# Estimation of genetic parameters for fertility and prolificacy in the Lacaune meat sheep population carrying a hyperprolific gene

Emilie Cobo, Jérôme Raoul, Loys Bodin

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Emilie Cobo, Jérôme Raoul, Loys Bodin. Estimation of genetic parameters for fertility and prolificacy in the Lacaune meat sheep population carrying a hyperprolific gene. Small Ruminant Research, 2025, 253, pp.107630. 10.1016/j.smallrumres.2025.107630. hal-05339004

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# Estimation of genetic parameters for fertility and prolificacy in the Lacaune meat sheep population carrying a hyperprolific gene

Emilie Cobo<sup>a,*</sup>, Jérôme Raoul<sup>a,b</sup>, Loys Bodin<sup>a</sup>

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## ARTICLE INFO

**Keywords:**

Meat Sheep

Fertility

Prolificacy

Hyperprolific gene

Genetic evaluation

## ABSTRACT

From an economic perspective, meat sheep farms depend on several components, such as reproductive traits, which can be influenced by numerous factors of variation. In this study, the effects of two kinds of oestrus, a) induced oestrus (IO) and b) the first natural return oestrus (FRO), and of the genotype encoding a hyperprolific gene (FecL) on prolificacy and fertility were tested. The genetic parameters of these two traits were also estimated using BLUP animal models with ASReml software. The data related to 68,652 matings were recorded from 2010 to 2020 for 20,770 ewes from the Lacaune Ovi-Test population. The oestrus type, which is associated with the use of hormonal treatment, improved the two reproductive traits studied. As expected, one copy of the mutated allele of the FecL gene had an effect on the prolificacy of + 0.5 lambs per lambing but had no effect on fertility. Moreover, the animal variances estimated during this study for prolificacy and fertility after IO and on FRO were low but still explained a share of the phenotype variance of ± 0.5 lambs per litter and ± 0.20 fertility points around the average. The repeatabilities and heritabilities of prolificacy and fertility were low but consistent with the literature. Owing to the structural relationship between the oestrus type in our dataset, less data were available on FRO, making its genetic evaluation less accurate than the genetic evaluation on IO.

## 1. Introduction

In meat sheep farming, numerical productivity is one of the key factors in the sector's competitiveness. The relationship between prolificacy and farmers' income has been highlighted in several studies (Amer et al., 1999; Oliván and Pardos, 2000; Ludemann, 2009). Against this backdrop, the French sheep industry has made improving productivity its main objective to maintain production potential and economic security (Cheype et al., 2013). In addition to numerical productivity, which is the ratio of the number of lambs produced (sold or kept for replacement) per ewe joined, weight productivity is also an economic indicator for breeders (Cobo et al., 2021). This productivity indicator, expressed in kilograms of lambs produced, corresponds to the sum of the weights of lambs produced (sold or kept for replacement) over the number of ewes joined. Productivity is the result of several components, notably, prolificacy and fertility.

Prolificacy, which corresponds to the number of lambs born, live or dead (including runts) over the number of lambings (including abortions), is an easy-to-measure trait on farms. The genetic parameters of litter size in meat sheep are well known (Rosati et al., 2002), as are its

variation factors (Ajafar et al., 2022). These variation factors include a) the use of pregnant mare serum gonadotropin (PMSG) in hormonal treatments for oestrus synchronization, which is responsible for 0.2-to-0.3-point increases in prolificacy in most breeds (Bodin and Elsen, 1989), and b) the presence of major genes involved in ovulation, as first evidenced based on average increases in prolificacy and the appearance of exceptional litter sizes. Numerous hyperovulation genes are known in various meat sheep breeds (Davis, 2005; Bodin et al., 2011), notably BMP15 and GDF9. In the Lacaune meat breed, the causal FecL mutation (Bodin et al., 2007; Drouilhet et al., 2009), located on chromosome 11, has an effect of + 0.5 lambs per lambing in the heterozygous state (Martin et al., 2014). To avoid excessively large litter sizes, which has a negative impact on ewe performance and on the farmer's work and income, the mutated allele at the FecL gene is managed on the basis of intragenotype selection and an individual mating plan aimed at avoiding the creation of homozygous carrier individuals (Raoul et al., 2018).

Fertility, which corresponds to the ratio of ewes lambing to ewes joining, is a more difficult measure in multi-sire breeding situations, as it requires greater rigour in the recording of information related to reproduction including accuracy of sire assignment (Juengel et al.,

<footnote><sup>a</sup> GenPhySE, INRAE, University of Toulouse, INPT, ENVT, Castanet-Tolosan 31326, France</footnote>

<footnote><sup>b</sup> Institut de l'Elevage, BP 42118, Castanet-Tolosan 31321, France</footnote>

<footnote>* Correspondence to: INRAE, UMR1388-GenPhySE, 24, chemin de Borde Rouge, CS 52627, Castanet-Tolosan 31326, France.</footnote>

<footnote>E-mail addresses: emilie.cobo@inrae.fr (E. Cobo), jerome.raoul@inrae.fr (J. Raoul), loys.bodin@inrae.fr (L. Bodin).</footnote>
2019). As a result, this trait is easiest to study after inducing oestrus in the context of artificial insemination (AI) because this practice is better controlled and the data are more accurate (David et al., 2007).

In the present study, the two main components of productivity, fertility (FERT) and prolificacy (PROL), were studied in the context of AI on induced oestrus (IO) and on the first natural return oestrus (FRO) breeding in the meat sheep Lacaune population of the Ovi-Test breeding programme, in which a major ovulation gene, the FecL gene, segregates. Given the advanced knowledge of prolificacy, we first evaluated prolificacy to assess the quality of our dataset. Subsequently, we examined fertility, in particular to assess the effect of the major ovulation gene and to estimate its genetic parameters on IO and FRO.

## 2. Material and methods

### 2.1. Reproductive traits, data origins, and preprocessing

Two main reproductive traits were studied: PROL and FERT. Prolificacy corresponds to the total number of lambs born, whether alive or dead (including runts). To estimate FERT, a preliminary analysis was required to identify the matings (on OI or FRO) that resulted or did not result in lambing. Fertility was then converted into binary data: 1 for successful mating and 0 for unsuccessful mating.

This study focused on the Ovi-Test Lacaune meat sheep population, in which the FecL hyperprolific major gene segregates. Mating and lambing records of ewes from 26 flocks of breeders from the Ovi-Test breeding association between 2010 and 2020 were extracted from the French official national genetic database. The data collected on the farms was analysed in the UMR GenPhySE laboratory at the INRAE Occitanie Toulouse center in France. The analysis focused on the results of artificial insemination after IO and of natural mating on the first return when it occurred. In these flocks, inseminated ewes are systematically joined with rams to ensure better global fertility but only 10 days after insemination to be able to determine the paternity of future lambs. The fertility of AI was considered successful if lambing occurred within a range of ± 5 days after the AI date increased by the gestation duration (153 days); otherwise, it was considered unsuccessful, whereas the fertility of the first return oestrus was considered successful if lambing occurred within the same range increased by the duration of the oestrus cycle (17 days). In total, the study covered 20,770 ewes with 68,652 matings (including 55,262 artificial inseminations after IO and 13,390 on FRO; Table 1), resulting in 49,248 lambings. In our study, PROL and FERT on the first return oestrus of ewes which remain barren after insemination implied that the numbers of PROL and FERT on FRO were lower than those of PROL and FERT on IO. As a result, the genetic parameters of the two reproduction traits were estimated less precisely on FRO than on IO. For each ewe involved in the study, their pedigree over five generations to build the pedigree matrix and their FecL genotypes were also retrieved. In our dataset, 90 % of the ewes had a known sire (i. e. 621 different sires), and more than half were genotyped (57.8 %). Among the 12,010 genotyped ewes, 61.8 % were homozygous wild type (++), 34.5 % were heterozygous type (L+), and 3.6 % were homozygous mutated type (LL). The number of matings per type of oestrus and genotype is shown in Table 1.

<caption>**Table 1**</caption>

Number of matings by oestrus type (IO: induced oestrus and FRO: first natural return oestrus) and the FecL genotype.

<table><thead><tr><th rowspan="2">Genotype</th><th colspan="2">Oestrus Type</th></tr><tr><th>IO</th><th>FRO</th></tr></thead><tbody><tr><td>Homozygous Wild + +</td><td>979</td><td>212</td></tr><tr><td>Heterozygous L+</td><td>10217</td><td>2136</td></tr><tr><td>Homozygous Mutated LL</td><td>20455</td><td>5125</td></tr><tr><td>Unknown</td><td>23611</td><td>5917</td></tr></tbody></table>

### 2.2. Genetic models

Each trait, PROL or FERT, was the subject of two independent genetic studies. Analyses were performed using an animal model in which the records referred to the ewe.

Prolificacy was analysed with a bi-traits BLUP animal model considering PROL_IO and PROL_FRO as two different traits. The fixed effects in the model included the type of oestrus (induced or first return), the genotype (4 levels: homozygous wild type, heterozygous, homozygous mutated and unknown genotype, named ++, L+, LL and unknown, respectively), the previous litter size (3 levels), the previous parity number (4 levels), the postpartum interval (4 levels), the month of mating (3 levels) and the age of the female at mating (3 levels). In addition to the animal effect, which represents the additive genetic effect, two additional random effects were taken into account, an animal permanent environment effect and a herd-year-season effect (1,085 levels), and a residual effect was estimated. The genetic correlation between PROL_IO and PROL_FRO was also estimated.

The genetic study of FERT involved two single-trait models, one for FERT_IO and the other for FERT_FRO. Each of these models was designed with an animal model with six fixed effects and four random effects. The six fixed effects were the genotype, previous litter size, previous parity number, postpartum interval, month of mating and age of the female at mating. The four random effects were an animal effect, an animal permanent environment effect, a herd-year-season effect, and an estimated residual effect.

Genetic models were run via a restricted maximum likelihood method implemented in ASReml software (Gilmour et al., 2014). The effect of the FecL genotype was assessed via a parametric Wald test provided by ASReml models.

## 3. Results

### 3.1. Prolificacy by method of reproduction and genotype

The average raw PROL in the sample was 2.00 ± 0.86, which was consistent with the breed standard (Coppin et al., 2022). As shown in Table 2, the estimated value of PROL_IO was higher than the estimated value of PROL_FRO by + 0.23 lambs, while their variabilities were roughly equal.

Compared with the estimated prolificacy of homozygous wild ewes (++), the estimated prolificacy of mutated ewes was higher: + 0.38 lambs for heterozygous ewes (L+) and + 0.45 lambs for homozygous mutated ewes (LL) (Table 2).

### 3.2. Genetic parameters of prolificacy

The genetic parameters of prolificacy estimated using a two-trait model for PROL_IO and PROL_FRO are presented in Table 3. All the fixed effects included in the model were highly significant (P < 0.001). Considering the standard error of the parameters, the additive genetic variances did not differ between the two methods of reproduction,

<caption>**Table 2**</caption>

Least square means¹ (LSMeans) and their standard errors (S.E.) of Lacaune meat ewes' prolificacy (n = 20,770) for the two breeding methods (IO: induced oestrus and FRO: first natural return oestrus) and the FecL genotype.

<table><thead><tr><th rowspan="2">Trait</th><th rowspan="2"></th><th colspan="2">Prolificacy</th></tr><tr><th>LSMeans</th><th>S.E.</th></tr></thead><tbody><tr><td rowspan="2">Method of Reproduction</td><td>IO</td><td>2.07</td><td>0.03</td></tr><tr><td>FRO</td><td>1.84</td><td>0.03</td></tr><tr><td rowspan="2">FecL Genotype</td><td>Homozygous Wild + +</td><td>1.72</td><td>0.03</td></tr><tr><td>Heterozygous L+</td><td>2.10</td><td>0.03</td></tr><tr><td rowspan="2"></td><td>Homozygous Mutated LL</td><td>2.17</td><td>0.04</td></tr><tr><td>Unknown</td><td>1.82</td><td>0.03</td></tr></tbody></table>
<caption>**Table 3**</caption>

Estimates of variance components, repeatability, and heritability (S.E. in brackets) of prolificacy on induced oestrus (IO; n = 40,896) and on first natural return oestrus (FRO; n = 8,352).

<table><thead><tr><th>Trait</th><th colspan="2">Prolificacy</th></tr><tr><th>Two Traits Model</th><th>IO</th><th>FRO</th></tr></thead><tbody><tr><td>Additive Genetic Variance σ<sup>2</sup><sub>a</sub></td><td>0.046 (0.005)</td><td>0.043 (0.008)</td></tr><tr><td>Animal Permanent Environment Variance σ<sup>2</sup><sub>pe</sub></td><td>0.020 (0.005)</td><td>0.040 (0.012)</td></tr><tr><td>Residual Variance σ<sup>2</sup><sub>e</sub></td><td>0.643 (0.006)</td><td>0.419 (0.012)</td></tr><tr><td>Repeatability r</td><td>0.09 (0.01)</td><td>0.16 (0.02)</td></tr><tr><td>Heritability h<sup>2</sup></td><td>0.06 (0.01)</td><td>0.09 (0.01)</td></tr><tr><td>Genetic Correlation rg</td><td>0.95 (0.05)</td><td></td></tr></tbody></table>

whereas the variance of the animal permanent environment effects was twice as high for PROL_FRO as it was for PROL_IO. However, the residual variance for PROL_IO was higher than that for PROL_FRO by + 0.224. The estimated repeatability of PROL_IO (r = 0.09 ± 0.01) was almost twice as low as that of PROL_FRO (r = 0.16 ± 0.02). The standard error of repeatability for IO was also lower than that for FRO. Heritabilities were of a similar order of magnitude (PROL_IO, h² = 0.06 ± 0.01 and PROL_FRO, h² = 0.09 ± 0.01). Prolificacies after IO and on FRO were strongly correlated at approximately 0.95 ± 0.05.

### 3.3. Fertility according to the FecL genotype

Overall, the raw fertility averages for IO and FRO were 73.7 % and 52.1 %, respectively.

The fertility of IO ewes was consistently higher than that of FRO ewes regardless of the FecL phenotype: 0.13 points for ++, 0.19 points for L+, and 0.23 points for LL ewes. Within the IO, the estimated fertility was similar regardless of genotype, ranging from 0.59 to 0.62 points. For FRO, the estimated fertility varied slightly according to genotype: – 0.03 points for L+ ewes and – 0.08 points for LL ewes compared with ++ ewes (Table 4).

Genotype had a significant effect on FERT regardless of the method of reproduction, with P < 0.001 for FERT_IO and P = 0.005 for FERT_FRO. Nevertheless, the difference between the estimated values of the FecL genotype for each method of reproduction was slight, particularly between L+ and ++ ewes. Indeed, the estimated values of fertility for heterozygous (L+) and wild-type (++) ewes were 0.62 ± 0.01 and 0.59 ± 0.01, respectively, for FERT_IO and 0.43 ± 0.03 and 0.46 ± 0.03, respectively, for FERT_FRO (Table 4). For IO, FERT was slightly better for L+ ewes than for ++ ewes, whereas the opposite was true for FRO. Mutation of an allele had little influence on ewe FERT and tended to increase FERT_IO in F+ ewes compared with ++ ewes but decreased FERT_FRO.

### 3.4. Genetic parameters of fertility

For fertility, animal variances estimated on IO and FRO using a single-trait model were equivalent ($\sigma_a^2 = 0.007$; Table 5). The animal permanent environment variances of FERT were of the same order of magnitude. In contrast, the residual variance of FERT_IO was 0.027 points lower than that of FERT_FRO. For IO, the repeatability and

<caption>**Table 4**</caption>

Least square means¹ (LSMeans) and their standard errors (S.E.) of Lacaune meat ewe fertility (n = 20,770) for each breeding method (IO: induced oestrus and FRO: first natural return oestrus) by FecL genotype.

<table><caption>**Table 5**</caption><thead><tr><th>Trait</th><th colspan="2">Fertility</th></tr><tr><th>Oestrus type</th><th>IO</th><th>FRO</th></tr></thead><tbody><tr><td>FecL Genotype</td><td>LSMeans (S.E.)</td><td>LSMeans (S.E.)</td></tr><tr><td>Homozygous Wild + +</td><td>0.59 (0.01)</td><td>0.46 (0.03)</td></tr><tr><td>Heterozygous L+</td><td>0.62 (0.01)</td><td>0.43 (0.03)</td></tr><tr><td>Homozygous Mutated LL</td><td>0.61 (0.02)</td><td>0.38 (0.04)</td></tr><tr><td>Unknown</td><td>0.61 (0.01)</td><td>0.44 (0.03)</td></tr></tbody></table>

Estimates of variance components, repeatability, and heritability (S.E. in brackets) of fertility on induced oestrus (IO) and on first natural return oestrus (FRO).

<table><thead><tr><th>Trait</th><th colspan="2">Fertility</th></tr><tr><th>One Trait Model</th><th>IO</th><th>FRO</th></tr></thead><tbody><tr><td>Additive Genetic Variance σ<sup>2</sup><sub>a</sub></td><td>0.007 (0.001)</td><td>0.007 (0.002)</td></tr><tr><td>Animal Permanent Environment Variance σ<sup>2</sup><sub>pe</sub></td><td>0.012 (0.001)</td><td>0.009 (0.003)</td></tr><tr><td>Residual Variance σ<sup>2</sup><sub>e</sub></td><td>0.161 (0.001)</td><td>0.188 (0.004)</td></tr><tr><td>Repeatability r</td><td>0.10 (0.01)</td><td>0.08 (0.01)</td></tr><tr><td>Heritability h<sup>2</sup></td><td>0.04 (0.01)</td><td>0.03 (0.01)</td></tr></tbody></table>

heritability of FERT were estimated at 0.10 ± 0.01 and 0.04 ± 0.01, respectively. The FRO values were 0.08 ± 0.01 and 0.03 ± 0.01, for repeatability and heritability respectively.

## 4. Discussion

### 4.1. Gene effect on prolificacy

As expected, the hyperprolific gene had a major effect on the average prolificacy of ewes, in favour of the L+ genotype. In our study, the estimated effect of FecL on the performance of 5,775 genotyped Lacaune ewes was in line with the study conducted by Martin et al. (2014). This gene effect, estimated ten years ago, was therefore confirmed using a larger dataset with 3.5 times more ewes.

The increase of just under 0.5 lambs for one copy of the mutant allele was less (Bindon et al., 1985; Hanrahan et al., 2004; Monteagudo et al., 2009; Demars et al., 2013) or greater (Hanrahan et al., 2004; Nicol et al., 2009; Silva et al., 2011; Demars et al., 2013; Vage et al., 2013) than that of other known major hyperprolific genes. Unlike other hyperprolific genes, the FecL gene also seems to affect litter size distribution by increasing the variability of the trait within the genotype, which can result from specific physiological mechanisms regulated by the Lacaune gene (Martin et al., 2014). The effect of the FecL mutated allele can be considered strong even though the estimated average prolificacy of ++ ewes is high.

### 4.2. Genetic parameters of PROL_IO and PROL_FRO

A bivariate model to estimate the genetic parameters of litter size on induced or natural oestrus has been applied in other studies (Bodin, 1979; Janssens et al., 2004; Baelden et al., 2005). However, to our knowledge, no reference is available in the literature concerning the prolificacy of the first return oestrus after hormonal synchronization, its genetic parameters, and its relationship with the prolificacy on the induced oestrus. Performance on a first return oestrus after synchronization was not comparable to that after a natural oestrus since, in the case of FRO, only ewes that were not pregnant after IO were available for FRO. For this study, the dataset was constructed in the following unique way: a first set (IO) with all synchronized ewes, resulting in pregnant or not pregnant ewes, and a second set (FRO) including the nonpregnant ewes of the first set and those that were pregnant or not pregnant on their first return oestrus. By definition, fertile ewes on IO do not have immediate performance on FRO.

In our study, the proportions of variance explained by the direct animal effect were similar, regardless of the method of reproduction. Although it appeared to be low, this animal genetic variance was consistent with the literature (Baelden et al., 2005). Considering an animal variance equal to 0.046 points and 2.5 genetic standard deviations, extreme ewes varied genetically by ± 0.5 lambs around the average. When the genotype effect was not considered in the BLUP animal bi-traits model, the animal variances increased by 0.02 points (data not shown), which corresponded to the variance due to the effect of the genotype on prolificacy (P < 0.001).

The repeatability, which measures the correlation between repeated
measurements of the same individual, was higher on PROL_FRO (+0.07 points) than on PROL_IO. The repeatabilities reported in the literature are on the same order of magnitude following IO or natural mating (Baelden et al., 2005). The difference between the repeatability estimated for PROL_IO and PROL_FRO in our study could be attributed to variations in the residual variance of the two reproduction methods (0.643 and 0.419, respectively). Nevertheless, both estimates remained much lower than those reported for Texel sheep raised in Belgium (0.20 for IO and 0.25 for natural mating; Janssens et al., 2004).

As usual, regardless of mating type, the estimated heritability values of prolificacy were low but different from 0. Even if the comparison between performances on natural mating and those on FRO was slightly biased, we could still compare heritability values estimated for these two traits. Therefore, the heritability estimated for FRO was close to that for natural mating reported by Altarriba et al. (1998), Janssens et al. (2004) and Baelden et al. (2005) and certainly not significantly different from the slightly higher values reported in 1997 by Matos et al. (0.16) or the lower estimation reported by Lee et al. (2000) in Rambouillet sheep (0.05). The heritability values of prolificacy for IO reported in our study and in the literature (Bodin, 1979; Janssens et al., 2004; Baelden et al., 2005) had the same order of magnitude, that is, low but different from 0.

Our results showed that PROL_IO and PROL_FRO were strongly genetically correlated. The genetic correlation was quite high in our study (0.95 ± 0.05) compared with those reported between prolificacy after induced and natural oestrus in most studies. Genetic correlations equal to 0.73, 0.74 and 0.75 have been reported in Ile de France, Blanche du Massif Central and Mouton Vendéen, respectively (Baelden et al., 2005), from 0.57 to 0.64 in Suffolk, and from 0.75 to 0.81 in Texel (Janssens et al., 2004).

### 4.3. Effect of the *FecL* gene on fertility

Several studies have demonstrated that major genes controlling prolificacy, such as BMP15 (Bodin et al., 2007; Demars et al., 2013; Martin et al., 2014) and GDF9 (Monteagudo et al., 2009; Nicol et al., 2009), can have a slight effect on FERT_IO. In the study by Martin et al. (2014), on the basis of fewer records, a mutated allele of the FecL gene increased FERT_IO by 0.07 points compared with FERT_IO for ++ ewes, with a significant genotype effect (P = 0.016). This effect of the FecL gene on fertility might be explained by the endocrine profile of LL ewes. In fact, after sponge withdrawal following classical progestative treatment, FSH and LH surges appear 20 h earlier in LL ewes than in ++ ewes (Drouilhet et al., 2010). Consequently, an equivalent delay between the end of the treatment and artificial insemination for all genotypes could be suboptimal for all of them, although in Drouilhet's experiment, these differences were observed in the absence of PMSG injection, which normally follows the progestative treatment.

In contrast with these previous results, in our study, mutation of an allele of the FecL gene seemed to have very little influence on ewe FERT_IO. Breeding management practices, such as selective use of AI in ewes with extreme prolificacy or adjustments in synchronization protocols, may have influenced the results. Moreover, sample composition may have biased fertility estimates, as ewes returning to oestrus after AI represent a non-random sub-sample of the population. Therefore, the interaction between genotype and mating type was not considered in the analyses.

### 4.4. Genetic parameters of FERT_IO and FERT_FRO

Given that several studies have shown that linear or threshold models yield similar results under certain conditions in terms of the incidence of categorical traits (Meijering and Gianola, 1985; Boichard and Manfredi, 1994), fertility was considered as a continuous variable and analysed using linear models. David et al. (2007) demonstrated that for sheep populations with extensive use of artificial insemination, estimation of breeding values for female fertility might be implemented

with simple linear models.

To our knowledge, few genetic studies on sheep fertility and very few studies on FERT_IO and FERT_FRO are available in the literature. In a linear animal model, Matos et al. (1997) estimated animal variances after IO of 0.004 and 0.0059 in two US meat sheep populations (Rambouillet and Finsheep). Using a linear model that provided joint estimates of male and female fertility, David et al. (2008) reported animal variances ranging from 0.0093 to 0.0178 in five dairy sheep populations under intensive AI schemes in France. In our study, whether after IO or on FRO, the estimated animal variances for fertility were intermediate; they were low but different from 0 ($\sigma_a^2$ = 0.007). Even if the genotype was not considered as a fixed effect in each of the single trait models, the share of animal variance remained the same, equal to 0.007 (data not shown); consequently, the animal variance explained by the FecL gene was almost zero. The animal permanent environment variances of FERT_IO and FERT_FRO were of the same order of magnitude but slightly lower than the literature values (David et al., 2008). The residual variance after IO was slightly lower than the values reported in the literature (David et al., 2008) and lower than the residual variance on FRO. The fixed effects in the FERT_IO and FERT_FRO models were the same, but if the records were available, other factors of variation could be integrated into the FERT_FRO model to reduce the amount of residual variance, such as the number of rams per ewe in a mating flock. The repeatability after IO estimated in our study corresponded to the low range of literature values. Repeatabilities ranging from 0.103 to 0.133 were reported in a study by David et al. (2008). Compared with the repeatability after IO, the estimated repeatability on FRO was lower and almost three times more variable. This phenomenon was explained by a higher residual variance since animal genetic and animal permanent environment variance were not different between IO and FRO. As expected for reproductive traits, heritabilities were low. They did not differ for IO or FRO and were different from zero. Heritability after IO was in line with the heritabilities estimated in previous studies in Lacaune ewes (David et al., 2008) and with heritabilities estimated in other species: from 0.02 to 0.06 for Holstein cows (Gonzalez-Recio and Alenda, 2005) and 0.052 and 0.045 for Saanen and Alpine goats, respectively (Clément et al., 2023).

## 5. Conclusion

Our estimation of the genetic components of both fertility and prolificacy after induced oestrus or on first natural return oestrus were in line with most of the values reported in the literature. Nevertheless, in this study repeatabilities and heritabilities often corresponded to the lowest rank of recent values in the literature because of the importance of the influence of uncontrolled effects of the environment.

As a result, the FecL gene did not impair the fertility of ewes in a relevant way, and its management allowed prolificacy while limiting the number of large litter sizes. The impact of hyperprolific gene on other reproductive traits, such as fertility, was almost non-existent, but as with any trait, the interaction with other traits of interest must be studied.

To our knowledge, there are no genetic studies on the effects of ewes' fertility on the first return to oestrus after IO. Despite the impossibility of using a bi-traits model for fertility in the case of its genetic study after insemination and on first natural return, this study provides interesting insight. If the ewe becomes pregnant after induced oestrus, it is no longer available upon natural oestrus return, which leads to an imbalance in the number of matings between the induced oestrus and the first natural return oestrus. As a result, to increase the accuracy of the genetic parameters estimated on the first natural return oestrus, more data could be used once collected. This approach could also be improved by the collection of new factors, such as the number of rams per ewe in a mating flock, for example, to allow refinement of the genetic models associated with this breeding practice.
CRediT authorship contribution statement

**Loys Bodin:** Writing – review & editing, Supervision. **Emilie Cobo:** Writing – original draft, Methodology, Formal analysis. **Jérôme Raoul:** Writing – review & editing, Supervision.

Research grants

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Declaration of Competing Interest

Authors declare that there are no known conflicts of interest, which could have affected the outcome of this publication.

Acknowledgements

The authors would like to thank the OVI-Test breeding association for the data provided, and AJE company for linguistic revision.

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