Abstract
Currently, there is a lack of information regarding the employment of genomic prediction in tropical forages when compared to other crops and temperate forages. Moreover, genomic prediction models have been extensively developed for diploid species, whereas to apply those to polyploids most studies consider the genotypic information parametrized for diploids. This simplification can reduce the accuracy to estimate genetic effects and, consequently, the genomic breeding values. Another challenge is that agronomical and nutritional traits in forages frequently are negatively correlated and may have low heritability. To circumvent those problems one attractive alternative is the use of multi-trait prediction models. Therefore, we compared the impact of the ploidy parametrization over the prediction accuracy of agronomical and nutritional traits in Urochloa spp. hybrids using single and multi-trait models. GBLUP-A (additive) and GBLUP-AD (additive + dominance) showed similar prediction abilities in both single and multi-trait models. Conversely, combining GBLUP-AD and tetraploid information may improve the selection coincidence. Furthermore, the multi-trait Validation Scheme 2, where one trait is not evaluated for some individuals, can provide an increment of up to 30% of prediction ability. Therefore, it is an excellent strategy for traits with low heritability. Overall, all genomic selection models provided greater genetic gains than phenotypic selection. Similarly, the allele dosage associated with additive, dominance, and multi-trait factors increased the accuracy of genomic prediction models for interspecific polyploid hybrids. Finally, genomic prediction should be used in tropical forages breeding programs in order to reduce time.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Alves FC, Granato ÍSC, Galli G, Lyra DH, Fritsche-Neto R, de los Campos G (2019) Bayesian analysis and prediction of hybrid performance. Plant Methods 15:14. https://doi.org/10.1186/s13007-019-0388-x
Andrews S (2010) FastQC: a quality control tool for high throughput sequence data. In: line. http://www.bioinformatics.babraham.ac.uk/projects/fastqc
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 16:1020. https://doi.org/10.1186/s12864-015-2212-y
Bassil NV, Davis TM, Zhang H, Ficklin S, Mittmann M, Webster T, Mahoney L, Wood D, Alperin ES, Rosyara UR, Koehorst-vanc Putten H, Monfort A, Sargent DJ, Amaya I, Denoyes B, Bianco L, van Dijk T, Pirani A, Iezzoni A, Main D, Peace C, Yang Y, Whitaker V, Verma S, Bellon L, Brew F, Herrera R, van de Weg E (2015) Development and preliminary evaluation of a 90 K Axiom® SNP array for the allo-octoploid cultivated strawberry Fragaria × ananassa. BMC Genomics 16. https://doi.org/10.1186/s12864-015-1310-1
Bauer AM, Léon J (2008) Multiple-trait breeding values for parental selection in self-pollinating crops. Theor Appl Genet 116:235–242. https://doi.org/10.1007/s00122-007-0662-6
Bennetzen JL, Schmutz J, Wang H, Percifield R, Hawkins J, Pontaroli AC, Estep M, Feng L, Vaughn JN, Grimwood J, Jenkins J, Barry K, Lindquist E, Hellsten U, Deshpande S, Wang X, Wu X, Mitros T, Triplett J, Yang X, Ye C-Y, Mauro-Herrera M, Wang L, Li P, Sharma M, Sharma R, Ronald PC, Panaud O, Kellogg EA, Brutnell TP, Doust AN, Tuskan GA, Rokhsar D, Devos KM (2012) Reference genome sequence of the model plant Setaria. Nat Biotechnol 30(6):555-561. https://doi.org/10.1038/nbt.2196
Biazzi E, Nazzicari N, Pecetti L, Brummer EC, Palmonari A, Tava A, Annicchiarico P (2017) Genome-wide association mapping and genomic selection for alfalfa (Medicago sativa) forage quality traits. PLoS One 12:e0169234. https://doi.org/10.1371/journal.pone.0169234
Bouckaert RR, Frank E (2004) Evaluating the replicability of significance tests for comparing learning algorithms. In: Dai H, Srikant R, Zhang C (eds) Advances in knowledge discovery and data mining. PAKDD 2004. Lecture Notes in Computer Science, vol 3056. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24775-3_3
Bourke PM, Voorrips RE, Visser RGF, Maliepaard C (2018) Tools for genetic studies in experimental populations of polyploids. Front Plant Sci. https://doi.org/10.3389/fpls.2018.00513
Burgueño J, de los Campos G, Weigel K, Crossa J (2012) Genomic prediction of breeding values when modeling genotype × environment interaction using pedigree and dense molecular markers. Crop Sci 52:707–719. https://doi.org/10.2135/cropsci2011.06.0299
Butler DG, Cullis BR, Gilmour AR, Gogel BJ (2009) ASReml-R reference manual mixed models for S language environments. Train. Ser. QE02001 149
Combs E, Bernardo R (2013) Accuracy of genomewide selection for different traits with constant population size, heritability, and number of markers. Plant Genome 6:0. https://doi.org/10.3835/plantgenome2012.11.0030
Crossa J, Pérez-Rodriguez P, Cuevas J, et al (2017) Genomic selection in plant breeding: methods, models, and perspectives
Daetwyler HD, Calus MPL, Pong-Wong R, de los Campos G, Hickey JM (2013) Genomic prediction in animals and plants: simulation of data, validation, reporting, and benchmarking. Genetics 193:347–365
De Almeida Filho JE, Guimarães JFR, E Silva FF et al (2016) The contribution of dominance to phenotype prediction in a pine breeding and simulated population. Heredity (Edinb) 117:33–41. https://doi.org/10.1038/hdy.2016.23
de Bem Oliveira I, Resende MF, Ferrao F, et al (2018) Genomic prediction of autotetraploids; influence of relationship matrices, allele dosage, and continuous genotyping calls in phenotype prediction. bioRxiv 432179. doi: https://doi.org/10.1101/432179
Figueiredo UJ, Nunes JAR, do Valle CB (2012) Estimation of genetic parameters and selection of Brachiaria humidicola progenies using a selection index. Crop Breed Appl Biotechnol 12:237–244. https://doi.org/10.1590/S1984-70332012000400002
de los Campos G, Hickey JM, Pong-Wong R, Daetwyler HD, Calus MPL (2013) Whole-genome regression and prediction methods applied to plant and animal breeding. Genetics 193:327–345. https://doi.org/10.1534/genetics.112.143313
Depristo MA, Banks E, Poplin R et al (2011) A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat Genet 43:491–501. https://doi.org/10.1038/ng.806
Desta ZA, Ortiz R (2014) Genomic selection: genome-wide prediction in plant improvement. Trends Plant Sci 19:592–601
Dias KODG, Gezan SA, Guimarães CT, et al (2018) Improving accuracies of genomic predictions for drought tolerance in maize by joint modeling of additive and dominance effects in multi-environment trials. Heredity (Edinb). 1–14. https://doi.org/10.1038/s41437-018-0053-6
Dos Santos JPR, De Castro Vasconcellos RC, Pires LPM et al (2016) Inclusion of dominance effects in the multivariate GBLUP model. PLoS One 11:e0152045. https://doi.org/10.1371/journal.pone.0152045
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:e19379. https://doi.org/10.1371/journal.pone.0019379
Enciso-Rodriguez F, Douches D, Lopez-Cruz M, et al (2018) Genomic selection for late blight and common scab resistance in tetraploid potato ( Solanum tuberosum ). G3: genes|genomes|genetics g3.200273.2018. doi: https://doi.org/10.1534/g3.118.200273
Endelman JB, Carley CAS, Bethke PC, Coombs JJ, Clough ME, da Silva WL, de Jong WS, Douches DS, Frederick CM, Haynes KG, Holm DG, Miller JC, Muñoz PR, Navarro FM, Novy RG, Palta JP, Porter GA, Rak KT, Sathuvalli VR, Thompson AL, Yencho GC (2018) Genetic variance partitioning and genome-wide prediction with allele dosage information in autotetraploid potato. Genetics 209:77–87. https://doi.org/10.1534/genetics.118.300685
Euclides VPB, Montagner DB, Barbosa RA, Valle CB, Nantes NN (2016) Animal performance and sward characteristics of two cultivars of Brachiaria brizantha (BRS Paiagu{á}s and BRS Piat{ã}). Rev Bras Zootec 45:85–92
Fernandes SB, Dias KOG, Ferreira DF, Brown PJ (2018) Efficiency of multi-trait, indirect, and trait-assisted genomic selection for improvement of biomass sorghum. Theor Appl Genet 131:747–755. https://doi.org/10.1007/s00122-017-3033-y
Ferrão LFV, Benevenuto J, Oliveira I de B et al (2018) Insights into the genetic basis of blueberry fruit-related traits using diploid and polyploid models in a GWAS context. Front Ecol Evol 6:107. https://doi.org/10.3389/fevo.2018.00107
Gouy M, Rousselle Y, Bastianelli D, Lecomte P, Bonnal L, Roques D, Efile JC, Rocher S, Daugrois J, Toubi L, Nabeneza S, Hervouet C, Telismart H, Denis M, Thong-Chane A, Glaszmann JC, Hoarau JY, Nibouche S, Costet L (2013) Experimental assessment of the accuracy of genomic selection in sugarcane. Theor Appl Genet 126:2575–2586. https://doi.org/10.1007/s00122-013-2156-z
Guo Z, Tucker DM, Lu J, Kishore V, Gay G (2012) Evaluation of genome-wide selection efficiency in maize nested association mapping populations. Theor Appl Genet 124:261–275. https://doi.org/10.1007/s00122-011-1702-9
Guo G, Zhao F, Wang Y, Zhang Y, du L, Su G (2014) Comparison of single-trait and multiple-trait genomic prediction models. BMC Genet 15:30. https://doi.org/10.1186/1471-2156-15-30
Hackett CA, McLean K, Bryan GJ (2013) Linkage analysis and QTL mapping using SNP dosage data in a tetraploid potato mapping population. PLoS One 8:e63939. https://doi.org/10.1371/journal.pone.0063939
Hayes BJ, Visscher PM, Goddard ME (2009) Increased accuracy of artificial selection by using the realized relationship matrix. Genet Res (Camb) 91:47–60. https://doi.org/10.1017/S0016672308009981
Hayes BJ, Cogan NOI, Pembleton LW, Goddard ME, Wang J, Spangenberg GC, Forster JW (2013) Prospects for genomic selection in forage plant species. Plant Breed 132:133–143. https://doi.org/10.1111/pbr.12037
Heffner EL, Sorrells ME, Jannink JL (2009) Genomic selection for crop improvement. Crop Sci 49(1):12
Jank L, Barrios SC, do Valle CB, Simeão RM, Alves GF (2014) The value of improved pastures to Brazilian beef production. Crop Pasture Sci 65:1132–1137. https://doi.org/10.1071/CP13319
Jia Y, Jannink J-L (2012) Multiple-trait genomic selection methods increase genetic value prediction accuracy. Genetics 192:1513–1522. https://doi.org/10.1534/genetics.112.144246
Jonas E, De Koning DJ (2013) Does genomic selection have a future in plant breeding? Trends Biotechnol 31:497–504
Keller-Grein G, Maass BL, Hanson J (1996) Natural variation in brachiaria and existing germplasm. In: Brachiaria: biology, agronomy and improvement. pp 16–42
Kempthorne O (1957) An introduction to genetic statistics
Lehermeier C, Schön CC, de los Campos G (2015) Assessment of genetic heterogeneity in structured plant populations using multivariate whole-genome regression models. Genetics 201:323–337. https://doi.org/10.1534/genetics.115.177394
Li H (2011) A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics 27:2987–2993. https://doi.org/10.1093/bioinformatics/btr509
Li H, Durbin R (2009) Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25(14):1754-1760. https://doi.org/10.1093/bioinformatics/btp324
Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R, 1000 Genome Project Data Processing Subgroup (2009) The sequence alignment/map format and SAMtools. Bioinformatics 25:2078–2079. https://doi.org/10.1093/bioinformatics/btp352
Li X, Wei Y, Acharya A, Hansen JL, Crawford JL, Viands DR, Michaud R, Claessens A, Brummer EC (2015) Genomic prediction of biomass yield in two selection cycles of a tetraploid alfalfa breeding population. Plant Genome 8:0. https://doi.org/10.3835/plantgenome2014.12.0090
Liaw A, Wiener M (2002) Classification and Regression by randomForest R news 2:18–22. doi: https://doi.org/10.1177/154405910408300516
Lopez-Cruz M, Crossa J, Bonnett D, et al (2015) Increased prediction accuracy in wheat breeding trials using a marker x environment interaction genomic selection model. G3: Genes|Genomes|Genetics 5:569–82. doi: https://doi.org/10.1534/g3.114.016097
Lyra DH, de Freitas Mendonça L, Galli G, Alves FC, Granato ÍSC, Fritsche-Neto R (2017) Multi-trait genomic prediction for nitrogen response indices in tropical maize hybrids. Mol Breed 37. https://doi.org/10.1007/s11032-017-0681-1
Marten GC, Brink GE, Buxton DR, Halgerson JL, Hornstein JS (1984) Near infrared reflectance spectroscopy analysis of forage quality in four legume species1.Crop Sci 24(6):1179. https://doi.org/10.2135/cropsci1984.0011183X002400060040x
Martin M (2011) Cutadapt removes adapter sequence from high-throughput sequencing reads. EMBnet.journal 17:10–12. https://doi.org/10.14806/ej.17.1.200
Matias FI, Barrios SCL, do Valle CB et al (2016) Estimate of genetic parameters in Brachiaria decumbens hybrids. Crop Breed Appl Biotechnol 16:115–122. https://doi.org/10.1590/1984-70332016v16n2a18
Matias F, Barrios S, Lucas B, Do Valle C et al (2018) Contribution of additive and dominance effects on agronomical and nutritional traits, and multivariate selection on Urochloa spp. hybrids. Crop Sci. https://doi.org/10.2135/cropsci2018.04.0261
McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, Garimella K, Altshuler D, Gabriel S, Daly M, DePristo MA (2010) The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res 20:1297–1303. https://doi.org/10.1101/gr.107524.110
Mendes-Bonato AB, Pagliarini MS, Forli F, Borges do Valle C, de Oliveira Penteado MI (2002) Chromosome numbers and microsporogenesis in Brachiaria brizantha (Gramineae). Euphytica 125:419–425. https://doi.org/10.1023/A:1016026027724
Meuwissen THE, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819–1829. doi: 11290733
Meuwissen T, Hayes B, Goddard M (2016) Genomic selection: a paradigm shift in animal breeding. Anim Front 6:6–14. https://doi.org/10.2527/af.2016-0002
Montagner DB, do Nascimento Júnior D, Sousa BM de L et al (2012) Morphogenesis in guinea grass pastures under rotational grazing strategies. Rev Bras Zootec 41:883–888
Nyine M, Uwimana B, Blavet N, Hřibová E, Vanrespaille H, Batte M, Akech V, Brown A, Lorenzen J, Swennen R, Doležel J (2018) Genomic prediction in a multiploid crop: genotype by environment interaction and allele dosage effects on predictive ability in banana. Plant Genome 11:0. https://doi.org/10.3835/plantgenome2017.10.0090
Ouyang S, Zhu W, Hamilton J, Lin H, Campbell M, Childs K, Thibaud-Nissen F, Malek RL, Lee Y, Zheng L, Orvis J, Haas B, Wortman J, Buell CR (2006) The TIGR rice genome annotation resource: improvements and new features. Nucleic Acids Res 35(Database):D883-D887. https://doi.org/10.1093/nar/gkl976
Peng Z, Fan W, Wang L, Paudel D, Leventini D, Tillman BL, Wang J (2017) Target enrichment sequencing in cultivated peanut (Arachis hypogaea L.) using probes designed from transcript sequences. Mol Gen Genomics 292:955–965. https://doi.org/10.1007/s00438-017-1327-z
Pérez-Rodríguez P, Gianola D, González-Camacho JM, Crossa J, Manès Y, Dreisigacker S (2012) Comparison between linear and non-parametric regression models for genome-enabled prediction in wheat. G3: Genes, Genomes, Genetics 2:1595–1605. https://doi.org/10.1534/g3.112.003665
Pérez P, de los Campos G (2013) BGLR: a statistical package for whole genome regression and prediction. R package version 1:2. http://genomics.cimmyt.org/BGLR-extdoc.pdf
Resende RMS, Casler MD, de Resende MDV et al (2014) Genomic selection in forage breeding: accuracy and methods. Crop Sci 54:143–156. https://doi.org/10.2135/cropsci2013.05.0353
Rosyara UR, De Jong WS, Douches DS, Endelman JB (2016) Software for genome-wide association studies in autopolyploids and its application to potato. Plant Genome 9:1–10. https://doi.org/10.3835/plantgenome2015.08.0073
Schmitz Carley CA, Coombs JJ, Douches DS, Bethke PC, Palta JP, Novy RG, Endelman JB (2017) Automated tetraploid genotype calling by hierarchical clustering. Theor Appl Genet 130:717–726. https://doi.org/10.1007/s00122-016-2845-5
Schnable PS, Ware D, Fulton RS, Stein JC, Wei F, Pasternak S, Liang C, Zhang J, Fulton L, Graves TA, Minx P, Reily AD, Courtney L, Kruchowski SS, Tomlinson C, Strong C, Delehaunty K, Fronick C, Courtney B, Rock SM, Belter E, Du F, Kim K, Abbott RM, Cotton M, Levy A, Marchetto P, Ochoa K, Jackson SM, Gillam B, Chen W, Yan L, Higginbotham J, Cardenas M, Waligorski J, Applebaum E, Phelps L, Falcone J, Kanchi, Thane T, Scimone A, Thane N, Henke J, Wang T, Ruppert J, Shah N, Rotter K, Hodges J, Ingenthron E, Cordes M, Kohlberg S, Sgro J, Delgado B, Mead K, Chinwalla A, Leonard S, Crouse K, Collura K, Kudrna D, Currie J, He R, Angelova A, Rajasekar S, Mueller T, Lomeli R, Scara G, Ko A, Delaney K, Wissotski M, Lopez G, Campos D, Braidotti M, Ashley E, Golser W, Kim H, Lee S, Lin J, Dujmic Z, Kim W, Talag J, Zuccolo A, Fan C, Sebastian A, Kramer M, Spiegel L, Nascimento L, Zutavern T, Miller B, Ambroise C, Muller S, Spooner W, Narechania A, Ren L, Wei S, Kumari S, Faga B, Levy MJ, McMahan L, Van Buren P, Vaughn MW, Ying K, Yeh C-T, Emrich SJ, Jia Y, Kalyanaraman A, Hsia A-P, Barbazuk WB, Baucom RS, Brutnell TP, Carpita NC, Chaparro C, Chia J-M, Deragon J-M, Estill JC, Fu Y, Jeddeloh JA, Han Y, Lee H, Li P, Lisch DR, Liu S, Liu Z, Nagel DH, McCann MC, SanMiguel P, Myers AM, Nettleton D, Nguyen J, Penning BW, Ponnala L, Schneider KL, Schwartz DC, Sharma A, Soderlund C, Springer NM, Sun Q, Wang H, Waterman M, Westerman R, Wolfgruber TK, Yang L, Yu Y, Zhang L, Zhou S, Zhu Q, Bennetzen JL, Dawe RK, Jiang J, Jiang N, Presting GG, Wessler SR, Aluru S, Martienssen RA, Clifton SW, McCombie WR, Wing RA, Wilson RK (2009) The B73 maize genome: complexity, diversity, and dynamics. Science 326(5956):1112-1115. https://doi.org/10.1126/science.1178534
Schulthess AW, Wang Y, Miedaner T, Wilde P, Reif JC, Zhao Y (2016) Multiple-trait- and selection indices-genomic predictions for grain yield and protein content in rye for feeding purposes. Theor Appl Genet 129:273–287. https://doi.org/10.1007/s00122-015-2626-6
Serang O, Mollinari M, Garcia AAF (2012) Efficient exact maximum a posteriori computation for Bayesian SNP genotyping in polyploids. PLoS One 7:e30906. https://doi.org/10.1371/journal.pone.0030906
Sharma SK, MacKenzie K, McLean K, Dale F, Daniels S, Bryan GJ (2018) Linkage disequilibrium and evaluation of genome-wide association mapping models in tetraploid potato G3 (Bethesda) g3.200377.2018. doi: https://doi.org/10.1534/g3.118.200377
Song J, Yang X, Resende MFR, Neves LG, Todd J, Zhang J, Comstock JC, Wang J (2016) Natural allelic variations in highly polyploidy Saccharum Complex. Front Plant Sci 7. https://doi.org/10.3389/fpls.2016.00804
Souza MB e, Cuevas J, Couto EG de O, et al (2017) Genomic-enabled prediction in maize using Kernel models with genotype × environment interaction. G3: Genes|Genomes|Genetics g3.117.042341. doi: https://doi.org/10.1534/g3.117.042341
Sverrisdóttir E, Byrne S, Sundmark EHR, Johnsen HØ, Kirk HG, Asp T, Janss L, Nielsen KL (2017) Genomic prediction of starch content and chipping quality in tetraploid potato using genotyping-by-sequencing. Theor Appl Genet 130:2091–2108. https://doi.org/10.1007/s00122-017-2944-y
Tan B, Grattapaglia D, Wu HX, Ingvarsson PK (2018) Genomic relationships reveal significant dominance effects for growth in hybrid Eucalyptus. Plant Sci 267:84–93. https://doi.org/10.1016/j.plantsci.2017.11.011
Uitdewilligen JGAML, Wolters AMA, D’hoop BB et al (2013) A next-generation sequencing method for genotyping-by-sequencing of highly heterozygous autotetraploid potato. PLoS One. https://doi.org/10.1371/journal.pone.0062355
Vitezica ZG, Varona L, Legarra A (2013) On the additive and dominant variance and covariance of individuals within the genomic selection scope. Genetics 195:1223–1230. https://doi.org/10.1534/genetics.113.155176
Voorrips RE, Gort G, Vosman B (2011) Genotype calling in tetraploid species from bi-allelic marker data using mixture models. BMC Bioinformatics. https://doi.org/10.1186/1471-2105-12-172
Worthington M, Heffelfinger C, Bernal D, Quintero C, Zapata YP, Perez JG, de Vega J, Miles J, Dellaporta S, Tohme J (2016) A parthenogenesis gene candidate and evidence for segmental allopolyploidy in apomictic Brachiaria decumbens. Genetics 203:1117–1132. https://doi.org/10.1534/genetics.116.190314
You Q, Yang X, Peng Z, Xu L, Wang J (2018) Development and applications of a high throughput genotyping tool for polyploid crops: single nucleotide polymorphism (SNP) array. Front Plant Sci. https://doi.org/10.3389/fpls.2018.00104
Zingaretti L, Monfort A, Pérez-Enciso M (2018) pSBVB: a versatile simulation tool to evaluate genomic selection in polyploid species. G3: Genes|Genomes|Genetics g3.200942.2018. doi: https://doi.org/10.1534/g3.118.200942
Acknowledgments
National Council for Scientific and Technological Development (CNPq), Brazilian Agricultural Research Corporation (EMBRAPA) for financial support. National Center for High-Performance Processing in São Paulo (CENAPAD) and Center for High Throughput Computing (CHTC) for computing support. This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.
Author information
Authors and Affiliations
Contributions
FIM and RFN designed the study. SCLB and CBdoV conducted the field experiment and collected the phenotypic data. KGXM performed the DNA extraction. FIM performed the SNP calling and filtering. FIM and FCA performed the data analyses and interpretation. FIM, FCA, JBE, and RFN wrote the paper. JBE provided analytical expertise and edited the manuscript. RFN supervised the whole study. All authors read and approved the final version of the manuscript for publication.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Key message
The allele dosage associated with additive, dominance, and multi-trait factors increases the accuracy of genomic prediction models for interspecific polyploid hybrids.
Electronic supplementary material
ESM 1
(DOCX 999 kb)
Rights and permissions
About this article
Cite this article
Matias, F.I., Alves, F.C., Meireles, K.G.X. et al. On the accuracy of genomic prediction models considering multi-trait and allele dosage in Urochloa spp. interspecific tetraploid hybrids. Mol Breeding 39, 100 (2019). https://doi.org/10.1007/s11032-019-1002-7
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s11032-019-1002-7