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Genetic analysis of robustness in meat sheep through body weight and body condition score changes over time.

ABSTRACT: Animal robustness may be defined as a complex trait characterizing the ability of an individual to be adapted, productive and healthy under contrasted and fluctuating environmental situations. Such a trait is now considered an essential criterion in order to meet the more ambitious goals of farming sustainability. In ruminants, one of the key mechanisms via which robustness is expressed is the capacity to mobilize or restore body reserves (BR) to cope with the challenges of negative energy balances. The objectives of this work were as follows: 1) to estimate the genetic parameters related to BR dynamics in ewes over successive production cycles and 2) to investigate BR management relationships between different physiological stages. For this, historical individual BW and BCS data from 2,920 phenotyped ewes were used for genetic analysis. The changes in BW (∆BW) and BCS (∆BCS) over time were analyzed. Eight physiological stages were considered to investigate these changes over time: mating, early pregnancy, mid-pregnancy, lambing, early suckling, mid-suckling, weaning, and postweaning. The estimated heritability were low for both ∆BW (h2 = 0.13 to 0.18) and ∆BCS (h2 = 0.04 to 0.16). Moderate to high positive genetic correlations (from 0.48 to 0.91) were obtained between BR mobilization phases and between BR accretion phases. Similarly, moderate to high negative genetic correlations (from -0.36 to -0.75) were estimated between the BR mobilization and accretion periods, suggesting that mechanisms driving BR mobilization and accretion processes were genetically correlated. This is the first study in ruminants that demonstrate that the extent and temporal changes in profiles of BR mobilization and accretion are heritable and genetically linked, indicating that such traits could be considered in genetic programs aimed at improving robustness. Nevertheless, further research is needed for a more comprehensive understanding of BR dynamics, notably by including other physiological parameters (i.e., metabolites and hormones) and additional information on the productive and reproductive life of the ewe.

SUBMITTER: Mace T

PROVIDER: S-EPMC6247844 | biostudies-other | 2018 Nov

REPOSITORIES: biostudies-other

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Genetic analysis of robustness in meat sheep through body weight and body condition score changes over time.

Macé Tiphaine T González-García Eliel E Pradel Julien J Parisot Sara S Carrière Fabien F Douls Sebastien S Foulquié Didier D Hazard Dominique D

Journal of animal science 20181101 11

Animal robustness may be defined as a complex trait characterizing the ability of an individual to be adapted, productive and healthy under contrasted and fluctuating environmental situations. Such a trait is now considered an essential criterion in order to meet the more ambitious goals of farming sustainability. In ruminants, one of the key mechanisms via which robustness is expressed is the capacity to mobilize or restore body reserves (BR) to cope with the challenges of negative energy balan ...[more]

PMID: 30085118

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Project description:The relationship between ewe body condition score (BCS) and liveweight (LW) has been exploited previously to predict the former from LW, LW-change, and previous BCS records. It was hypothesized that if fleece weight and conceptus-free liveweight and LW-change, and in addition, height at withers were used, the accuracy of current approaches to predicting BCS would be enhanced. Ewes born in 2017 (n = 429) were followed from 8 mo to approximately 42 mo of age in New Zealand. Individual ewe data were collected on LW and BCS at different stages of the annual production cycle (i.e., prebreeding, at pregnancy diagnosis, prelambing, and weaning). Additionally, individual lambing dates, ewe fleece weight, and height at withers data were collected. Linear regression models were fitted to predict current BCS at each ewe age and stage of the annual production cycle using two LW-based models, namely, unadjusted for conceptus weight and fleece weight (LW alone1) and adjusted (LW alone2) models. Furthermore, another two models based on a combination of LW, LW-change, previous BCS, and height at withers (combined models), namely, unadjusted (combined1) and adjusted for conceptus and fleece weight (combined2), were fitted. Combined models gave more accurate (with lower root mean square error: RMSE) BCS predictions than models based on LW records alone. However, applying adjusted models did not improve BCS prediction accuracy (or reduce RMSE) or improve model goodness of fit (R 2) (P > 0.05). Furthermore, in all models, both LW-alone and combined models, a great proportion of variability in BCS, could not be accounted for (0.25 ≥ R 2 ≥ 0.83) and there was substantial prediction error (0.33 BCS ≥ RMSE ≥ 0.49 BCS) across age groups and stages of the annual production cycle and over time (years). Therefore, using additional ewe data which allowed for the correction of LW for fleece and conceptus weight and using height at withers as an additional predictor did not improve model accuracy. In fact, the findings suggest that adjusting LW data for conceptus and fleece weight offer no additional value to the BCS prediction models based on LW. Therefore, additional research to identify alternative methodologies to account for individual animal variability is still needed.

Project description:The relationship between energy status and fertility in dairy cattle was retrospectively analyzed by comparing fertility with body condition score (BCS) near artificial insemination (AI; experiment 1), early postpartum changes in BCS (experiment 2), and postpartum changes in body weight (BW; experiment 3). To reduce the effect of cyclicity status, all cows were synchronized with Double-Ovsynch protocol before timed AI. In experiment 1, BCS of lactating dairy cows (n = 1,103) was evaluated near AI. Most cows (93%) were cycling at initiation of the breeding Ovsynch protocol (first GnRH injection). A lower percentage pregnant to AI (P/AI) was found in cows with lower (≤ 2.50) versus higher (≥ 2.75) BCS (40.4 vs. 49.2%). In experiment 2, lactating dairy cows on 2 commercial dairies (n = 1,887) were divided by BCS change from calving until the third week postpartum. Overall, P/AI at 70-d pregnancy diagnosis differed dramatically by BCS change and was least for cows that lost BCS, intermediate for cows that maintained BCS, and greatest for cows that gained BCS [22.8% (180/789), 36.0% (243/675), and 78.3% (331/423), respectively]. Surprisingly, a difference existed between farms with BCS change dramatically affecting P/AI on one farm and no effect on the other farm. In experiment 3, lactating dairy cows (n = 71) had BW measured weekly from the first to ninth week postpartum and then had superovulation induced using a modified Double-Ovsynch protocol. Cows were divided into quartiles (Q) by percentage of BW change (Q1 = least change; Q4 = most change) from calving until the third week postpartum. No effect was detected of quartile on number of ovulations, total embryos collected, or percentage of oocytes that were fertilized; however, the percentage of fertilized oocytes that were transferable embryos was greater for cows in Q1, Q2, and Q3 than Q4 (83.8, 75.2, 82.6, and 53.2%, respectively). In addition, percentage of degenerated embryos was least for cows in Q1, Q2, and Q3 and greatest for Q4 (9.6, 14.5, 12.6, and 35.2% respectively). In conclusion, for cows synchronized with a Double-Ovsynch protocol, an effect of low BCS (≤ 2.50) near AI on fertility was detected, but change in BCS during the first 3 wk postpartum had a more profound effect on P/AI to first timed AI. This effect could be partially explained by the reduction in embryo quality and increase in degenerate embryos byd 7 after AI in cows that lost more BW from the first to third week postpartum.

Project description:Background:Artificial insemination is a preferred breeding method for beef heifers as it advances the genetic background, produces a predictive and profitable calving season, and extends the heifer's reproductive life span. As reproductive efficiency in heifers is key for the success of beef cattle production systems, following artificial insemination, heifers are exposed to a bull for the remainder of the breeding season. Altogether, up to 95% of heifers might become pregnant in their first breeding season. Heifers that do not become pregnant at the end of the breeding season represent an irreparable economical loss. Additionally, heifers conceiving late in the breeding season to natural service, although acceptable, poses serious losses to producers. To minimize losses due to reproductive failure, different phenotypic parameters can be assessed and utilized as selection tools. Here, we tested the hypothesis that in a group of pre-selected heifers, records of weaning weight, age at weaning, age at artificial insemination, and age of dam differ among heifers of varied reproductive outcomes during the first breeding season. Results:None of the parameters tested presented predictive ability to discriminate the heifers based on the response variable ('pregnant to artificial insemination', 'pregnant to natural service', 'not pregnant'). Heifers categorized with body condition score?=?6 and reproductive tract score ??4 had the greatest proportion of pregnancy to artificial insemination (49% and 44%, respectively). Furthermore, it was notable that heifers presenting body condition score?=?6 and reproductive tract score?=?5 presented the greatest pregnancy rate at end of the breeding season (89%). Heifers younger than 368?d at the start of the breeding season did not become pregnant to artificial insemination. Those young heifers had 12.5% chance to become pregnant in their first breeding season, compared to 87.5% if the heifers were older than 368?days. Conclusion:Our results suggest that beef heifers with body condition score?=?6 and reproductive tract score ??4 are more likely to become pregnant to artificial insemination. Careful assessment should be undertaken when developing replacement heifers that will not reach 12?months of age by the beginning of the breeding season.

Project description:BackgroundIncreased physical activity (PA), reduced time spent sedentary (SED), healthier diet and reduced body weight may all have a positive impact on cardiometabolic risk. The relative importance of change in each of these variables on cardiometabolic risk, however, is unclear. We therefore sought to investigate the relative contributions of changes in PA, SED, diet and body weight on cardiometabolic risk.MethodsThis is a secondary analysis of data collected from the EuroFIT randomised controlled trial, which was a 12-week group-based lifestyle intervention for overweight middle-aged men delivered by coaches in football club stadia aiming to improve PA, SED, diet, and body weight. PA and SED were assessed by accelerometry, diet using the Dietary Instrument for Nutrition Education (DINE). An overall cardiometabolic risk score was derived from combining z-scores for glucose, HbA1c, insulin, lipids and blood pressure. In total, 707 men (from the overall cohort of 1113) with complete data for these variables at baseline and 12-month follow-up were included in the multivariable linear regression analyses.ResultsIn multivariable analyses, change in number of steps (explaining 5.1% of R2) and dietary factors (less alcohol, fatty and sugary food, and more fruit and vegetables) (together explaining 4.5% of R2), but not changes in standing time or SED, were significantly associated with change in body weight. Changes in number of steps (R2 = 1.7%), fatty food score (R2 = 2.4%), and sugary food score (R2 = 0.4%) were significantly associated with change in cardiometabolic risk score in univariable models. However, in multivariable models which included changes in weight as well as changes in steps and dietary variables, change in weight explained a substantially larger proportion of the change in cardiometabolic risk score, explaining 14.1% of R2 (out of an overall model R2 of 19.0%). When baseline (as well as change) values were also included in the model, 38.8% of R2 for change in cardiometabolic risk score was explained overall, with 14.1% of R2 still explained by change in weight.ConclusionChange in body weight, together with baseline cardiometabolic risk explained most of the change in cardiometabolic risk. Thus, the benefits of increasing physical activity and improving diet on cardiometabolic risk appear to act largely via an effect on changes in body weight.Trial registrationInternational Standard Randomised Controlled Trials, ISRCTN-81935608. Registered 06052015. https://www.isrctn.com/ISRCTN81935608?q=&filters=recruitmentCountry:Portugal&sort=&offset=7&totalResults=92&page=1&pageSize=10&searchType=basic-search.

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Genetic analysis of robustness in meat sheep through body weight and body condition score changes over time.

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Genetic analysis of robustness in meat sheep through body weight and body condition score changes over time.

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