期刊
FRONTIERS IN PLANT SCIENCE
卷 7, 期 -, 页码 -出版社
FRONTIERS MEDIA SA
DOI: 10.3389/fpls.2016.00133
关键词
perennial ryegrass; genomic selection; BLUP; machine learning; forage crop
资金
- Industrial Partnership Award scheme from the BBSRC [BB/J006955/1]
- Germinal Holdings LTD.
- BBSRC [BB/J006955/1, BBS/E/W/10962A01D, BBS/E/W/10962A01B, BB/M028267/1] Funding Source: UKRI
- Biotechnology and Biological Sciences Research Council [BB/M028267/1, BB/J006955/1, BBS/E/W/10962A01B] Funding Source: researchfish
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据