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Genomic prediction of grain yield in contrasting environments for white lupin genetic resources

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Abstract

Germplasm collections hold several thousands of white lupin (Lupinus albus L.) accessions. Genome-enabled models with good predictive ability for specific environments could provide a cost-efficient means to identify promising genetic resources for breeding programmes. This study provided an unprecedented assessment of genome-enabled predictions for white lupin grain yield, focusing on (i) a world collection of 109 landraces and 8 varieties phenotyped in three European sites with contrasting climate (Mediterranean, subcontinental or oceanic) and sowing time (data set 1); (ii) 78 geographically diversified landrace genotypes and three variety genotypes phenotyped in moisture-favourable and severely drought-prone managed environments (data set 2). The interest of predictions for individual genotypes was justified by large within-landrace variation for yield responses. Ridge regression BLUP (rrBLUP) and Bayesian Lasso (BL) models exploited allele frequencies (estimated from 3 to 4 genotypes per landrace) of 10,782 polymorphic SNPs for data set 1, and allele values of 9937 polymorphic SNPs for data set 2, following ApeKI-based genotyping-by-sequencing characterization. Compared with BL, rrBLUP displayed similar predictive ability for data set 1 and better predictive ability for data set 2. Best-predictive models displayed intra-environment predictive ability for the five test environments in the range 0.47–0.76. Cross-environment predictions between pairs of environments with positive genetic correlation, i.e., autumn-sown subcontinental vs Mediterranean sites, and moisture-favourable vs drought-prone environments, exhibited a predictive ability range of 0.40–0.51 and a predictive accuracy range of 0.48–0.61. Our results support the exploitation of genomic predictions and provide economic justification for the genotyping of germplasm collections of white lupin.

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Acknowledgements

We are grateful to the Elshire Group Ltd. for the excellent GBS genotyping service and to A. Passerini, P. Gaudenzi, S. Proietti, P. Manunza, G. Rochas and N. Rousseau for technical assistance.

Funding

The experiment data for this study were generated by the project ‘Legumes for the agriculture of tomorrow (LEGATO)’ funded by the FP7 of the European Commission (Grant agreement No. 613551) and the projects ‘Increase of protein feed production’ and ‘Plant Genetic Resources - FAO Treaty’ funded by the Italian Ministry of Agricultural, Food and Forestry Policies.

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Authors and Affiliations

  1. Research Centre for Animal Production and Aquaculture, Council for Agricultural Research and Economics (CREA), Lodi, Italy

    Paolo Annicchiarico, Nelson Nazzicari, Barbara Ferrari, Antonio M. Carroni, Massimo Romani & Luciano Pecetti

  2. Jouffray-Drillaud Semences, Station de Recherche La Litière, Saint Sauvant, France

    Nathalie Harzic

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  1. Paolo Annicchiarico
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  2. Nelson Nazzicari
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  3. Barbara Ferrari
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  4. Nathalie Harzic
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  5. Antonio M. Carroni
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  6. Massimo Romani
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  7. Luciano Pecetti
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Contributions

PA designed and supervised the research work, obtained financial resources, analysed the phenotypic data and drafted the manuscript. NN was responsible for the bioinformatics pipeline and the definition of genome-enabled models. PA, AMC, NH, MR and LP were responsible for phenotyping experiments. BF collected and verified the quality of DNA samples. All authors approved the manuscript.

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Correspondence to Paolo Annicchiarico.

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Annicchiarico, P., Nazzicari, N., Ferrari, B. et al. Genomic prediction of grain yield in contrasting environments for white lupin genetic resources. Mol Breeding 39, 142 (2019). https://doi.org/10.1007/s11032-019-1048-6

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