Implementation of Genomic Prediction in Lolium perenne (L.) Breeding Populations.
Implementation of Genomic Prediction in Lolium perenne (L.) Breeding Populations.
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DOI：
10.3389/fpls.2016.00133
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发表时间：
2016
影响因子：
5.6
通讯作者：
Skøt L
中科院分区：
文献类型：
--
作者：
Grinberg NF;Lovatt A;Hegarty M;Lovatt A;Skøt KP;Kelly R;Blackmore T;Thorogood D;King RD;Armstead I;Powell W;Skøt L
多年生黑麦草（Lolium perenne L.）是温带农业中最广泛种植的牧草之一。为了维持和增加其在畜牧业中作为饲料的使用，持续需要改善生物质产量、质量、抗病性和种子产量。生物量产量等性状的遗传增益相对较小。这是由于它的繁殖周期长，必须使用基于种群的繁殖方法。由于基因分型技术的最新进展，人们对基因组选择越来越感兴趣，从基因组选择中获得基因组估计的育种值。在本文中，我们比较了经典的RRBLUP模型与最先进的机器学习技术，应该很容易产生自己在GS中使用，并展示其应用于预测数量性状的育种群体的L。多年生植物预测精度从0到0.59不等，这取决于性状、预测模型和训练群体的组成。BLUP模型对大多数性状和训练群体的预测精度最高。与产量相关性状相比，牧草品质性状的准确性最高。训练群体组成对预测准确度的影响似乎没有明确的模式。牧草品质性状的遗传力普遍高于产量相关性状的遗传力，这可以部分解释精度的差异。一些人口结构是明显的育种群体，可能有助于不同的影响，训练人口的预测。相邻标记间的平均连锁不平衡值为0.121 ~ 0.215。更高的标记密度和更大的训练群体与测试群体密切相关，可能会提高预测精度。
Perennial ryegrass (Lolium perenne L.) is one of the most widely grown forage grasses in temperate agriculture. In order to maintain and increase its usage as forage in livestock agriculture, there is a continued need for improvement in biomass yield, quality, disease resistance, and seed yield. Genetic gain for traits such as biomass yield has been relatively modest. This has been attributed to its long breeding cycle, and the necessity to use population based breeding methods. Thanks to recent advances in genotyping techniques there is increasing interest in genomic selection from which genomically estimated breeding values are derived. In this paper we compare the classical RRBLUP model with state-of-the-art machine learning techniques that should yield themselves easily to use in GS and demonstrate their application to predicting quantitative traits in a breeding population of L. perenne. Prediction accuracies varied from 0 to 0.59 depending on trait, prediction model and composition of the training population. The BLUP model produced the highest prediction accuracies for most traits and training populations. Forage quality traits had the highest accuracies compared to yield related traits. There appeared to be no clear pattern to the effect of the training population composition on the prediction accuracies. The heritability of the forage quality traits was generally higher than for the yield related traits, and could partly explain the difference in accuracy. Some population structure was evident in the breeding populations, and probably contributed to the varying effects of training population on the predictions. The average linkage disequilibrium between adjacent markers ranged from 0.121 to 0.215. Higher marker density and larger training population closely related with the test population are likely to improve the prediction accuracy.