Implementation of genomic prediction in Lolium perenne (L.) breeding populations

Nastasiya F Grinberg, Alan Lovatt, Matt Hegarty, Andi Lovatt, Kirsten P Skøt, Rhys Kelly, Tina Blackmore, Danny Thorogood, Ross D King, Ian Armstead, Wayne Powell, Leif Skøt

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    Abstract

    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.

    Original languageEnglish
    Article number133
    JournalFrontiers in Plant Science
    Volume7
    Early online date12 Feb 2016
    DOIs
    Publication statusFirst published - 12 Feb 2016

    Fingerprint

    Lolium perenne
    genomics
    prediction
    breeding
    forage quality
    agriculture
    forage grasses
    breeding methods
    artificial intelligence
    biomass
    linkage disequilibrium
    breeding value
    quantitative traits
    marker-assisted selection
    genotyping
    genetic improvement
    seed yield
    disease resistance
    population structure
    heritability

    Cite this

    Grinberg, N. F., Lovatt, A., Hegarty, M., Lovatt, A., Skøt, K. P., Kelly, R., ... Skøt, L. (2016). Implementation of genomic prediction in Lolium perenne (L.) breeding populations. Frontiers in Plant Science, 7, [133]. https://doi.org/10.3389/fpls.2016.00133
    Grinberg, Nastasiya F ; Lovatt, Alan ; Hegarty, Matt ; Lovatt, Andi ; Skøt, Kirsten P ; Kelly, Rhys ; Blackmore, Tina ; Thorogood, Danny ; King, Ross D ; Armstead, Ian ; Powell, Wayne ; Skøt, Leif. / Implementation of genomic prediction in Lolium perenne (L.) breeding populations. In: Frontiers in Plant Science. 2016 ; Vol. 7.
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    abstract = "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.",
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    Grinberg, NF, Lovatt, A, Hegarty, M, Lovatt, A, Skøt, KP, Kelly, R, Blackmore, T, Thorogood, D, King, RD, Armstead, I, Powell, W & Skøt, L 2016, 'Implementation of genomic prediction in Lolium perenne (L.) breeding populations', Frontiers in Plant Science, vol. 7, 133. https://doi.org/10.3389/fpls.2016.00133

    Implementation of genomic prediction in Lolium perenne (L.) breeding populations. / Grinberg, Nastasiya F; Lovatt, Alan; Hegarty, Matt; Lovatt, Andi; Skøt, Kirsten P; Kelly, Rhys; Blackmore, Tina; Thorogood, Danny; King, Ross D; Armstead, Ian; Powell, Wayne; Skøt, Leif.

    In: Frontiers in Plant Science, Vol. 7, 133, 12.02.2016.

    Research output: Contribution to journalArticleResearchpeer-review

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    AU - Grinberg, Nastasiya F

    AU - Lovatt, Alan

    AU - Hegarty, Matt

    AU - Lovatt, Andi

    AU - Skøt, Kirsten P

    AU - Kelly, Rhys

    AU - Blackmore, Tina

    AU - Thorogood, Danny

    AU - King, Ross D

    AU - Armstead, Ian

    AU - Powell, Wayne

    AU - Skøt, Leif

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