论文标题
估计与不平衡数据资源的跨责任遗传相关性
Estimating trans-ancestry genetic correlation with unbalanced data resources
论文作者
论文摘要
本文的目的是提出一种新的估计方法，即使用遗传预测的观测值来估计跨熟悉的遗传相关性，该方法描述了整个基因组关联研究（GWAS）中复杂性状的遗传结构在人群中的变化。我们的新估计器纠正了由高维弱GWAS信号引起的预测误差，同时解决了跨种族的GWAS数据的异质性，例如连锁不平衡（LD）差异，这可能会导致同质性 - 敏感分析的偏见。此外，我们的估计量只要求一个人群具有较大的GWAS样本量，而第二个人群只能少数参与者数量少得多（例如，数百个）。它旨在专门解决不平衡的数据资源，以至于欧洲人口的GWAS样本量通常大于非欧洲血统群体。英国生物银行研究中30个复杂性状的大量模拟和实际数据分析表明，我们的方法能够对广泛的复杂性状提供可靠的估计。我们的结果为人口特异性遗传发现的转移性提供了深刻的见解。
The aim of this paper is to propose a novel estimation method of using genetic-predicted observations to estimate trans-ancestry genetic correlations, which describes how genetic architecture of complex traits varies among populations, in genome-wide association studies (GWAS). Our new estimator corrects for prediction errors caused by high-dimensional weak GWAS signals, while addressing the heterogeneity of GWAS data across ethnicities, such as linkage disequilibrium (LD) differences, which can lead to biased findings in homogeneity-agnostic analyses. Moreover, our estimator only requires one population to have a large GWAS sample size, and the second population can only have a much smaller number of participants (for example, hundreds). It is designed to specifically address the unbalanced data resources such that the GWAS sample size for European populations is usually larger than that of non-European ancestry groups. Extensive simulations and real data analyses of 30 complex traits in the UK Biobank study show that our method is capable of providing reliable estimates of a wide range of complex traits. Our results provide deep insights into the transferability of population-specific genetic findings.
