Application of structural equation models for genetic evaluation of lifetime reproductive traits and age at first lambing in Moghani sheep

Jafaroghli, M.; Ghafouri‐Kesbi, Farhad; Khorami, Seyed Javad; Barazandeh, Arsalan; Mokhtari, M.S.

doi:10.1016/j.smallrumres.2022.106761

Cited by 5 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, differences in RP4 were not significant, meaning that the Romanov ewes in the continuous mating system can be mated at a relatively large age span without hindering their reproductive efficiency in later life. This seems like valuable information, given that the age at first mating/lambing is considered one of the main determinants of ewes' productive life (Kenyon et al, 2011;Foster and Hileman, 2015) and has a negative genetic correlation with lifetime reproduction (Jafaroghli et al, 2022). Indeed, it was shown that age at first lambing affects stayability (Mclaren et al, 2020) and lifetime reproductive performance of ewes in pelt (Schoeman et al, 1991), dairy (Hernandez et al, 2011), and meat (Thomson et al, 2021) production systems.…”

Section: Discussionmentioning

confidence: 99%

Prediction of reproductive performance of ewes based on the early production data, ewe birth rank, dam age, and dam birth rank

et al. 2023

View full text Add to dashboard Cite

Abstract. This research aimed to analyze whether ewes' total reproductive performance up to the fourth year of life (RP4) can be predicted based on the data available at an early stage of a ewe's productive life. The RP4 of 133 Romanov ewes was measured in terms of the total number of lambs born per ewe (TNLE) and total birth weight of lambs per ewe (TBLE). Multiple regression was used to analyze whether early reproductive performance indicators (first litter size – FLS, age at first lambing – AFL, first lambing interval – FLI), ewe birth rank, dam age, and dam birth rank can be used as the predictors of RP4. Predicted R2 and 95 % prediction intervals were used as indicators of the precision of prediction. Average TNLE and TBLE at the end of fourth year of ewe life were 11.84 lambs and 37.96 kg, respectively. FLS and FLI significantly (P<0.05) influenced TNLE and TBLE, while AFL was not a significant (P>0.05) variable. Ewes with shorter FLI had significantly (P<0.05) higher TNLE (10.94 lambs) and TBLE (36.17 kg) than ewes with long FLI (TNLE = 9.12 lambs and TBLE = 28.05 kg). R2 predicted for TNLE and TBLE was 7.54 % and 11.49 %, respectively. The ewe's birth rank and the dam's birth rank significantly (P<0.05) influenced TNLE and TBLE. Ewes born as singletons and ewes from singleton-born dams had significantly (P<0.05) lower TNLE and TBLE than ewes born as triplets and ewes from triplet-born dams. R2 predicted for TNLE was 16.76 %, and 25.69 % for TBLE. FLS and FLI are better predictors of RP4 than AFL. The birth rank of ewe and dam also proved significant predictors of RP4. For both sets of predictors (early reproductive indicators and birth rank data), low values of R2 predicted indicate that precise prediction of RP4 cannot be made.

show abstract

Section: Discussionmentioning

confidence: 99%

Prediction of reproductive performance of ewes based on the early production data, ewe birth rank, dam age, and dam birth rank

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Our results imply that SEMs may also be valuable in actual breeding programmes. When selecting for higher weaning weight in sheep (Jafaroghli et al, 2022), for example, the undesired response on birth weight can be lowered up to the limit imposed by true genetic correlations. The same could be argued in connection to the relationship between litter size and weight in piglets (Varona et al, 2007).…”

Section: Implications For Animal Breeding Programmesmentioning

confidence: 99%

Conditioning on the causal network prevents indirect response to selection

Bonamy,

Fernández,

Giovambattista

et al. 2023

J Animal Breeding Genetics

View full text Add to dashboard Cite

Multiple trait animal models (MTM) allow to estimate the breeding values (BV) of several traits simultaneously while accounting for genetic and environmental correlations among them. However, relationships among traits may not be reciprocal but rather causal in nature. In these cases, and given a causal network, structural equations models (SEM) arise as a more appropriate methodology. Although MTM and SEM have been shown to be parametrically equivalent, the estimated breeding value (EBV) obtained from either one or the other should be interpreted differently. In this study, we investigated the impact of using these estimates on the response to selection for a causal network comprising five different traits through a stochastic simulation experiment. Three different selection targets were assayed, involving traits located upstream, midstream and downstream this causal network. We first considered the case in which traits were causally related but not genetically correlated. The current results support our hypothesis that MTM will absorb causal relationships as genetic correlations and, consequently, change the response to selection achieved as compared with SEM. We found no differences on the response to selection when the target trait was located at the top of the causal network, but noticeable differences were detected on upstream traits when selection pressure was placed on midstream or downstream traits. We also assayed a scenario in which causal effects and genetic correlations act simultaneously and found that selection based on BVs estimated using SEM diminished the indirect response in traits upstream the causal network.

show abstract