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使用纳米孔测序进行基因组预测的推算策略

H J Lamb¹, L T Nguyen², J P Copley²

¹Centre for Animal Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St. Lucia, QLD, 4067, Australia. harrison.lamb@uq.edu.au.

|December 9, 2023

概括

牛的基因组预测使用低覆盖率的牛津纳米孔技术 (ONT) 测序数据是准确的. 这种通过测序的快速基因型定型方法提高了归算准确度,并缩短了预测时间.

科学领域:

基因组学就是基因组学.
动物育种动物育种
生物信息学是一种生物信息学.

背景情况:

基因组预测利用SNP基因型来预测各种物种中复杂的特征.
基因型测序 (GBS) 与基因型归算是基因组预测的一个不断增长的方法.
牛津纳米孔技术 (ONT) MinION提供便携式和快速的GBS.

研究的目的:

评估基因组预测的速度和准确性,使用牛的低覆盖率ONT序列数据.
评估四种归算方法以及SNP参考小组大小对归算性能的影响.

主要方法:

用了62只肉牛的SNP阵列和ONT序列数据进行基因组估计繁殖价值 (GEBV) 计算.
采用了四种归算方法,包括 QUILT 包.
研究了不同的测序覆盖范围 (低至0.1×) 和SNP参考面板大小 (高达4800万SNP).

主要成果:

当从序列数据中使用全基因组侧边SNP进行归算时,GEBV的准确性显著增加.
在ONT和低密度SNP阵列GEBV之间的相关性超过0.91,在0.1×覆盖率达到0.97.
通过减少侧面序列SNP来减少推算时间;0.1×覆盖GBS比低密度SNP阵列推算更准确.

结论:

准确的基因组预测可以通过ONT序列数据实现,覆盖率低至0.1×.
输入可以快速执行,每个样本的时间最短为10分钟.
低覆盖率的GBS (0.1×) 可以超过牛中低密度SNP阵列的归算.

关键词:

基因组预测基因组预测基因型的归算基因型的归算通过测序进行基因型鉴定.牛津纳米孔技术测序测序扫描整个基因组的测序.

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