The effects of training population design on genomic prediction accuracy in wheat
The effects of training population design on genomic prediction accuracy in wheat
复制标题
训练群体设计对小麦基因组预测准确性的影响
DOI：
10.1101/443267
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Edwards S
中科院分区：
文献类型：
--
作者：
Edwards S
基因组选择为提高植物育种计划的遗传增益或效率提供了几条途径。在不同种类的家畜中，有经验证据表明，通过使用基因组选择来针对育种者方程的不同方面，遗传增益率会增加。对基因组育种价值的准确预测对此至关重要，而训练集的设计反过来又是实现足够准确水平的核心。总而言之，少量近亲和大量远亲有望使预测具有更高的准确性。为了量化训练集的某些属性对作物基因组选择准确性的影响，我们进行了一项广泛的田间冬小麦试验。综上所述，本试验构建了44个F2：4双亲和三亲群体，其中2992个品系在4个田间地点种植，并测定了产量。对于每个品系，产生了25K分离的SNP标记的基因型数据。产量的总遗传力估计为0.65，家系内估计在0.10~0.85之间。使用两种不同的交叉验证方法，产量Blues的基因组预测精度为0.125-0.127，并且通常随着训练集大小的增加而增加。在训练和验证集中使用相关杂交通常会导致比使用不相关杂交更高的预测精度。这项研究的结果强调了训练小组设计与所产生的预测模型将应用于的遗传物质有关的重要性。
Genomic selection offers several routes for increasing the genetic gain or efficiency of plant breeding programmes. In various species of livestock, there is empirical evidence of increased rates of genetic gain from the use of genomic selection to target different aspects of the breeder’s equation. Accurate predictions of genomic breeding value are central to this, and the design of training sets is in turn central to achieving sufficient levels of accuracy. In summary, small numbers of close relatives and very large numbers of distant relatives are expected to enable predictions with higher accuracy. To quantify the effect of some of the properties of training sets on the accuracy of genomic selection in crops, we performed an extensive field-based winter wheat trial. In summary, this trial involved the construction of 44 F2:4bi- and tri-parental populations, from which 2992 lines were grown on four field locations and yield was measured. For each line, genotype data were generated for 25 K segregating SNP markers. The overall heritability of yield was estimated to 0.65, and estimates within individual families ranged between 0.10 and 0.85. Genomic prediction accuracies of yield BLUEs were 0.125–0.127 using two different cross-validation approaches and generally increased with training set size. Using related crosses in training and validation sets generally resulted in higher prediction accuracies than using unrelated crosses. The results of this study emphasise the importance of the training panel design in relation to the genetic material to which the resulting prediction model is to be applied.
登录
查看更多内容
影响因子：
3.5
作者：
Jenko, J.;Wiggans, G. R.;Woolliams, J. A.
通讯作者：
Woolliams, J. A.
基因分型的潜力。
影响因子：
56.9
作者：
J. Leon
通讯作者：
J. Leon
DOI：
10.5023/jappstat.25.73
发表时间：
1996
期刊：
Japanese Journal of Applied Statistics
影响因子：
--
作者：
Mihoko Minami
通讯作者：
Mihoko Minami
影响因子：
3
作者：
Cendron, Filippo;Perini, Francesco;Cassandro, Martino
通讯作者：
Cassandro, Martino
影响因子：
5
作者：
Mackay I;Ober E;Hickey J
通讯作者：
Hickey J