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Meta-analysis of results from quantitative trait loci mapping studies on pig chromosome 4

机译:对猪4号染色体定量性状位点作图研究结果的Meta分析

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Meta-analysis of results from multiple studies could lead to more precise quantitative trait loci (QTL) position estimates compared to the individual experiments. As the raw data from many different studies are not readily available, the use of results from published articles may be helpful. In this study, we performed a meta-analysis of QTL on chromosome 4 in pig, using data from 25 separate experiments. First, a meta-analysis was performed for individual traits: average daily gain and backfat thickness. Second, a meta-analysis was performed for the QTL of three traits affecting loin yield: loin eye area, carcass length and loin meat weight. Third, 78 QTL were selected from 20 traits that could be assigned to one of three broad categories: carcass, fatness or growth traits. For each analysis, the number of identified meta-QTL was smaller than the number of initial QTL. The reduction in the number of QTL ranged from 71% to 86% compared to the total number before the meta-analysis. In addition, the meta-analysis reduced the QTL confidence intervals by as much as 85% compared to individual QTL estimates. The reduction in the confidence interval was greater when a large number of independent QTL was included in the meta-analysis. Meta-QTL related to growth and fatness were found in the same region as the FAT1 region. Results indicate that the meta-analysis is an efficient strategy to estimate the number and refine the positions of QTL when QTL estimates are available from multiple populations and experiments. This strategy can be used to better target further studies such as the selection of candidate genes related to trait variation.
机译:与单个实验相比,对多项研究结果进行的荟萃分析可能会导致更精确的数量性状基因座(QTL)位置估计。由于来自许多不同研究的原始数据不易获得,因此使用已发表文章的结果可能会有所帮助。在这项研究中,我们使用来自25个独立实验的数据对猪4号染色体上的QTL进行了荟萃分析。首先,对个体特征进行荟萃分析:平均日增重和背脂厚度。其次,对影响腰肉产量的三个特征的QTL进行了荟萃分析:腰眼面积,car体长度和腰肉重量。第三,从20个性状中选择了78个QTL,可以将其分配为三大类之一:car体,脂肪或生长性状。对于每个分析,已识别的meta-QTL的数量小于初始QTL的数量。与荟萃分析前的总数相比,QTL的减少幅度为71%至86%。此外,与单独的QTL估计相比,荟萃分析将QTL置信区间减少了多达85%。当荟萃分析中包含大量独立的QTL时,置信区间的减小更大。在与FAT1区域相同的区域中发现了与生长和脂肪相关的Meta-QTL。结果表明,当可以从多个人群和实验中获得QTL估计值时,荟萃分析是一种有效的策略,可以估计QTL的数量并改善QTL的位置。该策略可用于更好地针对进一步的研究,例如与性状变异相关的候选基因的选择。

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