298只萨宁奶山羊全基因组变异资源。

IF 6.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Kai Zhang, Jianqing Zhao, Shirong Mi, Jiqiang Liu, Jun Luo, Jianxin Liu, Hengbo Shi
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引用次数: 0

摘要

Saanen品种经常被用作杂交的终端品种,在全球乳制品行业中发挥着重要作用。然而,目前仍然缺乏关于Saanen奶山羊的基因组学信息。全基因组测序为鉴定与经济性状相关的遗传标记和发现新的候选基因提供了一种很有前途的方法。这可以有效地利用遗传资源,加快育种进程,提高萨宁奶山羊的泌乳性能。在这项研究中,我们展示了298只Saanen奶山羊的基因组。通过严格的测序和质量控制,平均测序深度为14.6X,高质量数据占92.3% (Q30 > 90%),平均作图率为99.9%,结果可靠。通过将我们的数据与Saanen奶山羊的参考基因组进行比较,我们确定了1459万个单核苷酸多态性(snp)和134万个插入-缺失(InDels)。该数据集极大地丰富了奶山羊的公共数据库,并为研究遗传多样性、改良品种和开发新品种提供了宝贵的资源。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Whole-genome variants resource of 298 Saanen dairy goats.

Whole-genome variants resource of 298 Saanen dairy goats.

Whole-genome variants resource of 298 Saanen dairy goats.

Whole-genome variants resource of 298 Saanen dairy goats.

The Saanen breeds are often used as terminal sires for hybridization and play an important role in the global dairy food industry. However, there is still a lack of genomics information on the Saanen dairy goats. Whole-genome sequencing offers a promising approach to identify genetic markers associated with economic traits and discover new candidate genes. This can effectively utilize genetic resources to accelerate breeding processes and improve lactation performance in Saanen dairy goats. In this study, we present the genomes of 298 Saanen dairy goats. Through rigorous sequencing and quality control, we achieved an average sequencing depth of 14.6X, with 92.3% of high-quality (Q30 > 90%) data and an average mapping ratio of 99.9%, indicating reliable results. By comparing our data to a reference genome of Saanen dairy goats, we identified14.59 million single nucleotide polymorphisms (SNPs) and 1.34 million insertions-deletions (InDels). This dataset significantly contributes to enriching public databases in dairy goats and provides valuable resources for studying genetic diversity, improving breeds, and developing new varieties.

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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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