Department of Botany, University of Wyoming, Laramie, WY 82071, USA.
Mol Ecol Resour. 2012 Nov;12(6):1168-76. doi: 10.1111/1755-0998.12009.x. Epub 2012 Sep 14.
Introgression in admixed populations can be used to identify candidate loci that might underlie adaptation or reproductive isolation. The Bayesian genomic cline model provides a framework for quantifying variable introgression in admixed populations and identifying regions of the genome with extreme introgression that are potentially associated with variation in fitness. Here we describe the bgc software, which uses Markov chain Monte Carlo to estimate the joint posterior probability distribution of the parameters in the Bayesian genomic cline model and designate outlier loci. This software can be used with next-generation sequence data, accounts for uncertainty in genotypic state, and can incorporate information from linked loci on a genetic map. Output from the analysis is written to an HDF5 file for efficient storage and manipulation. This software is written in C++. The source code, software manual, compilation instructions and example data sets are available under the GNU Public License at http://sites.google.com/site/bgcsoftware/.
在混合群体中，渗入可以用来识别可能是适应或生殖隔离基础的候选基因座。贝叶斯基因组渐变模型为量化混合群体中可变渗入提供了一个框架，并确定了基因组中具有极端渗入的区域，这些区域可能与适应度的变化有关。在这里，我们描述了 bgc 软件，它使用马尔可夫链蒙特卡罗来估计贝叶斯基因组渐变模型参数的联合后验概率分布，并指定异常基因座。该软件可与下一代测序数据一起使用，考虑基因型状态的不确定性，并可将遗传图谱上连锁基因座的信息纳入其中。分析的输出写入到 HDF5 文件中，以便于高效存储和处理。该软件是用 C++编写的。源代码、软件手册、编译说明和示例数据集可根据 GNU 公共许可证在 http://sites.google.com/site/bgcsoftware/ 获得。
