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Optimized grouping to increase accuracy of prediction of breeding values based on group records in genomic selection breeding programs

机译:优化分组,以提高基于基因组选择育种计划的群体记录的育种值预测精度

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Phenotypic records of group means or group sums are a good alternative to individual records for some difficult to measure, but economically important traits such as feed efficiency or egg production. Accuracy of predicted breeding values based on group records increases with increasing relationships between group members. The classical way to form groups with more closely-related animals is based on pedigree information. When genotyping information is available before phenotyping, its use to form groups may further increase the accuracy of prediction from group records. This study analyzed two grouping methods based on genomic information: (1) unsupervised clustering implemented in the STRUCTURE software and (2) supervised clustering that models genomic relationships. Using genomic best linear unbiased prediction (GBLUP) models, estimates of the genetic variance based on group records were consistent with those based on individual records. When genomic information was available to constitute the groups, genomic relationship coefficients between group members were higher than when random grouping of paternal half-sibs and of full-sibs was applied. Grouping methods that are based on genomic information resulted in higher accuracy of genomic estimated breeding values (GEBV) prediction compared to random grouping. The increase was ~ 1.5% for full-sibs and ~ 11.5% for paternal half-sibs. In addition, grouping methods that are based on genomic information led to lower coancestry coefficients between the top animals ranked by GEBV. Of the two proposed methods, supervised clustering was superior in terms of accuracy, computation requirements and applicability. By adding surplus genotyped offspring (more genotyped offspring than required to fill the groups), the advantage of supervised clustering increased by up to 4.5% compared to random grouping of full-sibs, and by 14.7% compared to random grouping of paternal half-sibs. This advantage also increased with increasing family sizes or decreasing genome sizes. The use of genotyping information for grouping animals increases the accuracy of selection when phenotypic group records are used in genomic selection breeding programs.
机译:组的表型或群组总和是个体记录的替代方案,以便一些难以测量,但经济上重要的特征,如饲料效率或鸡蛋生产。基于组记录的预测繁殖值的准确性随着组成员之间的关系而增加。用更密相关的动物形成组的古典方式是基于血统信息。当在表型之前可获得基因分型信息时,其用于形成组的用途可以进一步提高组记录预测的准确性。本研究分析了基于基因组信息的两种分组方法:(1)结构软件中实施的无监督聚类和(2)模拟基因组关系的监督聚类。使用基因组最佳线性无偏的预测(GBLUP)模型,基于组记录的遗传方差的估计与基于个别记录的遗传差异一致。当基因组信息可用于组成组时,组成员之间的基因组关系系数高于当彼得氏半标率和全部SIBs的随机分组时。与随机分组相比,基于基因组信息的基于基因组信息的分组方法导致基因组估计育种值(GEBV)预测的准确性。全部SIBs的增加〜1.5%,父足生物半SIBs的〜11.5%。另外,基于基因组信息的分组方法导致了通过GEBV排名的顶部动物之间的较低的共粒系数。在两个提出的方法中,监督聚类在准确性,计算要求和适用性方面都是优越的。通过添加剩余基因分型后代(比填充组所需的更基因分型后代),与随机分组的随机分组相比,监督聚类的优势增加了4.5%,与父子半SIB的随机分组相比,14.7% 。随着家庭尺寸的增加或降低基因组大小,这种优势也增加。使用用于分组动物的基因分型信息增加了在基因组选择育种计划中使用表型群体记录时选择的精度。

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