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2016
S. M. Coser, Motoike, S. Y., Corrêa, T. R., Pires, T. P., and Resende, M. D. V., Breeding of Acrocomia aculeata using genetic diversity parameters and correlations to select accessions based on vegetative, phenological, and reproductive characteristics, vol. 15, no. 4, p. -, 2016.
Conflicts of interestThe authors declare no conflict of interest.ACKNOWLEDGMENTSThe authors thank Petrobras SA for funding the research project and Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) for granting scholarships. REFERENCESBerton LHC, Azevedo Filho JA, Siqueira WJ, Colombo CA, et al (2013). Seed germination and estimates of genetic parameters of promising macaw palm (Acrocomia aculeata) progenies for biofuel production. Ind. Crops Prod. 51: 258-266. http://dx.doi.org/10.1016/j.indcrop.2013.09.012 Corrêa TR, Motoike SY, Coser SM, Silveira G, et al (2015). Estimation of genetic parameters for in vitro oil palm characteristics (Elaeis guineensis Jacq.) and selection of genotypes for cloning capacity and oil yield. Ind. Crops Prod. 77: 1033-1038. http://dx.doi.org/10.1016/j.indcrop.2015.09.066 Falconer DS and Mackay TFC (1996). Introduction to quantitative genetic. Fourth edition Essex: Longman. FAO (2013). Food and Agriculture Organization of the United Nations. FAOSTAT Database [http://faostat3.fao.org/home/E]. Accessed February 20, 2015. Farias Neto JT, Resende MDV, Oliveira MSP, Nogueira OL, et al (2008). Estimativas de parâmetros genéticos e ganhos de seleção em progênies de polinização aberta de açaizeiro. Rev. Bras. Frutic. 30: 1051-1056. http://dx.doi.org/10.1590/S0100-29452008000400035 Farias Neto JT, Clement CR, Resende MDV, et al (2013). Estimativas de parâmetros genéticos e ganho de seleção para produção de frutos em progênies de polinização aberta de pupunheira no estado do Pará, Brasil. Bragantia 32: 122-126. http://dx.doi.org/10.1590/S0006-87052013000200002 Gan PY, Li ZD, et al (2013). Econometric study Malaysia’s palm oil position in the word market to 2035. Renew. Sustain. Energy Rev. 39: 740-747. http://dx.doi.org/10.1016/j.rser.2014.07.059 Govindaraj M, Vetriventhan M, Srinivasan M, et al (2015). Importance of genetic diversity assessment in crop plants and its recent advances: an overview of its analytical perspectives. Genet. Res. Int. 2015: 431487. http://dx.doi.org/10.1155/2015/431487 Lanes ECM, Motoike SY, Kuki KN, Nick C, et al (2015). Molecular characterization and population structure of the macaw palm, Acrocomia aculeata (Arecaceae), ex situ germplasm collection using microsatellites markers. J. Hered. 106: 102-112. http://dx.doi.org/10.1093/jhered/esu073 Lopes R, Cunha RNV, Resende MDV, et al (2012). Produção de cachos e parâmetros genéticos de híbridos de caiaué com dendezeiro. Pesqui. Agropecu. Bras. 47: 1496-1503. http://dx.doi.org/10.1590/S0100-204X2012001000012 Manfio CE, Motoike SY, Resende MDV, de Santos CEM, et al (2012). Avaliação de progênies de macaúba na fase juvenil e estimativas de parâmetros genéticos e diversidade genética. Pesq. Florest. Bras 32: 63-69. http://dx.doi.org/10.4336/2012.pfb.32.69.63 Massaro RAM, Bonine CAV, Scarpinati EA, Paula RC, et al (2010). Viabilidade de aplicação da seleção precoce em testes clonais de Eucalyptus spp. Cienc. Florest. 20: 597-609. http://dx.doi.org/10.5902/198050982418 Motoike S, Kuki K, et al (2009). The potential of macaw palm (Acrocomia Aculeata) as source of biodiesel in Brazil. Int. Rev. Chem. Eng. Rapid Commun 1: 632-635. Mulamba NN, Mock JJ, et al (1978). Improvement of yield potential of the Eto Blanco maize (Zea mays L.) population by breeding for plant traits. Egypt. J. Genet. Cytol. 7: 40-51. Oliveira DA, Melo Júnior AF, Brandão MM, Rodrigues LA, et al (2012). Genetic diversity in populations of Acrocomia aculeata (Arecaceae) in the northern region of Minas Gerais, Brazil. Genet. Mol. Res. 11: 531-538. http://dx.doi.org/10.4238/2012.March.8.1 Resende MDV (1997). Avanços da genética biométrica Florestal. In: Encontro sobre temas de Genética e Melhoramento. Piracicaba. Anais. Piracicaba: ESALQ-USP.150-158. Resende MDV (2002). Genética Biométrica e Estatística no Melhoramento de Plantas Perenes. Embrapa Informação Tecnológica, Brasília. Roscoe R, Richetti A, Maranho E, et al (2007). Análise de viabilidade técnica de oleaginosas para produção de biodiesel em Mato Grosso do Sul. RPA 1: 48-59. Sokal RR and Rohlf FJ (1995). Biometry. Freeman Press, San Francisco. Wandeck FA and Justo PGA (1988). Macaúba, fonte energética e insumo industrial: sua significação econômica no Brasil. In: Simpósio Sobre o Cerrado, Savanas, 6. 1988, Brasília. Anais. Planaltina: EMBRAPA, CPAC, 541-577.  
I. S. C. Granato, Fritsche-Neto, R., Resende, M. D. V., Silva, F. F., Granato, I. S. C., Fritsche-Neto, R., Resende, M. D. V., and Silva, F. F., Effects of using phenotypic means and genotypic values in GGE biplot analyses on genotype by environment studies on tropical maize (Zea mays), vol. 15, p. -, 2016.
I. S. C. Granato, Fritsche-Neto, R., Resende, M. D. V., Silva, F. F., Granato, I. S. C., Fritsche-Neto, R., Resende, M. D. V., and Silva, F. F., Effects of using phenotypic means and genotypic values in GGE biplot analyses on genotype by environment studies on tropical maize (Zea mays), vol. 15, p. -, 2016.
V. S. Santos, S. Filho, M., Resende, M. D. V., Azevedo, C. F., Lopes, P. S., Guimarães, S. E. F., and Silva, F. F., Genomic prediction for additive and dominance effects of censored traits in pigs, vol. 15, no. 4, p. -, 2016.
Conflicts of interestThe authors declare no conflict of interest.ACKNOWLEDGMENTSThe first author would like to thank the CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior) for a Sandwich Doctorate scholarship (grant #BEX 9415/14-9). Research supported by CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico) and FAPEMIG (Fundação de Amparo à Pesquisa do Estado de Minas Gerais). REFERENCESAzevedo CF, de Resende MD, E Silva FF, Viana JMS, et al (2015). Ridge, Lasso and Bayesian additive-dominance genomic models. BMC Genet. 16: 105. http://dx.doi.org/10.1186/s12863-015-0264-2 Band GO, Guimarães SEF, Lopes PS, Peixoto JDO, et al (2005). Relationship between the Porcine Stress Syndrome gene and carcass and performance traits in F2 pigs resulting from divergent crosses. Genet. Mol. Biol. 28: 92-96. http://dx.doi.org/10.1590/S1415-47572005000100016 Costa EV, Diniz DB, Veroneze R, Resende MD, et al (2015). Estimating additive and dominance variances for complex traits in pigs combining genomic and pedigree information. Genet. Mol. Res. 14: 6303-6311. http://dx.doi.org/10.4238/2015.June.11.4 Cox DR, et al (1972). Regression models and life tables (with discussion). J. R. Stat. Soc. Series B Stat. Methodol. 34: 187-220. de Los Campos G, Gianola D, Rosa GJM, et al (2009). Reproducing kernel Hilbert spaces regression: a general framework for genetic evaluation. J. Anim. Sci. 87: 1883-1887. http://dx.doi.org/10.2527/jas.2008-1259 Ducrocq V, Sölkner J and Mészáros G (2010). Survival Kit v6-a Software Package for Survival Analysis (ID232). Proceedings of the 9th World Congress of Genetic and Applied Livestock Production, Leipzig, 232. Ertl J, Legarra A, Vitezica ZG, Varona L, et al (2014). Genomic analysis of dominance effects on milk production and conformation traits in Fleckvieh cattle. Genet. Sel. Evol. 46: 40. http://dx.doi.org/10.1186/1297-9686-46-40 de Almeida Filho JE, Guimarães JFR, E Silva FF, de Resende MD, et al (2016). The contribution of dominance to phenotype prediction in a pine breeding and simulated population. Heredity (Edinb) 117: 33-41. http://dx.doi.org/10.1038/hdy.2016.23 Giolo SR, Demétrio CGB, et al (2011). A frailty modeling approach for parental effects in animal breeding. J. Appl. Stat. 38: 619-629. http://dx.doi.org/10.1080/02664760903521492 Guo SF, Gianola D, Rekaya R, Short T, et al (2001). Bayesian analysis of lifetime performance and prolificacy in Landrace sows using a linear mixed model with censoring. Livest. Prod. Sci. 72: 243-252. http://dx.doi.org/10.1016/S0301-6226(01)00219-6 Hollander CA, Knol EF, Heuven HCM, van Grevenhof EM, et al (2015). Interval from last insemination to culling: II. Culling reasons from practise and the correlation with longevity. Livest. Sci. 181: 25-30. http://dx.doi.org/10.1016/j.livsci.2015.09.018 Hou Y, Madsen P, Labouriau R, Zhang Y, et al (2009). Genetic analysis of days from calving to first insemination and days open in Danish Holsteins using different models and censoring scenarios. J. Dairy Sci. 92: 1229-1239. http://dx.doi.org/10.3168/jds.2008-1556 Kärkkäinen HP, Sillanpää MJ, et al (2013). Fast genomic predictions via Bayesian G-BLUP and multilocus models of threshold traits including censored Gaussian data. G3 (Bethesda) 3: 1511-1523. http://dx.doi.org/10.1534/g3.113.007096 Mészáros G, Pálos J, Ducrocq V, Sölkner J, et al (2010). Heritability of longevity in Large White and Landrace sows using continuous time and grouped data models. Genet. Sel. Evol. 42: 13. http://dx.doi.org/10.1186/1297-9686-42-13 Morota G, Boddhireddy P, Vukasinovic N, Gianola D, et al (2014). Kernel-based variance component estimation and whole-genome prediction of pre-corrected phenotypes and progeny tests for dairy cow health traits. Front. Genet. 5: 56. http://dx.doi.org/10.3389/fgene.2014.00056 Mrode RA (2005). Linear models for the prediction of animal breeding values. CAB International, Wallingford. Muñoz PR, Resende MFJrGezanSA, Resende MDV, et al (2014). Unraveling additive from nonadditive effects using genomic relationship matrices. Genetics 198: 1759-1768. http://dx.doi.org/10.1534/genetics.114.171322 Nishio M, Satoh M, et al (2014). Including dominance effects in the genomic BLUP method for genomic evaluation. PLoS One 9: e85792. http://dx.doi.org/10.1371/journal.pone.0085792 Onteru SK, Fan B, Nikkilä MT, Garrick DJ, et al (2011). Whole-genome association analyses for lifetime reproductive traits in the pig. J. Anim. Sci. 89: 988-995. http://dx.doi.org/10.2527/jas.2010-3236 Ornella L, Pérez P, Tapia E, González-Camacho JM, et al (2014). Genomic-enabled prediction with classification algorithms. Heredity (Edinb) 112: 616-626. http://dx.doi.org/10.1038/hdy.2013.144 Pankratz VS, de Andrade M, Therneau TM, et al (2005). Random-effects Cox proportional hazards model: general variance components methods for time-to-event data. Genet. Epidemiol. 28: 97-109. http://dx.doi.org/10.1002/gepi.20043 Pérez P, de los Campos G, et al (2014). Genome-wide regression and prediction with the BGLR statistical package. Genetics 198: 483-495. http://dx.doi.org/10.1534/genetics.114.164442 Pinheiro JC and Bates DM (2000). Mixed-Effects Models in S and S-PLUS. Springer-Verlag, New York. R Development Core Team (2016). R: A Language and Environment for Statistical Computing. Available at http://www.R-project.org. Accessed March 16, 2016. Resende MDV, Silva FF and Azevedo CF (2014). Estatística matemática, biométrica e computacional: modelos mistos, multivariados, categóricos e generalizados (REML/BLUP), Inferência Bayesiana, Regressão Aleatória, Seleção Genômica, QTL-GWAS, Estatística Espacial e Temporal, Competição, Sobrevivência. Editora Suprema, Viçosa. Santos VS, Martins Filho S, Resende MDV, Azevedo CF, et al (2015). Genomic selection for slaughter age in pigs using the Cox frailty model. Genet. Mol. Res. 14: 12616-12627. http://dx.doi.org/10.4238/2015.October.19.5 Schaeffer L (2013). Survival. In: History of genetic evaluation methods in dairy cattle (Grosu H, Schaeffer L, Oltenacu PA, et al., eds.) 279-298. Available at [https://xa.yimg.com/kq/groups/18395782/1926111600/name/FINAL_BOOK_29.04.2013.pdf]. Accessed 12 April, 2016 Schneider MdelP, Strandberg E, Ducrocq V, Roth A, et al (2005). Survival analysis applied to genetic evaluation for female fertility in dairy cattle. J. Dairy Sci. 88: 2253-2259. http://dx.doi.org/10.3168/jds.S0022-0302(05)72901-5 Serenius T, Stalder KJ, Puonti M, et al (2006). Impact of dominance effects on sow longevity. J. Anim. Breed. Genet. 123: 355-361. http://dx.doi.org/10.1111/j.1439-0388.2006.00614.x Silva FF, de Resende MD, Rocha GS, Duarte DA, et al (2013). Genomic growth curves of an outbred pig population. Genet. Mol. Biol. 36: 520-527. http://dx.doi.org/10.1590/S1415-47572013005000042 Smith BJ, et al (2007). boa: An R Package for MCMC Output Convergence Assessment and Posterior Inference. J. Stat. Softw. 21: 1-37. http://dx.doi.org/10.18637/jss.v021.i11 Sobczyńska M, Blicharski T, et al (2015). Phenotypic and genetic variation in longevity of Polish Landrace sows. J. Anim. Breed. Genet. 132: 318-327. http://dx.doi.org/10.1111/jbg.12135 Sorensen DA, Gianola D, Korsgaard IR, et al (1998). Bayesian mixed‐effects model analysis of a censored normal distribution with animal breeding applications. Acta Agric. Scand. Anim. Sci. 48: 222-229. Su G, Christensen OF, Ostersen T, Henryon M, et al (2012). Estimating additive and non-additive genetic variances and predicting genetic merits using genome-wide dense single nucleotide polymorphism markers. PLoS One 7: e45293. http://dx.doi.org/10.1371/journal.pone.0045293 Therneau T (2012). Mixed effects Cox models. R package version 2.2-3. http://cran.r-project.org/web/packages/coxme/vignettes/coxme.pdf. Accessed April 12, 2016. VanRaden PM, et al (2008). Efficient methods to compute genomic predictions. J. Dairy Sci. 91: 4414-4423. http://dx.doi.org/10.3168/jds.2007-0980 Verardo LL, Silva FF, Varona L, Resende MDV, et al (2015). Bayesian GWAS and network analysis revealed new candidate genes for number of teats in pigs. J. Appl. Genet. 56: 123-132. http://dx.doi.org/10.1007/s13353-014-0240-y Wang C, Da Y, et al (2014). Quantitative genetics model as the unifying model for defining genomic relationship and inbreeding coefficient. PLoS One 9: e114484. http://dx.doi.org/10.1371/journal.pone.0114484 Yazdi MH, Visscher PM, Ducrocq V, Thompson R, et al (2002). Heritability, reliability of genetic evaluations and response to selection in proportional hazard models. J. Dairy Sci. 85: 1563-1577. http://dx.doi.org/10.3168/jds.S0022-0302(02)74226-4  
J. E. Almeida Filho, Tardin, F. D., Guimarães, J. F. R., Resende, M. D. V., Silva, F. F., Simeone, M. L., Menezes, C. B., Queiroz, V. A. V., Filho, J. E. Almeida, Tardin, F. D., Guimarães, J. F. R., Resende, M. D. V., Silva, F. F., Simeone, M. L., Menezes, C. B., and Queiroz, V. A. V., Multi-trait BLUP model indicates sorghum hybrids with genetic potential for agronomic and nutritional traits, vol. 15, p. -, 2016.
J. E. Almeida Filho, Tardin, F. D., Guimarães, J. F. R., Resende, M. D. V., Silva, F. F., Simeone, M. L., Menezes, C. B., Queiroz, V. A. V., Filho, J. E. Almeida, Tardin, F. D., Guimarães, J. F. R., Resende, M. D. V., Silva, F. F., Simeone, M. L., Menezes, C. B., and Queiroz, V. A. V., Multi-trait BLUP model indicates sorghum hybrids with genetic potential for agronomic and nutritional traits, vol. 15, p. -, 2016.
C. F. Azevedo, Resende, M. D. V., Silva, F. F., Viana, J. M. S., Valente, M. S. F., Resende, Jr, M. F. R., Oliveira, E. J., Azevedo, C. F., Resende, M. D. V., Silva, F. F., Viana, J. M. S., Valente, M. S. F., Resende, Jr, M. F. R., and Oliveira, E. J., New accuracy estimators for genomic selection with application in a cassava (Manihot esculenta) breeding program, vol. 15, p. -, 2016.
C. F. Azevedo, Resende, M. D. V., Silva, F. F., Viana, J. M. S., Valente, M. S. F., Resende, Jr, M. F. R., Oliveira, E. J., Azevedo, C. F., Resende, M. D. V., Silva, F. F., Viana, J. M. S., Valente, M. S. F., Resende, Jr, M. F. R., and Oliveira, E. J., New accuracy estimators for genomic selection with application in a cassava (Manihot esculenta) breeding program, vol. 15, p. -, 2016.
R. D. Castro, Peternelli, L. A., Resende, M. D. V., Marinho, C. D., Costa, P. M. A., Barbosa, M. H. P., Moreira, E. F. A., Castro, R. D., Peternelli, L. A., Resende, M. D. V., Marinho, C. D., Costa, P. M. A., Barbosa, M. H. P., Moreira, E. F. A., Castro, R. D., Peternelli, L. A., Resende, M. D. V., Marinho, C. D., Costa, P. M. A., Barbosa, M. H. P., and Moreira, E. F. A., Selection between and within full-sib sugarcane families using the modified BLUPIS method (BLUPISM), vol. 15, p. -, 2016.
R. D. Castro, Peternelli, L. A., Resende, M. D. V., Marinho, C. D., Costa, P. M. A., Barbosa, M. H. P., Moreira, E. F. A., Castro, R. D., Peternelli, L. A., Resende, M. D. V., Marinho, C. D., Costa, P. M. A., Barbosa, M. H. P., Moreira, E. F. A., Castro, R. D., Peternelli, L. A., Resende, M. D. V., Marinho, C. D., Costa, P. M. A., Barbosa, M. H. P., and Moreira, E. F. A., Selection between and within full-sib sugarcane families using the modified BLUPIS method (BLUPISM), vol. 15, p. -, 2016.
R. D. Castro, Peternelli, L. A., Resende, M. D. V., Marinho, C. D., Costa, P. M. A., Barbosa, M. H. P., Moreira, E. F. A., Castro, R. D., Peternelli, L. A., Resende, M. D. V., Marinho, C. D., Costa, P. M. A., Barbosa, M. H. P., Moreira, E. F. A., Castro, R. D., Peternelli, L. A., Resende, M. D. V., Marinho, C. D., Costa, P. M. A., Barbosa, M. H. P., and Moreira, E. F. A., Selection between and within full-sib sugarcane families using the modified BLUPIS method (BLUPISM), vol. 15, p. -, 2016.
2011
R. G. Freitas, Missio, R. F., Matos, F. S., Resende, M. D. V., and Dias, L. A. S., Genetic evaluation of Jatropha curcas: an important oilseed for biodiesel production, vol. 10, pp. 1490-1498, 2011.
Basha SD and Sujatha M (2007). Inter and intra-population variability of Jatropha curcas (L.) characterized by RAPD and ISSR markers and development of population-specific SCAR markers. Euphytica 156: 375-386. doi:10.1007/s10681-007-9387-5 Basha SD, Francis G, Makkar HPS and Becker K (2009). A comparative study of biochemical traits and molecular markers for assessment of genetic relationships between Jatropha curcas L. germplasm from different countries. Plant Sci. 176: 812-823. doi:10.1016/j.plantsci.2009.03.008 Bernardo R (2002). Breeding for Quantitative Traits in Plants. Stemma Press, Woodbury. Cruz CD (2006). Genes Versão 2006.4.1: Programa Genes Versão Windows. Universidade Federal de Viçosa, Viçosa. Dahmer N, Schifino-Wittmann MT and Dias LAS (2009). Chromosome numbers of Jatropha curcas L.: an important agrofuel plant. Crop Breed. Appl. Biotechnol. 9: 386-389. Dias LAS, Leme LP, Laviola BG and Pallini A (2007). Cultivo de Pinhão Manso (Jatropha curcas L.) para Produção de Óleo Combustível. Universidade Federal de Viçosa, Viçosa. Divakara BN, Upadhyaya HD, Wani SP and Gowda CLL (2009). Biology and genetic improvement of Jatropha curcas L.: a review. Appl. Energy 87: 732-742. doi:10.1016/j.apenergy.2009.07.013 Ginwal HS, Rawat OS and Srivastava RL (2004). Seed source variation in growth performance and oil yield of Jatropha curcas Linn. in Central India. Silvae Genet. 53: 186-192. Ginwal HS, Phartyal SS, Rawat OS and Srivastava RL (2005). Seed source variation in morphology, germination and seedling growth of Jatropha curcas Linn. in Central India. Silvae Genet. 54: 76-80. Heller J (1996). Physic Nut (Jatropha curcas L.). Promoting the Conservation and Use of Underutilized and Neglected Crops. International Board for Plant Genetic Resources, Roma, 161. Jongschaap REE, Corré WJ, Bindraban OS and Brandenburg WA (2007). Claims and facts on Jatropha curcas L.: global Jatropha curcas evaluation, breeding and propagation programme. Plant Res. Int. Report 158. Kaushik N, Kumar K, Kumar S, Kaushikb N, et al. (2007). Genetic variability and divergence studies in seed traits and oil content of Jatropha (Jatropha curcas L.) accessions. Biomass Bioenergy 31: 497-502. doi:10.1016/j.biombioe.2007.01.021 Mishra DK (2009). Selection of candidate plus phenotypes of Jatropha curcas L. using method of paired comparisons. Biomass Bioenergy 33: 542-545. doi:10.1016/j.biombioe.2008.08.004 Rao GR, Korwar GR, Shanker AK and Ramakrishna YS (2008). Genetic associations, variability and diversity in seed characters, growth, reproductive phenology and yield in Jatropha curcas (L.) accessions. Trees 22: 697-709. doi:10.1007/s00468-008-0229-4 Resende MDV (2007). Software Selegen-Reml/Blup. Embrapa Floresta, Colombo. SAS Institute Inc. (1989). SAS/STAT User’s Guide: Version 6. 4th edn. SAS Institute, Cary. Sun QB, Li LF, Li Y, Wu GJ, et al. (2008). SSR and AFLP markers reveal low genetic diversity in the biofuel plant Jatropha curcas in China. Crop Sci. 48: 1865-1871. doi:10.2135/cropsci2008.02.0074