Estimating Trans-Ancestry Genetic Correlation with Unbalanced Data Resources

Abstract

The aim of this article is to propose a novel method for estimating trans-ancestry genetic correlations in genome-wide association studies (GWAS) using genetically predicted observations. These correlations describe how genetic architecture of complex traits varies among populations. Our new estimator corrects for biases arising from prediction errors in high-dimensional weak GWAS signals, while addressing the ethnic diversity inherent in GWAS data, such as linkage disequilibrium (LD) differences. A distinguishing feature of our approach is its flexibility regarding sample sizes: it necessitates a large GWAS sample only from one population, while the secondary population may have a much smaller cohort, even in the hundreds. This design directly addresses the existing imbalance in GWAS data resources, where datasets for European populations typically outnumber those of non-European ancestries. Through extensive simulations and real data analysis from the UK Biobank study encompassing 26 complex traits, we validate the reliability of our method. Our results illuminate the broader implications of transferring genetic findings across diverse populations. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Keywords:

• Data heterogeneity
• GWAS
• High-dimensional prediction
• Trans-ancestry genetic correlation
• UK Biobank

Supplementary Materials

Supplementary Materials:Technical proofs, more discussions of special cases, additional simulation results, and results of the real data analysis using the UKB database.

Acknowledgements

We would like to thank Ziliang Zhu, Yue Yang, and Fei Zou for their helpful discussions. We would also like to thank the anonymous referees, the Associate Editor, and the Editor for their constructive comments that substantially improved the quality of this article. We thank the individuals represented in the UK Biobank for their participation and the research teams for their work in collecting, processing, and disseminating these datasets for analysis.

Disclosure Statement

The authors report there are no competing interests to declare.

Additional information

Funding

Research reported in this publication was supported by the National Institute On Aging of the National Institutes of Health under Award Number RF1AG082938. This research has been conducted using the UK Biobank resource (application number 76139), subject to a data transfer agreement.