Genome position specific priors for genomic prediction.
Genome position specific priors for genomic prediction.
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DOI：
10.1186/1471-2164-13-543
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发表时间：
2012-10-10
期刊：
影响因子：
4.4
通讯作者：
Hayes BJ
中科院分区：
文献类型：
--
作者：
Brøndum RF;Su G;Lund MS;Bowman PJ;Goddard ME;Hayes BJ
基因组预测的准确性高度依赖于参考群体的大小。对于小群体，包括来自其他群体的信息可以提高这种准确性。通常的策略是汇集来自不同种群的数据;然而，这并没有被证明是成功的，因为希望与远亲品种。BayesRS是一种跨种群共享信息进行基因组预测的新方法。该方法允许捕获信息，即使在SNP等位基因和重复突变等位基因的相位在群体之间逆转，或者实际重复突变在群体之间不同但影响相同基因的情况下。从一个群体中导出沿着基因组沿着固定大小的片段中SNP效应的四分布混合物的比例，并将其设置为目标群体的SNP效应分布的位置特异性先验比例。该模型进行了测试，使用不同品种的奶牛种群：540澳大利亚泽西公牛，2297澳大利亚荷斯坦公牛和5214北欧荷斯坦公牛。研究的性状是蛋白质、脂肪和牛奶产量。基因型数据是Illumina 777 K SNP，真实的或插补的。结果显示，与没有特定位置先验的模型相比，使用具有来自澳大利亚荷斯坦牛先验的BayesRS时，泽西种群的准确性提高了高达3.5%。然而，除了脂肪产量的情况外，准确性的增加低于当参考群体被组合以估计SNP效应时所实现的。Jersey验证集的规模较小，这意味着使用Hotelling-Williams t检验在5%水平下准确度的这些改善并不显著。在澳大利亚荷斯坦牛人口中观察到的准确性增加1-2%，当使用来自北欧荷斯坦牛人口的先验相比，使用没有先验信息。使用Hotelling威廉姆斯t检验蛋白质和脂肪产量，这些改善是显著的（P<0.05）。对于某些性状的方法可能是有利的，相比池的参考数据的远亲群体，但需要进一步的调查，以确认结果。对于密切相关的人群，该方法的表现并不比合并参考数据更好。然而，与仅基于一个参考群体的分析相比，它确实提供了更高的准确性，而不会增加计算负担。这里描述的方法提供了一个一般的设置，包括位置特定的先验：该方法可以用来包括生物信息的基因组预测。
The accuracy of genomic prediction is highly dependent on the size of the reference population. For small populations, including information from other populations could improve this accuracy. The usual strategy is to pool data from different populations; however, this has not proven as successful as hoped for with distantly related breeds. BayesRS is a novel approach to share information across populations for genomic predictions. The approach allows information to be captured even where the phase of SNP alleles and casuative mutation alleles are reversed across populations, or the actual casuative mutation is different between the populations but affects the same gene. Proportions of a four-distribution mixture for SNP effects in segments of fixed size along the genome are derived from one population and set as location specific prior proportions of distributions of SNP effects for the target population. The model was tested using dairy cattle populations of different breeds: 540 Australian Jersey bulls, 2297 Australian Holstein bulls and 5214 Nordic Holstein bulls. The traits studied were protein-, fat- and milk yield. Genotypic data was Illumina 777K SNPs, real or imputed. Results showed an increase in accuracy of up to 3.5% for the Jersey population when using BayesRS with a prior derived from Australian Holstein compared to a model without location specific priors. The increase in accuracy was however lower than was achieved when reference populations were combined to estimate SNP effects, except in the case of fat yield. The small size of the Jersey validation set meant that these improvements in accuracy were not significant using a Hotelling-Williams t-test at the 5% level. An increase in accuracy of 1-2% for all traits was observed in the Australian Holstein population when using a prior derived from the Nordic Holstein population compared to using no prior information. These improvements were significant (P<0.05) using the Hotelling Williams t-test for protein- and fat yield. For some traits the method might be advantageous compared to pooling of reference data for distantly related populations, but further investigation is needed to confirm the results. For closely related populations the method does not perform better than pooling reference data. However, it does give an increased accuracy compared to analysis based on only one reference population, without an increased computational burden. The approach described here provides a general setup for inclusion of location specific priors: the approach could be used to include biological information in genomic predictions.
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