多年生黑麦草饲料营养价值的基因组预测

Agnieszka Konkolewska, Michael Dineen, Rachel Keirse, Patrick Conaghan, Dan Milbourne, Susanne Barth, Aonghus Lawlor, Stephen Byrne
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引用次数: 0

摘要

背景尽管多年生黑麦草具有重要的动物生产潜力,但其饲料营养价值的遗传增益在多年生黑麦草育种中受到限制。本研究的目的是对训练群体进行表型分析,并建立预测模型,以评估利用基因分型测序数据预测有机物质消化率(OMD)和中性洗涤纤维(NDF)的潜力。方法建立OMD和NDF的近红外反射光谱校准方法,并利用n = 1606的间隔植株训练群体的基因型测序数据进行表型分析,建立基因组选择模型。我们还对来自训练人群的f2家族在草地样地的OMD和NDF进行了评估,并用于实证验证预测模型。结果繁殖群体中存在足够的基因型变异,可以提高饲料营养价值,并确定了有助于校准的光谱波段。基于基因组数据的OMD和NDF预测精度适中(预测能力分别在0.51 ~ 0.59和0.33 ~ 0.57之间),基于单株建立的模型优于基于科均值建立的模型。令人鼓舞的是,在亲本植物上建立的基因组预测模型可以预测作为竞争植物生长的后代的OMD。结论基因组预测模型的应用可加快饲草营养价值的遗传改良。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Genomic prediction of forage nutritive value in perennial ryegrass

Genomic prediction of forage nutritive value in perennial ryegrass

Background

Despite its importance to animal production potential, genetic gain for forage nutritive value has been limited in perennial ryegrass (Lolium perenne L.) breeding. The objective of this study was to phenotype a training population and develop prediction models to assess the potential of predicting organic matter digestibility (OMD) and neutral detergent fiber (NDF) with genotyping-by-sequencing data.

Methods

Near infra-red reflectance spectroscopy calibrations for OMD and NDF were developed and used to phenotype a spaced plant training population of n = 1606, with matching genotype-by-sequencing data, for developing genomic selection models. F 2 families derived from the training population were also evaluated for OMD and NDF in sward plots and used to empirically validate prediction models.

Results

Sufficient genotypic variation exists in breeding populations to improve forage nutritive value, and spectral bands contributing to calibrations were identified. OMD and NDF can be predicted from genomic data with moderate accuracy (predictive ability in the range of 0.51–0.59 and 0.33–0.57, respectively) and models developed on individual plants outperform those developed from family means. Encouragingly, genomic prediction models developed on parental plants can predict OMD in subsequent generations grown as competitive swards.

Conclusions

These findings suggest that genetic improvement in forage nutritive value can be accelerated through the application of genomic prediction models.

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