Implementation of genomic prediction in Lolium perenne (L.) breeding populations
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. Gene...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2016-02-01
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| Series: | Frontiers in Plant Science |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fpls.2016.00133/full |
| _version_ | 1819266516535738368 |
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| author | Nastasiya F Grinberg Alan eLovatt Matthew eHegarty Andrea eLovatt Kirsten Pape Skot Rhys eKelly Tina eBlackmore Danny eThorogood Ian eArmstead Ross eKing Wayne ePowell Wayne ePowell Leif eSkot |
| author_facet | Nastasiya F Grinberg Alan eLovatt Matthew eHegarty Andrea eLovatt Kirsten Pape Skot Rhys eKelly Tina eBlackmore Danny eThorogood Ian eArmstead Ross eKing Wayne ePowell Wayne ePowell Leif eSkot |
| author_sort | Nastasiya F Grinberg |
| collection | DOAJ |
| description | 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 (GEBV) are derived. In this paper we compare the classical RRBLUP model with state-of-the-art machine learning (ML) 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 (LD) 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. |
| first_indexed | 2024-12-23T21:02:31Z |
| format | Article |
| id | doaj.art-13c90a1846574032a1835d56e4e41a11 |
| institution | Directory Open Access Journal |
| issn | 1664-462X |
| language | English |
| last_indexed | 2024-12-23T21:02:31Z |
| publishDate | 2016-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj.art-13c90a1846574032a1835d56e4e41a112022-12-21T17:31:19ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2016-02-01710.3389/fpls.2016.00133166522Implementation of genomic prediction in Lolium perenne (L.) breeding populationsNastasiya F Grinberg0Alan eLovatt1Matthew eHegarty2Andrea eLovatt3Kirsten Pape Skot4Rhys eKelly5Tina eBlackmore6Danny eThorogood7Ian eArmstead8Ross eKing9Wayne ePowell10Wayne ePowell11Leif eSkot12University of ManchesterAberystwyth UniversityAberystwyth UniversityAberystwyth UniversityAberystwyth UniversityAberystwyth UniversityAberystwyth UniversityAberystwyth UniversityAberystwyth UniversityUniversity of ManchesterAberystwyth UniversityCGIAR ConsortiumAberystwyth UniversityPerennial 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 (GEBV) are derived. In this paper we compare the classical RRBLUP model with state-of-the-art machine learning (ML) 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 (LD) 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.http://journal.frontiersin.org/Journal/10.3389/fpls.2016.00133/fullmachine learninggenomic selectionPerennial ryegrassBLUPforage crop. |
| spellingShingle | Nastasiya F Grinberg Alan eLovatt Matthew eHegarty Andrea eLovatt Kirsten Pape Skot Rhys eKelly Tina eBlackmore Danny eThorogood Ian eArmstead Ross eKing Wayne ePowell Wayne ePowell Leif eSkot Implementation of genomic prediction in Lolium perenne (L.) breeding populations Frontiers in Plant Science machine learning genomic selection Perennial ryegrass BLUP forage crop. |
| title | Implementation of genomic prediction in Lolium perenne (L.) breeding populations |
| title_full | Implementation of genomic prediction in Lolium perenne (L.) breeding populations |
| title_fullStr | Implementation of genomic prediction in Lolium perenne (L.) breeding populations |
| title_full_unstemmed | Implementation of genomic prediction in Lolium perenne (L.) breeding populations |
| title_short | Implementation of genomic prediction in Lolium perenne (L.) breeding populations |
| title_sort | implementation of genomic prediction in lolium perenne l breeding populations |
| topic | machine learning genomic selection Perennial ryegrass BLUP forage crop. |
| url | http://journal.frontiersin.org/Journal/10.3389/fpls.2016.00133/full |
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