机构地区:[1]江西农业大学动物生物技术国家重点实验室培育基地,南昌330045
出 处:《中国农业科学》2014年第3期564-573,共10页Scientia Agricultura Sinica
基 金:国家"973"计划项目(2012CB722502);国家自然科学基金项目(31101697);江西省教育厅项目(GJJ12234)
摘 要:【目的】利用全基因组关联分析(GWAS)方法搜寻与苏太猪肉质性状相关的候选基因及分子标记。【方法】屠宰测定了150头苏太猪的背最长肌和半膜肌72 h pH值(包括72 h pH、45 min至72 h pH下降值)及72 h肉色性状(包括红度a,黄度b,亮度L和主观评分)。利用Illumina猪60 K SNP芯片,对这些个体进行基因型判定,用PLINK v1.07对获得的基因型数据进行质量控制,剔除检出率<99.7%、次等位基因频率(minor allele frequency,MAF)<0.05、偏离哈代温伯格(Hardy-Weinberg Equilibrium,HWE)P≤10-5的SNP标记和检出率<90%的个体,最终有150个个体和43 760个SNP用于GWAS研究。利用R语言环境下的GenABEL软件包中的广义线性混合模型,对每个SNP与性状作关联分析,采用Bonferroni方法确定关联显性阈值。群体层化效应的检测通过QQ-plot的结果展示,它通过比较无效假设关联显著性的分布与实际关联性分布的差异来展示可能的群体结构或者显著关联位点。【结果】1.背最长肌72 h pH和半膜肌72 h pH、背最长肌45 min至72 h pH下降值和半膜肌45 min至72 h pH下降值、背最长肌和半膜肌的72 h肌肉黄度、背最长肌和半膜肌的肉色主观评分与肌肉亮度L性状间均为高度相关,且均达到显著水平(P<0.05)。2.群体层化分析没有发现明显的整体系统偏差,也不存在明显群体层化效应。3.关联分析结果表明共有39个SNPs达到染色体显著水平,分布于基因组上的20个区域(≤10 Mb);其中,与pH显著关联的SNPs有17个,除了标记ASGA0082337没有定位在猪基因组序列上,其余16个SNPs分别位于3、4、10、14、X号染色体上;与肉色显著关联的SNPs有22个,它们分别位于1、3、7、10、12、14、15号染色体上;但在背最长肌的红度、亮度和肉色主观评分及半膜肌的亮度和肉色主观评分性状中未检测到显著SNPs。背最长肌和半膜肌的45 min至72 h pH下降值最强关联的SNP位点都为14号染色体上的M1G【Objective】 The objective of this study is to identify candidate genes and molecular markers associated with meat quality traits of Sutai pigs. 【Method】 Genome-wide association analysis was conducted by using Illumina 60K SNP Bead-chip genotypes of 150 Sutai pigs. Phenotypic data of each animal included postmortem 72 h muscle pH(pH 72 h), pH drop from postmortem 45 min to 72 h (pHdrop_45 min_72 h), 72 h Minolta a, b, L (ColorM_a72 h, ColorM_b72 h, ColorM_L72 h) and subjective color score (ColorScore_72 h) of longissimus dorsi (LM) and semimembranosus (SM) muscle. Quality control was carried out using PLINK v1.07. SNP markers were removed if they had genotype-missing rates 〉 0.03 or minor allele frequencies (MAF) 〈 0.05 or Hardy-Weinherg P≤10-5. Samples were removed on low (〈90%) call rate. After the quality control, all 150 samples passed the filter and a final set of 43 760 SNPs were selected for GWAS. The association analyses were conducted using GenABEL in the R software. SNPs were individually tested for association with all studied traits using a generalized linear mixed model and the genome-wide significance threshold was determined by the Bonferroni method. The influence of population stratification was assessed by examining the distribution of test statistics generated from the thousands of association tests and assessing their deviation from the null distribution in a quantile-quantile (Q-Q) plot. 【Result】 The relationship between LM_pH72 h and SM_pH72 h, between LM_pHdrop_45 min_72 h and SM_pHdrop_45 min_72 h, between LM_ColorM_b72 h and SM_ColorM_b72 h, between LM_ColorM_L72 h and LM_ColorScore_72 h and between SM_ColorM_L72 h and SM_ColorScore_72 h were significant (P 〈 0.05). There was no clear overall systematic bias in all studied traits and no very strong stratification existed. In total, 39 SNPs reached Bonferroni chromosome-wise significance and fell into 20 genomic regions of approximately 10 Mb or less. Among them, 17 SNPs significa
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