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基于人口统计学的家犬选择下基因组区域评估

Demographically-Based Evaluation of Genomic Regions under Selection in Domestic Dogs.

作者信息

Freedman Adam H, Schweizer Rena M, Ortega-Del Vecchyo Diego, Han Eunjung, Davis Brian W, Gronau Ilan, Silva Pedro M, Galaverni Marco, Fan Zhenxin, Marx Peter, Lorente-Galdos Belen, Ramirez Oscar, Hormozdiari Farhad, Alkan Can, Vilà Carles, Squire Kevin, Geffen Eli, Kusak Josip, Boyko Adam R, Parker Heidi G, Lee Clarence, Tadigotla Vasisht, Siepel Adam, Bustamante Carlos D, Harkins Timothy T, Nelson Stanley F, Marques-Bonet Tomas, Ostrander Elaine A, Wayne Robert K, Novembre John

机构信息

Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, California, United States of America.

National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, United States of America.

出版信息

PLoS Genet. 2016 Mar 4;12(3):e1005851. doi: 10.1371/journal.pgen.1005851. eCollection 2016 Mar.

Abstract

Controlling for background demographic effects is important for accurately identifying loci that have recently undergone positive selection. To date, the effects of demography have not yet been explicitly considered when identifying loci under selection during dog domestication. To investigate positive selection on the dog lineage early in the domestication, we examined patterns of polymorphism in six canid genomes that were previously used to infer a demographic model of dog domestication. Using an inferred demographic model, we computed false discovery rates (FDR) and identified 349 outlier regions consistent with positive selection at a low FDR. The signals in the top 100 regions were frequently centered on candidate genes related to brain function and behavior, including LHFPL3, CADM2, GRIK3, SH3GL2, MBP, PDE7B, NTAN1, and GLRA1. These regions contained significant enrichments in behavioral ontology categories. The 3rd top hit, CCRN4L, plays a major role in lipid metabolism, that is supported by additional metabolism related candidates revealed in our scan, including SCP2D1 and PDXC1. Comparing our method to an empirical outlier approach that does not directly account for demography, we found only modest overlaps between the two methods, with 60% of empirical outliers having no overlap with our demography-based outlier detection approach. Demography-aware approaches have lower-rates of false discovery. Our top candidates for selection, in addition to expanding the set of neurobehavioral candidate genes, include genes related to lipid metabolism, suggesting a dietary target of selection that was important during the period when proto-dogs hunted and fed alongside hunter-gatherers.

摘要

控制背景人口统计学效应对于准确识别近期经历正选择的基因座至关重要。迄今为止,在确定狗驯化过程中处于选择状态的基因座时,尚未明确考虑人口统计学的影响。为了研究驯化早期狗谱系上的正选择,我们检查了六个犬科动物基因组中的多态性模式,这些基因组先前被用于推断狗驯化的人口统计学模型。利用推断出的人口统计学模型,我们计算了错误发现率(FDR),并在低FDR水平下确定了349个与正选择一致的异常区域。前100个区域中的信号经常集中在与脑功能和行为相关的候选基因上,包括LHFPL3、CADM2、GRIK3、SH3GL2、MBP、PDE7B、NTAN1和GLRA1。这些区域在行为本体类别中存在显著富集。排名第三的命中基因CCRN4L在脂质代谢中起主要作用,我们的扫描中揭示的其他与代谢相关的候选基因,包括SCP2D1和PDXC1,也支持这一点。将我们的方法与一种不直接考虑人口统计学的经验性异常值方法进行比较,我们发现这两种方法之间只有适度的重叠,60%的经验性异常值与我们基于人口统计学的异常值检测方法没有重叠。考虑人口统计学的方法具有较低的错误发现率。我们选择的顶级候选基因,除了扩大神经行为候选基因集外,还包括与脂质代谢相关的基因,这表明在原始狗与狩猎采集者一起狩猎和觅食的时期,饮食选择目标很重要。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/32b2/4778760/704b6c7b4654/pgen.1005851.g001.jpg

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