnic genome-wide association studies: advantages and challenges of mapping in diverse populations

Abstract

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.Electronic supplementary materialThe online version of this article (doi:10.1186/s13073-014-0091-5) contains supplementary material, which is available to authorized users.

Human Demographic History Impacts Genetic Risk Prediction across Diverse Populations

“…9 At the highly complex end of the polygenicity spectrum, we and others have shown that the utility of polygenic risk inferences and the heritable phenotypic variance explained in diverse populations is improved with more diverse cohorts. 92,95 Standard single-ancestry GWASs typically apply linear mixed model approaches and/or incorporate principal components as covariates to control for confounding from population structure with primarily Europeandescent cohorts.…”

Challenges and progress in interpretation of non-coding genetic variants associated with human disease

“…To reduce the LD background, one interesting approach is to identify conserved GWAS associations from distinct ethnicity background, a method known as trans-ethnic analysis. 59 While the method has been proven to improve the overall prediction of causality, such approach may lose certain ethnicity-specific GWAS signals originating from ethnic-specific heterozygosity of the region.…”

High-Resolution Genetic Maps Identify Multiple Type 2 Diabetes Loci at Regulatory Hotspots in African Americans and Europeans

“…Since the lead SNP is unlikely to be the causal variant, this low replication rate is a general problem for trans-ethnic studies. 5 The inability to account for genetic distance between neighboring SNPs and genetic heterogeneity (e.g., locus and allele heterogeneity and variation in LD between populations) are both potential thwarting factors in the endeavor to identify trans-ethnic disease loci.…”