...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Molecular biology and evolution >Genome Scans for Detecting Footprints of Local Adaptation Using a Bayesian Factor Model
【24h】

Genome Scans for Detecting Footprints of Local Adaptation Using a Bayesian Factor Model

机译：基因组扫描，使用贝叶斯因子模型检测局部适应的足迹

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

There is a considerable impetus in population genomics to pinpoint loci involved in local adaptation. A powerful approach to find genomic regions subject to local adaptation is to genotype numerous molecular markers and look for outlier loci. One of the most common approaches for selection scans is based on statistics that measure population differentiation such as F_(ST). However, there are important caveats with approaches related to F_(ST) because they require grouping individuals into populationsand they additionally assume a particular model of population structure. Here, we implement a more flexible individual-based approach based on Bayesian factor models. Factor models capture population structure with latent variables called factors, whichcan describe clustering of individuals into populations or isolation-by-distance patterns. Using hierarchical Bayesian modeling, we both infer population structure and identify outlier loci that are candidates for local adaptation. In order to identifyoutlier loci, the hierarchical factor model searches for loci that are atypically related to population structure as measured by the latent factors. In a model of population divergence, we show that it can achieve a 2-fold or more reduction of false discovery rate compared with the software BayeScan or with an F_(ST) approach. We show that our software can handle large data sets by analyzing the single nucleotide polymorphisms of the Human Genome Diversity Project. The Bayesian factor model is implemented in the open-source PCAdapt software.
机译：群体基因组学有很大的动力来查明参与局部适应的基因座。查找受局部适应影响的基因组区域的有效方法是对众多分子标记进行基因分型，并寻找异常基因座。选择扫描的最常见方法之一是基于测量种群差异的统计数据，例如F_（ST）。但是，与F_（ST）有关的方法存在一些重要警告，因为它们需要将个体分组到群体中，并且它们还假定了特定的群体结构模型。在这里，我们基于贝叶斯因子模型实现了一种更加灵活的基于个体的方法。因子模型使用称为因子的潜在变量捕获种群结构，该变量可以描述将个体聚集成种群或按距离隔离的模式。使用分层贝叶斯建模，我们既可以推断种群结构，也可以确定离群位点，这些位点适合进行局部适应。为了识别异常基因座，分层因素模型搜索与潜在因素测得的非典型与种群结构有关的基因座。在人口差异模型中，我们证明与软件BayeScan或F_（ST）方法相比，它可以减少2倍或更多的错误发现率。我们证明了我们的软件可以通过分析人类基因组多样性计划的单核苷酸多态性来处理大型数据集。贝叶斯因子模型在开源PCAdapt软件中实现。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号