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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  
L. M. Moura, Carneiro, P. C. S., Vale, N. M., Barili, L. D., Silva, L. C., Carneiro, J. E. S., Cruz, C. D., Moura, L. M., Carneiro, P. C. S., Vale, N. M., Barili, L. D., Silva, L. C., Carneiro, J. E. S., and Cruz, C. D., Diallel analysis to choose parents for black bean (Phaseolus vulgaris L.) breeding, vol. 15, p. -, 2016.
L. M. Moura, Carneiro, P. C. S., Vale, N. M., Barili, L. D., Silva, L. C., Carneiro, J. E. S., Cruz, C. D., Moura, L. M., Carneiro, P. C. S., Vale, N. M., Barili, L. D., Silva, L. C., Carneiro, J. E. S., and Cruz, C. D., Diallel analysis to choose parents for black bean (Phaseolus vulgaris L.) breeding, vol. 15, p. -, 2016.
G. N. Silva, Tomaz, R. S., Sant’Anna, I. C., Carneiro, V. Q., Cruz, C. D., Nascimento, M., Silva, G. N., Tomaz, R. S., Sant’Anna, I. C., Carneiro, V. Q., Cruz, C. D., Nascimento, M., Silva, G. N., Tomaz, R. S., Sant’Anna, I. C., Carneiro, V. Q., Cruz, C. D., and Nascimento, M., Evaluation of the efficiency of artificial neural networks for genetic value prediction, vol. 15, p. -, 2016.
G. N. Silva, Tomaz, R. S., Sant’Anna, I. C., Carneiro, V. Q., Cruz, C. D., Nascimento, M., Silva, G. N., Tomaz, R. S., Sant’Anna, I. C., Carneiro, V. Q., Cruz, C. D., Nascimento, M., Silva, G. N., Tomaz, R. S., Sant’Anna, I. C., Carneiro, V. Q., Cruz, C. D., and Nascimento, M., Evaluation of the efficiency of artificial neural networks for genetic value prediction, vol. 15, p. -, 2016.
G. N. Silva, Tomaz, R. S., Sant’Anna, I. C., Carneiro, V. Q., Cruz, C. D., Nascimento, M., Silva, G. N., Tomaz, R. S., Sant’Anna, I. C., Carneiro, V. Q., Cruz, C. D., Nascimento, M., Silva, G. N., Tomaz, R. S., Sant’Anna, I. C., Carneiro, V. Q., Cruz, C. D., and Nascimento, M., Evaluation of the efficiency of artificial neural networks for genetic value prediction, vol. 15, p. -, 2016.
F. R. F. Teixeira, Nascimento, M., Nascimento, A. C. C., Silva, F. Fe, Cruz, C. D., Azevedo, C. F., Paixão, D. M., Barroso, L. M. A., Verardo, L. L., de Resende, M. D. V., Guimarães, S. E. F., Lopes, P. S., Teixeira, F. R. F., Nascimento, M., Nascimento, A. C. C., Silva, F. Fe, Cruz, C. D., Azevedo, C. F., Paixão, D. M., Barroso, L. M. A., Verardo, L. L., de Resende, M. D. V., Guimarães, S. E. F., and Lopes, P. S., Factor analysis applied to genome prediction for high-dimensional phenotypes in pigs, vol. 15, p. -, 2016.
F. R. F. Teixeira, Nascimento, M., Nascimento, A. C. C., Silva, F. Fe, Cruz, C. D., Azevedo, C. F., Paixão, D. M., Barroso, L. M. A., Verardo, L. L., de Resende, M. D. V., Guimarães, S. E. F., Lopes, P. S., Teixeira, F. R. F., Nascimento, M., Nascimento, A. C. C., Silva, F. Fe, Cruz, C. D., Azevedo, C. F., Paixão, D. M., Barroso, L. M. A., Verardo, L. L., de Resende, M. D. V., Guimarães, S. E. F., and Lopes, P. S., Factor analysis applied to genome prediction for high-dimensional phenotypes in pigs, vol. 15, p. -, 2016.
G. M. P. de Faria, Sanchez, C. F. B., de Carvalho, L. P., M. Oliveira, daSilva, and Cruz, C. D., Genetic gains from selection for fiber traits in Gossypium hirsutum L., vol. 15, no. 4, p. -, 2016.
Conflicts of interestThe authors declare no conflict of interest.ACKNOWLEDGMENTSThe authors would like to thank Professor Cosme Damião Cruz from the Department of Biology at Universidade Federal de Viçosa, for his helpful advice and proofreading the English style of our manuscript. Research supported by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), Brazil, Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Brazil, and Embrapa Cotton, Brazil. REFERENCESAguiar AM (2003). Use of the design III with molecular markers for the genetic analysis of grain yield and its components in maize. PhD thesis. Escola Superior de Agricultura Luiz de Queiroz, Piracicaba. Akhtar MM, Azhar FM, Ali Z, et al (2008). Genetic basis of fiber quality attributes in upland cotton (Gossypium hirsutum) germplasm. Int. J. Agric. Biol. 10: 217-220. Ali MA, Khan IA, Awan SI, Ali S, et al (2008). Genetics of fibre quality traits in cotton (Gossypium hirsutum L.). Aust. J. Crop Sci. 2: 10-17. Basra AS (2000). Cotton Fibers: Developmental Biology, Quality Improvement, and Textile Processing. Food Products Press, New York. Bayles MB, Verhalen LM, Johnson WM, Barnes BR, et al (2005). Trends over time among cotton cultivars released by the Oklahoma Agricultural Experiment Station. Crop Sci. 45: 966-980. http://dx.doi.org/10.2135/cropsci2004.0453 Braden CA, Smith CW, et al (2004). Fiber length development in near-long staple upland cotton. Crop Sci. 44: 1553-1559. http://dx.doi.org/10.2135/cropsci2004.1553 Bridge RR, Meredith WR, et al (1983). Comparative performance of obsolete and current cotton cultivars. Crop Sci. 23: 949-952. http://dx.doi.org/10.2135/cropsci1983.0011183X002300050032x Bridge RR, Meredith WR, Chism JF, et al (1971). Comparative performance of obsolete and current varieties of upland cotton. Crop Sci. 11: 29-32. http://dx.doi.org/10.2135/cropsci1971.0011183X001100010010x Carvalho SP (1995). Alternative methods for estimating path coefficients and selection indices under multicollinearity. PhD thesis. Universidade Federal de Viçosa, Viçosa. Comstock RE, Robinson HF, et al (1948). The components of genetic variance in populations of biparental progenies and their use in estimating the average degree of dominance. Biometrics 4: 254-266. http://dx.doi.org/10.2307/3001412 Cruz CD, et al (2013). GENES - a software package for analysis in experimental statistics and quantitative genetics. Acta Scientiarum 35: 271-276. Cruz CD and Carneiro PCS (2003). Modelos biométricos aplicados ao melhoramento genético (volume 2). UFV, Viçosa. Culp TW, Green CC, et al (1992). Performance of obsolete and current cotton cultivars and Pee Dee germplasm lines of cotton. Crop Sci. 32: 35-41. http://dx.doi.org/10.2135/cropsci1992.0011183X003200010008x de Magalháes Bertini CHC, Schuster I, Sediyama T, de Barros EG, et al (2006). Characterization and genetic diversity analysis of cotton cultivars using microsatellites. Genet. Mol. Biol. 29: 321-329. http://dx.doi.org/10.1590/S1415-47572006000200021 Eyherabide GH, Hallauer AR, et al (1991). Reciprocal full-sib recurrent selection in maize: I. Direct and indirect responses. Crop Sci. 31: 952-959. http://dx.doi.org/10.2135/cropsci1991.0011183X003100040023x Falconer DS (1987). Introdução à genética quantitativa. Trad. Silva MA and Silva JC. UFV, Viçosa. Falconer DS and Mackay TFC (1996). Introduction to quantitative genetics. Longman, London. Furtado MR (1996). Selection alternatives in Design I of the Comstock and Robinson, in maize. PhD thesis. Universidade Federal de Viçosa, Viçosa. Gonçalves GM, Viana AP, Neto FVB, Pereira MG, et al (2007). Selection and heritability in the prediction of genetic gain in yellow passion fruit. Pesq. Agropec. Bras. 42: 193-198. Hallauer AR, Carena MJ and Miranda Filho JB (1988). Quantitative genetics in maize breeding. Iowa State University Press, Ames. IBGE (Instituto Brasileiro de Geografia e Estatística) Censo Agropecuário 2016. Available at [http://www.ibge.gov.br/home/estatistica/indicadores/agropecuaria/lspa/201607_5]. Accessed September 3, 2016. Miller PA, Rawlings JO, et al (1967). Selection for increased lint yield and correlated responses in upland cotton, Gossypium hirsutum L. Crop Sci. 7: 637-640. http://dx.doi.org/10.2135/cropsci1967.0011183X000700060024x Neves LG, Bruckner CH, Cruz CD, Duarte LP, et al (2011). Genetic gain prediction using the Design I in a population of yellow passion fruit. Rev. Cienc. Agron. 42: 495-501. http://dx.doi.org/10.1590/S1806-66902011000200032 Preetha S, Raveendren TS, et al (2008). Genetic appraisal of yield and fibre quality traits in cotton using interspecific F2, F3 and F4 population. Int. J. Integr. Biol 3: 136-142. Queiroz NL, Filho JLS, Silva MNB, Neto FCV, et al. (2011). Capacidade de combinação entre genótipos de algodoeiro de diferentes bases genéticas para características de fibra. 8 Congresso brasileiro de algodoeiro; COTTON EXPO, 1, 2011, São Paulo. Evolução da cadeia para construção de um setor forte. Anais: Campina Grande. Riaz M, Farooq J, Sakhawat G, Mahmood A, et al (2013). Genotypic variability for root/shoot parameters under water stress in some advanced lines of cotton (Gossypium hirsutum L.). Genet. Mol. Res. 12: 552-561. http://dx.doi.org/10.4238/2013.February.27.4 Santos RF, Kouri J and Santos JW (2008). Crisis and recovery in the Brazilian market of the agricultural feedstock. In: O Agronegócio do Algodão no Brasil 2. ed. (Beltrão NE de M, Azevedo DMP de, eds.). Embrapa Informação Tecnológica, Brasília. Schwartz BM, Smith CW, et al (2008). Genetic gain in fiber properties of upland cotton under varying plant densities. Crop Sci. 48: 1321-1327. http://dx.doi.org/10.2135/cropsci2007.05.0308 Smith CW, Hague S, Hequet EF, Thaxton PS, et al (2008). Development of extra-long staple upland cotton. Crop Sci. 48: 1823-1831. http://dx.doi.org/10.2135/cropsci2008.01.0052 Turner JH, Ramey Jr and Worley Jr (1976). Trends for yield and quality in cotton breeding since 1960. In: Beltwide Cotton Conference, 1976. Proceedings National Cotton Council of America, Las Vegas.  
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.