Model training across multiple breeding cycles significantly improves genomic prediction accuracy in rye (Secale cereale L.)

Auinger, Hans-Jürgen; Schönleben, Manfred; Lehermeier, Christina; Schmidt, Malthe; Korzun, Viktor; Geiger, H. H.; Piepho, Hans‐Peter; Gordillo, Andrés; Wilde, Peer; Bauer, Eva; Schön, Chris‐Carolin

doi:10.1007/s00122-016-2756-5

Cited by 90 publications

(79 citation statements)

References 36 publications

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“…The results that we obtained are in accordance with the predictive abilities reported by Auinger et al [4], which ranged between 0.39 and 0.58 (with an average heritability of 0.83) and were based on GP-FV. The validation sets VS 1 :GCA1-2012 and VS 2 :GCA1-2013 could be predicted more accurately than VS 3 :GCA1-2014.…”

Section: Discussionsupporting

confidence: 93%

Genomic prediction in early selection stages using multi-year data in a hybrid rye breeding program

Bernal-Vasquez

Gordillo²,

Schmidt³

et al. 2017

BMC Genet

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BackgroundThe use of multiple genetic backgrounds across years is appealing for genomic prediction (GP) because past years’ data provide valuable information on marker effects. Nonetheless, single-year GP models are less complex and computationally less demanding than multi-year GP models. In devising a suitable analysis strategy for multi-year data, we may exploit the fact that even if there is no replication of genotypes across years, there is plenty of replication at the level of marker loci. Our principal aim was to evaluate different GP approaches to simultaneously model genotype-by-year (GY) effects and breeding values using multi-year data in terms of predictive ability. The models were evaluated under different scenarios reflecting common practice in plant breeding programs, such as different degrees of relatedness between training and validation sets, and using a selected fraction of genotypes in the training set. We used empirical grain yield data of a rye hybrid breeding program. A detailed description of the prediction approaches highlighting the use of kinship for modeling GY is presented.ResultsUsing the kinship to model GY was advantageous in particular for datasets disconnected across years. On average, predictive abilities were 5% higher for models using kinship to model GY over models without kinship. We confirmed that using data from multiple selection stages provides valuable GY information and helps increasing predictive ability. This increase is on average 30% higher when the predicted genotypes are closely related with the genotypes in the training set. A selection of top-yielding genotypes together with the use of kinship to model GY improves the predictive ability in datasets composed of single years of several selection cycles.ConclusionsOur results clearly demonstrate that the use of multi-year data and appropriate modeling is beneficial for GP because it allows dissecting GY effects from genomic estimated breeding values. The model choice, as well as ensuring that the predicted candidates are sufficiently related to the genotypes in the training set, are crucial.Electronic supplementary materialThe online version of this article (doi:10.1186/s12863-017-0512-8) contains supplementary material, which is available to authorized users.

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Section: Discussionsupporting

confidence: 93%

Genomic prediction in early selection stages using multi-year data in a hybrid rye breeding program

Bernal-Vasquez

Gordillo²,

Schmidt³

et al. 2017

BMC Genet

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“…Hence, a prerequisite to obtain large predictive ability is that the training set represents well the calibration set and that the calibration set represents well the TPG (Rincent et al 2012; Crossa et al 2013; Albrecht et al 2014; Auinger et al 2016). …”

Section: Discussionmentioning

confidence: 99%

“…Marker effects are estimated on the training set of genotypes, and subsequently, genotypic values are calculated for all genotypes in the training and validation set. For accurate genomic prediction of the genotypic values in the validation set, training and validation sets should have similar genetic diversity, reflected in large kinship coefficients (Saatchi et al 2011; Auinger et al 2016). This condition is more likely to be met if the training set covers the whole genotypic, say genetic, space of the calibration set.…”

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confidence: 99%

Improvement of Predictive Ability by Uniform Coverage of the Target Genetic Space

Bustos-Korts

Malosetti

Chapman

et al. 2016

G3 Genes|Genomes|Genetics

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Genome-enabled prediction provides breeders with the means to increase the number of genotypes that can be evaluated for selection. One of the major challenges in genome-enabled prediction is how to construct a training set of genotypes from a calibration set that represents the target population of genotypes, where the calibration set is composed of a training and validation set. A random sampling protocol of genotypes from the calibration set will lead to low quality coverage of the total genetic space by the training set when the calibration set contains population structure. As a consequence, predictive ability will be affected negatively, because some parts of the genotypic diversity in the target population will be under-represented in the training set, whereas other parts will be over-represented. Therefore, we propose a training set construction method that uniformly samples the genetic space spanned by the target population of genotypes, thereby increasing predictive ability. To evaluate our method, we constructed training sets alongside with the identification of corresponding genomic prediction models for four genotype panels that differed in the amount of population structure they contained (maize Flint, maize Dent, wheat, and rice). Training sets were constructed using uniform sampling, stratified-uniform sampling, stratified sampling and random sampling. We compared these methods with a method that maximizes the generalized coefficient of determination (CD). Several training set sizes were considered. We investigated four genomic prediction models: multi-locus QTL models, GBLUP models, combinations of QTL and GBLUPs, and Reproducing Kernel Hilbert Space (RKHS) models. For the maize and wheat panels, construction of the training set under uniform sampling led to a larger predictive ability than under stratified and random sampling. The results of our methods were similar to those of the CD method. For the rice panel, all training set construction methods led to similar predictive ability, a reflection of the very strong population structure in this panel.

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“…The latter involves selection across multiple breeding generations, which might not necessarily suffer from strong population heterogeneity (Sallam et al 2015, Auinger et al 2016) but could nonetheless benefit from robust multi-population models for potential increase in persistency of accuracy over generations (Habier et al 2007). In this context, IS procedures could also be interesting, for example if a subset of non-selected individuals may be assayed phenotypically during the breeding program.…”

Section: Applications and Prospectsmentioning

confidence: 99%

“…The present work describes promising methods for increasing accuracy and robustness of predictions in situations where heterogeneous data sources are combined, for example when the CS incorporates data from historical trials (Dawson et al 2013, Rutkoski et al 2015 or from multiple generations of a dynamic breeding program (Sallam et al 2015, Auinger et al 2016. Table 1 describes the abbreviations about panels, traits and procedures used to this study.…”

Section: Introductionmentioning

confidence: 99%

Extensions of BLUP models for genomic prediction in heterogeneous populations: Application in a diverse switchgrass sample

Ramstein

Casler

2017

Preprint

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Genomic prediction is a useful tool to accelerate genetic gain in selection using DNA marker information. However, this technology usually relies on models that are not designed to accommodate population heterogeneity, which results from differences in marker signals across genetic backgrounds. Previous studies have proposed to cope with population heterogeneity using diverse approaches: (i) either ignoring it, therefore relying on the robustness of standard approaches; (ii) reducing it, by selecting homogenous subsets of individuals in the sample; or (iii) modelling it by using interactive models. In this study we assessed all three possible approaches, applying existing and novel procedures for each of them. All procedures developed are based on deterministic optimizations, can account for heteroscedasticity, and are applicable in contexts of admixed populations. In a case study on a diverse switchgrass sample, we compared the procedures to a control where predictions rely on homogeneous subsamples. Ignoring heterogeneity was often not detrimental, and sometimes beneficial, to prediction accuracy, compared to the control. Reducing heterogeneity did not result in further increases in accuracy. However, in scenarios of limited subsample sizes, a novel procedure, which accounted for redundancy within subsamples, outperformed the existing procedure, which only considered relationships to selection candidates. Modelling heterogeneity resulted in substantial increases in accuracy, in the cases where accounting for population heterogeneity yielded a highly significant improvement in fit. Our study exemplifies advantages and limits of the various approaches that are promising in various contexts of population heterogeneity, e.g. prediction based on historical datasets or dynamic breeding.

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Model training across multiple breeding cycles significantly improves genomic prediction accuracy in rye (Secale cereale L.)

Cited by 90 publications

References 36 publications

Genomic prediction in early selection stages using multi-year data in a hybrid rye breeding program

Genomic prediction in early selection stages using multi-year data in a hybrid rye breeding program

Improvement of Predictive Ability by Uniform Coverage of the Target Genetic Space

Extensions of BLUP models for genomic prediction in heterogeneous populations: Application in a diverse switchgrass sample

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