近代大多数畜禽品种都经历过强度较高的人工选择，品种历史悠久的安徽皖南黑猪也不例外，这种选择痕迹可以通过基因组选择信号检测出来。随着基因组技术的发展，重测序成本在不断下降，但对于畜禽基因组选择应用来说成本还是很高。一个可能的解决方案是采用低覆盖度的NGS重测序，构建低覆盖度NGS数据的统计分析处理技术，降低成本。基于此，本项目拟利用NGS技术对皖南黑猪群体进行低覆盖度的基因组重测序，获得低覆盖度的基因组SNPs数据，摸索低覆盖度SNPs数据集缺失基因型的填充方法，构建统计模型，检测皖南黑猪群体的全基因组选择信号，捕获皖南黑猪受选择影响的相关功能基因，优化基因组选择技术在动物遗传改良中的应用策略。同时，探索低覆盖度NGS数据高效的选择信号检测新方法。

A common feature of farm animal populations is that they are subject to a selection pressure which clearly exceeds the level of selective forces in natural selection, Anhui Wannan black pig is also no exception, which has a long breeding history. It is of interest to identify regions in the genome that show a measurable reaction to selection pressure. With the development of genome technology, the heavy cost of re-sequencing is in declining, but for genomic selection breeding application in livestock is still very high. One possible solution is to use next generation sequencing (NGS) of low coverage and set up for statistical analysis method for low coverage NGS data, reduce the costs. Based on this, the project aims to obtain low coverage genome SNPs information using NGS technology of low coverage in Wannan black pig population, gropes for the genotype imputation methods for low coverage SNPs data set, builds statistical model of detecting genomic selection signatures, trapping the affected function genes by selection in Wannan black pigs. In addition, novel approach regarding detecting selection signatures of low coverage NGS data will be proposed.