一个计算框架,用于改进从5,061只绵羊中识别遗传变异的测序数据
Shangqian Xie1, Karissa Isaacs2, Gabrielle Becker1
1Department of Animal, Veterinary & Food Sciences, University of Idaho, Moscow, ID, USA.
Journal of animal science and biotechnology
|October 1, 2023
概括
一个新的计算框架通过优化多个样本的变异识别来增强对人口规模基因类型的联合调用. 这种方法提高了准确性,并识别了对动物繁殖重要的罕见遗传变异,而不需要额外的成本.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 人口遗传学 人口遗传学
背景情况:
- 全基因组学提供了全面的遗传变异特征.
- 联合调用结合了样本中的变异,但对人口规模的基因型定型的改进有限.
- 优化相互支持信息对于增强变种识别至关重要.
研究的目的:
- 开发一个计算框架,用于共同调用基因变异在人口规模的基因造型.
- 通过结合序列错误和优化相互支持信息来提高变种识别的准确性.
- 识别与绵羊经济重要特征相关的低频率和罕见遗传变异.
主要方法:
- 开发了一个四步计算框架,用于联合调用遗传变异.
- 使用 Poisson 模型对 GATK 和 Freebayes 算法的内置序列错误概率.
- 使用多个样本和算法的变体构建了一个原始的高可信度识别 (rHID) 数据库.
- 实施虚假发现率 (FDR) 控制和对变体的重新检查,以拯救潜在的真实阳性.
主要成果:
- 与原始变体相比,SNP和Indels的一致性明显提高了12%至32%.
- 在个体绵羊中成功识别了低频变异,这些变异与乳头数,疹病理,繁殖,外套颜色和虫病毒易感性等特征有关.
- 该框架准确地从5,061个绵羊样本中确定了变异.
结论:
- 计算策略有效地减少了假阳性,并增强了遗传变异识别.
- 这种方法改善了对动物育种应用至关重要的罕见变异的识别.
- 不需要额外的样本或测序数据,使该策略具有成本效益.
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