Department of Biostatistics, University of Iowa, Iowa City, Iowa, USA.
Genet Epidemiol. 2021 Jul;45(5):427-444. doi: 10.1002/gepi.22384. Epub 2021 May 16.
Many genetic studies that aim to identify genetic variants associated with complex phenotypes are subject to unobserved confounding factors arising from environmental heterogeneity. This poses a challenge to detecting associations of interest and is known to induce spurious associations when left unaccounted for. Penalized linear mixed models (LMMs) are an attractive method to correct for unobserved confounding. These methods correct for varying levels of relatedness and population structure by modeling it as a random effect with a covariance structure estimated from observed genetic data. Despite an extensive literature on penalized regression and LMMs separately, the two are rarely discussed together. The aim of this review is to do so while examining the statistical properties of penalized LMMs in the genetic association setting. Specifically, the ability of penalized LMMs to accurately estimate genetic effects in the presence of environmental confounding has not been well studied. To clarify the important yet subtle distinction between population structure and environmental heterogeneity, we present a detailed review of relevant concepts and methods. In addition, we evaluate the performance of penalized LMMs and competing methods in terms of estimation and selection accuracy in the presence of a number of confounding structures.
许多旨在识别与复杂表型相关的遗传变异的遗传研究都受到环境异质性引起的未观察到的混杂因素的影响。这给检测感兴趣的关联带来了挑战，并且已知在未被考虑时会产生虚假关联。惩罚线性混合模型 (LMM) 是一种纠正未观察到的混杂因素的有吸引力的方法。这些方法通过将其建模为具有协方差结构的随机效应来纠正不同程度的相关性和群体结构，该协方差结构是从观察到的遗传数据中估计得出的。尽管关于惩罚回归和 LMM 的文献很多，但很少将它们放在一起讨论。本综述的目的是在检查遗传关联环境中惩罚 LMM 的统计特性的同时做到这一点。具体来说，惩罚 LMM 在存在环境混杂的情况下准确估计遗传效应的能力尚未得到很好的研究。为了澄清群体结构和环境异质性之间重要而微妙的区别，我们详细回顾了相关概念和方法。此外，我们还评估了惩罚 LMM 和竞争方法在存在多种混杂结构时在估计和选择准确性方面的性能。
