西门塔尔牛部分生长性状全基因组低密度芯片筛选

发布时间：2018-01-22 05:55

  本文关键词： 西门塔尔牛 低密度标记 基因组预测 交叉验证　出处：《吉林农业大学》2015年硕士论文　论文类型：学位论文

【摘要】：基因组选择作为动物遗传育种领域的研究热点,在动物育种工作中得到了广泛的应用,并对肉牛育种工作产生了巨大的推动作用。但考虑到成本问题,利用高密度SNP芯片进行基因组选择在实际育种中并不适用。因此,研究者提出采用低密度芯片进行基因组选择,以降低育种成本。本研究以1,059头出生于2008至2012年的西门塔尔牛为研究群体,利用Illumina770k SNP芯片,针对宰前活重(Body weight,BW)、胴体重(Carcass weight,CW)和育肥期日增重(Average daily gain,ADG)三个性状,开展了低密度芯片基因组选择的研究。研究中利用均匀抽取、基于BayesB所得效应值大小和基于GWAS所得P值大小三种不同筛选方式,分别筛选出三种不同类别的低密度芯片(均匀抽取的ELD芯片、根据效应值大小筛选的SLD芯片和根据P值大小筛选的PLD芯片),进行低密度芯片的基因组预测研究,并采用交叉验证方法评价预测准确性。结果表明,随着标记数目的增多,三种类别的低密度芯片基因组预测准确性均呈现逐渐增加趋势。且基于BayesB估计效应值大小值筛选的SLD低密度芯片效果要好于另两种芯片。当SNP标记数目达到10,000时,在BayesB方法下,SLD低密度芯片三个性状的基因组预测准确性分别达到了宰前活重0.22±0.01,胴体重0.21±0.02和平均日增重0.15±0.01。在不同性状中,三种低密度芯片所表现出的预测能力不同,宰前活重和育肥期平均日增重两个性状中,SLD芯片的预测准确性,除GBLUP方法下较低外,其他均高于另两种芯片。这就说明,不同类型低密度芯片基因组预测能力与目标性状的遗传结构有关,因此针对不同应用情况仍需进行详细的育种学分析。本研究以西门塔尔牛为群体,针对宰前活重、胴体重和育肥期增重三个性状,设计了不同类型低密度芯片,并对不同低密度标记基因组选择进行了系统的研究,探讨了相关问题,为制作准确度高、使用方便的低密度芯片和肉牛低密度芯片基因组选择的实施提供了依据。
[Abstract]:As a research hotspot in animal genetics and breeding, genome selection has been widely used in animal breeding, and has played an important role in beef cattle breeding. Using high-density SNP microarray for genome selection is not suitable in actual breeding. Therefore, the researchers proposed to use low-density microarray to select genome to reduce the breeding cost. A total of 0 59 Simmental cattle, born between 2008 and 2012, were used to study body weight using Illumina770k SNP chip. BWN, Carcass weight (CW) and average daily gain (ADG) in fattening stage. The genome selection of low density microarray was studied. There were three different screening methods: uniform extraction, effect value based on BayesB and P value based on GWAS. Three different types of low-density chips were selected (evenly extracted ELD chips, SLD chips selected according to the size of the effect value and PLD chips screened according to the P value). The genome prediction of low density microarray was studied and the accuracy of prediction was evaluated by cross validation. The results showed that the number of markers increased with the increase of the number of markers. The accuracy of genome prediction of the three kinds of low density chips showed an increasing trend, and the SLD low density chip based on the size of the estimated effect value of BayesB was better than the other two chips. When SN was used, the accuracy of the low density chip was better than that of the other two kinds of chips. The number of P markers was 10. At #number0#, the accuracy of genome prediction for the three traits of low density microarray was 0.22 卤0.01 under BayesB. The carcass weight was 0.21 卤0.02 and the average daily gain was 0.15 卤0.01. Among different traits, the three low-density microarrays showed different predictive abilities. The prediction accuracy of SLD-chip was higher than that of the other two microarrays except for the lower one under GBLUP method in the two traits of live weight before slaughter and average daily gain during fattening period. Different types of low-density microarray genome prediction ability is related to the genetic structure of target traits, so it is still necessary to carry out detailed breeding analysis for different applications. In this study, Simmental cattle were selected as the population. Different types of low density microarray were designed for three traits, I. E. live weight, carcass weight and weight gain during fattening period, and the selection of different low density marker genomes was studied systematically, and the related problems were discussed. It provides a basis for the implementation of genome selection of low density chips and beef cattle low density chips with high accuracy and convenient use.
【学位授予单位】：吉林农业大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：S823
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