Despite massive research efforts, the molecular etiology of bovine polledness and the developmental pathways involved in horn ontogenesis are still poorly understood. In a recent article, we provided evidence for the existence of at least two different alleles at the Polled locus and identified candidate mutations for each of them. None of these mutations was located in known coding or regulatory regions, thus adding to the complexity of understanding the molecular basis of polledness. We confirm previous results here and exhaustively identify the causative mutation for the Celtic allele (PC) and four candidate mutations for the Friesian allele (PF). We describe a previously unreported eyelash-and-eyelid phenotype associated with regular polledness, and present unique histological and gene expression data on bovine horn bud differentiation in fetuses affected by three different horn defect syndromes, as well as in wild-type controls. We propose the ectopic expression of a lincRNA in PC/p horn buds as a probable cause of horn bud agenesis. In addition, we provide evidence for an involvement of OLIG2, FOXL2 and RXFP2 in horn bud differentiation, and draw a first link between bovine, ovine and caprine Polled loci. Our results represent a first and important step in understanding the genetic pathways and key process involved in horn bud differentiation in Bovidae.

In mammals, anti-Müllerian hormone (AMH) expression is detected in the granulosa cells of all growing follicles and is highest in healthy small antral follicles, which contribute most significantly to AMH endocrine levels. AMH is a reliable endocrine marker of this population of gonadotrophin-responsive follicles in ruminants and, over the longer term, plasma AMH concentrations are characteristic of individual animals. In the cow, plasma AMH concentrations follow specific dynamic profiles throughout the prepubertal period, the oestrous cycle and the change from gestation to the post partum period, with the alterations most likely reflecting numerical changes in the population of high AMH-producing follicles. In granulosa cells, bone morphogenetic proteins (BMP) enhance AMH gene expression and AMH synthesis, with these effects antagonised by FSH. BMP could both support follicular growth and contribute significantly to the induction and/or maintenance of AMH expression in small growing follicles. AMH expression decreases sharply in large follicles when they become oestrogenic, suggesting a role for FSH and/or oestradiol in these changes, but the underlying mechanisms remain hypothetical. A better understanding of the factors and mechanisms regulating AMH production is needed to propose new strategies for managing the reserve of primordial and small growing follicles, as well as for improving embryo production.

BackgroundGenotyping with the medium-density Bovine SNP50 BeadChip® (50K) is now standard in cattle. The high-density BovineHD BeadChip®, which contains 777 609 single nucleotide polymorphisms (SNPs), was developed in 2010. Increasing marker density increases the level of linkage disequilibrium between quantitative trait loci (QTL) and SNPs and the accuracy of QTL localization and genomic selection. However, re-genotyping all animals with the high-density chip is not economically feasible. An alternative strategy is to genotype part of the animals with the high-density chip and to impute high-density genotypes for animals already genotyped with the 50K chip. Thus, it is necessary to investigate the error rate when imputing from the 50K to the high-density chip.MethodsFive thousand one hundred and fifty three animals from 16 breeds (89 to 788 per breed) were genotyped with the high-density chip. Imputation error rates from the 50K to the high-density chip were computed for each breed with a validation set that included the 20% youngest animals. Marker genotypes were masked for animals in the validation population in order to mimic 50K genotypes. Imputation was carried out using the Beagle 3.3.0 software.ResultsMean allele imputation error rates ranged from 0.31% to 2.41% depending on the breed. In total, 1980 SNPs had high imputation error rates in several breeds, which is probably due to genome assembly errors, and we recommend to discard these in future studies. Differences in imputation accuracy between breeds were related to the high-density-genotyped sample size and to the genetic relationship between reference and validation populations, whereas differences in effective population size and level of linkage disequilibrium showed limited effects. Accordingly, imputation accuracy was higher in breeds with large populations and in dairy breeds than in beef breeds. More than 99% of the alleles were correctly imputed if more than 300 animals were genotyped at high-density. No improvement was observed when multi-breed imputation was performed.ConclusionIn all breeds, imputation accuracy was higher than 97%, which indicates that imputation to the high-density chip was accurate. Imputation accuracy depends mainly on the size of the reference population and the relationship between reference and target populations.

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Background Selective breeding is a relatively recent practice in aquaculture species compared to terrestrial livestock. Nevertheless, the genetic variability of farmed salmonid lines, which have been selected for several generations, should be assessed. Indeed, a significant decrease in genetic variability due to high selection intensity could have occurred, potentially jeopardizing the long-term genetic progress as well as the adaptive capacities of populations facing change(s) in the environment. Thus, it is important to evaluate the impact of selection practices on genetic diversity to limit future inbreeding. The current study presents an analysis of genetic diversity within and between six French rainbow trout ( Oncorhynchus mykiss ) experimental or commercial lines based on a medium-density single nucleotide polymorphism (SNP) chip and various molecular genetic indicators: fixation index ( F ST ), linkage disequilibrium (LD), effective population size ( N e ) and inbreeding coefficient derived from runs of homozygosity (ROH). Results Our results showed a moderate level of genetic differentiation between selected lines ( F ST ranging from 0.08 to 0.15). LD declined rapidly over the first 100 kb, but then remained quite high at long distances, leading to low estimates of N e in the last generation ranging from 24 to 68 depending on the line and methodology considered. These results were consistent with inbreeding estimates that varied from 10.0% in an unselected experimental line to 19.5% in a commercial line, and which are clearly higher than corresponding estimates in ruminants or pigs. In addition, strong variations in LD and inbreeding were observed along the genome that may be due to differences in local rates of recombination or due to key genes that tended to have fixed favorable alleles for domestication or production. Conclusions This is the first report on ROH for any aquaculture species. Inbreeding appeared to be moderate to high in the six French rainbow trout lines, due to founder effects at the start of the breeding programs, but also likely to sweepstakes reproductive success in addition to selection for the selected lines. Efficient management of inbreeding is a major goal in breeding programs to ensure that populations can adapt to future breeding objectives and SNP information can be used to manage the rate at which inbreeding builds up in the fish genome. Electronic supplementary material The online version of this article (10.1186/s12711-019-0468-4) contains supplementary material, which is available to authorized users.

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The aim of this study was to assess the potential consequences of selection for less aggressive or active animals during handling by estimating genetic correlations between different criteria of temperament and Limousin heifer breeding traits. Data consisted of the records collected from 1992 to 2004 at the progeny test station of Limousin AI sires. Six traits recorded during the same behavioural test – the so-called docility test – were considered to describe the heifer's temperament: aggressiveness against the handler, running time and number of escapes per minute of test period in presence or absence of the handler and a synthetic docility score accounting for the five previous components. Eight breeding traits were also considered simultaneously in the joint analysis with the five elementary temperament components: weights at 12 months and after calving (for measuring heifer growth), age at first observed oestrus (for measuring puberty) and fertility (for measuring heifer reproductive performance), calving ease score and pelvic opening (for measuring calving performance), maternal behaviour at calf's birth and milk yield (for measuring the suckling ability of the primiparous cow). REML (co)variance estimates were derived using linear multitrait sire models. Estimates of heritability were in the range of values given in the literature. Estimates of genetic correlation between temperament traits indicated that, genetically, aggressive animals also attempt to escape a lot. Consequently, their elimination will reduce both working risk and handling time for the breeder. Genetic correlations between temperament traits and breeding traits ranged from zero to favourable estimates and confirmed the very few results of the literature indicating a slight trend of less fearful heifers also being more productive, mainly because of a higher reproduction and calving performance, and, to a lesser extent, because of higher maternal abilities such as behaviour at calf's birth and milk yield.

Single-breed genomic selection (GS) based on medium single nucleotide polymorphism (SNP) density (~50,000; 50K) is now routinely implemented in several large cattle breeds. However, building large enough reference populations remains a challenge for many medium or small breeds. The high-density BovineHD BeadChip (HD chip; Illumina Inc., San Diego, CA) containing 777,609 SNP developed in 2010 is characterized by short-distance linkage disequilibrium expected to be maintained across breeds. Therefore, combining reference populations can be envisioned. A population of 1,869 influential ancestors from 3 dairy breeds (Holstein, Montbéliarde, and Normande) was genotyped with the HD chip. Using this sample, 50K genotypes were imputed within breed to high-density genotypes, leading to a large HD reference population. This population was used to develop a multi-breed genomic evaluation. The goal of this paper was to investigate the gain of multi-breed genomic evaluation for a small breed. The advantage of using a large breed (Normande in the present study) to mimic a small breed is the large potential validation population to compare alternative genomic selection approaches more reliably. In the Normande breed, 3 training sets were defined with 1,597, 404, and 198 bulls, and a unique validation set included the 394 youngest bulls. For each training set, estimated breeding values (EBV) were computed using pedigree-based BLUP, single-breed BayesC, or multi-breed BayesC for which the reference population was formed by any of the Normande training data sets and 4,989 Holstein and 1,788 Montbéliarde bulls. Phenotypes were standardized by within-breed genetic standard deviation, the proportion of polygenic variance was set to 30%, and the estimated number of SNP with a nonzero effect was about 7,000. The 2 genomic selection (GS) approaches were performed using either the 50K or HD genotypes. The correlations between EBV and observed daughter yield deviations (DYD) were computed for 6 traits and using the different prediction approaches. Compared with pedigree-based BLUP, the average gain in accuracy with GS in small populations was 0.057 for the single-breed and 0.086 for multi-breed approach. This gain was up to 0.193 and 0.209, respectively, with the large reference population. Improvement of EBV prediction due to the multi-breed evaluation was higher for animals not closely related to the reference population. In the case of a breed with a small reference population size, the increase in correlation due to multi-breed GS was 0.141 for bulls without their sire in reference population compared with 0.016 for bulls with their sire in reference population. These results demonstrate that multi-breed GS can contribute to increase genomic evaluation accuracy in small breeds.