An evaluation of the predictive performance and mapping power of the BayesR model for genomic prediction.
An evaluation of the predictive performance and mapping power of the BayesR model for genomic prediction.
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DOI：
10.1093/g3journal/jkab225
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发表时间：
2021-10-19
期刊：
影响因子：
--
通讯作者：
Croiseau P
中科院分区：
文献类型：
--
作者：
Mollandin F;Rau A;Croiseau P
技术进步和成本下降导致了越来越密集的基因分型数据的增加，使识别潜在的因果标记成为可能。定制的基因分型芯片将中等密度的基因分型与定制的基因分型面板相结合，可以利用这些候选基因来潜在地提高基因组预测的准确性和可解释性。为此，一个特别有希望的模型是BayesR，它将标记分为四个效果大小类。贝叶斯回归已被证明能够在真实数据应用中提供准确的预测和数量性状基因座(QTL)定位的前景，但目前还缺乏在模拟数据中进行广泛的基准测试。基于一组真实的基因类型，我们生成了在各种遗传结构和表型遗传力下的模拟数据，并评估了排除或包括因果标记在基因类型中的影响。我们定义了几个QTL定位的统计标准，包括几个基于滑动窗口的标准来解释连锁不平衡(LD)。我们比较和对比这些统计数据以及它们准确区分已知因果标记的优先顺序的能力。总体而言，我们确认了BayesR在中等到高度可遗传特征上的强大预测性能，特别是对于50k定制数据。在遗传力低或在50k基因型中具有因果标记的弱LD的情况下，无论使用什么标准，QTL定位都是一个挑战。贝叶斯回归是一种很有前途的方法，可以同时获得准确的预测和可解释的SNPs分类到有效大小的类。我们举例说明了BayesR在各种仿真场景中的性能，并比较了每种场景的优势和局限性。
Technological advances and decreasing costs have led to the rise of increasingly dense genotyping data, making feasible the identification of potential causal markers. Custom genotyping chips, which combine medium-density genotypes with a custom genotype panel, can capitalize on these candidates to potentially yield improved accuracy and interpretability in genomic prediction. A particularly promising model to this end is BayesR, which divides markers into four effect size classes. BayesR has been shown to yield accurate predictions and promise for quantitative trait loci (QTL) mapping in real data applications, but an extensive benchmarking in simulated data is currently lacking. Based on a set of real genotypes, we generated simulated data under a variety of genetic architectures and phenotype heritabilities, and we evaluated the impact of excluding or including causal markers among the genotypes. We define several statistical criteria for QTL mapping, including several based on sliding windows to account for linkage disequilibrium (LD). We compare and contrast these statistics and their ability to accurately prioritize known causal markers. Overall, we confirm the strong predictive performance for BayesR in moderately to highly heritable traits, particularly for 50k custom data. In cases of low heritability or weak LD with the causal marker in 50k genotypes, QTL mapping is a challenge, regardless of the criterion used. BayesR is a promising approach to simultaneously obtain accurate predictions and interpretable classifications of SNPs into effect size classes. We illustrated the performance of BayesR in a variety of simulation scenarios, and compared the advantages and limitations of each.
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影响因子：
30.8
作者：
Marchini, Jonathan;Howie, Bryan;Donnelly, Peter
通讯作者：
Donnelly, Peter
影响因子：
4.5
作者：
Moser G;Lee SH;Hayes BJ;Goddard ME;Wray NR;Visscher PM
通讯作者：
Visscher PM
影响因子：
4.4
作者：
MacLeod IM;Bowman PJ;Vander Jagt CJ;Haile-Mariam M;Kemper KE;Chamberlain AJ;Schrooten C;Hayes BJ;Goddard ME
通讯作者：
Goddard ME
影响因子：
3.5
作者：
Brondum, R. F.;Su, G.;Lund, M. S.
通讯作者：
Lund, M. S.
DOI：
10.1186/s12711-016-0259-0
发表时间：
2016-11-04
期刊：
Genetics, selection, evolution : GSE
影响因子：
--
作者：
van den Berg I;Boichard D;Lund MS
通讯作者：
Lund MS