文章来源：https://www.cambridge.org/core/journals/genetics-research/article/increased-accuracy-of-artificial-selection-by-using-the-realized-relationship-matrix/52CE559816ADC28084C9B68EA82E28AE
通过使用已实现的关系矩阵，提高了人工选择的准确性
摘要
密集的标记基因型允许构建个体之间的已实现关系矩阵，其原理是成对个体之间通过血缘(IBD)同源的基因组的现实比例。在本文中，我们证明了在育种值的最佳线性无偏预测(BLUP)中，通过用已实现的关系矩阵代替从系谱中得到的平均关系矩阵，育种值的准确性可以大大提高，特别是对于没有自身表型的个体。我们进一步证明，这种预测育种值的方法完全等同于基因组选择方法，在该方法中，导致性状变异的数量性状基因座的效应被假定为正态分布。对于已知的家族关系，例如半同胞，可以确定性地预测使用BLUP方程中的已实现关系矩阵预测的育种值的准确性。
前言
In best linear unbiased prediction (BLUP) of breeding values, information from performance of relatives is incorporated through the use of a relationship matrix.
在育种值的最佳线性无偏预测(BLUP)中，来自其他亲缘个体的表型信息通过使用关系矩阵而被合并。
Elements of this matrix are derived as the predicted proportion of the genome that is identical by descent (IBD) among two individuals given their pedigree relationship.
这个矩阵的元素是根据两个个体之间在系谱关系中血缘同源(IBD)的基因组预测比例推导出来的。
However, Mendelian sampling during gamete formation results in variation in the realized
proportion of the genome, which is IBD between pairs of individuals with the same predicted relation-ship coefficients.
然而，在具有相同预测关系系数个体对间的IBD中，其真实基因组比例在配子形成阶段由于孟德尔抽样发生了变化。
例如，在全同胞个体之间IBD基因组的预测比例是0.5，而对于一个有30条染色体，每条长度为1米的物种，其标准偏差是0.04。
DNA marker information can be used to calculate the realized relationship matrix with elements the actual proportion of the genome that is IBD between two individuals, with a high degree of precision, provided that a sufficient number of markers are used.
只要使用足够数量的标记，就可以使用DNA标记信息高精度地计算现实关系矩阵中的元素，即两个个体之间基因组的实际比例即IBD。
Nejati-Javaremi et al. (1997) demonstrated with simulation that if the loci contributing to trait variation were known, and the alleles at these loci were used to derive the realized relationship matrix, the accuracy of breeding values calculated using this matrix could be higher than that calculated using the predicted relationship matrix.
Nejati-javaremi等。 （1997）用模拟证明，如果已知有助于特征变异的基因座，并且这些基因座的等位基因用于预测现实的关系矩阵，则使用该矩阵计算的育种值准确性可以高于使预测关系矩阵（A矩阵）计算的准确性。
In practice, all the loci contributing to trait variation are unlikely to have been identified.
事实上，解释性状差异的位点都不太可能被识别出来。
Villanueva et al. (2005) demonstrated by simulation that using the realized relationship matrix derived from markers rather than the predicted relationship matrix in the calculation of estimated breeding values (EBVs) could lead to higher accuracies of selection.
Villanueva等。 （2005）通过模拟证明，使用从标记导出的实现关系矩阵而不是预测的关系矩阵在计算估计的繁殖值（EBVS）中可能导致更高的选择精度。
They proposed that marker information used in this way could offer benefits in selection programmes when no quantum trait locus (QTL) has been mapped or when the underlying genetic model can be considered the infinitesimal model, where no individual QTL has a moderate to large effect on the trait.
他们提出以这种方式使用的标记信息可以在没有数量性状基因座（QTL）或者当潜在的遗传模型可以被认为是微效多基因遗传模型时提供益处。
For some traits such as height in humans, this is indeed the case, with the largest reported QTLs explaining only a small fraction of the genetic variance.
对于某些特征，如人类的高度，这确实如此，具有最大报告的QTLS仅解释遗传方差的一小部分
While Villanueva et al. (2005) considered estimating realized relationships conditional on a known pedigree (exploiting linkage information) realized relationship coefficients can also be estimated for ‘unrelated’ individuals within a population.
Villanueva等人(2005年)认为，估计已实现关系的条件是已知的系谱(利用连锁信息)，而已实现的关系系数也可以为群体中“不相关”的个体进行估计。
This requires sufficient marker density to identify chromosome segments in two individuals that are descended from the same common, but unknown ancestor.
这需要足够的标记密度来识别来自同一共同但未知祖先的两个个体的染色体片段。
An alternative method by which DNA marker data can be used to estimate breeding values is genomic selection (Meuwissen et al., 2001).
DNA标记数据可用于估计育种值的另一种方法是基因组选择(Meuwissen等人，2001)。
In this method, the markers are used to track QTLs whose effects are estimated and summed to predict the breeding value of each individual.
在这种方法中，标记被用来跟踪其效应被估计和相加的QTL，以预测每个个体的育种值。
However, if there are many QTLs whose effects are normally distributed with constant variance, then genomic selection can be equivalent to the use of the realized relationship matrix
但是，如果存在许多QTL的效果通常以恒定方差分布，则基因组选择可以等同于实现关系矩阵的使用。
Currently, there is no analytical method available to predict the accuracy of EBVs calculated using the genomic relationship matrix considering information from relatives.
目前，没有可用的分析方法来预测使用包含了亲缘信息的基因组关系矩阵计算EBV的准确性。
Analytical expressions would be desirable to guide the design of experiments aiming to achieve a given accuracy of genomic breeding values (GEBVs).
为了达到基因组育种值(GEBV)的给定准确度，需要用解析表达式来指导实验设计。
Our objective was to derive such expressions for the accuracy of GEBV considering information from relatives. We also modify the expression of Goddard (2008) for the accuracy of GEBV in random mating populations to improve the predictions.
我们的目标是在考虑亲属信息的情况下，推导出GEBV的准确性表达式。我们还修改了Goddard(2008)的表达式，以提高随机交配群体中GEBV的准确性，以改善预测。
Our starting point for all derivations was the equivalent genomic selection model.
我们所有衍生的起点是等效基因组选择模型。
We then verified the analytical predictions using two simulation approaches.
然后，我们使用两种模拟方法验证了分析预测。
First, we derive a prediction of the accuracy based on the prediction error variance (PEV) where the realized relationship matrix is determined by a large number of informative markers.
第一种，我们基于预测误差方差(PEV)推导出精度预测，其中实现的关系矩阵由大量信息标记确定。
Secondly, we derive accuracy from simulations with both markers and QTLs segregating as the correlation between true and predicted breeding values. We then investigate the sensitivity of the results to the number of markers used, the number of QTLs and effective population size.
第二，QTL和marks的相分离作为真实育种值与预测育种值之间的相关性为我们从模拟中得到的准确性。
We then investigate the sensitivity of the results to the number of markers used, the number of QTLs and effective population size.
然后，我们研究了结果对所使用的标记数量、QTL数量和有效群体大小的敏感性。