绒肉兼用山羊基因组选择方法的模拟研究

中国山羊的主要产区是内蒙古，凭借饲养量大，产绒量高，绒毛品质一流和肉质鲜美等优势，在国际上享有盛誉，是中国对外贸易的主要部分, 具有一定的研究价值。传统育种方法使绒山羊产绒量和体重上获得较大的遗传进展，然而纤维直径呈现变粗的趋势。随着测序技术的发展，希望通过基因组选择最大化绒山羊产业的经济效益。. 以早期内蒙古阿尔巴斯白绒山羊生产性能的传统遗传评估结果为基础，选择低等和中等偏高遗传两个性状。根据NCBI公布的山羊基因组信息，模拟不同的标记芯片密度，设置相关的基因组参数和世代传递过程，获得历史群、基础群以及后代群体的表型和基因型数据，然后从基础群后代中随机抽取群体规模大小不等的参考群体和验证群体，利用BLUP和Bayes 系列方法，进行山羊基因组育种值估计。最后，通过基因组育种值准确性的评估，揭示影响山羊基因组选择的因素,为后期利用真实数据进行山羊基因组选择提供理论基础。

Inner Mongolia is the main producing area of Chinese goats. The goats with multiple purposes in China were well known by all over the world for the advantage of large quantity, high cashmere yield, superior fiber quality and delicious meat. The products from goats are main parts of foreign trade in China. Considering the excellent characteristic, it is worth of doing some works on the Chinese goats. The genetic gains of cashmere yield and body weight in Inner Mongolia Cashmere Goats (IMCGs) was observed by traditional breeding plan in many years, however, the fiber diameter had a decreasing trend in recent years. With the development of sequencing technology, it is feasible that genomic selection of goats is performed to maximize the economic benefits of Cashmere goat industry. .Based on the results of genetic evaluation in products traits of IMCGs, the two traits with low and medium-high heritability were selected to make the simulation study. The different marker densities were simulated in the light of goats genomic sequence information. The phenotype and genotype data of history, founder and offspring populations were obtained by setting the genomic parameters and process of generation transmission. Then, the reference and validation population with different sizes were selected randomly from the offspring of founders and used to obtain genomic estimated breeding value through BLUP and Bayes methods. Lastly, the accuracy evaluation of genomic estimated breeding value was used to reveal the factors that influence on genomic selection of goats. It will provide theoretical basis for executing genomic selection by using real data of production traits in goats in future.