使用低覆盖范围的全基因组测序来对结构和单核酸变体进行高性能归算
Manu Kumar Gundappa1,2, Diego Robledo3, Alastair Hamilton4
1Animal Breeding and Genomics, Wageningen University & Research, P.O. Box 338, 6700 AH, Wageningen, The Netherlands. manu.gundappa@wur.nl.
Genetics, selection, evolution : GSE
|March 29, 2025
概括
低覆盖范围的全基因组测序 (WGS) 与基因型归算相结合,有效地将大西洋鱼的结构变异 (SV) 基因型化. 这种具有成本效益的方法通过准确地捕获单核酸变体 (SNVs) 以及单核酸变体 (SNVs) 的SVs来增强基因组研究.
科学领域:
- 人口基因组学是人口的基因组学.
- 水产养殖遗传学 水产养殖遗传学
- 生物信息学是一种生物信息学.
背景情况:
- 全基因组测序 (WGS) 提供了优势,但并没有取代单核酸变异 (SNV) 的向基因型定型.
- 结构变异 (SV) 具有显著的特征效应,但很难准确地定型基因.
- 具有基因型归因的低覆盖WGS是全基因组变异覆盖的成本有效策略,但其对SVs的应用尚未得到充分探索.
研究的目的:
- 通过在大西洋鱼中使用低覆盖范围的WGS数据,调查联合SNV和SV归算的有效性.
- 为了评估在各种WGS深度 (1x4x) 中对参考面板内部和外部样本的归算性能.
主要方法:
- 使用了365个野生大西洋鱼的参考小组,具有高可靠性SNV和SV基因型.
- 生成了20个商业人口样本的15倍WGS数据,这些样本与参考小组外部.
- 采用GLIMPSE归算方法,评估WGS深度1x,2x,3x和4x的性能.
主要成果:
- 在所有测试的WGS深度中,即使在参考面板之外的样本中,SNVs也被以高精度和回忆计算.
- 在将SV基因型概率 (GLs) 与SNV链接不平衡 (LD) 结合起来时,SV归算准确性得到了改善,在3-4倍深度下表现最佳.
- 综合策略捕获了84%的参考面板删除,在1x深度准确度为87%. SV长度影响了归算性能,较长的SV从SV GL中获益最多.
结论:
- 使用低覆盖率WGS的参考小组归算显示了对SNV和SV两种基因型的显著前景.
- 这种方法提供了新的途径,通过结合 SV 数据来增强全基因组关联研究的解决方案.
- 这些发现支持低覆盖率的WGS的成本效益和准确性,用于在水产养殖中进行全面的基因组变异分析.
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