Zhou Geyu, Qie Xinyue, Zhao Hongyu
Department of Biostatistics, Yale School of Public Health, Yale University, New Haven, CT 06511, USA.
Genetics. 2025 Apr 17;229(4). doi: 10.1093/genetics/iyaf018.
Polygenic risk score has become increasingly popular for predicting the value of complex traits. In many settings, polygenic risk score is used as a covariate in regression analysis to study the association between different phenotypes. However, measurement error in polygenic risk score causes attenuation bias in the estimation of regression coefficients. In this paper, we employ a Bayesian approach to accounting for the measurement error of polygenic risk score and correcting the attenuation bias in linear and logistic regression. Through simulation, we show that our approach is able to obtain approximately unbiased estimation of coefficients and credible intervals with correct coverage probability. We also empirically compare our Bayesian measurement error model with the conventional regression model by analyzing real traits in the UK Biobank. The results demonstrate the effectiveness of our approach as it significantly reduces the error in coefficient estimates.
多基因风险评分在预测复杂性状的值方面越来越受欢迎。在许多情况下，多基因风险评分被用作回归分析中的协变量，以研究不同表型之间的关联。然而，多基因风险评分中的测量误差会导致回归系数估计中的衰减偏差。在本文中，我们采用贝叶斯方法来考虑多基因风险评分的测量误差，并校正线性和逻辑回归中的衰减偏差。通过模拟，我们表明我们的方法能够获得系数的近似无偏估计和具有正确覆盖概率的可信区间。我们还通过分析英国生物银行中的真实性状，将我们的贝叶斯测量误差模型与传统回归模型进行了实证比较。结果证明了我们方法的有效性，因为它显著降低了系数估计中的误差。
