中文摘要：

【目的】通过全基因组关联分析定位和筛选相关基因,寻找与奶牛乳房炎抗性相关的分子标记,以进行下一步的标记辅助选择。【方法】对2 093头北京地区中国荷斯坦牛SCC进行对数转化,依据LASCS=log2∑SCC/n和SCS-SD=log2∑（scc-u）~2/n-1将测定日记录SCC转化为服从正态分布的统计量LASCS和SCS-SD。同时将LASCS和SCS-SD进行半个标准差（half of standard deviation,0.5 SD）和一个标准差（one standard deviation,1 SD）的划分,将牛只划分为乳房炎易感牛（Case）及抗性牛（Control）。将54 001个SNPs进行质控,剔除不符合条件的SNPs,剔除的条件是：SNPs的call rate〈90%,严重偏离哈迪-温伯格平衡（HWE）（P〈10E-6）和最小等位基因频率（MAF）〈0.03。然后通过ROADTRIPS软件（版本1.2）的3种检验：RM检验、RCHI检验和RW检验对LASCS和SCS-SD进行Case-control方法的全基因组关联分析。通过Bonferroni方法对关联分析结果进行校正,并针对牛的每条染色体分别制定各条染色体的显著水平,以0.05分别除每条染色体上的SNP数目,作为每条染色体的显著性水平。同时,将所有个体的LASCS和SCS-SD作为连续性状通过线性混合模型进行全基因组关联分析,将结果进行比较,以确定显著SNPs的位置。【结果】通过0.5 SD/1 SD的标准将群体划分后,分别有1371/708个个体用于LASCS性状的关联分析,和1385/716个个体用于SCS-SD性状的关联分析。通过质控将不符合的SNPs剔除之后,共有43781/43671（43817/43704）个SNPs分别可用于LASCS（SCS-SD）的0.5 SD/1 SD的关联分析。对LASCS和SCS-SD进行全基因组关联分析,经染色体水平上的Bonferroni校正（P〈0.05）,共发现5个SNPs达到显著水平,其中3个SNPs定位到X染色体上,其它2个SNPs分别定位到7和28号染色体上。通过对基于0.5 SD的SCS-SD的乳房炎抗性进行全基因组关联分析发现一个全基因组水平显著的SNP（Hapmap48573-BTA-104531,P=1.11E-06

英文摘要：

【Objective】In order to conduct maker assisted selection, the aim of this study is to find molecular markers related to mastitis resistance by identifying and screening relevant genes through genome-wide association study.【Method】A total of 2093 Chinese Holsteins SCC in Beijing region were log-transformed to LASCS and SCS-SD which were following normal distribution according to formula LASCS=log2∑SCC/n andSCS-SD=log2∑（scc-u）~2/n-1. LASCS and SCS-SD were divided into two groups including mastitis susceptive cows（Case） and resistant cows（Control） based on half of standard deviation（0.5 SD） and one standard deviation（1 SD） of LASCS and SCS-SD. The unqualified SNPs were deleted after quality control for 54 001 SNPs by the criteria： call rate 90 %, SNPs deviated extremely from Hardy-Weinberg equilibrium（P 10E-6） and minor allele frequency？ 0.03. Case-control association testing for LASCS and SCS-SD were further performed by ROASTRIPS software（version 1.2） which contains 3 tests： RM test, RCHI test and RW test. Bonferroni multiple testing was adopted to adjust the results of association analysis on each chromosome level. The number of SNPs on each chromosome divided by 0.05 was considered as the significant chromosome level. At the same time, LASCS and SCS-SD of all the individuals were considered as the continual traits for association analysis by using linear mixed model method. The results were further confirmed after comparing to case-control method.【Result】For the divided population by 0.5 SD and 1 SD, a total of 1371 and 708 individuals were used for LASCS association analysis as well as 1385 and 716 individuals used for SCS-SD association analysis, respectively. After quality control by deleting unqualified SNPs, a total of 43781/43671（43817/43704） SNPs were available for 0.5 SD/1 SD of LASCS（SCS-SD）, respectively. Through Bonferroni correction at chromosome level（P 0.05） after association analysis, 5 SNPs were detected significantly including 3 SNPs l