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通过低覆盖度测序,对鱼的关键经济特征进行具有成本效益的基因组预测

Hailiang Song¹, Tian Dong¹, Wei Wang¹

¹Fisheries Science Institute, Beijing Academy of Agriculture and Forestry Sciences & Beijing Key Laboratory of Fisheries Biotechnology, Beijing 100068, China; Key Laboratory of Sturgeon Genetics and Breeding, Ministry of Agriculture and Rural Affairs, Hangzhou 311799, China; National Innovation Center for Digital Seed Industry, Beijing 100097, China.

|June 5, 2024

概括

低覆盖全基因组测序 (LCS) 提供了一种可负担的养方法. 这种方法在基因型归因和基因组预测方面实现了高精度,使其对水产养殖遗传改进具有价值.

科学领域:

水产养殖的基因组学
动物繁殖动物繁殖
基因组选择基因组选择

背景情况:

鱼繁殖缺乏SNP芯片,并面临高的全基因组测序成本.
低覆盖度测序 (LCS) 是一个具有成本效益的替代方案.

研究的目的:

评估LCS对俄罗斯的基因型归算和基因组预测的效率.
在水产养殖应用中确定LCS的最佳参数.

主要方法:

643个俄罗斯鱼基因组的测序,覆盖面大约为13.68×.
使用BaseVar+STITCH进行基因型归算.
评估各种测序深度和SNP密度的预测准确性.
应用增量特征选择.

主要成果:

使用BaseVar+STITCH在2倍的测序深度和300个样本以上的测序深度实现了最高的基因型准确性.
0.5×测序深度与50KSNP显示了与全基因组测序可比的预测准确性.
增量特征选择显示了提高预测准确性的潜力.

结论:

与归算相结合的LCS是鱼遗传改进的成本效益高的策略.
这种方法可以增强水产养殖中经济重要特征的遗传收益.
低基因组系统 (LCS) 促进了基因组资源有限的物种的基因组选择.

关键词:

基因组预测基因组预测计入算法是指指计入算法.增量特征选择增量特征选择链接不平衡的剪裁低覆盖范围的全基因组测序.鱼是一种鱼.

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