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

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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 (rm) 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 (Nmax = 254,373) and European (Nmax = 1,220,901) populations. Among these phenotypes, rm 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.

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All data generated or analyzed during this study are included in this published article and its supplementary information file.

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Acknowledgements

We thank all the GWAS consortia for making summary statistics publicly available for us and are grateful to all the investigators and participants contributed to those studies. The data analyses in the present study were carried out with the high-performance computing cluster that was supported by the special central finance project of local universities for Xuzhou Medical University. We are especially grateful to two anonymous referees for making a lot of constructive comments that have led to substantial improvements of our manuscript.

Funding

The research of Ping Zeng was supported in part by the Youth Foundation of Humanity and Social Science funded by Ministry of Education of China (18YJC910002), the Natural Science Foundation of Jiangsu Province of China (BK20181472), the China Postdoctoral Science Foundation (2018M630607 and 2019T120465), the QingLan Research Project of Jiangsu Province for Outstanding Young Teachers, the Six-Talent Peaks Project in Jiangsu Province of China (WSN-087), the Training Project for Youth Teams of Science and Technology Innovation at Xuzhou Medical University (TD202008), the Postdoctoral Science Foundation of Xuzhou Medical University, the National Natural Science Foundation of China (81402765), and the Statistical Science Research Project from National Bureau of Statistics of China (2014LY112). The research of Shuiping Huang was supported in part by the Social Development Project of Xuzhou City (KC19017). The research of Ting Wang was supported in part by the Social Development Project of Xuzhou City (KC20062).

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  1. Haojie Lu and Ting Wang are joint first authors.

Authors and Affiliations

  1. Department of Epidemiology and Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China

    Haojie Lu, Ting Wang, Jinhui Zhang, Shuo Zhang, Shuiping Huang & Ping Zeng

  2. Center for Medical Statistics and Data Analysis, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China

    Shuiping Huang & Ping Zeng

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  1. Haojie Lu
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Contributions

PZ conceived the idea for the study. PZ, TW, and SH obtained and cleared the datasets; PZ, HL, TW, JZ, and SZ performed the data analyses. PZ, HL, and TW interpreted the results of the data analyses. PZ and HL wrote the manuscript with the help from other authors.

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Correspondence to Ping Zeng.

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Lu, H., Wang, T., Zhang, J. et al. Evaluating marginal genetic correlation of associated loci for complex diseases and traits between European and East Asian populations. Hum Genet 140, 1285–1297 (2021). https://doi.org/10.1007/s00439-021-02299-8

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