标题： 生物银行规模关联研究的定量响应的强大转换 显示英文标题

标题： A powerful transformation of quantitative responses for biobank-scale association studies

摘要： 在具有非高斯误差的线性回归模型中，响应变量的变换在广泛的应用中被广泛使用。 受各种遗传关联研究的启发，假设检验的变换方法受到了广泛关注。 近年来，生物银行规模的遗传研究迅速发展，这些研究具有大量参与者，可能达到数十万，这推动了新的变换方法的需求，这些方法既能够有效检测微弱的遗传信号，又在大规模数据上计算高效。 在此工作中，我们提出了一种新颖的变换方法，该方法利用了误差密度的信息。 这种变换导致局部最有力的检验，因此对于检测微弱信号具有强大的功效。 为了使计算能够扩展到生物银行规模的研究，我们利用了弱遗传信号的特性，并提出了一个一致且计算高效的变换函数估计器。 通过广泛的模拟和对英国生物银行中肺功能特征的基于基因的分析，我们验证了我们的方法在严格控制第一类错误率方面表现良好，并且相较于现有方法显著提高了统计功效。 显示英文摘要

摘要： In linear regression models with non-Gaussian errors, transformations of the response variable are widely used in a broad range of applications. Motivated by various genetic association studies, transformation methods for hypothesis testing have received substantial interest. In recent years, the rise of biobank-scale genetic studies, which feature a vast number of participants that could be around half a million, spurred the need for new transformation methods that are both powerful for detecting weak genetic signals and computationally efficient for large-scale data. In this work, we propose a novel transformation method that leverages the information of the error density. This transformation leads to locally most powerful tests and therefore has strong power for detecting weak signals. To make the computation scalable to biobank-scale studies, we harnessed the nature of weak genetic signals and proposed a consistent and computationally efficient estimator of the transformation function. Through extensive simulations and a gene-based analysis of spirometry traits from the UK Biobank, we validate that our approach maintains stringent control over type I error rates and significantly enhances statistical power over existing methods.