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作 者:苏玉虹[1] 熊远著[1] 张勤[2] 蒋思文[1] 余雳[1] 雷明刚[1] 郑嵘[1] 邓昌彦[1]
机构地区:[1]华中农业大学农业部猪遗传育种重点实验室 [2]中国农业大学动物科技学院,北京100094
出 处:《Acta Genetica Sinica》2002年第7期607-611,共5页
基 金:973国家重大基础研究项目 (G2 0 0 0 0 1610 5 );湖北省自然科学基金 (2 0 0 0J10 6)资助~~
摘 要:以大白猪和梅山猪为父母本建立F2 资源家系 ,在 2 0 0 0年 ,随机选留 6 6头F2 代个体 ,获得出生重、6 0日龄体重、出生至 6 0日龄平均日增重及 6 0日龄至屠宰前平均日增重的表型数据。结合 4 8个微卫星标记构建的猪 1、2、3、4、6和 7号染色体遗传连锁图谱 ,用线性模型最小二乘法对各数量性状进行QTL区间作图 ,利用置换法(permutation)确定显著性阈值。研究发现 ,猪 4号染色体上有一个染色体水平极显著 (P <0 0 1)的QTL影响 6 0日龄至屠宰前平均日增重 ,并达到基因组显著水平 (P <0 0 5 )。在染色体水平 ,出生至 6 0日龄平均日增重QTL位于 2号染色体 ,6 0日龄体重QTL位于 1号染色体。 6号染色体的出生至 6The development of molecular biology techniques and the application of these techniques to farm animals have progressed rapidly and have opened new vistas for investigators wishing to identify genes that control quantitative traits. Now that a comprehensive map has been developed for the porcine genome, genomic scans to detect quantitative trait loci (QTL) can begin. In order to locate the genetic regions in the swine genome that are responsible for economically important traits, a resource population was developed by intercross with three Large White and seven Meishan pigs. In subsequent generations, 66 F 2 offsprings in 2000 were recorded for four growth traits including birth weight (BWT), body weight at 60 day (WT60), average daily gain from birth to 60 day (ADG1) and average daily gain from 60 day to the end of test (ADG2), and genotyped for 48 microsatellite markers spanning six chromosomes. Association analyses were performed using interval mapping by regression under an outbred line cross model on chromosomes 1,2,3,4,6 and 7. The F threshold values were determined by permutation. A total of 12 QTL were detected at suggestive level for the four traits evaluated in this study. Of the 12 suggestive QTL, 3 and 1 QTL were significant at the chromosome wise and genome wise levels. There was a QTL for ADG2 at genome wide level on chromosome 4 explained additive variance 2.19%. A chromosome wide QTL affecting ADG1 and WT60 were detected on chromosomes 2 and 1 explained additive variance 0.01% and 26.01%, respectively.
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