Genomic prediction of grain yield in contrasting environments for white lupin genetic resources
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.
KeywordsDrought tolerance Germplasm Genomic selection Genotype × environment interaction Lupinus albus
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.
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.
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.
Compliance with ethical standards
The authors declare that they have no competing interests.
- Annicchiarico P (2009) Coping with and exploiting genotype × environment interactions. In: Ceccarelli S, Guimarães EP, Weltzien E (eds) Plant breeding and farmer participation. FAO, Rome, pp 519–564Google Scholar
- Annicchiarico P, Nazzicari N, Li X, Wei Y, Pecetti L, Brummer EC (2015) Accuracy of genomic selection for alfalfa biomass yield in different reference populations. BMC Genomics 16:1020Google Scholar
- Atkins CA, Smith PMC, Gupta S, Jones MGK, Caligari PDS (1998) Genetics, cytology and biotechnology. In: Gladstones JS, Atkins C, Hamblin J (eds) Lupins as crop plants: biology, production and utilization. CAB International, Wallingford, UK, pp 67–92Google Scholar
- Brown AHD (2000) The genetic structure of crop landraces and the challenge to conserve them in situ on farms. In: Brush SB (ed) Genes in the field. On-farm conservation of crop diversity. IPGRI/IDRC/Lewis Publishers, Boca Raton, FL, pp 19–48Google Scholar
- Buirchell BJ, Cowling WA (1998) Genetic resources in lupins. In: Gladstones JS, Atkins C, Hamblin J (eds) Lupins as crop plants: biology, production and utilization. CAB International, Wallingford, UK, pp 41–66Google Scholar
- Burstin J, Salloignon P, Chabert-Martinello M, Magnin-Robert J-B, Siol M, Jacquin F et al. (2015) Genetic diversity and trait genomic prediction in a pea diversity panel. BMC Genomics 16:105Google Scholar
- Casella G, George EI (1992) Explaining the Gibbs sampler. Am Stat 46:167–174Google Scholar
- DeLacy IH, Basford KE, Cooper M, Bull IK, McLaren CG (1996) Analysis of multi-environment trials – an historical perspective. In: Cooper M, Hammer GL (eds) Plant adaptation and crop improvement. CAB International, Wallingford, UK, pp 39–124Google Scholar
- Gladstones JS (1998) Distribution, origin, taxonomy, history and importance. In: Gladstones JS, Atkins C, Hamblin J (eds) Lupins as crop plants: biology, production and utilization. CAB International, Wallingford, UK, pp 1–39Google Scholar
- Ma Y, Reif JC, Jiang Y, Wen Z, Wang D, Liu Z et al. (2016) Potential of marker selection to increase prediction accuracy of genomic selection in soybean (Glycine max L.). Mol Breed 36:113Google Scholar
- Nazzicari N, Biscarini F (2017) GROAN: genomic regression workbench (version 1.0.0). https://cran.r-project.org/package=GROAN. Accessed 30 April 2019
- Papineau J, Huyghe C (2004) Le lupin doux protéagineux. Editions France Agricole, ParisGoogle Scholar
- SAS Institute (2011) SAS/STAT® 9.3 User's guide. SAS Institute Inc, Cary, NCGoogle Scholar
- Searle SR, Casella G, McCulloch CE (2009) Variance components. John Wiley & Sons, New YorkGoogle Scholar