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High-Throughput Sequencing With the Preselection of Markers Is a Good Alternative to SNP Chips for Genomic Prediction in Broilers

机译:预选标记的高通量测序是对肉鸡基因组预测的SNP芯片的替代方案

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The choice of a genetic marker genotyping platform is important for genomic prediction in livestock and poultry. High-throughput sequencing can produce more genetic markers, but the genotype quality is lower than that obtained with single nucleotide polymorphism (SNP) chips. The aim of this study was to compare the accuracy of genomic prediction between high-throughput sequencing and SNP chips in broilers. In this study, we developed a new SNP marker screening method, the pre-marker-selection (PMS) method, to determine whether an SNP marker can be used for genomic prediction. We also compared a method which preselection marker based results from genome-wide association studies (GWAS). With the two methods, we analysed body weight at the12 ~(th) week (BW) and feed conversion ratio (FCR) in a local broiler population. A total of 395 birds were selected from the F2 generation of the population, and 10X specific-locus amplified fragment sequencing (SLAF-seq) and the Illumina Chicken 60K SNP Beadchip were used for genotyping. The genomic best linear unbiased prediction method (GBLUP) was used to predict the genomic breeding values. The accuracy of genomic prediction was validated by the leave-one-out cross-validation method. Without SNP marker screening, the accuracies of the genomic estimated breeding value (GEBV) of BW and FCR were 0.509 and 0.249, respectively, when using SLAF-seq, and the accuracies were 0.516 and 0.232, respectively, when using the SNP chip. With SNP marker screening by the PMS method, the accuracies of GEBV of the two traits were 0.671 and 0.499, respectively, when using SLAF-seq, and 0.605 and 0.422, respectively, when using the SNP chip. Our SNP marker screening method led to an increase of prediction accuracy by 0.089–0.250. With SNP marker screening by the GWAS method, the accuracies of genomic prediction for the two traits were also improved, but the gains of accuracy were less than the gains with PMS method for all traits. The results from this study indicate that our PMS method can improve the accuracy of GEBV, and that more accurate genomic prediction can be obtained from an increased number of genomic markers when using high-throughput sequencing in local broiler populations. Due to its lower genotyping cost, high-throughput sequencing could be a good alternative to SNP chips for genomic prediction in breeding programmes of local broiler populations.
机译:遗传标记基因分型平台的选择对于畜禽和家禽的基因组预测是重要的。高通量测序可以产生更多的遗传标记,但基因型质量低于单核苷酸多态性(SNP)芯片获得的基因型质量。本研究的目的是比较肉鸡中高通量测序和SNP芯片之间的基因组预测的准确性。在这项研究中,我们开发了一种新的SNP标记筛选方法,预标记 - 选择(PMS)方法,以确定SNP标记是否可用于基因组预测。我们还比较了一种基于基因组关联研究(GWA)的基于标记的结果的方法。用这两种方法,在局部肉鸡群体中分析了12〜(β)周(BW)和饲料转化率(FCR)的体重。从群体的F2生成中选择总共395只鸟类,并且10x特异性基因座扩增的片段测序(SLAF-SEQ)和Illumina Chicket 60k SnP珠芯片用于基因分型。基因组最佳线性无偏的预测方法(GBLUP)用于预测基因组育种值。通过休养次级交叉验证方法验证了基因组预测的准确性。在没有SNP标记筛选的情况下,使用SLAF-SEQ时,BW和FCR的基因组估计育种值(GEBV)的准确性分别为0.509和0.249,并且在使用SNP芯片时,精度分别为0.516和0.232。通过PMS方法通过SNP标记筛选,当使用SNP芯片时,分别使用SLAF-SEQ和0.605和0.422分别为0.671和0.499的GeBV的精度分别为0.671和0.499。我们的SNP标记筛选方法导致预测精度的增加0.089-0.250。通过GWAS方法筛选SNP标记筛选,两个特征的基因组预测的准确性也得到了改善,但精度的收益小于所有特征的PMS方法的收益。本研究的结果表明,我们的PMS方法可以提高GeBV的准确性,并且当在局部肉鸡群中使用高通量测序时,可以从增加的基因组标记数量获得更准确的基因组预测。由于其较低的基因分型成本,高通量测序可能是对当地肉鸡种群育种计划中的基因组预测的SNP芯片的良好替代品。

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