Abstract
Genome-wide association studies (GWASs) facilitated the discovery of countless disease-associated variants. However, GWASs have mostly been conducted in European ancestry samples. Recent studies have reported that these European-based association results may reduce disease prediction accuracy when applied in non-Europeans. Therefore, previously reported variants should be validated in non-European populations to establish reliable scientific evidence for precision medicine. In this study, we validated known associations with type 2 diabetes (T2D) and related metabolic traits in 125,850 samples from a Korean population genotyped by the Korea Biobank Array (KBA). At the end of December 2020, there were 8,823 variants associated with glycemic traits, lipids, liver enzymes, and T2D in the GWAS catalog. Considering the availability of imputed datasets in the KBA genome data, publicly available East Asian T2D summary statistics, and the linkage disequilibrium among the variants (r2 < 0.2), 2,900 independent variants were selected for further analysis. Among these, 1,837 variants (63.3%) were statistically significant (p ≤ 0.05). Most of the non-replicated variants (n = 1,063) showed insufficient statistical power and decreased minor allele frequencies compared with the replicated variants. Moreover, most of known variants showed <10% genetic heritability. These results could provide valuable scientific evidence for future study designs, the current power of GWASs, and future applications in precision medicine in the Korean population.
Highlights
Common diseases are a result of the complex interactions between genetic and environmental factors [1]
Genome-wide association studies (GWASs) have been used as an efficient research tool for revealing numerous genetic variants associated with various diseases and traits [3]
There were about 290K variants recorded in the NHGRI-EBI genome-wide association studies (GWASs) catalog [4]
Summary
Common diseases are a result of the complex interactions between genetic and environmental factors [1]. Genome-wide association studies (GWASs) have been used as an efficient research tool for revealing numerous genetic variants associated with various diseases and traits [3]. Most GWASs have been conducted in European ancestry samples, and this Eurocentric bias may produce a significant reduction in disease prediction accuracy for non-Europeans [5,6]. This discrepancy might be caused by a difference in allele frequency distribution across populations and population-specific genetic effects [7,8]
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