Accuracy of Genomewide Selection for Different Traits with Constant Population Size, Heritability, and Number of Markers
Accuracy of Genomewide Selection for Different Traits with Constant Population Size, Heritability, and Number of Markers
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DOI：
10.3835/plantgenome2012.11.0030
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发表时间：
2013-03-01
期刊：
影响因子：
4.2
通讯作者：
Bernardo, Rex
中科院分区：
文献类型：
--
作者：
Combs, Emily;Bernardo, Rex
在全基因组选择中，预测性能和真实基因型值之间的预期相关性是训练群体大小（N）、基于条目平均值的遗传力（h（2））和性状潜在的有效染色体片段数（M-e）的函数。我们的目标是（i）确定不同性状的预测准确度如何响应N，h（2）和标记数（N-M）的变化，以及（ii）确定如果N，h（2）和N-M保持不变，预测准确度是否在性状之间相等。在玉米（Zea mays L.）的模拟群体和四个经验群体中，大麦（Hordeum vulgare L.），和小麦（Triticum aestivum L.），我们在表型数据中加入随机非遗传效应，使h（2）分别减少到0.50、0.30和0.20。正如预期的那样，增加N、h（2）和N M增加了预测精度。对于同一群体中的同一性状，N和h（2）的不同组合导致相同的Nh（2），预测精度是恒定的。然而，不同的性状，即使在N，h（2），和N-M是恒定的，在他们的预测精度不同。尽管N、h（2）和N-M为常数，但产量性状的预测精度低于其他性状。在设计培训人群时，需要关于不同特征的可预测性的经验证据和经验。
In genomewide selection, the expected correlation between predicted performance and true genotypic value is a function of the training population size (N), heritability on an entry-mean basis (h(2)), and effective number of chromosome segments underlying the trait (M-e). Our objectives were to (i) determine how the prediction accuracy of different traits responds to changes in N, h(2), and number of markers (N-M) and (ii) determine if prediction accuracy is equal across traits if N, h(2), and N-M are kept constant. In a simulated population and four empirical populations in maize (Zea mays L.), barley (Hordeum vulgare L.), and wheat (Triticum aestivum L.), we added random nongenetic effects to the phenotypic data to reduce h(2) to 0.50, 0.30 and 0.20. As expected, increasing N, h(2), and N M increased prediction accuracy. For the same trait within the same population, prediction accuracy was constant for different combinations of N and h(2) that led to the same Nh(2). Different traits, however, varied in their prediction accuracy even when N, h(2), and N-M were constant. Yield traits had lower prediction accuracy than other traits despite the constant N, h(2), and N-M. Empirical evidence and experience on the predictability of different traits are needed in designing training populations.