在非模型物种的低覆盖基因组序列中准确运行同卵性估计
Rebecca S Taylor1, Micheline Manseau1,2, Paul J Wilson2
1Landscape Science and Technology, Environment and Climate Change Canada, Ottawa, Ontario, Canada.
Molecular ecology resources
|December 3, 2025
概括
在野生动物中使用低覆盖率的全基因组进行同卵性 (ROH) 分析的运行现在更加准确. 针对鹿优化的ROHan软件,改善了用于保护工作的同胞繁殖检测.
科学领域:
- 保护基因组学 保护基因组学
- 人口遗传学 人口遗传学
- 生物信息学是一种生物信息学.
背景情况:
- 全基因组测序 (WGS) 对于分析非模型物种的同卵性 (ROH) 运行至关重要.
- 像Plink这样的现有工具在低测序深度 (<10×) 上低估了ROH,可能导致有缺陷的保护决策.
- 对于非模型物种和低覆盖率数据,需要优化ROH和异构结合性估计工具.
研究的目的:
- 为了评估ROHan软件的ROH和在鹿 (非模型物种) 中的异构结合性估计,使用低覆盖范围的WGS数据.
- 评估测序深度,rohmu参数和人口历史对ROH推断准确性的影响.
- 为优化野生动物的ROH推断提供建议,使用低覆盖的WGS.
主要方法:
- 利用来自22只大鹿的高覆盖率WGS数据,在不同的测序深度 (1-15×) 上测试ROHan性能.
- 研究了"rohmu"参数 (ROH内的耐受性异构性率) 和人口史对ROH和异构性估计的影响.
- 通过使用优化参数,重新分析了一个孤立的鹿种群的低覆盖范围WGS数据.
主要成果:
- 精确的ROH估计 (基因组和长度的百分比) 在测序深度低至3-5×时可以实现.
- "rohmu"参数和个体的人口历史显著影响了ROH推断和异构性估计.
- 低覆盖度测序高估了异构性,但优化的ROHan参数揭示了在一个孤立的群体中以前未被检测到的高近亲繁殖.
结论:
- 罗汉可以准确地推断ROH和非模型物种如鹿的近亲繁殖水平,即使使用低覆盖范围的WGS数据.
- 优化"rohmu"参数和仔细解释结果对于在野生动物保护中进行强大的ROH推断至关重要.
- 这项研究为利用低覆盖范围的WGS提供了一个框架,以有效地保护危物种的基因组学.
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