This work aims to estimate the genetic parameters of seminal and production traits in a paternal line of rabbits selected for ADG during the fattening period. The considered traits were male libido (Lib) defined as successful mounting of an artificial vagina; presence of urine (Ur) and calcium carbonate deposits (Ca) in the ejaculate; semen pH; individual sperm motility (IM); the suitability for AI of the ejaculate (Sui), which involves the subjective combination of several quality traits; the average ejaculate volume (Vol); sperm concentration (Conc); and the average sperm production per ejaculate (Prod = Vol × Conc). The genetic relationship between all of these traits with ADG is also provided. Male libido and seminal data came either from routine evaluations of the ejaculates in an AI center or from 2 experiments in which bucks from the same population were used. Two consecutive ejaculates per male and per week were collected, leaving 7 d within weekly collections. A linear tri-trait model was used to analyze Conc, Vol, and ADG, whereas linear and threshold-linear 2-trait models were used to analyze male libido and the remaining seminal traits with ADG. A Bayesian approach was adopted for inference. Approximately 38% of ejaculates were rejected for AI primarily due to low IM scores. Variables related to the quality of the ejaculate (Ur, Ca, pH, IM, Sui) and Lib were found to be lowly heritable (h(2) ranged from 0.04 to 0.11), but repeatable. This indicates performance of bucks for seminal quality traits and libido in AI centers would be more strongly affected by management practices rather than genetic selection. Semen production traits exhibited moderate values of h(2) (0.22, 0.27, and 0.23 for Conc, Vol, and Prod, respectively), suggesting the possibility of effective selection for these traits. A moderate to high negative genetic correlation (r(g); posterior mean; highest posterior density at 95%, HPD(95%)) was estimated between Conc and Vol (-0.53, HPD(95%) = -0.76, -0.27). The ADG was estimated to have an h(2) of 0.16, to have a low, positive r(g) with Conc (0.21, HPD(95%) = -0.03, 0.48), to have a low, negative r(g) with Vol (-0.19, HPD(95%) = -0.47, 0.08), and to be genetically uncorrelated with all remaining traits analyzed. Therefore, selection for increasing ADG in paternal lines is expected to have no detrimental effects on Ur, Ca, pH, IM, Sui, and Lib and little to no effect on Conc, Vol, and Prod.
Failures in fertilization or embryogenesis have been shown to be partly the result of poor semen quality. When AI is practiced, fertilization rate depends on the number and quality of spermatozoa in the insemination dose around the time of application. Individual variation in the male effect on fertility (success or failure to conceive; Fert) and prolificacy (total number of kids born per litter; TB) could also depend on these factors, and it could be better observed under limited conditions of AI, such as decreased sperm concentration, small or null preselection of ejaculates for any semen quality trait, or a long storage period of the AI doses. The aim of this research was to determine if an interaction existed between male genotype and the AI conditions for male effects on Fert and TB after AI was performed under different conditions. Fertility and TB were assumed to be different traits and were analyzed in 2 sets of independent analyses. In the first step, the different conditions were determined uniquely by the sperm dosage. Artificial insemination was performed at 10 and 40 × 10(6) spermatozoa/mL. In the second step, the different conditions were determined by all the factors involved in the AI process as a whole (conditions and duration of the storage period of the dose, genetic type of the female, and environmental conditions on the farm). Data from AI from the former experiment were analyzed with data from AI performed under different conditions. Threshold and linear 2-trait models were assumed for Fert and TB, respectively. The sperm dosage had a clear effect on Fert and TB, which favored the greater dosage (+0.13% and +1.25 kids born, respectively). Prolificacy was more sensitive to sperm reduction than was fertility. Male heritabilities for Fert were 0.09 for both sperm dosages, and were 0.08 and 0.06 for male TB with a smaller and larger sperm dosage, respectively. No genotype × sperm dosage interaction was found. Therefore, the same response to selection to improve male Fert and TB could be achieved at any sperm concentration. However, an interaction between male genotype and the AI conditions as a whole seemed to exist, indicating that the AI conditions for selection for Fert and TB could be modified to maximize genetic progress. Consequently, the optimization of a breeding program for male Fert and TB under a given set of semen utilization conditions is achievable.
This work aimed to study the relationship between pH of the semen and fertility (Fert, defined as the success or failure of conception), which is of special interest because pH of the semen can be considered a global marker of the expression of some seminal quality traits. Different methods used to model the relationship between Fert and pH are presented here: 1) ignoring genetic and environmental correlations and including pH either as a covariate or as a cross-classified effect on fertility, 2) a bivariate mixed model, and 3) recursive bivariate mixed models. A total of 653 pH records and 6,365 Fert records after AI were used. Crossbreed does from 2 maternal lines were artificially inseminated with buck semen from a paternal line in a commercial environment. A negative, and almost linear, effect of pH on Fert was detected. The posterior median of pH and Fert heritabilities, and the highest posterior density interval at 95% (in parentheses) were approximately 0.18 (0.05, 0.29) and approximately 0.10 (0.02, 0.20) across all the models, respectively. Genetic correlations between traits were negative, but the highest posterior density interval at 95% included zero [i.e., -0.31 (-0.91, 0.33) in the bivariate mixed model and -0.17 (-0.99, 0.48) and -0.44 (-0.99, 0.10) in the recursive bivariate mixed models including pH as a covariate or as a cross-classified effect, respectively]. All models predicted Fert data reasonably well (i.e., 76 and 62% correct predictions for success and failure, respectively). No differences in the prediction of the EBV for male fertility were encountered between models, showing a good concordance in the animals ranked by their EBV (the correlation between EBV in all models was close to 1). Thus, no differences in results were obtained considering, or not considering, genetic and environmental correlations between pH and Fert and assuming, or not assuming, recursiveness between each trait. This is because the magnitude of the effect of pH on Fert was not large enough; therefore, the same results were obtained even though the models were of different complexity.
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