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High-throughput and Cost-effective Chicken Genotyping Using Next-Generation Sequencing

机译:使用新一代测序的高通量和低成本鸡基因分型

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摘要

Chicken genotyping is becoming common practice in conventional animal breeding improvement.Despite the power of high-throughput methods for genotyping, their high cost limits large scale use inanimal breeding and selection. In the present paper we optimized the CornellGBS, an efficient and costeffectivegenotyping by sequence approach developed in plants, for its application in chickens. Herewe describe the successful genotyping of a large number of chickens (462) using CornellGBS approach.Genomic DNA was cleaved with the PstI enzyme, ligated to adapters with barcodes identifyingindividual animals, and then sequenced on Illumina platform. After filtering parameters were applied,134,528 SNPs were identified in our experimental population of chickens. Of these SNPs, 67,096 hada minimum taxon call rate of 90% and were considered ‘unique tags’. Interestingly, 20.7% of theseunique tags have not been previously reported in the dbSNP. Moreover, 92.6% of these SNPs wereconcordant with a previous Whole Chicken-genome re-sequencing dataset used for validation purposes.The application of CornellGBS in chickens showed high performance to infer SNPs, particularly inexonic regions and microchromosomes. This approach represents a cost-effective (~US$50/sample)and powerful alternative to current genotyping methods, which has the potential to improve wholegenomeselection (WGS), and genome-wide association studies (GWAS) in chicken production.
机译:鸡的基因分型已成为常规动物育种改良中的普遍做法。尽管高通量基因分型方法的功能强大,但它们的高成本限制了其在动物育种和选择中的大规模应用。在本文中,我们优化了CornellGBS,这是一种通过在植物中开发的序列方法进行的高效且具有成本效益的基因分型,用于其在鸡中的应用。在此我们描述了使用CornellGBS方法成功地对大量鸡(462)进行基因分型的方法。用PstI酶切割基因组DNA,将其连接到带有识别个体动物的条形码的衔接子上,然后在Illumina平台上测序。应用过滤参数后,在我们的实验鸡群中鉴定出134,528个SNP。在这些SNP中,有67,096个的最低分类单元检出率为90%,被视为“唯一标签”。有趣的是,这些独特标签中的20.7%以前尚未在dbSNP中报告。此外,这些SNP的92.6%与先前用于验证目的的全鸡基因组重测序数据集相符.CornellGBS在鸡中的应用显示出较高的推断SNP的性能,特别是在非遗传区域和微染色体上。这种方法代表了一种经济有效的方法(每个样本约50美元),是当前基因分型方法的有力替代方法,它有可能改善鸡生产中的全基因组选择(WGS)和全基因组关联研究(GWAS)。

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