Estimating Trans-Ancestry Genetic Correlation with Unbalanced Data Resources
提出一种新方法，利用遗传预测观测值估计跨祖先遗传相关性，仅需一个群体的大样本，另一群体样本量可小至数百，解决了数据资源不平衡问题。
The aim of this paper 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.