Abstract
Studies that traverse ancestrally diverse populations may increase power to detect novel loci and improve fine-mapping resolution of causal variants by leveraging linkage disequilibrium differences between ethnic groups. The inclusion of African ancestry samples may yield further improvements because of low linkage disequilibrium and high genetic heterogeneity. We investigate the fine-mapping resolution of trans-ethnic fixed-effects meta-analysis for five type II diabetes loci, under various settings of ancestral composition (European, East Asian, African), allelic heterogeneity, and causal variant minor allele frequency. In particular, three settings of ancestral composition were compared: (1) single ancestry (European), (2) moderate ancestral diversity (European and East Asian), and (3) high ancestral diversity (European, East Asian, and African). Our simulations suggest that the European/Asian and European ancestry-only meta-analyses consistently attain similar fine-mapping resolution. The inclusion of African ancestry samples in the meta-analysis leads to a marked improvement in fine-mapping resolution.
Highlights
Numerous genome-wide association studies (GWASs) have been carried out, resulting in the identification of many susceptibility loci for a wide range of complex traits.[1]
As we found a clear improvement in the trans-ethnic meta-analyses composed of European, East Asian, and African populations, but not in those without African samples, we investigated fine-mapping in African-only meta-analyses of perfect data
In the locus heterogeneity scenario, we found that null cohorts (East Asian, African) had a dramatic impact on the power to detect the causal variant that was associated in only two (European) of the six cohorts (Table 4)
Summary
Numerous genome-wide association studies (GWASs) have been carried out, resulting in the identification of many susceptibility loci for a wide range of complex traits.[1] The detection of additional loci has resulted from GWAS meta-analyses (primarily in populations of European descent) and has been aided by imputation that allows the prediction of genotypes not typed on GWAS chips, but present in a higher density reference. The joint effects of the loci identified to date have only accounted for a small proportion of the heritability of complex traits. Because of linkage disequilibrium (LD), many variants within identified loci have indistinguishable signals. This LD is beneficial to GWAS, as it increases the power to detect new associations, when the causal variant is not directly typed. The caveat to this is that it limits the potential of fine-mapping efforts to refine the location of causal variants
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