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2016
A. F. Costa, Teodoro, P. E., Bhering, L. L., Leal, N. R., Tardin, F. D., Daher, R. F., Costa, A. F., Teodoro, P. E., Bhering, L. L., Leal, N. R., Tardin, F. D., and Daher, R. F., Biplot analysis of strawberry genotypes recommended for the State of Espírito Santo, vol. 15, p. -, 2016.
A. F. Costa, Teodoro, P. E., Bhering, L. L., Leal, N. R., Tardin, F. D., Daher, R. F., Costa, A. F., Teodoro, P. E., Bhering, L. L., Leal, N. R., Tardin, F. D., and Daher, R. F., Biplot analysis of strawberry genotypes recommended for the State of Espírito Santo, vol. 15, p. -, 2016.
L. A. Peixoto, Bhering, L. L., and Cruz, C. D., Determination of the optimal number of markers and individuals in a training population necessary for maximum prediction accuracy in F2 populations by using genomic selection models, vol. 15, no. 4, p. -, 2016.
ACKNOWLEDGMENTSWe are thankful to CAPES (Coordenação de Aperfeiçoamento de Pessoal do Ensino Superior), CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico), FAPEMIG (Fundação de Amparo à Pesquisa de Minas Gerais), and Universidade Federal de Viçosa for financial support. We also thank the Biometric Lab (Universidade Federal de Viçosa, Brazil) where all analyses were performed by remote access.REFERENCESAllard RW (1999). Principles of plant breeding. John Wiley & Sons, New York. Ashraf M, Akram NA, Mehboob-Ur-RahmanFoolad MR, et al (2012). Marker-assisted selection in plant breeding for salinity tolerance. Methods Mol. Biol. 913: 305-333. Asoro FG, Newell MA, Beavis WD, Scott MP, et al (2011). Accuracy and training population design for genomic selection on quantitative traits in elite North American oats. Plant Genome 4: 132-144. http://dx.doi.org/10.3835/plantgenome2011.02.0007 Bassi FM, Bentley AR, Charmet G, Ortiz R, et al (2016). Breeding schemes for the implementation of genomic selection in wheat (Triticum spp.). Plant Sci. 242: 23-36. http://dx.doi.org/10.1016/j.plantsci.2015.08.021 Belaj A, del Carmen Dominguez-García M, Atienza SG, Urdíroz NM, et al (2012). Developing a core collection of olive (Olea europaea L.) based on molecular markers (DArTs, SSRs, SNPs) and agronomic traits. Tree Genet. Genomes 8: 365-378. http://dx.doi.org/10.1007/s11295-011-0447-6 Beyene Y, Semagn K, Mugo S, Tarekegne A, et al (2015). Genetic gains in grain yield through genomic selection in eight bi-parental maize populations under drought stress. Crop Sci. 55: 154-163. http://dx.doi.org/10.2135/cropsci2014.07.0460 Bhering LL, Junqueira VS, Peixoto LA, Cruz CD, et al (2015). Comparison of methods used to identify superior individuals in genomic selection in plant breeding. Genet. Mol. Res. 14: 10888-10896. http://dx.doi.org/10.4238/2015.September.9.26 Boichard D, Chung H, Dassonneville R, David X, Bovine LD Consortiumet al (2012). Design of a bovine low-density SNP array optimized for imputation. PLoS One 7: e34130. http://dx.doi.org/10.1371/journal.pone.0034130 Borém A and Miranda GV (2013). Melhoramento de Plantas. UFV, Viçosa. Cros D, Denis M, Sánchez L, Cochard B, et al (2015). Genomic selection prediction accuracy in a perennial crop: case study of oil palm (Elaeis guineensis Jacq.). Theor. Appl. Genet. 128: 397-410. http://dx.doi.org/10.1007/s00122-014-2439-z Cruz CD, et al (2013). GENES: a software package for analysis in experimental statistics and quantitative genetics. Acta Sci. Agron. 35: 271-276. http://dx.doi.org/10.4025/actasciagron.v35i3.21251 de Los Campos G, Vazquez AI, Fernando R, Klimentidis YC, et al (2013). Prediction of complex human traits using the genomic best linear unbiased predictor. PLoS Genet. 9: e1003608. http://dx.doi.org/10.1371/journal.pgen.1003608 Desta ZA, Ortiz R, et al (2014). Genomic selection: genome-wide prediction in plant improvement. Trends Plant Sci. 19: 592-601. http://dx.doi.org/10.1016/j.tplants.2014.05.006 Dirlewanger E, Pronier V, Parvery C, Rothan C, et al (1998). Genetic linkage map of peach [Prunus persica (L.) Batsch] using morphological and molecular markers. Theor. Appl. Genet. 97: 888-895. http://dx.doi.org/10.1007/s001220050969 Erbe M, Gredler B, Seefried FR, Bapst B, et al (2013). A function accounting for training set size and marker density to model the average accuracy of genomic prediction. PLoS One 8: e81046. http://dx.doi.org/10.1371/journal.pone.0081046 Falconer D and Mackay T (1996). Introduction to Quantitative Genetics. Longman Scientific & Technical, Harlow, UK. Frascaroli E, Schrag TA, Melchinger AE, et al (2013). Genetic diversity analysis of elite European maize (Zea mays L.) inbred lines using AFLP, SSR, and SNP markers reveals ascertainment bias for a subset of SNPs. Theor. Appl. Genet. 126: 133-141. http://dx.doi.org/10.1007/s00122-012-1968-6 Gianola D, de los Campos G, Hill WG, Manfredi E, et al (2009). Additive genetic variability and the Bayesian alphabet. Genetics 183: 347-363. http://dx.doi.org/10.1534/genetics.109.103952 Gouy M, Rousselle Y, Bastianelli D, Lecomte P, et al (2013). Experimental assessment of the accuracy of genomic selection in sugarcane. Theor. Appl. Genet. 126: 2575-2586. http://dx.doi.org/10.1007/s00122-013-2156-z Habier D, Fernando RL, Dekkers JC, et al (2009). Genomic selection using low-density marker panels. Genetics 182: 343-353. http://dx.doi.org/10.1534/genetics.108.100289 He J, Zhao X, Laroche A, Lu Z-X, et al (2014). Genotyping-by-sequencing (GBS), an ultimate marker-assisted selection (MAS) tool to accelerate plant breeding. Front. Plant Sci. 5: 484. http://dx.doi.org/10.3389/fpls.2014.00484 Heaton MP, Harhay GP, Bennett GL, Stone RT, et al (2002). Selection and use of SNP markers for animal identification and paternity analysis in U.S. beef cattle. Mamm. Genome 13: 272-281. http://dx.doi.org/10.1007/s00335-001-2146-3 Isidro J, Jannink J-L, Akdemir D, Poland J, et al (2015). Training set optimization under population structure in genomic selection. Theor. Appl. Genet. 128: 145-158. http://dx.doi.org/10.1007/s00122-014-2418-4 Langer M, Maixner M, et al (2004). Molecular characterisation of grapevine yellows associated phytoplasmas of the stolbur-group based on RFLP-analysis of non-ribosomal DNA. VITIS-Journal of Grapevine Research 43: 191-199. Lightfoot DA, et al (2015). Two Decades of Molecular Marker-Assisted Breeding for Resistance to Soybean Sudden Death Syndrome. Crop Sci. 55: 1460-1484. http://dx.doi.org/10.2135/cropsci2014.10.0721 Lynch M, Milligan BG, et al (1994). Analysis of population genetic structure with RAPD markers. Mol. Ecol. 3: 91-99. http://dx.doi.org/10.1111/j.1365-294X.1994.tb00109.x Meuwissen THE, Hayes BJ, Goddard ME, et al (2001). Prediction of total genetic value using genome-wide dense marker maps. Genetics 157: 1819-1829. Ogutu JO, Schulz-Streeck T, Piepho H-P, et al (2012). Genomic selection using regularized linear regression models: ridge regression, lasso, elastic net and their extensions. BMC Proc. 6 (Suppl 2): S10. http://dx.doi.org/10.1186/1753-6561-6-S2-S10 Ordas B, Butron A, Alvarez A, Revilla P, et al (2012). Comparison of two methods of reciprocal recurrent selection in maize (Zea mays L.). Theor. Appl. Genet. 124: 1183-1191. http://dx.doi.org/10.1007/s00122-011-1778-2 Pandey MK, Rani NS, Sundaram RM, Laha GS, et al (2013). Improvement of two traditional Basmati rice varieties for bacterial blight resistance and plant stature through morphological and marker-assisted selection. Mol. Breed. 31: 239-246. http://dx.doi.org/10.1007/s11032-012-9779-7 Poland J, Endelman J, Dawson J, Rutkoski J, et al (2012). Genomic selection in wheat breeding using genotyping-by-sequencing. Plant Genome 5: 103-113. http://dx.doi.org/10.3835/plantgenome2012.06.0006 R Core Team (2015). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna. Ren R, Ray R, Li P, Xu J, et al (2015). Construction of a high-density DArTseq SNP-based genetic map and identification of genomic regions with segregation distortion in a genetic population derived from a cross between feral and cultivated-type watermelon. Mol. Genet. Genomics 290: 1457-1470. http://dx.doi.org/10.1007/s00438-015-0997-7 Soldati MC, Fornes L, Van Zonneveld M, Thomas E, et al (2013). An assessment of the genetic diversity of Cedrela balansae C. DC. (Meliaceae) in Northwestern Argentina by means of combined use of SSR and AFLP molecular markers. Biochem. Syst. Ecol. 47: 45-55. http://dx.doi.org/10.1016/j.bse.2012.10.011 Spindel J, Begum H, Akdemir D, Virk P, et al (2015). Genomic selection and association mapping in rice (Oryza sativa): effect of trait genetic architecture, training population composition, marker number and statistical model on accuracy of rice genomic selection in elite, tropical rice breeding lines. PLoS Genet. 11: e1004982. http://dx.doi.org/10.1371/journal.pgen.1004982 Tang B, Jenkins JN, McCarty J, Watson C, et al (1993). F2 hybrids of host plant germplasm and cotton cultivars: II. Heterosis and combining ability for fiber properties. Crop Sci. 33: 706-710. http://dx.doi.org/10.2135/cropsci1993.0011183X003300040013x Tang B, Jenkins J, Watson C, McCarty J, et al (1996). Evaluation of genetic variances, heritabilities, and correlations for yield and fiber traits among cotton F2 hybrid populations. Euphytica 91: 315-322. http://dx.doi.org/10.1007/BF00033093 Wellmann R, Preuß S, Tholen E, Heinkel J, et al (2013). Genomic selection using low density marker panels with application to a sire line in pigs. Genet. Sel. Evol. 45: 28. http://dx.doi.org/10.1186/1297-9686-45-28 Würschum T, Reif JC, Kraft T, Janssen G, et al (2013). Genomic selection in sugar beet breeding populations. BMC Genet. 14: 85. http://dx.doi.org/10.1186/1471-2156-14-85 Yaniv E, Raats D, Ronin Y, Korol AB, et al (2015). Evaluation of marker-assisted selection for the stripe rust resistance gene Yr15, introgressed from wild emmer wheat. Mol. Breed. 35: 1-12. http://dx.doi.org/10.1007/s11032-015-0238-0 Zhang J, Song Q, Cregan PB, Jiang G-L, et al (2016). Genome-wide association study, genomic prediction and marker-assisted selection for seed weight in soybean (Glycine max). Theor. Appl. Genet. 129: 117-130. http://dx.doi.org/10.1007/s00122-015-2614-x  
P. E. Teodoro, Bhering, L. L., Costa, R. D., Rocha, R. B., Laviola, B. G., Teodoro, P. E., Bhering, L. L., Costa, R. D., Rocha, R. B., and Laviola, B. G., Mixed models for selection of Jatropha progenies with high adaptability and yield stability in Brazilian regions, vol. 15, p. -, 2016.
P. E. Teodoro, Bhering, L. L., Costa, R. D., Rocha, R. B., Laviola, B. G., Teodoro, P. E., Bhering, L. L., Costa, R. D., Rocha, R. B., and Laviola, B. G., Mixed models for selection of Jatropha progenies with high adaptability and yield stability in Brazilian regions, vol. 15, p. -, 2016.
A. A. C. de Azeredo, Bhering, L. L., Brasileiro, B. P., Cruz, C. D., Barbosa, M. H. P., de Azeredo, A. A. C., Bhering, L. L., Brasileiro, B. P., Cruz, C. D., and Barbosa, M. H. P., Selection in sugarcane based on inbreeding depression, vol. 15, p. -, 2016.
A. A. C. de Azeredo, Bhering, L. L., Brasileiro, B. P., Cruz, C. D., Barbosa, M. H. P., de Azeredo, A. A. C., Bhering, L. L., Brasileiro, B. P., Cruz, C. D., and Barbosa, M. H. P., Selection in sugarcane based on inbreeding depression, vol. 15, p. -, 2016.
L. A. Silva, Resende, R. T., Ferreira, R. A. D. C., Silva, G. N., Kist, V., Barbosa, M. H. P., Nascimento, M., Bhering, L. L., Silva, L. A., Resende, R. T., Ferreira, R. A. D. C., Silva, G. N., Kist, V., Barbosa, M. H. P., Nascimento, M., and Bhering, L. L., Selection index using the graphical area applied to sugarcane breeding, vol. 15, p. -, 2016.
L. A. Silva, Resende, R. T., Ferreira, R. A. D. C., Silva, G. N., Kist, V., Barbosa, M. H. P., Nascimento, M., Bhering, L. L., Silva, L. A., Resende, R. T., Ferreira, R. A. D. C., Silva, G. N., Kist, V., Barbosa, M. H. P., Nascimento, M., and Bhering, L. L., Selection index using the graphical area applied to sugarcane breeding, vol. 15, p. -, 2016.
A. M. Corrêa, Pereira, M. I. S., de Abreu, H. K. A., Sharon, T., de Melo, C. L. P., Ito, M. A., Teodoro, P. E., and Bhering, L. L., Selection of common bean genotypes for the Cerrado/Pantanal ecotone via mixed models and multivariate analysis, vol. 15, no. 4, p. -, 2016.
Conflicts of interestThe authors declare no conflict of interest.ACKNOWLEDGMENTSResearch supported by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) and Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq). REFERENCESCarbonell SAM, Chioratto AF, Resende MDV, Dias LAS, et al (2007). Estabilidade de cultivares e linhagens de feijoeiro em diferentes ambientes no Estado de São Paulo. Bragantia 66: 193-201. http://dx.doi.org/10.1590/S0006-87052007000200003 Conab - Companhia Nacional de Abastecimento (2015). Acompanhamento da safra brasileira: Grãos 2013/2014. Décimo levantamento/Agosto 2014. Available at [http://www.conab.gov.br]. Accessed July 10, 2015. Corrêa AM, Lima ARS, Braga DC, Ceccon G, et al (2015). Agronomic performance and genetic variability among common bean genotypes in Savanna/Pantanal ecotone. J. Agron. 14: 175-179. http://dx.doi.org/10.3923/ja.2015.175.179 Corrêa AM, Gonçalves MC, Teodoro PE, et al (2016a). Pattern analysis of multi-environment trials in common bean genotypes. Biosci. J. 32: 328-336. http://dx.doi.org/10.14393/BJ-v32n2a2016-29572 Corrêa AM, Teodoro PE, Gonçalves MC, Barroso LM, et al (2016b). Adaptability and phenotypic stability of common bean genotypes through Bayesian inference. Genet. Mol. Res. 15: .http://dx.doi.org/10.4238/gmr.15028260 Corrêa AM, Teodoro PE, Gonçalves MC, Barroso LM, et al (2016c). Artificial intelligence in the selection of common bean genotypes with high phenotypic stability. Genet. Mol. Res. 15: .http://dx.doi.org/10.4238/gmr.15028230 Cruz CD, et al (2013). Genes: a software package for analysis in experimental statistics and quantitative genetics. Acta Sci. Agron. 35: 271-276. http://dx.doi.org/10.4025/actasciagron.v35i3.21251 Cruz CD, Torres RA, Vencovsky R, et al (1989). An alternative approach to the stability analysis proposed by Silva and Barreto. Rev. Bras. Genet. 12: 567-580. Cruz CD, Carneiro PCS and Regazzi AJ (2012). Modelos biométricos aplicados ao melhoramento genético. 3rd ed. Editora UFV, Viçosa. Fao - Food and Agriculture Organization of United Nations (2015). Food and agricultural commodities production. Available at [http://www.fao.org]. Accessed July 10, 2015. Finlay KW, Wilkinson GN, et al (1963). The analysis of adaptation in a plant-breeding programme. Aust. J. Agric. Res. 14: 742-754. http://dx.doi.org/10.1071/AR9630742 Martins SM, Melo PG, Faria LC, Souza TL, et al (2016). Genetic parameters and breeding strategies for high levels of iron and zinc in Phaseolus vulgaris L. Genet. Mol. Res. 15. http://dx.doi.org/10.4238/gmr.15028011 Regitano Neto A, Ramos Júnior EA, Gallo PB, Freitas JG, et al (2013). Comportamento de genótipos de arroz de terras altas no estado de São Paulo. Rev. Cienc. Agron. 44: 512-519. http://dx.doi.org/10.1590/S1806-66902013000300013 Resende MDV (2007). SELEGEN-REML/BLUP: sistema estatístico e seleção genética computadorizada via modelos lineares mistos. Embrapa Florestas, Colombo. Rocha RB, Muro-Abad JI, Araújo EF, Cruz CD, et al (2005). Avaliação do método centróide para estudo da estabilidade e adaptabilidade ao ambiente. Cienc. Florest. 15: 255-266. http://dx.doi.org/10.5902/198050981863 Silva MG, Arf O, Teodoro PE, et al (2015). Nitrogen topdressing and application ways of fluazifop-p-butyl + fomesafen in weed control and agronomic performance of common bean. An. Acad. Bras. Cienc. 87: 2301-2307. http://dx.doi.org/10.1590/0001-3765201520140347 Teodoro PE, Oliveira-Júnior JF, Cunha ER, Correa CCG, et al (2016). Cluster analysis applied to the spatial and temporal variability of monthly rainfall in Mato Grosso do Sul State, Brazil. Meteorol. Atmos. Phys. 128: 197-209. http://dx.doi.org/10.1007/s00703-015-0408-y Torres FE, Teodoro PE, Sagrilo E, Ceccon G, et al (2015). Interação genótipo x ambiente em genótipos de feijão-caupi semiprostrado via modelos mistos. Bragantia 74: 255-260. http://dx.doi.org/10.1590/1678-4499.0099 Torres FE, Teodoro PE, Rodrigues EV, Santos A, et al (2016). Simultaneous selection for cowpea (Vigna unguiculata L.) genotypes with adaptability and yield stability using mixed models. Genet. Mol. Res. 15. http://dx.doi.org/10.4238/gmr.15028272