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在肉鸡基因组预测中,标记预选择的高通量测序是单核苷酸多态性芯片的良好替代方法。

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

作者信息

Liu Tianfei, Luo Chenglong, Ma Jie, Wang Yan, Shu Dingming, Su Guosheng, Qu Hao

机构信息

State Key Laboratory of Livestock and Poultry Breeding, Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, China.

Guangdong Provincial Key Laboratory of Animal Breeding and Nutrition, Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, China.

出版信息

Front Genet. 2020 Feb 27;11:108. doi: 10.3389/fgene.2020.00108. eCollection 2020.

Abstract

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 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.

摘要

对于畜禽的基因组预测而言,选择一种基因标记基因分型平台至关重要。高通量测序能够产生更多的基因标记,但基因型质量低于单核苷酸多态性(SNP)芯片所获得的质量。本研究的目的是比较高通量测序和SNP芯片在肉鸡基因组预测方面的准确性。在本研究中,我们开发了一种新的SNP标记筛选方法,即预标记选择(PMS)方法,以确定一个SNP标记是否可用于基因组预测。我们还比较了一种基于全基因组关联研究(GWAS)结果进行标记预选的方法。使用这两种方法,我们分析了一个本地肉鸡群体12周龄时的体重(BW)和饲料转化率(FCR)。从该群体的F2代中总共选取了395只鸡,并使用10X特异性位点扩增片段测序(SLAF-seq)和Illumina鸡60K SNP芯片进行基因分型。采用基因组最佳线性无偏预测方法(GBLUP)预测基因组育种值。通过留一法交叉验证方法验证基因组预测的准确性。在不进行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,使用SNP芯片时,准确性分别为0.516和0.232。通过PMS方法进行SNP标记筛选时,使用SLAF-seq时,这两个性状的GEBV准确性分别为0.671和0.499,使用SNP芯片时,分别为0.605和0.422。我们的SNP标记筛选方法使预测准确性提高了0.089 - 0.250。通过GWAS方法进行SNP标记筛选时,这两个性状的基因组预测准确性也有所提高,但准确性的提升幅度小于所有性状使用PMS方法时的提升幅度。本研究结果表明,我们的PMS方法可以提高GEBV的准确性,并且在本地肉鸡群体中使用高通量测序时,通过增加基因组标记的数量可以获得更准确的基因组预测。由于其基因分型成本较低,高通量测序可能是本地肉鸡群体育种计划中基因组预测的SNP芯片的一个良好替代方案。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d185/7056902/e79460adce3b/fgene-11-00108-g001.jpg

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