论文标题
基于重复样品拆分的稳定且自适应的多基因检测方法
A stable and adaptive polygenic signal detection method based on repeated sample splitting
论文作者
论文摘要
在复杂性状的高维遗传关联研究中，我们着重于多基因检测，我们为广义线性模型开发了自适应测试，以适应不同的替代方法。为了促进对高维数据的有效挑选后推断，我们的研究遵守原始采样分解原理，但反复地这样做以提高推断的稳定性。我们显示了针对固定数量和不同变体数量的提议测试的渐近零分布。我们还显示了在局部替代方案下提出的测试的渐近性能，提供了有关为什么归因于可变选择和加权的能力增益的见解，可以补偿由于样本拆分而导致的效率损失。我们通过广泛的模拟研究和两种应用来支持我们的分析结果。所提出的过程在计算上是有效的，并且已作为R软件包DoubleCauchy实施。
Focusing on polygenic signal detection in high dimensional genetic association studies of complex traits, we develop an adaptive test for generalized linear models to accommodate different alternatives. To facilitate valid post-selection inference for high dimensional data, our study here adheres to the original sampling-splitting principle but does so, repeatedly, to increase stability of the inference. We show the asymptotic null distributions of the proposed test for both fixed and diverging number of variants. We also show the asymptotic properties of the proposed test under local alternatives, providing insights on why power gain attributed to variable selection and weighting can compensate for efficiency loss due to sample splitting. We support our analytical findings through extensive simulation studies and two applications. The proposed procedure is computationally efficient and has been implemented as the R package DoubleCauchy.
