在有致病变异的情况下,针对多步和单步基因组BLUP的替代SNP权重
Bruna Folegatti Santana1, Molly Riser1, El Hamidi A Hay2
1Animal Science Department, University of Connecticut, Storrs, Connecticut, USA.
概括
纳入致病变体 (QTN) 显著提高了乳制品遗传选择的准确性. 非线性权衡方法通常优于其他方法,尽管结果取决于特定的参数和特征复杂性.
科学领域:
- 动物遗传学动物遗传学
- 量化遗传学 量化遗传学
- 基因组预测 基因组预测
背景情况:
- 乳牛的遗传选择准确性对于育种计划至关重要.
- 新技术,包括序列数据和标记物的权重策略,可以提高预测准确性.
- 计算标记重量的最佳方法,特别是对致病变异的最佳方法,需要进一步调查.
研究的目的:
- 评估多步和单步基因组最佳线性无偏预测 (GBLUP) 方法的准确性.
- 为了比较使用单核酸多态 (SNP) 标记,因果定量特征核酸 (QTN) 或其组合的预测准确度.
- 评估不同SNP权重策略 (未加权,二次,非线性) 对预测准确性的影响.
主要方法:
- 单个特征的模拟数据,遗传率为0.3,有效种群大小约为200个.
- 使用了大量的血统 (44万只动物),大约有16800个基因型个体和49974个SNP标记.
- 模拟了100,1000和2000个因果QTN,并应用了GBLUP和单步GBLUP (ssGBLUP),使用未加权,二次和非线性权重方案.
主要成果:
- 将QTN纳入基因组小组导致预测准确度大幅提高.
- 与二次加权和未加权方法相比,非线性加权 (非线性A) 显示出更高的性能.
- 非线性A的有效性取决于方程参数,而未加权的方法更适合于不太多基因的特征.
结论:
- 整合致病变异可显著提高乳牛基因组预测的准确性.
- 非线性权重策略具有优势,但需要仔细选择参数,而未加权的方法对于更简单的遗传架构是可行的.
- 通过分析预测准确度的变化,SNP权重方法可以提供对复杂特征遗传结构的见解.
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