Evaluating marginal genetic correlation of associated loci for complex diseases and traits between European and East Asian populations

Abstract

Genome-wide association studies (GWASs) have successfully identified a large amount of single-nucleotide polymorphisms associated with many complex phenotypes in diverse populations. However, a comprehensive understanding of the genetic correlation of associated loci of phenotypes across populations remains lacking and the extent to which associations discovered in one population can be generalized to other populations or can be utilized for trans-ethnic genetic prediction is also unclear. By leveraging summary statistics, we proposed MAGIC to evaluate the trans-ethnic marginal genetic correlation (r_m) of per-allele effect sizes for associated SNPs (P < 5E-8) under the framework of measurement error models. We confirmed the methodological advantage of MAGIC over general approaches through simulations and demonstrated its utility by analyzing 34 GWAS summary statistics of phenotypes from the East Asian (N_max = 254,373) and European (N_max = 1,220,901) populations. Among these phenotypes, r_m was estimated to range from 0.584 (se = 0.140) for breast cancer to 0.949 (se = 0.035) for age of menarche, with an average of 0.835 (se = 0.045). We also uncovered that the trans-ethnic genetic prediction accuracy for phenotypes in the target population would substantially become low when using associated SNPs identified in non-target populations, indicating that associations discovered in the one population cannot be simply generalized to another population and that the accuracy of trans-ethnic phenotype prediction is generally dissatisfactory. Overall, our study provides in-depth insight into trans-ethnic genetic correlation and prediction for complex phenotypes across diverse populations.