整合局部空间关联指数的景观基因组模型的高性能计算
High performance computation of landscape genomic models integrating local indices of spatial association
摘要：
景观基因组学自问世以来，随着分子生物学和地形气候学数据的日益丰富，得到了迅速发展。该领域目前的挑战主要涉及处理大量模型和从人口统计学中分离选择。有几种方法可以解决后者，要么通过估计人口结构的中性模型，要么同时推断环境和人口的影响。在这里，我们提出了山姆$β$ ada，一个综合的方法来研究签名的地方适应，提供快速处理的全基因组数据，并使评估的空间关联使用分子标记。具体地说，候选基因座的适应识别自动评估基因组环境协会。作为补充，测量这些候选基因座的空间关联局部指标（LISA）可以检测相似的基因型是否倾向于在空间上聚集，这构成了个体之间可能的亲属关系的有用指示。在本文中，我们还分析了来自乌干达牛的SNP数据，以检测Sam $β$ ada，BayEnv，LFMM和离群值方法（Arlequin中的FDIST方法）的局部适应特征，并比较它们的结果。Sam $β$ ada是一款适用于Windows、Linux和MacOS X的开源软件，可从\url{http://lasig.epfl.ch/sambada}获得
英文摘要：
Since its introduction, landscape genomics has developed quickly with the increasing availability of both molecular and topo-climatic data. The current challenges of the field mainly involve processing large numbers of models and disentangling selection from demography. Several methods address the latter, either by estimating a neutral model from population structure or by inferring simultaneously environmental and demographic effects. Here we present Sam$β$ada, an integrated approach to study signatures of local adaptation, providing rapid processing of whole genome data and enabling assessment of spatial association using molecular markers. Specifically, candidate loci to adaptation are identified by automatically assessing genome-environment associations. In complement, measuring the Local Indicators of Spatial Association (LISA) for these candidate loci allows to detect whether similar genotypes tend to gather in space, which constitutes a useful indication of the possible kinship relationship between individuals. In this paper, we also analyze SNP data from Ugandan cattle to detect signatures of local adaptation with Sam$β$ada, BayEnv, LFMM and an outlier method (FDIST approach in Arlequin) and compare their results. Sam$β$ada is an open source software for Windows, Linux and MacOS X available at \url{http://lasig.epfl.ch/sambada}
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
