A study on the genetic and phenotypic factors affecting specific ewe productivity traits in Sangsari sheep

doi:10.21203/rs.3.rs-4286325/v1

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Research Article

A study on the genetic and phenotypic factors affecting specific ewe productivity traits in Sangsari sheep

https://doi.org/10.21203/rs.3.rs-4286325/v1

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published 01 Oct, 2024

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The study's objectives were to assess the genetic parameters of reproductive characteristics in Sangsari sheep. A data set of reproductive information with pedigree details from 1995 to 2016 was used. Studied traits were litter size at birth (LSB), litter size at weaning (LSW), litter mean weight per lamb born (LMWLB), litter mean weight per lamb weaned (LMWLW), total litter weight at birth (TLWB) and total litter weight at weaning (TLWW). Test significance of the environmental factors to be included in the model was conducted using the general linear model procedure of the SAS program. All traits were significantly affected by the year of lambing and ewe age at lambing (P < 0.01). The AI-REML procedure of the Wombat program was used to evaluate genetic parameters. A series of bivariate animal models were employed to calculate genetic (r_g) and phenotypic (r_p) correlations between traits. The total least square means ± standard error of LSB, LSW, LMWLB, LMWLW, TLWB, and TLWW were 1.04 ± 0.05, 0.96 ± 0.03, 3.12 ± 0.08, 15.40 ± 0.13, 3.28 ± 0.04, and 19.31 ± 0.16, respectively. The estimates of h² were relatively low and ranged from 0.063 ± 0.028 for LSW to 0.181 ± 0.063 for TLWW. Repeatability estimates varied from 0.101 for LSW to 0.241 for TLWW. The sire service effects for LMWLB, TLWB, and TLWW were 0.012 ± 0.004, 0.023 ± 0.006, and 0.039 ± 0.009, respectively. The traits studied showed a greater magnitude of genetic correlation than phenotypic correlation, with values ranging from − 0.59 (LSB-LMWLB) to 0.87 (LMWLB-TLWB). It appears that focusing on TLWW for selection could result in more significant improvements in the reproductive performance of Sangsari ewes.

Correlation

Heritability

Reproductive traits

Variance components

The Sangsari sheep is a breed indigenous to the province of Semnan in central Iran, known for its small size and fat-tailed characteristic. This breed is raised in an area known as Sangsar located in the northern region of the province. They are not only well adapted to dry and semi-arid climates, but they are also capable of traveling long distances. The migration of Sangsari sheep herds from Semnan to Mazandaran and Khorasan Provinces takes place to access both summer and winter ranges (Miraei-Ashtiani et al., 2007). This breed is prone to fattening and their meat efficiency is about 60% of their live weight. This makes them a more cost-effective option for meat production (Kasiriyan et al., 2011; Mirzaee Ilaly et al., 2019). Ajafar et al. (2022) report that red meat consumption has increased by 5 to 6% per year in developing countries, and the amount of meat produced doesn't meet the growing population's needs, so breeding programs must be conducted in the sheep industry to increase meat production efficiency. Reproductive traits, along with growth traits play an essential role in the biological aspect of meat production, in addition to growth traits.

Improvement in the reproductive efficiency of the ewe results in greater meat production (Aguirre et al., 2017; Areb et al., 2021). One of the influential factors in improving reproductive efficiency and consequently sheep meat production is to increase the number and weight of weaned lambs per ewe in a given year as noted by Mohammadi et al. (2012) and Jafaroghli et al. (2019). Sheep's reproductive performance is affected by not only genetic factors but also environmental factors. Hence, improving environmental factors, including management practices and nutrition, and utilizing genetic selection have been recommended as the main approaches for increasing reproductive efficiency in sheep. To design breeding programs for the genetic progress of reproductive traits, it is crucial to have an understanding of genetic parameters like heritability, repeatability, and genetic correlations (Pourtahmasbian Ahrabi et al., 2018). Earlier researchers (Notter et al., 2018; Tera et al., 2021; Habtegiorgis et al., 2022) have documented heritability, repeatability, and genetic correlations for reproductive traits in various sheep breeds. As far as we know, neither genetic parameter estimates nor adequate information on the reproductive characteristics of the Sangsari sheep are available in the literature. So, this research aimed to determine the genetic parameters for ewe reproductive traits in Sangsari sheep. Genetic and phenotypic correlations among traits were also evaluated.

Study area and environmental conditions

Semnan is located in the center of Iran, in a warm and dry climate region. The climate in this province is mild on the mountain slopes, cold in the mountains, and hot in the deserts. Near Damghan City in the north of Semnan province, there is a sheep breeding station called Sangsari. The lowest and highest recorded temperatures are 12.50°C and 23.90°C, respectively, with an annual precipitation of 127.7 mm. Furthermore, the research location was positioned at 1127 meters above sea level.

Animals and flock management

Sangsari sheep are primarily raised in Iran for mutton production, making them the primary source of protein in the country. This breed comes in various colors, including black, pea, light brown, and dark brown. Usually, rams and ewes are hornless, and males have horns that appear as small appendages. First mating occurs at 18 months for ewes, while at 18 months or two years of age for rams. The mating season commenced in August and concluded in September. A fertile ram mated with about 10–12 ewes. The lambing season starts in January and lasts until February. Pedigree information, sex, date, and type of birth of the lambs were registered immediately after birth. In approximately 3 months, lambs were weaned. When the pasture conditions are suitable, the sheep are taken daily to pastures around the station, eat the fodder at the pastures during the day, and return to their station in the evening. Usually, sheep are kept indoors and hand-fed in late autumn and winter because of the harsh weather conditions.

Data sets and the analyzed traits

A dataset of reproductive information with pedigree details from 1995 to 2016 was used to investigate the reproductive efficiency of Sangsari sheep. Reproductive traits investigated were litter size at birth (LSB), defined as the number of lambs born per ewe lambing, litter size at weaning (LSW) was the number of lambs weaned per ewe lambing, litter mean weight per lamb born (LMWLB) was the average birth weights of lambs per ewe lambing, litter mean weight per lamb weaned (LMWLW) was the average weaning weight of lambs per ewe lambing, total litter weight at birth (TLWB) was the sum of the birth weights of all lambs born per ewe lambing and total litter weight at weaning (TLWW) was the sum of the weights of all lambs born per ewe lambing.

Data analysis

Test significance of the environmental factors to be involved in the model was conducted using the general linear model procedure of the SAS program (SAS, 2009). Preliminary analysis shows that the year of lambing (22 classes: 1995 to 2016) and the age of ewe at lambing (6 classes: 2–7 years old) have a significant influence on all reproductive traits (p < 0.01). No significant interaction was found among the fixed effects (p > 0.05), therefore it was not incorporated into the final model. The influence of various fixed effects on reproductive traits was assessed using the following general linear model:

Y_ijk = µ + B_i + D_j + e_ijk

In this equation, Y_ijk, µ, and e_ijk are the reproductive traits of the ewe, the overall mean of each trait, and residual error, respectively, B_i represents the year of lambing and D_j denotes the age of the ewe at lambing. The least-square mean (LSM) was used to compare the means among subgroups. Utilizing the average information restricted maximum likelihood (AIREML) algorithm, the genetic parameters for the traits under investigation were calculated in the Wombat program (Meyer, 2012). The animal models in use were as follows:

Model 1: y = X_b + Z_aa + e

Model 2: y = X_b + Z_aa + W_pepe + e

Model 3: y = X_b + Z_aa + Z_ss + e

Model 4: y = X_b + Z_aa + Z_ss + W_pepe + e

Where:

y = Vector of observations for the studied traits; b, a, s, pe, and e = Vectors of fixed effects, direct additive genetic effects, service sire effects, maternal permanent environmental effects, and residual effects, respectively; X, Z_a, Z_s, and W_pe = Design matrices relating fixed effects, direct additive genetic effects, service sire effects, and maternal permanent environmental effects to observations, respectively. The following matrix shows the (co)variance structure:

$$\text{V}\text{a}\text{r}\left(\begin{array}{c}\text{a}\\ \text{s}\\ \begin{array}{c}\text{p}\text{e}\\ \text{e}\end{array}\end{array}\right)=\left(\begin{array}{cc}\begin{array}{c}{\text{A}{\sigma }}_{\text{a}}^{2}\\ 0\\ \begin{array}{c}0\\ 0\end{array}\end{array}& \begin{array}{cc}\begin{array}{c}0\\ {{\text{I}}_{\text{s}}{\sigma }}_{\text{s}}^{2}\\ \begin{array}{c}0\\ 0\end{array}\end{array}& \begin{array}{cc}\begin{array}{c}\begin{array}{c}0\\ 0\end{array}\\ {{\text{I}}_{\text{d}}{\sigma }}_{\text{p}\text{e}}^{2}\\ 0\end{array}& \begin{array}{c}0\\ 0\\ \begin{array}{c}0\\ {{\text{I}}_{\text{n}}{\sigma }}_{\text{e}}^{2}\end{array}\end{array}\end{array}\end{array}\end{array}\right)$$

In the structure of this matrix, ${{\sigma }}_{\text{a}}^{2}$, ${{\sigma }}_{\text{s}}^{2}$, ${{\sigma }}_{\text{p}\text{e}}^{2}$, and ${{\sigma }}_{\text{e}}^{2}$ = Variances of direct additive genetic, sire service, maternal permanent environmental, and residual, respectively, and A = Additive relationship matrix. I_s, I_d, and I_n = Identity matrices for the numbers of sires, ewes, and records, respectively. It is supposed that the direct additive genetic effects, service sire effects, permanent environmental effects, and residual effects follow a normal distribution with a mean of 0 and variances of ${\text{A}{\sigma }}_{\text{a}}^{2}$, ${{\text{I}}_{\text{s}}{\sigma }}_{\text{s}}^{2}$, ${{\text{I}}_{\text{d}}{\sigma }}_{\text{p}\text{e}}^{2}$, and ${{\text{I}}_{\text{n}}{\sigma }}_{\text{e}}^{2}$, respectively. Based on the model following parameters were calculated:

Direct heritability as: ${\text{h}}^{2}= \frac{{{\sigma }}_{\text{a}}^{2}}{{{\sigma }}_{\text{p}}^{2}}$, service sire varaiance ratio as: ${\text{s}}^{2}= \frac{{{\sigma }}_{\text{s}}^{2}}{{{\sigma }}_{\text{p}}^{2}}$, maternal environmental variance ratio as: ${\text{p}\text{e}}^{2}= \frac{{{\sigma }}_{\text{p}\text{e}}^{2}}{{{\sigma }}_{\text{p}}^{2}}$, and repeatability as: $\text{r}=\frac{{{\sigma }}_{\text{a}}^{2}+{{\sigma }}_{\text{p}\text{e}}^{2}}{{{\sigma }}_{\text{P}}^{2}}$.

Where, ${{\sigma }}_{\text{a}}^{2}$ = direct genetic variance, ${{\sigma }}_{\text{s}}^{2}$ = service sire variance, ${{\sigma }}_{\text{p}\text{e}}^{2}$ = ewe permanent environmental variance, and ${{\sigma }}_{\text{P}}^{2}$ = phenotypic variance, and, ${{\sigma }}_{\text{P}}^{2}={{\sigma }}_{\text{a}}^{2}+ {{\sigma }}_{\text{s}}^{2}+ {{\sigma }}_{\text{p}\text{e}}^{2}+ {{\sigma }}_{\text{e}}^{2}$. A selection of the best-fitted model was carried out by using Akaike's information criterion (AIC), as illustrated below:

AIC_i = -2 log L_i + 2pi

In this equation, log L_i and p_i are maximized of the model at convergence and the number of parameters derived from each model, respectively. The best model was considered to be the one with the lowest AIC. Genetic (r_g) and phenotypic (r_p) associations between reproductive traits were determined using bivariate analyses, incorporating the same fixed effects as the univariate models.

Environmental factors and reproductive performance

A summary of the information statistical analysis, and least square means of the studied traits are donated in Tables 1 and 2. LMWLB and LSW were determined to have the lowest and highest variability, with coefficients of variation (CV) of 16.35% and 50.00%, respectively. Small CV values suggest limited variability in animals, greater uniformity of traits, diminished impact of environmental causes on traits, and increased response to selection (Salako, 2006b). The mean values for LSB, LSW, LMWLB, LMWLW, TLWB, and TLWW were 1.04, 0.96, 3.12, 15.40, 3.28, and 19.31, respectively. The least-squares analysis revealed a significant impact of the lambing year and the dam's age at lambing on all reproductive traits (p < 0.01). As ewes aged from 2 to 6 years, both TLWB and TLWW increased, then declined for older ewes. Both 2-year-old and 7-year-old dams showed restricted reproductive abilities in total traits except for LSB and LSW.

Table 1

Summary of descriptive statistics for reproduction characteristics of Sangsari ewes
			Ttaits^a
Item	LSB	LSW	LMWLB (kg)	LMWLW (kg)	TLWB (kg)	TLWW (kg)
No animal in pedigree	1382	1298	1382	1298	1382	1298
No. of records	3382	3102	3382	3102	3382	3102
No. of ewes	295	276	295	276	295	276
No. of sires of the ewes	63	60	63	60	63	60
No. service sires	81	79	81	79	81	79
Mean	1.04	0.96	3.12	15.40	3.28	19.31
S.D.	0.18	0.48	0.51	3.98	0.86	4.21
C.V. (%)	17.31	50.00	16.35	25.84	26.22	21.80
^a LSB: litter size at birth; LSW: litter size at weaning; LMWLB: litter mean weight per lamb born; LMWLW: litter mean weight per lamb weaned; TLWB: total litter weight at birth; TLWW: total litter weight at weaning.

Table 2

Least-squares means ± standard errors for the traits of interest.
Fixed effects				Traits^a
Fixed effects	LSB	LSW	LMWLB (kg)	LMWLW (kg)	TLWB (kg)	TLWW (kg)
Overall mean	1.05	0.94	3.12	15.41	3.28	19.31
Dam age (year)	**	**	**	**	**	**
2	1.02^b ± 0.05	0.89^b ± 0.02	3.03^c ± 0.04	14.98^c ± 0.18	3.17^b ± 0.07	19.51^c ± 0.20
3	1.02^b ± 0.05	0.88^b ± 0.02	3.08^c ± 0.05	15.50^b ± 0.19	3.20^b ± 0.08	19.64^c ± 0.24
4	1.07^a ± 0.06	1.03^a ± 0.03	3.21^a ± 0.06	15.81^a ± 0.22	3.28^ab ± 0.09	20.37^b ± 0.37
5	1.03^a ± 0.06	1.02^a ± 0.03	3.16^b ± 0.06	15.83^a ± 0.22	3.26^b ± 0.09	20.59^b ± 0.32
6	1.02^a ± 0.08	1.01^a ± 0.05	3.19^ab ± 0.09	16.01^a ± 0.24	3.38^a ± 0.11	21.63^a ± 0.31
7	1.01^a ± 0.08	0.86^b ± 0.07	3.04^c ± 0.10	14.94^c ± 0.29	3.19^b ± 0.13	20.50^b ± 0.31
Lambing year	**	**	**	**	**	**
^a LSB: litter size at birth; LSW: litter size at weaning; LMWLB: litter mean weight per lamb born; LMWLW: litter mean weight per lamb weaned; TLWB: total litter weight at birth; TLWW: total litter weight at weaning.
** P < 0.01: Significant effect at p < 0.01.

Genetic parameters

Table 3 contains the (co)variance components and genetic parameters for the assessed traits. The best models for estimating genetic parameters of LSB, LSW, LMWLB, LMWLW, TLWB, and TLWW traits were identified as models number 2, 2, 2, 4, 4, and 4 based on AIC values. A survey of variance components resulting from the most appropriate model revealed that 0.459, 0.27, 0.410, 2.934, 1.542, and 2.725 of the total variations in LSB, LSW, LMWLB, LMWLW, TLWB, and TLWB can be attributed to additive genetic impacts. The aforementioned values comprise 9.56%, 6.30%, 16.53%, 12.28%, 10.77%, and 18.15% of the phenotypic variance, indicating a restricted influence of additive genetic effects. Estimated values of h² were in the range between 0.063 (LSW) and 0.181 (TLWW), according to the most appropriate model. Significant service sire effects were observed in the LMWLB, TLWB, and TLWW traits, explaining 1.17%, 1.27%, and 3.92% of the phenotypic variance. Pe² had values of 0.056 for LSB, 0.038 for LSW, 0.026 for LMWLB, 0.058 for TLWB, 0.059 for TLWB, and 0.060 for TLWW, respectively. The estimated repeatability (r) was higher than the respective h² values, ranging from 0.101 for LSW to 0.241 for TLWW.

Table 3

Estimate variance components and genetic parameters of reproductive traits using the most appropriate model.
Traits^a	Model fitted	${\varvec{\sigma }}_{\mathbf{a}}^{2}$	${\varvec{\sigma }}_{\mathbf{p}\mathbf{e}}^{2}$	${\varvec{\sigma }}_{\mathbf{s}}^{2}$	${\varvec{\sigma }}_{\mathbf{e}}^{2}$	${\varvec{\sigma }}_{\mathbf{P}}^{2}$	h² ± SE	pe² ± SE	S² ± SE	r	AIC value
LSB	2	0.459	0.271	-	4.070	4.801	0.096 ± 0.033	0.056 ± 0.030	-	0.152	-2456.85
LSW	2	0.207	0.125	-	2.955	3.287	0.063 ± 0.028	0.038 ± 0.025	-	0.101	-1874.32
LMWLB	4	0.410	0.065	0.029	1.976	2.480	0.165 ± 0.054	0.026 ± 0.020	0.012 ± 0.004	0.192	-746.91
LMWLW	2	2.934	1.393	-	19.558	23.885	0.123 ± 0.045	0.058 ± 0.031	-	0.181	3784.64
TLWB	4	1.542	0.845	0.326	11.608	14.321	0.108 ± 0.039	0.059 ± 0.031	0.023 ± 0.006	0.167	2611.45
TLWW	4	2.725	0.899	0.589	10.802	15.015	0.181 ± 0.063	0.060 ± 0.033	0.039 ± 0.009	0.241	4855.15
${{\sigma }}_{\text{a}}^{2}$: Direct genetic variance; ${{\sigma }}_{\text{p}\text{e}}^{2}$: maternal environmental variance; ${{\sigma }}_{\text{s}}^{2}$: service sire variance; ${{\sigma }}_{\text{e}}^{2}$: residual variance; ${{\sigma }}_{\text{p}}^{2}$: phenotypic variance; h2: direct heritability; ${{\sigma }}_{\text{p}\text{e}}^{2}$: ratio of maternal permanent environmental variance on phenotypic variance; S²: ratio of service sire variance to phenotypic variance; r: repeatability of the ewe performance; AIC: Akaik’s Information Criterion.
^a LSB: litter size at birth; LSW: litter size at weaning; LMWLB: litter mean weight per lamb born; LMWLW: litter mean weight per lamb weaned; TLWB: total litter weight at birth; TLWW: total litter weight at weaning.

Correlations between traits measured

The values of r_g and r_p between reproductive traits are presented in Fig. 1. A low phenotypic correlation was observed in most of the reproductive traits. The estimated r_g for all traits studied was higher than r_p. LSB showed a low r_g with LMWLB and LMWLW. However, it had a significant genetic association with other reproductive traits. Positive and high values of r_g were obtained for LMWLB-LMWLW (0.85) and LMWLB-TLWB (0.87). The genetic correlation between TLWW and other reproductive traits was positive and high.

Fixed effects

The coefficient of variation (CV) is a measure used to determine the variability of a specific trait. The CV value is relatively high for reproductive traits, especially LSW, showing substantial variation among animals, considerable changes in traits due to environmental factors, and limited response to selection (Jafari et al., 2014). LSB showed the lowest coefficient of variation. Consistent with the present findings, Eteqadi et al. (2017) in Guilan sheep and Mohammadi et al. (2015) in Lori sheep observed greater values of CV for LSW compared to other traits. The value of CV for LSW found here was in accord with the results of Roshanfekr et al. (2015) in Arabi sheep. However, in contrast to the findings here, Saghi et al. (2017) reported higher CV estimates for TLWW and TLWB in Kordi ewes. The lower CV for pre-weaning traits may be attributed to more feeding of animals with mother's milk and consequently less influence of environmental factors. The Sangsari ewes showed comparable performance in terms of LSB (1.04) and LSW (0.96) to Moghani (Rashidi et al., 2011) (Rashidi et al., 2011) and Lori ewes (Mohammadi et al., 2015). Higher levels of LSB (1.27) and LSW (1.28) were also reported in the study of Jafaroghi et al. (2019) who investigated Balochi sheep. Roshanfekr et al. (2015) and Eteqadi et al. (2017) also found similar findings for TLWW, LMWLW, TLWB, and LMWLB, whereas Shariati et al. (2018) noted higher values. Differences in the performance of reproductive traits across studies are primarily due to the varying effects of environmental factors on various breeds (Aguirre et al., 2017). The development of management strategies to improve the genetic improvement of sheep reproductive traits depends on an understanding of environmental factors.

The year of lambing and the dam’s age had a significant impact on all the traits (p < 0.01). Fluctuations in climate conditions such as rainfall, temperature, humidity, management practices, sheep dependence on pasture, and lamb feeding may be responsible for the impacts of lambing year on the traits studied. Post-research (Shariati et al., 2017; Areb et al., 2021) indicated that lambing year significantly affects sheep reproductive traits. There is a general trend of improvement in reproductive traits with age, particularly from 4 years of age onwards. The 5- to 6-yr old ewes had the highest values for TLWW, TLWB, LMWLW, and LMWLB, whereas the lowest values for these traits were observed in 2-yr old ewes. According to several studies (Shariati et al., 2017; Amiri Roudbar., 2018), ewe age influences discussed traits. In contrast, the age of dams was not found to be significant for LSB and LSW in Meraban sheep (Yavarifard et al., 2015). Variations in milk production and mothering ability of ewes, nursing, and maternal behavior can affect the performance of reproductive traits at different ages of the ewe (Afolayan et al., 2008).

Genetic effects and heritability estimates

Estimating variance components with the best model for each trait implies that 0.459, 0.27, 0.410, 2.934, 1.542, and 2.725 total changes for characteristics LSW, LSW, LMWLB, LMWLW, TLWB, and TLWW are caused by additive genetic variance. LSW and TLWW had the highest and lowest values of heritability, respectively. Consistent with former investigation (Jafaroghli et al., 2019; Pourtahmasebian et al., 2020), the value of h² for LSB was 0.096, which was considered higher compared to the result of Eteqadi et al. (2017), Amou Poshte Masari et al. (2013) and Roshanfekr et al. (2015), who recorded measurements varying from 0.00 to 0.05. Contrary to those, greater values of h² have also been obtained for this trait. Mohammadi et al. (2015) and Aguirre et al. (2017) reported h² values of 0.18 and 0.12 for Lori and Santa Ines sheep breeds. The LSB has a considerable influence on lamb production efficiency, but the LSW is more influential as it reflects the ewe's reproductive potential and the survival of lambs (Notter et al., 2018).

An estimation of h² 0.063 was achieved for LSW in the current research. Similar outcomes were documented in different populations of sheep. A h² of 0.054 and 0.08 was recorded for LSW in Lori-Bakhtiari and Iran-Black sheep by Amiri Roudbar et al. (2018) and Baneh et al. (2020), respectively. The small values of h² for LSB and LSW, imply that non-genetic factors have a much greater influence on these traits than additive genetics, as demonstrated by Eteqadi et al. (2017) for Guilan sheep and Roshanfekr et al. (2015) for Arabi breed. In addition, the low heritability of litter traits can be attributed to their discontinuous distribution as indicated by Mohammadi et al. (2015) and Roshanfekr et al. (2015). Nonetheless, some studies have documented higher h² estimates for LSW (Yavari et al., 2015; Jafari et al., 2016). The estimate obtained by Rashidi et al. (2011) was lower (0.02) for this trait in Moghani sheep. Therefore, direct selection cannot greatly improve the reproductive efficiency of Sangsri sheep due to the low heritability estimates for LSW. Variations in heritability estimates may result from differences in sheep breeds and population size, data structure as well as a model of estimation. LSW has a lower heritability than LSB, suggesting that both environmental factors and the lamb's genotype significantly contribute to lamb loss from birth to weaning, rather than the ewe's genotype. The selection of superior animals may have resulted in a decrease in the genetic variance of the desired trait. The moderate estimate of h² for LMWLB (0.165) demonstrated similarity to the corresponding observation in Lori-Bakhtiari sheep (Amiri Roudbar et al., 2018). This suggests that there may be genetic variation in this trait that could potentially improve birth weight. The observed magnitude of h² for LMWLW (0.123) aligns with the results of Mohammadi et al. (2012a) in Makooei sheep but it was found to be lower than the levels obtained by Yavarifard et l. (2015), in Mehraban sheep. Conversely, Amou Poshte Masari et al. (2013) on Shall sheep and Baneh et al. (2020) on Iran-Black sheep breeds reported higher values of h² for LMWLW.

The heritability estimation of LMWLW was observed to be comparatively lower than that of LMWLB, primarily due to a more pronounced influence of environmental factors and lamb genotypes on LMWLW in comparison with ewe genotypes. The h² of TLWB (0.108) was comparable to the value documented by Pourtahmasebian et al. (2020) in Afshari sheep, although it was less than the value calculated in more recent publications (Nabavi et al., 2015; Saghi et al., 2017; Jafaroghli et al., 2019). The low heritability of TLWB is ascribed to environmental influences, such as insufficient nutrition in ewes during pregnancy. TLWW greatly influences the profitability aspects of industry sheep. It affects the reproductive and maternal capacities of ewes, the growth and survival rates of their lambs before weaning (Boareki et al., 2020). The heritability of TLWW was determined to be 0.181. Yavarifard et al. (2015) observed a corresponding value in Mehraban sheep. Contrary to our findings, previous studies have also suggested lower (Eteqadi et al., 2017; Jafaroghli et al., 2019) and higher estimates of h² for TLWW (Nabavi et al., 2015). Selection based on TLWW is more efficient than TLWB due to a higher level of heritability. One possible reason for the low h² of reproductive traits in the present study could be the high phenotypic variance caused by various environmental factors. Consequently, focusing on management and activities that include improving ewe nutrition before mating and in the latter phases of pregnancy may result in the improvement of ewe reproductive performance. There was a significant service sire effect observed in the LMWLB, TLWB, and TLWW traits in Sangsari sheep. The findings align with the outcomes documented in the Mehraban (Yavarifard et al., 2015), Arman (Shariati et al., 2017), and Iran-Black (Baneh et al., 2020) sheep populations. Our analysis indicates that the estimated service sire effect ranges from 0.012 to 0.039, as reported by scholarly sources (Mohammadi et al., 2015; Amiri Roudbar et al., 2018). According to Amiri Roudbar. (2018), the impact of service sire accounts for 1–4% of the overall phenotypic variance in Lori-Bakhtirai sheep.

The value of S² for LMWLB is estimated to be 0.012. A study by Mohammadi et al. (2015) showed that the service sire had a minimal effect on LMWLB in Lori sheep, with a recorded value of 0.014. Nevertheless, this finding was deemed statistically significant and consistent with the conclusions of the present study. Several scholarly sources (Boujenane et al., 2021; Areb et al., 2021) have proposed that the best model for analyzing reproductive traits should take into account additive genetic effects and maternal effects of the ewe. Based on our estimations, the maternal permanent environmental variance as a proportion of ${\sigma }_{P}^{2}$ (pe²) for traits studied is low. The values of pe² vary from 0.026 for LMWLB to 0.060 for TLWW. The findings are within the range of values documented in previous studies, which ranged from 0.00 (Amou Poshte Masari et al., 2013) in shall sheep to 0.23 in Mehraban sheep (Yavarifard et al., 2015). The pe² values exhibited lower estimates than h² for birth and weaning performance traits. This implies that the significance of additive genetic effects is greater regarding these traits. The same results were documented by Roshanfekr et al. (2015) in a study conducted on Arabi sheep. The study revealed that permanent environmental factors have a greater effect on LMWLB and TLWW. Improving environmental conditions, such as ewe feeding and management techniques, is thus crucial for increasing the reproductive efficiency of Sangsari sheep. The study indicates that permanent environmental factors have a greater impact on LMWLB and TLWW. The range of repeatability was from 0.101 for LSW to 0.241 for TLWW. The observed repeatability estimate of LSB (0.152) aligns with the findings documented by Shariati et al. (2017), Baneh et al. (2020), and Pourtahmasebian et al. (2020). However, Yavarifard et al. (2015) noted a higher repeatability of 0.40 in Mehraban sheep, contrasting with our study's estimation. The repeatability of LSW (0.101) observed in this study exhibited greater magnitude compared to the outcomes obtained by Roshanfekr et al. (2015) and Eteqadi et al. (2017).

The magnitude of repeatability for LSW (0.101) was greater than the results of Roshanfekr et al. (2015) and Eteqadi et al. (2017). However, this result agrees with the study of Mohammadi et al. (2012b), which found a repeatability of 0.10 for LSW in Zandi sheep. The value of repeatability for LMWLB was estimated to be 0.192, which falls within the reported range of 0.15 as indicated by Mohammadi et al. (2012a), and 0.39 as reported by Baneh et al. (2020). The current investigation revealed a higher repeatability value for LMWLW (0.181) when compared to earlier studies carried out by Mohammadi et al. (2015) and Eteqadi et al. (2017). The repeatability of TLWB and TLWW was comparable to that of Yavarifard et al. (2015) for TLWW and Amiri Roudbar et al. (2018) for TLWB. Reproductive traits show a greater value of repeatability than corresponding heritability, suggesting a greater influence of fixed effects and permanent environmental effects on these traits.

Correlation estimates

Figure 1 illustrates the correlation estimates derived from bivariate analyses. The lowest genetic correlation (r_g) was observed between LSB-LMWLB, at -0.59, while the highest correlation was found between LMWLB-TLWB and LSB-TLWB, at 0.87. LSB and LSW showed a high genetic correlation of 0.83, indicating that these traits are influenced by similar genes. Similar findings were also documented by Baneh et al. (2020) regarding Iran-Black and Jafaroghli et al. (2019) on Baluchi sheep breeds. However, Eteqadi et al. (2017) reported a negative r_g value (-0.99) for LSB-LSW. Both LSB and LSW exhibit negative genetic associations with the LMWLB and LMWLW. These findings suggest that an increased number of lambs in a litter can lead to decreased birth weights and weaning weights. Comparable outcomes were also documented by Mohammadi et al. (2015), Roshanfekr et al. (2015), and Jafaroghli et al. (2019). Eteqadi et al. (2017) and Shariati (et al. 2.17) also obtained comparable findings regarding the genetic correlations between LSB-TLWB (0.87), LSB-TLWW (0.80), LSW-TLWB (0.71), and LSW-TLWW (0.82).

Ewes with a larger litter size were expected to have a greater overall weight of lambs. LSB and LSW have limited direct selection due to their low h² values. Nonetheless, there is the possibility of improving these traits by using indirect selection and taking advantage of positive genetic associations among LSB-TLWB, LSB-TLWW, LSW-TLWB, and LSW-TLWW. Similar results have also been documented for Shall sheep by Amou Poshte Mesari et al. (2013), and in Lori sheep by Mohammadi et al. (2015). TLWB and TLWW have a genetic correlation of 0.82, indicating that genes associated with higher total birth weight also lead to an increase in total weight. The outcomes are in line with the results of Mohammadi et al. (2015), Shariati et al. (2017), and Baneh et al. (2020). In addition, genes that increase TLWB may also increase the mother’s milk and mothering abilities, since the lamb's weaning weight depends on the ewe's maternal ability besides its genotype. The r_p values ranged from − 0.19 for LSB-LMWLW to 0.66 for LSB-LSW, and these findings are according to previous studies (Mohammadi et al., 2013; Eteqadi et al., 2017; Baneh et al., 2020). Some of the studied reproductive traits show a medium and positive phenotypic correlation, suggesting that environmental factors that enhance one trait may also positively impact other traits. It was generally noted that there are positive and moderate r_g and r_p values among LMWLB and LMWLW, which is consistent with earlier findings (Amou Poshte Masari et al., 2015; Shariati et al., 2015).

Genetic improvement through direct selection has been limited by the low heritability of reproductive traits, despite the significant phenotypic variation in Sangsari sheep. Therefore, improving non-genetic factors, such as management and ewe’s nutrition before and after mating, can effectively improve the reproductive performance of ewes. Compared to other traits, TLWW showed greater heritability and repeatability. Furthermore, TLWW had positive and moderately high r_g with all traits. Hence, TLWW can be effective as a selection criterion in the breeding program for improving the efficiency of ewe reproduction.

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Competing Interests

The authors have no relevant financial or non-financial interests to disclose.

Data Availability

Not applicable.

Ethics approval

This study did not require manipulation or modification of the usual handling of the animals, since we have worked directly with the routine records.

Consent to participate

Not applicable.

Consent for publication

The authors give their consent for publication.

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A study on the genetic and phenotypic factors affecting specific ewe productivity traits in Sangsari sheep

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Abstract

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Introduction

Material and methods

Study area and environmental conditions

Animals and flock management

Data sets and the analyzed traits

Data analysis

Results

Environmental factors and reproductive performance

Genetic parameters

Correlations between traits measured

Discussion

Fixed effects

Genetic effects and heritability estimates

Correlation estimates

Conclusions

Declarations

References

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