 17 de maio de 2012
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 J.B.S. Ferraz*; Rezende, F.M.*; Silva, M.R.†; Silva, J.C.B. †; Silva, R.C.G.*; Eler*, J.P.; Meirelles, F.V. *; Guimarães, J.D. †† 
The Brazilian cattle herd size is around 200 million head, 80% of them with major participation of zebu (Bos indicus) breeds, mainly Nellore. The need of replacement bulls is around 450,000/year (Ferraz & Felicio, 2010), with an estimate of 350,000 Nellore young bulls. Among the selection criteria for replacement bulls, breeding soundness is essential to allow a candidate to become a bull, mainly because close to 95% of the Brazilian beef cows are naturally mate. Between 20 and 40% of bulls from an unselected population may have reduced fertility, few are completely sterile (Kastelic & Thundathil, 2008). Manuals and rules to evaluate breeding soundness are widely described in the literature (Chenoweth et al., 1993; Henry & Neves, 1998; Kennedy et al., 2002) and are intensively used in the Brazilian beef industry. DNA techniques are starting to be widely used in beef cattle genetic evaluation, to recognize uncertain paternity in multi-sire pastures (Van Eenennaam et al, 2007) and, following what is happening with several traits, can be very useful to speed up genetic gain in breeding programs. So, this research was realized to verify the association of DNA markers (single nucleotide polymorphisms – SNP) with ten different traits related to breeding soundness of Nellore young bulls, reared in tropical area of Brazil, close to parallel 20º. 
Data Breeding soundness exams of 1.470 young Nellore bulls, with ages between 18.3 and 30.9 months, reared in three different farms, located in geographical coordinates 20º37’S, 50º13”W; 20º22”S, 49º57”W and 20º28”S, 55º54”W, carried out from 2001 to 2006 and genotyped for 119 genetic markers were used to estimate the effect of those markers on ten traits: traits: scrotum circumference at the physical exam (SC), gross motility (GM), progressive sperm motility (MOT), sperm vigor (VIG), acrossomal defects (ACRO), major sperm defects (MAD), minor sperm defects (MID), total sperm defects (TD) and breeding soundness classification (CLAS). Breeding soundness evaluations were performed by the Animal Reproduction team of Federal University of Viçosa, following the standards established by Blom (1973), Henry & Neves (1998). Data bank maintenance and statistical analysis were performed at Animal Breeding and Biotechnology Group from the College of Animal Science and Feed Engineering, University of Sao Paulo, Brazil. Genotyping. DNA samples were obtained from blood or hair follicles. Genotypes came from DNA mass scpectrometry (Sequenom iPlexTM Mass Spec), carried out in laboratories, located in USA and licensed by IGENITY® (Duluth, Georgia), a Merial Ltd subdivision, the company that owns the exploration licenses rights on markers analyzed. A total of 119 markers were studied in this research, related to many metabolic routes. Markers were coded to protect property rights and confidentiality agreements. Statistical analyses. Allele and genotypic frequencies for T945M and UCP1 were obtained using PROC FREQ of SAS (2004). Associations between SNPs and traits were studied with using mixed model methodology, in a sire model, using PROC MIXED of SAS (2004), using the model: 
where Yij the phenotypic value of an animal, µ is the general mean of the trait, CGi is the fixed effect contemporary group, ß1 is the coefficient for the covariate age at exam, ß2 is the regression coefficient for a given genetic marker, Sj is the coefficient associate to random effect of sire, and eij is the random effect of the residual. Allele substitution effect was estimated as suggested by Falconer (1981), using ß2. F-statistic was considered significant for allelic substitution effect if the nominal P-value was lower than 0.05 (*) or 0.01 (**). As frequencies of alleles were very unequal, the effects were, also, considered "potential" for 0.05≤P≤0.10 (††) or 0.10≤P≤0.25 (†), respectively. 
The descriptive statistics of the traits analyzed are presented in Table 1.
N = nº of observations; AVG = average; STD = standard deviation; MIN, MAX = minimum or maximum values From the 119 markers, 100 were not used in the final analysis, due to several reasons, like fixation of one of the alleles, very low marker frequency or a small amount of genotyped animals. Only 19 markers were considered in the final analysis, although allele substitution effect was estimated for all of the markers were that was possible. Allelic and genotypic frequencies of the most important markers are shown in Table 2.
Homo 1 = homozygous for allele 1 (f= p); Homo 2 = homozygous for allele 2 (f= q), Hetero = heterozygous The analysis of genes frequencies indicate that a large amount of polymorphisms, that were described, originally, mainly in Bos Taurus, are fixed or have a very low frequency in the sample of Bos indicus analyzed. However, an important amount of markers showed polymorphisms that allow estimation allele substitution effects. The association of those 19 markers with the traits was studied and the results are presented in Table 3. The analysis of that table indicates that markers 58, 59, 97, 100 and 105 have statistically significant effects in several traits linked to breeding soundness, while markers 56, 91, 99, 101, 107, 109, 111, 112, 113 and 119 can be potential markers for those traits in Nellore breed. References, in literature, on genetic markers effect on breeding soundness on zebu cattle are very rare, what makes difficult to compare these results with other findings. The allele substitution effects were estimated for markers and some can be very important. For example, effect of marker 100 and 59 on SC were3.15 and 1.25% of the mean of the trait, while the effect of markers 105 and 59, on MD were 16.12 and 10.92% of its’ mean.
*P≤0.05, **P≤0.01, ††0.05≤P≤0.10, †0.10≤P≤0.25 
The use of genetic markers as auxiliary selection criteria can help in selection for breeding soundness of Nellore cattle. Improve the size of population sampled, searching for rare polymorphisms and improving allele frequencies, the inclusion of new markers, related to metabolic routes that are important to semen production will bring better results to such kind of studies. 
Blom, E. (1973). Nordisk Veterinarer Medicin, 25: 383-391. Chenoweth, P.J.; Hopkins, F.M.; Spitzer, J.C.; Larsen, R.E. Theriogenology Handbook, 1993. p. B-10. Falconer, D.S. Introduction to quantitative genetics. 2.ed. England: Longman, 1981. Ferraz, J.B.S.; Felício, P.E. (2010). Meat Science, 84: 238-243. Henry, M.; Neves, J.P. Manual para exame andrológico e avaliação de sêmen animal. 2.ed. Belo Horizonte: CBRA, 1998. 49p. Kennedy, S.P.; Spitzer, J.C.; Hopkins, F.M.; Higdon III, H.L.; Bridges Jr, W.C. (2002). Theriogenology, 58(5):947-961. Kastelic, J.C.; Thundathil, J.C. (2008). Reproduction in Domestic Animals, 43(2):368-373 SAS - STATISTICAL ANALYSIS SYSTEMS. User‘s guide: Version 9.1, Cary, 2004, Van Eenennaam, A.L., Weaber, R.L., Drake, D.J.; Penedo, M.C.T. Quaas, R.L., Garrick, D.J., Pollak, E.J. (2007). Journal of. Animal Science, 85:3159-3169. 1Research partially funded by FAPESP and CNPq, Brazil; *University of Sao Paulo, College of Animal Science and Food Engineering; Pirassununga, SP, Brazil, jbferraz@usp.br; †State University of Sao Paulo, College of Agricultural Sciences, Jaboticabal, SP; ††University of Viçosa, Veterinary School, Viçosa, MG, Brazil. IGENITY® é marca registrada da Merial Saúde Animal Ltda. |
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