Fertility is one of the key factors in the economic and productive success of the equine industry. Despite this, studies on the genetic causes affecting reproductive performance are scarce, especially in mares, where the genetic architecture of the reproductive traits is extremely complex. Today, with the increasing availability of new genomic methodologies for this species, we are presented with an interesting opportunity to understand the genetic basis of equine reproductive disorders. These include, among others, novel techniques for detecting chromosomal abnormalities, whose association with infertility in horses was established over 50 years ago; new sequencing technologies permitting an accurate detection of point mutations influencing fertility, as well as the study of inbreeding and molecular homozygosity, which has been widely suggested as one of the main causes of low reproductive performance in horses. Finally, over the last few years, reproductive performance has also been associated with copy number variants and candidate genes detected by genome-wide association studies on fertility traits. However, such studies are still scarce, probably because they depend on the existence of large and accurate phenotypic datasets of reproductive and/or fertility traits, which are still difficult to obtain in equines.

Despite the economic importance of fertility for the horse industry, few efforts have been made to achieve a better understanding of the genetic mechanisms underlying its control. This is probably due to the difficulty of obtaining reliable phenotypes and the complexity of modelling the environmental and management factors. This work is novel in that we propose to use reproductive efficiency (RE) as an indicator of mare fertility. To achieve this, we performed a genome-wide association study in the Pura Raza Español horse aimed at identifying genomic variants, regions, and candidate genes associated with fertility in mares. The dataset included 819 animals genotyped with the Affymetrix Axiom™ Equine 670 K single-nucleotide polymorphisms (SNPs) Genotyping Array and the deregressed breeding values for RE trait, obtained using a ssBLUP model, employed as pseudo-phenotypic data. Our results showed 28 SNPs potentially associated with RE, which explained 87.19% of the genetic variance and 6.61% of the phenotypic variance. Those results were further validated in BayesB, showing a correlation between observed and predicted RE of 0.57. In addition, 15 candidate genes (HTRA3, SPIRE1, APOE, ERCC1, FOXA3, NECTIN-2, KLC3, RSPH6A, PDPK1, MEIOB, PAQR4, NM3, PKD1, PRSS21, IFT140) previously related to fertility in mammals were associated with the markers and genomic regions significantly associated with RE. To our knowledge, this is the first genome-wide association study performed on mare fertility.

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.

Copy number variations (CNVs) are a new-fangled source of genetic variation that can explain changes in the phenotypes in complex traits and diseases. In recent years, their study has increased in many livestock populations. However, the study and characterization of CNVs in equines is still very limited. Our study aimed to investigate the distribution pattern of CNVs, characterize CNV regions (CNVRs), and identify the biological pathways affected by CNVRs in the Pura Raza Española (PRE) breed. To achieve this, we analyzed high-density SNP genotyping data (670,804 markers) from a large cohort of 654 PRE horses. In total, we identified 19,902 CNV segments and 1007 CNV regions in the whole population. The length of the CNVs ranged from 1.024 kb to 4.55 Mb, while the percentage of the genome covered by CNVs was 4.4%. Interestingly, duplications were more abundant than deletions and mixed CNVRs. In addition, the distribution of CNVs across the chromosomes was not uniform, with ECA12 being the chromosome with the largest percentage of its genome covered (19.2%), while the highest numbers of CNVs were found in ECA20, ECA12, and ECA1. Our results showed that 71.4% of CNVRs contained genes involved in olfactory transduction, olfactory receptor activity, and immune response. Finally, 39.1% of the CNVs detected in our study were unique when compared with CNVRs identified in previous studies. To the best of our knowledge, this is the first attempt to reveal and characterize the CNV landscape in PRE horses, and it contributes to our knowledge of CNVs in equines, thus facilitating the understanding of genetic and phenotypic variations in the species. However, further research is still needed to confirm if the CNVs observed in the PRE are also linked to variations in the specific phenotypical differences in the breed.

Background: Estimating inbreeding, which is omnipresent and inevitable in livestock populations, is a primary goal for management and animal breeding especially for those interested in mitigating the negative consequences of inbreeding. Inbreeding coefficients have been historically estimated by using pedigree information; however, over the last decade, genome-base inbreeding coefficients have come to the forefront in this field. The Pura Raza Española (PRE) horse is an autochthonous Spanish horse breed which has been recognised since 1912. The total PRE population (344,718 horses) was used to estimate Classical (F), Ballou’s ancestral, Kalinowski’s ancestral, Kalinowski’s new and the ancestral history coefficient values. In addition, genotypic data from a selected population of 805 PRE individuals was used to determine the individual inbreeding coefficient using SNP-by-SNP-based techniques (methods of moments -F_HOM-, the diagonal elements of the genomic -F_G-, and hybrid matrixes -F_H-) and ROH measures (F_RZ). The analyse of both pedigree and genomic based inbreeding coefficients in a large and robust population such as the PRE horse, with proven parenteral information for the last 40 years and a high degree of completeness (over 90% for the last 70 years) will allow us to understand PRE genetic variability better and the correlations between the estimations will give the data greater reliability. Results: The mean values of the pedigree-based inbreeding coefficients ranged from 0.01 (F for the last 3 generations -F3-) to 0.44 (ancestral history coefficient) and the mean values of genomic-based inbreeding coefficients varied from 0.05 (F_RZ for three generations, F_H and F_HOM) to 0.11 (F_RZ for nine generations). Significant correlations were also found between pedigree and genomic inbreeding values, which ranged between 0.58 (F3 with F_HOM) and 0.79 (F with F_RZ). In addition, the correlations between F_RZ estimated for the last 20 generations and the pedigree-based inbreeding highlight the fact that fewer generations of genomic data are required when comparing total inbreeding values, and the opposite when ancient values are calculated. Conclusions: Ultimately, our results show that it is still useful to work with a deep and reliable pedigree in pedigree-based genetic studies with very large effective population sizes. Obtaining a satisfactory parameter will always be desirable, but the approximation obtained with a robust pedigree will allow us to work more efficiently and economically than with massive genotyping.