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Genomic prediction using DArT-Seq technology for yellowtail kingfish Seriola lalandi

机译:利用DArT-Seq技术预测黄尾king Seriola lalandi的基因组

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摘要

BackgroundGenomic prediction using Diversity Arrays Technology (DArT) genotype by sequencing platform has not been reported in yellowtail kingfish (Seriola lalandi). The principal aim of this study was to address this knowledge gap and to assess predictive ability of genomic Best Linear Unbiased Prediction (gBLUP) for traits of commercial importance in a yellowtail kingfish population comprising 752 individuals that had DNA sequence and phenotypic records for growth traits (body weight, fork length and condition index). The gBLUP method was used due to its computational efficiency and it showed similar predictive performance to other approaches, especially for traits whose variation is of polygenic nature, such as body traits analysed in this study. The accuracy or predictive ability of the gBLUP model was estimated for three growth traits: body weight, folk length and condition index.
机译:背景技术尚未在黄尾金枪鱼(Seriola lalandi)中报道通过测序平台使用多样性阵列技术(DArT)基因型进行基因组预测。这项研究的主要目的是解决这一知识差距,并评估基因组最佳线性无偏预测(gBLUP)对具有商业重要性的黄尾翠鸟种群的预测能力,该种群包括752个具有生长性状的DNA序列和表型记录的个体(体重,前叉长度和状况指标)。使用gBLUP方法是由于其计算效率高,它显示出与其他方法相似的预测性能,尤其是对于变异具有多基因性状的特征,例如本研究中分析的身体特征。估计了gBLUP模型的三个生长性状的准确性或预测能力:体重,民间体长和状况指数。

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