Persistence of accuracy of genome-wide breeding values over generations when including a polygenic effect.
Persistence of accuracy of genome-wide breeding values over generations when including a polygenic effect.
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DOI：
10.1186/1297-9686-41-53
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发表时间：
2009-12-29
期刊：
影响因子：
--
通讯作者：
Meuwissen TH
中科院分区：
文献类型：
--
作者：
Solberg TR;Sonesson AK;Woolliams JA;Odegard J;Meuwissen TH
当估计基因组选择中的标记效应时，标记效应的估计可以简单地充当谱系的代理，即它们的效应可能部分归因于它们与优良亲本的关联，并且与任何致病QTL无关。因此，这些标记主要解释多基因效应而不是QTL效应。然而，如果贝叶斯模型中包含多基因效应，则预计这些标记的估计效应将在几代人中更加持久，而无需每一代都重新估计标记效应，并将导致准确性提高和偏差减少。使用贝叶斯方法进行基因组选择，当模型中包含多基因效应 (GWpEBV) 和不包含多基因效应 (GWEBV) 时，对不同标记密度的“BayesB”进行评估。通过模拟 Ne 为 100 的群体 1000 代以上，获得连锁不平衡和突变漂移平衡。无论标记密度如何，包含多基因效应的模型的选择准确性略高于不包含多基因效应的模型。在后代中，准确度有所下降，并且标记密度较低时，这种下降幅度更大。然而，两个模型之间的准确度没有显着差异。 TBV 对 GWEBV 和 GWpEBV 的线性回归被用作偏差的度量。当模型中包含多基因效应时，回归系数在几代人中更加稳定，并且对于最高标记密度始终在 0.98 和 1.00 之间。随着标记密度的降低，回归系数下降得更快。包括多基因效应对选择准确性没有影响，但显示出减少的偏差，当使用全基因组标记的估计来估计一代以上的育种值时，这一点尤其重要。
When estimating marker effects in genomic selection, estimates of marker effects may simply act as a proxy for pedigree, i.e. their effect may partially be attributed to their association with superior parents and not be linked to any causative QTL. Hence, these markers mainly explain polygenic effects rather than QTL effects. However, if a polygenic effect is included in a Bayesian model, it is expected that the estimated effect of these markers will be more persistent over generations without having to re-estimate the marker effects every generation and will result in increased accuracy and reduced bias. Genomic selection using the Bayesian method, 'BayesB' was evaluated for different marker densities when a polygenic effect is included (GWpEBV) and not included (GWEBV) in the model. Linkage disequilibrium and a mutation drift balance were obtained by simulating a population with a Ne of 100 over 1,000 generations. Accuracy of selection was slightly higher for the model including a polygenic effect than for the model not including a polygenic effect whatever the marker density. The accuracy decreased in later generations, and this reduction was stronger for lower marker densities. However, no significant difference in accuracy was observed between the two models. The linear regression of TBV on GWEBV and GWpEBV was used as a measure of bias. The regression coefficient was more stable over generations when a polygenic effect was included in the model, and was always between 0.98 and 1.00 for the highest marker density. The regression coefficient decreased more quickly with decreasing marker density. Including a polygenic effect had no impact on the selection accuracy, but showed reduced bias, which is especially important when estimates of genome-wide markers are used to estimate breeding values over more than one generation.
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影响因子：
3.3
作者：
Habier, D.;Fernando, R. L.;Dekkers, J. C. M.
通讯作者：
Dekkers, J. C. M.
影响因子：
4.1
作者：
MEUWISSEN, THE;LUO, Z
通讯作者：
LUO, Z
影响因子：
3.3
作者：
Calus, M P L;Meuwissen, T H E;Veerkamp, R F
通讯作者：
Veerkamp, R F
影响因子：
2.6
作者：
Muir, W. M.
通讯作者：
Muir, W. M.
影响因子：
3.3
作者：
Gianola, Daniel;Fernando, Rohan L.;Stella, Alessandra
通讯作者：
Stella, Alessandra