标题： 基因型与环境变量之间的相关性检测。 全基因组研究的快速计算方法 显示英文标题

标题： Detection of correlation between genotypes and environmental variables. A fast computational approach for genomewide studies

摘要： 显示与某些环境变量显著相关的基因组区域（或位点）很可能处于选择压力下，这是最近识别选择位点并获取其功能信息的方法的理论基础。 为了有效，这些方法需要能够将环境变量的潜在影响与种群历史的混杂影响区分开来。 对于全基因组数据集的常规分析，还需要快速的推断和模型选择算法。 我们提出了一种基于显式空间模型的方法，该模型是空间广义线性混合模型（SGLMM）的一个实例。 在推断方面，我们利用了Rue等人（2009）和Lindgren等人（2011）开发的INLA-SPDE理论和计算框架。 我们提出的方法可以量化基因型与环境变量之间的相关性。 它适用于最常见的遗传标记类型，无论是在个体层面还是种群层面获得的。 在模拟数据下，这些数据是在地统计模型和显式选择模型下生成的，我们证明了该方法是高效的。 我们还重新分析了一个涉及欧洲十九个云杉小蠹（Hylobius abietis）种群的数据集。 所提出的方法也作为测试遗传和环境变量之间关联的Mantel检验的统计上合理的选择。 显示英文摘要

摘要： Genomic regions (or loci) displaying outstanding correlation with some environmental variables are likely to be under selection and this is the rationale of recent methods of identifying selected loci and retrieving functional information about them. To be efficient, such methods need to be able to disentangle the potential effect of environmental variables from the confounding effect of population history. For the routine analysis of genome-wide datasets, one also needs fast inference and model selection algorithms. We propose a method based on an explicit spatial model which is an instance of spatial generalized linear mixed model (SGLMM). For inference, we make use of the INLA-SPDE theoretical and computational framework developed by Rue et al. (2009) and Lindgren et al (2011). The method we propose allows one to quantify the correlation between genotypes and environmental variables. It works for the most common types of genetic markers, obtained either at the individual or at the population level. Analyzing simulated data produced under a geostatistical model then under an explicit model of selection, we show that the method is efficient. We also re-analyze a dataset relative to nineteen pine weevils (Hylobius abietis}) populations across Europe. The method proposed appears also as a statistically sound alternative to the Mantel tests for testing the association between genetic and environmental variables.