Comparison of analyses of the XVth QTLMAS common dataset III: Genomic Estimations of Breeding Values

Roy, Pascale Le; Filangi, Olivier; Demeure, Olivier; Elsen, Jean Michel J. M.

doi:10.1186/1753-6561-6-s2-s3

Cited by 2 publications

(1 citation statement)

References 19 publications

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“…Potential bias of GP is another important element that could be used to compare models. We computed the bias as suggested by [ 32 , 33 ]. The comparison of the biases of all models followed a rather similar trend as the predictive abilities showed in Table 7 .…”

Section: Resultsmentioning

confidence: 99%

The importance of phenotypic data analysis for genomic prediction - a case study comparing different spatial models in rye

Bernal-Vasquez

Möhring

Schmidt³

et al. 2014

BMC Genomics

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BackgroundGenomic prediction is becoming a daily tool for plant breeders. It makes use of genotypic information to make predictions used for selection decisions. The accuracy of the predictions depends on the number of genotypes used in the calibration; hence, there is a need of combining data across years. A proper phenotypic analysis is a crucial prerequisite for accurate calibration of genomic prediction procedures. We compared stage-wise approaches to analyse a real dataset of a multi-environment trial (MET) in rye, which was connected between years only through one check, and used different spatial models to obtain better estimates, and thus, improved predictive abilities for genomic prediction. The aims of this study were to assess the advantage of using spatial models for the predictive abilities of genomic prediction, to identify suitable procedures to analyse a MET weakly connected across years using different stage-wise approaches, and to explore genomic prediction as a tool for selection of models for phenotypic data analysis.ResultsUsing complex spatial models did not significantly improve the predictive ability of genomic prediction, but using row and column effects yielded the highest predictive abilities of all models. In the case of MET poorly connected between years, analysing each year separately and fitting year as a fixed effect in the genomic prediction stage yielded the most realistic predictive abilities. Predictive abilities can also be used to select models for phenotypic data analysis. The trend of the predictive abilities was not the same as the traditionally used Akaike information criterion, but favoured in the end the same models.ConclusionsMaking predictions using weakly linked datasets is of utmost interest for plant breeders. We provide an example with suggestions on how to handle such cases. Rather than relying on checks we show how to use year means across all entries for integrating data across years. It is further shown that fitting of row and column effects captures most of the heterogeneity in the field trials analysed.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-646) contains supplementary material, which is available to authorized users.

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Section: Resultsmentioning

confidence: 99%

The importance of phenotypic data analysis for genomic prediction - a case study comparing different spatial models in rye

Bernal-Vasquez

Möhring

Schmidt³

et al. 2014

BMC Genomics

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A novel genomic selection method combining GBLUP and LASSO

Wang

Bao

2015

Genetica

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Genetic prediction of quantitative traits is a critical task in plant and animal breeding. Genomic selection is an accurate and efficient method of estimating genetic merits by using high-density genome-wide single nucleotide polymorphisms (SNP). In the framework of linear mixed models, we extended genomic best linear unbiased prediction (GBLUP) by including additional quantitative trait locus (QTL) information that was extracted from high-throughput SNPs by using least absolute shrinkage selection operator (LASSO). GBLUP was combined with three LASSO methods-standard LASSO (SLGBLUP), adaptive LASSO (ALGBLUP), and elastic net (ENGBLUP)-that were used for detecting QTLs, and these QTLs were fitted as fixed effects; the remaining SNPs were fitted using a realized genetic relationship matrix. Simulations performed under distinct scenarios revealed that (1) the prediction accuracy of SLGBLUP was the lowest; (2) the prediction accuracies of ALGBLUP and ENGBLUP were equivalent to or higher than that of GBLUP, except under scenarios in which the number of QTLs was large; and (3) the persistence of prediction accuracy over generations was strongest in the case of ENGBLUP. Building on the favorable computational characteristics of GBLUP, ENGBLUP enables robust modeling and efficient computation to be performed for genomic selection.

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Comparison of analyses of the XVth QTLMAS common dataset III: Genomic Estimations of Breeding Values

Cited by 2 publications

References 19 publications

The importance of phenotypic data analysis for genomic prediction - a case study comparing different spatial models in rye

The importance of phenotypic data analysis for genomic prediction - a case study comparing different spatial models in rye

A novel genomic selection method combining GBLUP and LASSO

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