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Mapping the genomic architecture ofadaptive traits with interspecific introg origin: a coalescent-based approach

机译:用三角性吉语引发来映射基因组结构的基因组结构:基于聚合的方法

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Recent studies of eukaryotes including human and Neandertal, mice, and butterflies have highlighted the major role that interspecific introgression has played in adaptive trait evolution. A common question arises in each case: what is the genomic architecture of the introgressed traits? One common approach that can be used to address this question is association mapping, which looks for genotypic markers that have significant statistical association with a trait. It is well understood that sample relatedness can be a confounding factor in association mapping studies if not properly accounted for. Introgression and other evolutionary processes (e.g., incomplete lineage sorting) typically introduce variation among local genealogies, which can also differ from global sample structure measured across all genomic loci. In contrast, state-of-the-art association mapping methods assume fixed sample relatedness across the genome, which can lead to spurious inference. We therefore propose a new association mapping method called Coal-Map, which uses coalescent-based models to capture local genealogical variation alongside global sample structure. Using simulated and empirical data reflecting a range of evolutionary scenarios, we compare the performance of Coal-Map against EIGENSTRAT, a leading association mapping method in terms of its popularity, power, and type I error control. Our empirical data makes use of hundreds of mouse genomes for which adaptive interspecific introgression has recently been described. We found that Coal-Map's performance is comparable or better than EIGENSTRAT in terms of statistical power and false positive rate. Coal-Map's performance advantage was greatest on model conditions that most closely resembled empirically observed scenarios of adaptive introgression. These conditions had: (1) causal SNPs contained in one or a few introgressed genomic loci and (2) varying rates of gene flow - from high rates to very low rates where incomplete lineage sorting dominated as a primary cause of local genealogical variation.
机译:最近对包括人和尼安德特,小鼠和蝴蝶在内的真核生物的研究突出了在适应性特质演变中所发挥的三种间隙所发挥的主要作用。每种情况都出现了一个常见的问题:什么是狭窄特征的基因组架构?可以用于解决这个问题的一种常见方法是关联映射,其查找与特征具有显着统计关联的基因型标记。很好地理解,如果没有适当占据的话,样本相关性可以是关联映射研究中的混淆因素。突出和其他进化过程(例如,不完全的谱系分类)通常在局部系中引入变异,这也可以与在所有基因组基因座上测量的全局样本结构不同。相反,最先进的关联映射方法假定全基因组的固定样本相关性,这可能导致杂散推理。因此,我们提出了一种新的关联映射方法,称为煤泥地图,该方法使用基于聚合的模型来捕获全局样本结构的局部族记变化。利用反映一系列进化场景的模拟和经验数据,我们在其普及,电力和I型错误控制方面比较煤地图对eigenstrat的性能,是一个领先的关联映射方法。我们的经验数据利用数百个小鼠基因组,用于最近描述了适应性间隙迟发。我们发现,在统计功率和假阳性率方面,煤层地图的性能比特征在于。煤层地图的性能优势在于模型条件最为伟大,最近似的经验观察到的适应性迟滞的情况。这些条件具有:(1)一种或多种基因组基因座中含有的因果SNP,(2)基因流量的不同速率 - 从高速率到非常低的速率,其中不完全分类为局部族裔变异的主要原因。

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