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Horse owners choose whether or not to train American Saddlebred horses (ASHs) to perform the 4-beat gaits called rack and slow gait. The rack and slow gait are similar to ambling gaits shown to be associated with variation in the DMRT3 gene in other breeds but are trained rather than naturally occurring gaits. A premature stop codon in the DMRT3 gene (DMRT3_Ser301STOP) caused by the Ch23:g.22999655C>A SNP has an effect on the pattern of locomotion in horses and allows for the pacing gait and strong association with performance of ambling gaits in diverse breeds. We used horse show records to identify ASHs trained to perform as 5-gaited horses and compared them to other Saddlebreds for the prevalence of the A allele of the Ch23:g.22999655C>A SNP of DMRT3. Genomic DNA was typed using a PCR-RFLP technique for 5-gaited ASHs (n = 55), 3-gaited or harness ASHs (n = 64) and ASHs not shown (n = 128). We did not observe differences in the frequencies of the Ch23:g.22999655C>A SNP of DMRT3 when comparing 5-gaited ASHs to other ASHs (P > 0.22). These results suggest that DMRT3 alleles do not affect the choice of breeders to train and show ASHs as 5-gaited horses., (© 2016 Stichting International Foundation for Animal Genetics.)

We investigated genetic associations between mature cow weight (MW) and weaning weight (WW), yearling weight (YW), weight gain from birth to weaning (GBW), weight gain from weaning to yearling (GWY), weaning hip height (WHH), yearling hip height (YHH), scrotal circumference (SC), and age at first calving (AFC). Data from 127,104 Nellore animals born between 1993 and 2006, belonging to Agropecuária Jacarezinho Ltda., were analyzed. (Co)variance components were obtained by the restricted maximum likelihood method, applying an animal model in a multi-traits analysis. The model included direct genetic and residual effects as random effects, the fixed effects of contemporary group, and the linear and quadratic effects of animal age at recording (except for AFC, GBW, and GWY) and age of cow at calving as covariates (except for MW). The numbers of days from birth to weaning and from weaning to yearling were included as covariates for GBW and GWY, respectively. Estimated direct heritabilities were 0.43 ± 0.02 (MW), 0.33 ± 0.01 (WW), 0.36 ± 0.01 (YW), 0.28 ± 0.02 (GBW), 0.31 ± 0.01 (GWY), 0.44 ± 0.02 (WHH), 0.48 ± 0.02 (YHH), 0.44 ± 0.01 (SC), and 0.16 ± 0.03 (AFC). Genetic correlations between MW and productive traits were positive and of medium to high magnitude (ranging from 0.47 ± 0.03 to 0.71 ± 0.01). A positive and low genetic correlation was observed between MW and SC (0.24 ± 0.04). A negative genetic correlation (-0.19 ± 0.03) was estimated between MW and AFC. Selection to increase weight or weight gains at any age, as well as hip height, will change MW in the same direction. Selection for higher SC may lead to a long-term increase in MW. The AFC can be included in selection indices to improve the reproductive performance of beef cattle without significant changes in MW.

The aim of this study was to analyze LEP and DGAT1 gene polymorphisms in 3 Nelore lines selected for growth and to evaluate their effects on growth and carcass traits. Traits analyzed were birth, weaning, and yearling weight, rump height, LM area, backfat thickness, and rump fat thickness obtained by ultrasound. Two SNP in the LEP gene [LEP 1620(A/G) and LEP 305(T/C)] and the K232A mutation in the DGAT1 gene were analyzed. The sample consisted of 357 Nelore heifers from 2 lines selected for yearling weight and a control line, established in 1980, at the Estação Experimental de Zootecnia de Sertãozinho (Sertãozinho, Brazil). Three genotypes were obtained for each marker. Differences in allele frequencies among the 3 lines were only observed for the DGAT1 K232A polymorphism, with the frequency of the A allele being greater in the control line than in the selected lines. The DGAT1 K232A mutation was associated only with rump height, whereas LEP 1620(A/G) was associated with weaning weight and LEP 305(T/C) with birth weight and backfat thickness. However, more studies, with larger data sets, are necessary before these makers can be used for marker-assisted selection.