Genomic selection efficiency and a priori estimation of accuracy in a structured dent maize panel
Population structure affects genomic selection efficiency as well as the ability to forecast accuracy using standard GBLUP.
Genomic prediction models usually assume that the individuals used for calibration belong to the same population as those to be predicted. Most of the a priori indicators of precision, such as the coefficient of determination (CD), were derived from those same models. But genetic structure is a common feature in plant species, and it may impact genomic selection efficiency and the ability to forecast prediction accuracy. We investigated the impact of genetic structure in a dent maize panel (“Amaizing Dent”) using different scenarios including within- or across-group predictions. For a given training set size, the best accuracies were achieved when predicting individuals using a model calibrated on the same genetic group. Nevertheless, a diverse training set representing all the groups had a certain predictive efficiency for all the validation sets, and adding extra-group individuals was almost always beneficial. It underlines the potential of such a generic training set for dent maize genomic selection applications. Alternative prediction models, taking genetic structure explicitly into account, did not improve the prediction accuracy compared to GBLUP. We also investigated the ability of different indicators of precision to forecast accuracy in the within- or across-group scenarios. There was a global encouraging trend of the CD to differentiate scenarios, although there were specific combinations of target populations and traits where the efficiency of this indicator proved to be null. One hypothesis to explain such erratic performances is the impact of genetic structure through group-specific allele diversity at QTLs rather than group-specific allele effects.
This research was supported by the “Investissement d’Avenir” project “Amaizing”. S. Rio is jointly funded by the program AdmixSel of the INRA metaprogram SelGen and by the breeding companies partners of the Amaizing project: Caussade-Semences, Euralis, KWS, Limagrain, Maisadour, RAGT and Syngenta. We thank Valerie Combes, Delphine Madur and Stephane Nicolas for DNA extraction, analysis and assembly of genotypic data. We thank Cyril Bauland and Carine Palaffre (INRA Saint-Martin de Hinx) for the panel assembly and the coordination of seed production, all breeding companies partners of the Amaizing project and Biogemma for field trials and Pierre Dubreuil (Biogemma) for the assembly and the analysis of phenotypic data.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
The authors declare that the experiments comply with the current laws of the countries in which the experiments were performed.
- Butler DG, Cullis BR, Gilmour AR, Gogel BJ (2009) ASReml-R reference manualGoogle Scholar
- Ganal MW, Durstewitz G, Polley A, Bérard A, Buckler ES, Charcosset A, Clarke JD, Graner E-M, Hansen M, Joets J, Le Paslier M-C, McMullen MD, Montalent P, Rose M, Schön C-C, Sun Q, Walter H, Martin OC, Falque M (2011) A large maize (zea mays l.) snp genotyping array: development and germplasm genotyping, and genetic mapping to compare with the b73 reference genome. PLoS ONE 6(12):1–15CrossRefGoogle Scholar
- Millet EJ, Welcker C, Kruijer W, Negro S, Coupel-Ledru A, Nicolas SD, Laborde J, Bauland C, Praud S, Ranc N, Presterl T, Tuberosa R, Bedo Z, Draye X, Usadel B, Charcosset A, Van Eeuwijk F, Tardieu F (2016) Genome-wide analysis of yield in Europe: allelic effects vary with drought and heat scenarios. Plant Physiol 172(2):749–764PubMedPubMedCentralGoogle Scholar
- Rincent R, Laloë D, Nicolas S, Altmann T, Brunel D, Revilla P, Rodríguez V, Moreno-Gonzalez J, Melchinger A, Bauer E, Schoen C-C, Meyer N, Giauffret C, Bauland C, Jamin P, Laborde J, Monod H, Flament P, Charcosset A, Moreau L (2012) Maximizing the reliability of genomic selection by optimizing the calibration set of reference individuals: comparison of methods in two diverse groups of maize inbreds (zea mays l.). Genetics 192(2):715–728CrossRefGoogle Scholar
- Rincent R, Nicolas S, Bouchet S, Altmann T, Brunel D, Revilla P, Malvar RA, Moreno-Gonzalez J, Campo L, Melchinger AE, Schipprack W, Bauer E, Schoen C-C, Meyer N, Ouzunova M, Dubreuil P, Giauffret C, Madur D, Combes V, Dumas F, Bauland C, Jamin P, Laborde J, Flament P, Moreau L, Charcosset A (2014) Dent and flint maize diversity panels reveal important genetic potential for increasing biomass production. Theor Appl Genet 127(11):2313–2331CrossRefGoogle Scholar
- Schopp P, Müller D, Wientjes YCJ, Melchinger AE (2017) Genomic prediction within and across biparental families: means and variances of prediction accuracy and usefulness of deterministic equations. G3 Genes Genom Genet 7(11):3571–3586Google Scholar
- Toosi A, Fernando R, Dekkers J (2013) Genomic selection in admixed and crossbred populations. J Anim Sci 130(1):10–19Google Scholar
- Unterseer S, Bauer E, Haberer G, Seidel M, Knaak C, Ouzunova M, Meitinger T, Strom TM, Fries R, Pausch H, Bertani C, Davassi A, Mayer KF, Schön C-C (2014) A powerful tool for genome analysis in maize: development and evaluation of the high density 600 k SNP genotyping array. BMC Genomics 15(1):823CrossRefGoogle Scholar