Foroutaifar Saheb
Department of Animal Science, College of Agriculture and Natural Resources, Razi University, Kermanshah, PO Box: 6715685418, Iran.
Stat Appl Genet Mol Biol. 2020 Aug 10;19(3):/j/sagmb.2020.19.issue-3/sagmb-2019-0007/sagmb-2019-0007.xml. doi: 10.1515/sagmb-2019-0007.
The main objectives of this study were to compare the prediction accuracy of different Bayesian methods for traits with a wide range of genetic architecture using simulation and real data and to assess the sensitivity of these methods to the violation of their assumptions. For the simulation study, different scenarios were implemented based on two traits with low or high heritability and different numbers of QTL and the distribution of their effects. For real data analysis, a German Holstein dataset for milk fat percentage, milk yield, and somatic cell score was used. The simulation results showed that, with the exception of the Bayes R, the other methods were sensitive to changes in the number of QTLs and distribution of QTL effects. Having a distribution of QTL effects, similar to what different Bayesian methods assume for estimating marker effects, did not improve their prediction accuracy. The Bayes B method gave higher or equal accuracy rather than the rest. The real data analysis showed that similar to scenarios with a large number of QTLs in the simulation, there was no difference between the accuracies of the different methods for any of the traits.
本研究的主要目的是使用模拟数据和真实数据，比较不同贝叶斯方法对具有广泛遗传结构的性状的预测准确性，并评估这些方法对其假设违背情况的敏感性。对于模拟研究，基于两个具有低或高遗传力以及不同数量QTL及其效应分布的性状实施了不同的情景。对于真实数据分析，使用了一个关于乳脂率、产奶量和体细胞评分的德国荷斯坦数据集。模拟结果表明，除了贝叶斯R方法外，其他方法对QTL数量和QTL效应分布的变化敏感。具有与不同贝叶斯方法估计标记效应时所假设的类似的QTL效应分布，并没有提高它们的预测准确性。贝叶斯B方法给出的准确性高于或等同于其他方法。真实数据分析表明，与模拟中具有大量QTL的情景类似，对于任何性状，不同方法的准确性之间没有差异。
