标题： 通过稀疏高维线性混合模型中的变量选择方法提高遗传力估计 显示英文标题

标题： Improving heritability estimation by a variable selection approach in sparse high dimensional linear mixed models

摘要： 受神经解剖学应用的启发，我们提出了一种新的方法来估计遗传力，该遗传力对应于可以由遗传因素解释的表型方差的比例。对神经解剖特征估计这一数量是精神病疾病研究中的一个基本挑战。由于表型变异可能仅由可用遗传信息的一小部分引起，因此我们提出了一种遗传力估计器，可以在高维稀疏线性混合模型中使用。我们的方法包括三个步骤。首先，进行变量选择阶段以恢复遗传效应的支持——也称为因果变异——即找到真正解释表型变异的遗传效应。其次，我们提出了一种最大似然策略来估计遗传力，该策略仅考虑第一步中找到的因果遗传效应。第三，我们通过一种非参数自助法计算我们的遗传力估计器的标准误差和95%置信区间。我们的主要贡献在于提供了一种遗传力估计，其标准误差明显小于在遗传效应非常稀疏时没有变量选择的方法。由于实际中真实的遗传结构通常未知，我们还提出了一种经验准则，使用户能够决定是否有必要应用基于变量选择的方法。我们在来自Imagen项目的合成和真实神经解剖数据上展示了我们方法的性能。我们还表明，我们的方法计算负担非常低，并且在统计学上非常高效。 显示英文摘要

摘要： Motivated by applications in neuroanatomy, we propose a novel methodology for estimating the heritability which corresponds to the proportion of phenotypic variance which can be explained by genetic factors. Estimating this quantity for neuroanatomical features is a fundamental challenge in psychiatric disease research. Since the phenotypic variations may only be due to a small fraction of the available genetic information, we propose an estimator of the heritability that can be used in high dimensional sparse linear mixed models. Our method consists of three steps. Firstly, a variable selection stage is performed in order to recover the support of the genetic effects -- also called causal variants -- that is to find the genetic effects which really explain the phenotypic variations. Secondly, we propose a maximum likelihood strategy for estimating the heritability which only takes into account the causal genetic effects found in the first step. Thirdly, we compute the standard error and the 95% confidence interval associated to our heritability estimator thanks to a nonparametric bootsrap approach. Our main contribution consists in providing an estimation of the heritability with standard errors substantially smaller than methods without variable selection when the genetic effects are very sparse. Since the real genetic architecture is in general unknown in practice, we also propose an empirical criterion which allows the user to decide whether it is relevant to apply a variable selection based approach or not. We illustrate the performance of our methodology on synthetic and real neuroanatomic data coming from the Imagen project. We also show that our approach has a very low computational burden and is very efficient from a statistical point of view.