不同贝叶斯方法在基因组预测中的准确性和敏感性。

IF 0.9 4区 数学 Q3 Mathematics
Saheb Foroutaifar
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引用次数: 1

摘要

本研究的主要目的是利用模拟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精度,并评估这些方法对违反其假设的敏感性。在模拟研究中,根据遗传力低或高的两个性状、不同的QTL数量及其效应分布,实施不同的情景。对于实际数据分析,使用了德国荷尔斯坦的乳脂率、产奶量和体细胞评分数据集。模拟结果表明,除Bayes R外,其他方法对QTL数量和QTL效应分布的变化较为敏感。有一个QTL效应的分布,类似于不同的贝叶斯方法估计标记效应的假设,并没有提高他们的预测精度。与其他方法相比,贝叶斯B方法给出了更高或相同的精度。实际数据分析表明,与模拟中qtl数量较多的情况类似,不同方法对任意性状的准确率均无差异。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Accuracy and sensitivity of different Bayesian methods for genomic prediction using simulation and real data.

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.

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来源期刊
CiteScore
1.20
自引率
11.10%
发文量
8
审稿时长
6-12 weeks
期刊介绍: Statistical Applications in Genetics and Molecular Biology seeks to publish significant research on the application of statistical ideas to problems arising from computational biology. The focus of the papers should be on the relevant statistical issues but should contain a succinct description of the relevant biological problem being considered. The range of topics is wide and will include topics such as linkage mapping, association studies, gene finding and sequence alignment, protein structure prediction, design and analysis of microarray data, molecular evolution and phylogenetic trees, DNA topology, and data base search strategies. Both original research and review articles will be warmly received.
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