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Genetic Parameters For Sheep Production Traits


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Genetic Parameters For Sheep Production Traits


Genetic correlations between 29 wool production and quality traits and live weight and ultrasound fat depth (FAT) and eye muscle depth (EMD) traits were estimated from the Information Nucleus (IN). The IN comprised 8 genetically linked flocks managed across a range of Australian sheep production environments. The data were from a maximum of 9,135 progeny born over 5 yr from 184 Merino sires and 4,614 Merino dams. The wool traits included records for yearling and adult fleece weight, fiber diameter (FD), staple length (SL), fiber diameter CV (FDCV), scoured color, and visual scores for breech and body wrinkle. We found high heritability for the major yearling wool production traits and some wool quality traits, whereas other wool quality traits, wool color, and visual traits were moderately heritable. The estimates of heritability for live weight generally increased with age as maternal effects declined. Estimates of heritability for the ultrasound traits were also higher when measured at yearling age rather than at postweaning age. The genetic correlations for fleece weight with live weights were positive (favorable) and moderate (approximately 0.5 0.1), whereas those with FD were approximately 0.3 (unfavorable). The other wool traits had lower genetic correlations with the live weights. The genetic correlations for FAT and EMD with FD and SL were positive and low, with FDCV low to moderate negative, but variable with wool weight and negligible for the other wool traits. The genetic correlations for FAT and EMD with postweaning weight were positive and high (0.61 0.18 to 0.75 0.14) but were generally moderate with weights at other ages. Selection for increased live weight will result in a moderate correlated increase in wool weight as well as favorable reductions in breech cover and wrinkle, along with some unfavorable increases in FD and wool yellowness but little impact on other wool traits. The ultrasound meat traits, FAT and EMD, were highly positively genetically correlated (0.8), and selection to increase them would result in a small unfavorable correlated increase in FD, moderately favorable reductions in breech cover and wrinkle, but equivocal or negligible changes in other wool traits. The estimated parameters provide the basis for calculation of more accurate Australian Sheep Breeding Values and selection indexes that combine wool and meat objectives in Merino breeding programs.


Breeding for host resistance to parasites has become an imperative in many sheep industries. Because of the widespread use of AI in sheep breeding schemes, it is important to understand how the performance of offspring from rams varies in different flock environments, both for resistance to parasites and key production traits. This study used both variance component and reaction norm models to investigate the level of genotype x environment interaction for fecal egg count (FEC) and important Merino production traits in a range of flock environments in Australia. These flocks were linked by the use of common rams in a sire-referencing scheme. Both linear and quadratic polynomial reaction norm models were used. The heritability of these traits and the genetic correlation between them and FEC also was investigated using the reaction norm model. A contemporary group (CG) was defined by a flock, year, age class, sex, and paddock combination. Each CG environment was characterized by the mean value of any given trait for that CG. The recorded data used in the study were analyzed in a standardized form. Standardization for each trait was achieved within a CG by subtracting the CG mean from each observation and dividing by the CG SD. The genotype x environment effect accounted for


For all the above-mentioned breeds, the genetic improvement for milk production is performed by the corresponding breeder association through official breeding programs, in which artificial insemination (AI) becomes a fundamental tool of progeny testing. AI plays a significant role because it is directly related to fertility and is the primary reproductive tool used for genetic improvement by introducing genes from superior sires into flocks [5]. Hence, AI is one of the most powerful biotechnological reproductive methods used in most breeding programs of livestock species.


In relation to AI, it is important to study the characteristics of semen production and semen quality. The number of doses produced per ram ejaculate depends on the volume, sperm concentration, and motility of the sperm, which are mainly affected by environmental and genetic factors [6].


To increase the efficiency of the dairy sheep AI centers, studies focused on the assessment of the environmental factors affecting semen production and the estimation of genetic parameters for seminal production and quality traits are required. In cattle, different authors have reported moderate heritabilities for some semen traits [5,7], suggesting the potential for genetic improvement not only for male reproduction traits but also for female reproductive traits, which are generally difficult to improve through direct selection [8].


In sheep, the limited studies addressing the estimation of genetic parameters for ram semen traits in different breeds show a wide variation [6,9,10], highlighting the importance of studying these traits for individual breeds. The estimation of genetic parameters is relevant not only to assess the possibility of the genetic improvement of semen traits but also to understand the interactions among the different traits and, therefore, help define the most efficient selection strategy and avoid undesired consequences of selection [5]. Finally, the inclusion of seminal traits in the male selection indices could be an aspect to consider and investigate in sheep breeds.


Regarding the genetic parameters presented in Table 3 and considering the three traits under study, we observed that the ASS, LCR, and MAN breeds showed similar heritability estimates (Table 3), with SC being the most heritable trait (0.20, 0.11, and 0.15, respectively). The VOL trait showed intermediate heritability estimates (0.12, 0.08, and 0.11, respectively), and the MOT trait showed estimates close to zero (0.03, 0.01, and 0.03, respectively). In the CHU breed, the SC trait showed similar heritability estimate (0.18) to those found in ASS and MAN, whereas the estimates obtained for VOL and MOT were substantially higher than those found in the other breeds (0.20 and 0.11, respectively). The heritability estimate for the VOL trait in CHU was the highest heritability reported in this work. Finally, the LCN breed showed similar trends of heritability estimates to those of CHU, although with lower values, with the VOL trait showing the highest heritability value (0.16) within this breed.


Genetic parameters for three semen traits analysed in five Spanish ovine dairy breeds. For each population (ASS, CHU, LCN, LCR, and MAN) heritabilities are presented in bold on the diagonal of the corresponding breed row. Also for each breed, the genetic and phenotypic correlations among the analysed traits are indicated above and below, respectively, the corresponding diagonal. The SE of each estimate is shown in brackets.


Milk yield is one of the most important economic traits in dairy sheep, but in practice (for genetic improvement), fertility is also a critical trait for the economic efficiency of dairy sheep flocks [14]. According to different studies in other species, semen quantity and quality are the main factors affecting the fertility of rams used for AI [15,16]. AI is becoming increasingly essential within sheep breeding programs, allowing the dissemination of the genetic gain to the whole population. Reproductive efficiency has become a key factor to consider because improved reproductive success could result in greater genetic progress for selected traits [17]. Therefore, the selection of rams should be focused not only on milk production traits but also on semen traits in which the estimation of genetic parameters is a required step.


The dataset compiled through this study has provided the opportunity to analyze different seminal traits rarely studied in the ovine species and to compare phenotypic and genetic parameters among five of the most important Spanish dairy sheep breeds. Traits such as volume, sperm concentration, and sperm motility are relevant traits to analyze in the ejaculate of rams.


In general, the mean values reported here for these traits were very similar to those obtained in other sheep breeds [6,18]. The mean value observed for the MOT trait in the MAN breed (3.61) was low compared with that of the rest of breeds analyzed here and also stood out compared with French breeds [6]. We think that the deviation from the normality and the subjectivity of the MOT has affected the accuracy of the genetic parameters estimated here for this trait, as will be discussed below.


Seminal traits are affected by environmental, management, and genetic effects and physiological status [6]. In our case, three AI centers located in different regions of Spain provided semen production data, and the main environmental factors affecting sperm production were collected at each center. In this study, because the analyses were performed individually for each breed and the different centers analyzed different breeds, the reported differences might be due not only to the breed factor but also to the different criteria to consider an ejaculate suitable for AI at each AI center. For example, for the MAN breed, a sequential evaluation of ejaculates is carried out in such a way that a minimum volume is required for an ejaculate; those ejaculates that pass this criterion are evaluated for motility, and once they pass this criterion, the concentration is measured. Therefore, all the ejaculates with concentration data will have volume and motility data above the respective thresholds. On the other hand, the AI center that analyzed the samples from LCN and LCR breeds implements more restrictive criteria to declare an ejaculate suitable for AI (a mass motility value of 4 and 0.3 for volume are required as minimum values). Hence, these breeds show a relatively low variability compared with the rest of breeds, especially for the SC and MOT traits. In the case of the breeds analyzed in the same AI centers, CHU-ASS and LCN-LCR, the differences reported between them are more likely to reflect genuine differences due to the breed effect. In any case, we acknowledge that the use of these quality thresholds can introduce certain biases on the genetic parameter estimates reported here (e.g., a lower heritability due to the artificially reduced phenotypic variance). Hence, the ideal situation for this kind of study would be that all semen quality measures were recorded without any censorship in all the AI centers; but this state is unrealistic in practical conditions. The impact of the thresholds will depend on the proportion of samples that are below the corresponding threshold in each breed and also the change in the variable distributions when including or discarding some of the data. 59ce067264






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