The aim of this study was to estimate genetic parameters for several female fertility criteria and to choose the most suitable selection index in Spanish Florida and Payoya goat breeds. In this study, we analyzed as fertility traits, the age at first kidding (AgFiKid), and the interval between the first and second kiddings (Int12Kid), between the second, third, and remaining kiddings (Int3toKid), and between all kiddings (IntAllKid) in 51,123 and 22,049 Florida and Payoya females, respectively. Genetic parameters were estimated by fitting animal models using restricted maximum likelihood (REML) methodology. We proposed six selection indices to compare the genetic responses for all traits included, based on a new selection index theory. The heritability and repeatability estimates of the traits were low, as expected. The genetic correlations among fertility traits covered a wide range of values from 0.07 (AgFiKid-Int12Kid) to 0.71 (Int3toKid-IntAllKid) in Florida and from −0.02 (AgFiKid-Int12Kid) to 0.82 (Int3toKid-IntAllKid) in Payoya. Overall, the results of this study indicate that IntAllKid gives the highest genetic responses in both breeds but is expressed late in a female’s life. However, AgFiKid and Int12Kid could be recommended as early selection criteria for female fertility in both breeds.

The native Spanish Merino breed was the founder of all the other Merino and Merino-derived breeds worldwide. Despite the fact that this breed was created and improved to produce the highest quality fine wool, the global wool market crisis led to the wholescale crossing of most of the herds with breeds for meat purposes. Nevertheless, there are still some purebred animals with a high potential for producing quality wool. The objective of this study was to characterize the current wool quality of the breed and identify genes associated with these parameters. To achieve this, over 12,800 records from the most representative animals of the breed (registered in the herd book) were analyzed using the Australian OFDA 2000 system, for parameters such as fiber diameter (FD), standard deviation (SD), coefficient of variation (CV), fibers over 15 microns (>15%), staple length (SL), and comfort factor (CRV). Additionally, animals with the most extreme FD values were whole-genome sequenced using NGS. Genome-wide association studies (GWAS) determined the association of 74 variants with the different traits studied, which were located in 70 different genes. Of these genes, EDN2, COL18A1, and LRP1B, associated with fibers over 15%, and FGF12 and ADAM17, associated with SL, play a key role in hair follicle growth and development. Our study reveals the great potential for recovering this breed for fine wool production, and identifies five candidate genes whose understanding may aid in that selection process.

Genetic parameters of litter size (LS) and number of kids born alive (NBA) were estimated across parities in Florida and Payoya goat breeds using a threshold random regression model (RRM). We analysed the reproductive records for the Florida and Payoya breeds separately, and a total of 130.849 and 67.478 reproductive records from the first to the seventh parities from Florida and Payoya, respectively, were included in the analysis. Random regressions on Legendre polynomials of standardised parity were included for permanent environmental and additive genetic effects. We based our estimation of all the covariance components on Bayesian inference under categorical distribution and considered heterogeneous residual variances in the model. The estimates of heritabilities ranged from null to 0.37 for LS and from 0.02 to 0.20 for NBA, while the repeatability estimates were between 0.008–0.60 and 0.03–0.44 for LS and NBA, respectively. Phenotypic correlations within-trait along parities ranged widely from −0.31 to 0.97, while the genetic correlations within-trait through parities showed a wider range and magnitude of values, from −0.98 to 0.99 for LS and from −0.72 to 0.99 for NBA. The estimates of genetic correlations between LS and NBA in the different parities were positive in the first two parities and negative in the later ones. Due to the noticeable changes in variance and the incomplete genetic correlations, the use of a random regression model to analyse LS and NBA is recommended in goat breeds.

A total of 1,031,143 records of daily dairy control test of Spanish Florida goats were used for this study. The database was edited, and only the records of the first three lactations were kept. The final database contained 340,654 daily-test somatic cell counts from 27,749 daughters of 941 males and 16,243 goats. The evolution of this count in the last 14 years was analyzed following French and American international associations’ criteria for the risk of mastitis in goats, and confirmed the slight increase in SCS in the last years and the importance of this problem (50% of dairy control tests show a risk of suffering mastitis). For the genetic analysis, the SCS records were log-transformed to normalize this variable. Two strategies were used for the genetic analysis: a univariate animal model for the SCS assuming that SCS does not vary throughout the parities, and a multi-character animal model, where SCS is not considered as the same character in the different parities. The heritabilities (h²) were higher in the multiple traits models, showings an upward trend from the first to the third parity (h² between 0.245 to 0.365). The genetic correlations of the same trait, as well as between breeding values (GVs) between different parities, were different from unity. The breeding values (EBVs) obtained for both models were subjected to a PCA: the first eigenvector (${\lambda }_1)$ explained most of the variations (between 74% to 90%), while the second ${\lambda }_2$accounted for between 9% to 20% of the variance, which shows that the selection will be proportionally favorable but not equivalent in all parities and that there are some variations in the type of response.

Inbreeding depression is a genetic phenomenon associated with the loss of fitness and mean phenotypic performance due to mating between relatives. Historically, inbreeding coefficients have been estimated from pedigree information. However, the onset of genomic selection programs provides large datasets of individuals genotyped using SNP arrays, enabling more precise assessment of an individual's genomic‐level inbreeding using genomic data. One of the traits most sensitive to issues stemming from increased inbreeding is reproduction. This is particularly important in equine, in which fertility is only moderate compared to other livestock species. To explore this further, we evaluated the effect of inbreeding on five reproductive traits (age at first foaling (AFF), average interval between foalings (AIF), total number of foalings (NF), productive life (PL) and reproductive efficiency (RE)) in Pura Raza Español mares using genomic data. Residual predicted phenotypes were obtained by purging these traits through the REML (wg_Residual) and ssGREML (g_Residual) approaches in reproductive data of 29,847 PRE mares using the BLUPF90+ program. Next, we used pedigree‐based (F_ped) and ROH‐based genomic (F_ROH) inbreeding coefficients derived from 1018 animals genotyped with 61,271 SNPs to estimate the inbreeding depression (linear regression). Our results indicated significant levels of inbreeding depression for all reproductive traits, with the exception of the AIF trait when F_pedwas used. However, all traits were negatively affected by the increase in genomic inbreeding, and F_ROHwas found to capture more inbreeding depression than F_ped. Likewise, REML models (ssGREML) using genomic data for estimated predicted residual phenotypes resulted in higher variance explained by the model compared with the models not using genomics (REML). Finally, a segmented regression analysis was conducted to evaluate the effect of inbreeding depression, revealing that the levels of genealogical and genomic homozygosity do not manifest uniformly in reproductive traits. In contrast, the levels of inbreeding depression ranged from low to high as homozygosity increased. This analysis also showed that reproductive traits are very sensitive to inbreeding depression, even with relatively low levels of homozygosity.

This study aimed to estimate genetic parameters of reproductive efficiency over a wide age range of females using a random regression model (RRM) in Spanish goat breeds. A total of 138,139 and 64,638 reproductive records from the first to the sixth parities from Florida and Payoya, respectively, were included in the analysis. Random regressions on Legendre polynomials of standardised age were included for permanent environmental and additive genetic effects. Estimation of the covariance components was based on the Bayesian inference using the GIBBS3F90 software. Differences among genetic variance components for reproductive efficiency were observed over the animal’s lifetime. The estimates of heritabilities were moderate, ranging from 0.21 to 0.32 for Florida and from 0.25 to 0.35 for Payoya, while the fractions of phenotypic variance explained by the permanent environmental effect were high, varying between 0.45–0.68 and 0.58–0.71 for Florida and Payoya, respectively. The correlations for permanent environmental effect over age ranged from 0.37 to 0.99, while the genetic correlations between the different ages varied from 0.36 to 0.98 for Florida and from 0.80 to 0.99 for Payoya. The results from this study support the validity of using an RRM to genetically analyse reproductive efficiency in Spanish dairy goats following the changes in variances and the genetic correlations different from unity over the animal’s lifetime. Moreover, reproductive efficiency is a highly heritable trait that is expressed early in a female’s life, and it could be used as a precocious selection criterion to improve female fertility in Spanish dairy goats.