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 is a preview of subscription content, log in to check access.
Buy single article
Instant access to the full article PDF.
Price includes VAT for USA
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
This is the net price. Taxes to be calculated in checkout.
Albrecht T, Auinger H-J, Wimmer V, Ogutu JO, Knaak C, Ouzunova M, Piepho H-P, Schön C-C (2014) Genome-based prediction of maize hybrid performance across genetic groups, testers, locations, and years. Theor Appl Genet 127(6):1375–1386
Alexander D, Novembre J, Lange K (2009) Fast model-based estimation of ancestry in unrelated individuals. Genome Res 19:1655–1664
Astle W, Balding DJ (2009) Population structure and cryptic relatedness in genetic association studies. Stat Sci 24(4):451–471
Brard S, Ricard A (2015) Is the use of formulae a reliable way to predict the accuracy of genomic selection? J Anim Breed Genet 132(3):207–217
Brøndum R, Rius-Vilarrasa E, Strandén I, Su G, Guldbrandtsen B, Fikse W, Lund M (2011) Reliabilities of genomic prediction using combined reference data of the Nordic Red dairy cattle populations. J Dairy Sci 94:4700–4707
Browning B, Browning S (2009) A unified approach to genotype imputation and haplotype-phase inference for large data sets of trios and unrelated individuals. Am J Hum Genet 84:210–223
Butler DG, Cullis BR, Gilmour AR, Gogel BJ (2009) ASReml-R reference manual
Carillier C, Larroque H, Robert-Granié C (2014) Comparison of joint versus purebred genomic evaluation in the french multi-breed dairy goat population. Genet Select Evol 46(1):67
Daetwyler HD, Villanueva B, Woolliams JA (2008) Accuracy of predicting the genetic risk of disease using a genome-wide approach. PLoS ONE 3(10):1–8
de Roos APW, Hayes BJ, Goddard ME (2009) Reliability of genomic predictions across multiple populations. Genetics 183(4):1545–1553
Duhnen A, Gras A, Teyssèdre S, Romestant M, Claustres B, Daydé J, Mangin B (2017) Genomic selection for yield and seed protein content in soybean: a study of breeding program data and assessment of prediction accuracy. Crop Sci 57:1–13
Elsen J-M (2016) Approximated prediction of genomic selection accuracy when reference and candidate populations are related. Genet Select Evol 48(1):18
Elshire RJ, Glaubitz JC, Sun Q, Poland JA, Kawamoto K, Buckler ES, Mitchell SE (2011) A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS ONE 6(5):1–10
Endelman JB, Atlin GN, Beyene Y, Semagn K, Zhang X, Sorrells ME, Jannink J (2014) Optimal design of preliminary yield trials with genome-wide markers. Crop Sci 54:48–59
Erbe M, Gredler B, Seefried FR, Bapst B, Simianer H (2013) A function accounting for training set size and marker density to model the average accuracy of genomic prediction. PLoS ONE 8(12):1–11
Esfandyari H, Sørensen AC, Bijma P (2015) A crossbred reference population can improve the response to genomic selection for crossbred performance. Genet Select Evol 47(1):76
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):1–15
Glaubitz JC, Casstevens TM, Lu F, Harriman J, Elshire RJ, Sun Q, Buckler ES (2014) Tassel-GBS: a high capacity genotyping by sequencing analysis pipeline. PLoS ONE 9(2):1–11
Goddard M, Hayes B, Meuwissen T (2011) Using the genomic relationship matrix to predict the accuracy of genomic selection. J Anim Breed Genet 128(6):409–421
Guo Z, Tucker DM, Basten CJ, Gandhi H, Ersoz E, Guo B, Xu Z, Wang D, Gay G (2014) The impact of population structure on genomic prediction in stratified populations. Theor Appl Genet 127(3):749–762
Hayes BJ, Bowman PJ, Chamberlain AC, Verbyla K, Goddard ME (2009) Accuracy of genomic breeding values in multi-breed dairy cattle populations. Genet Select Evol 41(1):51
Heslot N, Yang H, Sorrells ME, Jannink J (2012) Genomic selection in plant breeding: a comparison of models. Crop Sci 52:146–160
Isidro J, Jannink J-L, Akdemir D, Poland J, Heslot N, Sorrells ME (2015) Training set optimization under population structure in genomic selection. Theor Appl Genet 128(1):145–158
Karoui S, Carabaño MJ, Díaz C, Legarra A (2012) Joint genomic evaluation of French dairy cattle breeds using multiple-trait models. Genet Select Evol 44(1):39
Lehermeier C, Schön C-C, de los Campos G (2015) Assessment of genetic heterogeneity in structured plant populations using multivariate whole-genome regression models. Genetics 201(1):323–337
Makgahlela M, Mäntysaari E, Strandén I, Koivula M, Nielsen U, Sillanpää M, Juga J (2013) Across breed multi-trait random regression genomic predictions in the nordic red dairy cattle. J Anim Breed Genet 130(1):10–19
Meuwissen THE, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157(4):1819–1829
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):749–764
Olson KM, Van Raden PM, Tooker ME (2012) Multibreed genomic evaluations using purebred Holsteins, Jerseys, and Brown Swiss. J Dairy Sci 95(9):5378–5383
Plieschke L, Edel C, Pimentel EC, Emmerling R, Bennewitz J, Götz K-U (2015) A simple method to separate base population and segregation effects in genomic relationship matrices. Genet Select Evol 47(1):53
Pszczola M, Strabel T, Mulder H, Calus M (2012) Reliability of direct genomic values for animals with different relationships within and to the reference population. J Dairy Sci 95(1):389–400
Rabier C-E, Barre P, Asp T, Charmet G, Mangin B (2016) On the accuracy of genomic selection. PLoS ONE 11(6):1–23
Rincent R, Charcosset A, Moreau L (2017) Predicting genomic selection efficiency to optimize calibration set and to assess prediction accuracy in highly structured populations. Theor Appl Genet 130(11):2231–2247
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):715–728
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):2313–2331
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):3571–3586
Strandén I, Mäntysaari EA (2013) Use of random regression model as an alternative for multibreed relationship matrix. J Anim Breed Genet 130(1):4–9
Technow F, Burger A, Melchinger A E (2013) Genomic prediction of northern corn leaf blight resistance in maize with combined or separated training sets for heterotic groups. Genes–Genomes–Genetics 3(2):197–203
Thornton T, Tang H, Thomas J, Heather M, Bette J, Risch N (2012) Estimating kinship in admixed populations. Am J Hum Genet 91:122–138
Toosi A, Fernando R, Dekkers J (2013) Genomic selection in admixed and crossbred populations. J Anim Sci 130(1):10–19
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):823
VanRaden PM (2008) Efficient methods to compute genomic predictions. J Dairy Sci 91(11):4414–4423
Wientjes YC, Veerkamp RF, Bijma P, Bovenhuis H, Schrooten C, Calus MP (2015a) Empirical and deterministic accuracies of across-population genomic prediction. Genet Select Evol 47(1):5
Wientjes YC, Veerkamp RF, Calus MP (2015b) Using selection index theory to estimate consistency of multi-locus linkage disequilibrium across populations. BMC Genet 16(1):87
Wientjes YCJ, Bijma P, Vandenplas J, Calus MPL (2017) Multi-population genomic relationships for estimating current genetic variances within and genetic correlations between populations. Genetics 207(2):503–515
Wientjes YCJ, Bijma P, Veerkamp RF, Calus MPL (2016) An equation to predict the accuracy of genomic values by combining data from multiple traits, populations, or environments. Genetics 202(2):799–823
Wientjes YCJ, Calus MPL, Hayes BJ, Goddard ME, Hayes BJ (2015c) Impact of QTL properties on the accuracy of multi-breed genomic prediction. Genet Select Evol 47(1):42
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.
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.
Communicated by Benjamin Stich.
Electronic supplementary material
Below is the link to the electronic supplementary material.
About this article
Cite this article
Rio, S., Mary-Huard, T., Moreau, L. et al. Genomic selection efficiency and a priori estimation of accuracy in a structured dent maize panel. Theor Appl Genet 132, 81–96 (2019). https://doi.org/10.1007/s00122-018-3196-1