Publications
Found 18 results
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“Factor analysis applied to genome prediction for high-dimensional phenotypes in pigs”, vol. 15, p. -, 2016.
, “Factor analysis applied to genome prediction for high-dimensional phenotypes in pigs”, vol. 15, p. -, 2016.
, “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
“Comparison of dimensionality reduction methods to predict genomic breeding values for carcass traits in pigs”, vol. 14, pp. 12217-12227, 2015.
, “Estimating additive and dominance variances for complex traits in pigs combining genomic and pedigree information”, vol. 14, pp. 6303-6311, 2015.
, “Genomic selection for slaughter age in pigs using the Cox frailty model”, vol. 14, pp. 12616-12627, 2015.
, “Acute heat stress and dietary methionine effects on IGF-I, GHR, and UCP mRNA expression in liver and muscle of quails”, vol. 13, pp. 7294-7303, 2014.
, “Expression of immune response genes in peripheral blood of cattle infested with Rhipicephalus microplus”, vol. 13, pp. 4013-4021, 2014.
, , “Dietary methionine effects on IGF-I and GHR mRNA expression in broilers”, vol. 12, pp. 6414-6423, 2013.
, “Age-related changes in mitochondrial UCP, ANT and COX III gene expression in the breast muscle of quails (Coturnix coturnix japonica)”, vol. 11, pp. 1981-1989, 2012.
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