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.
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.
Polled and Multisystemic Syndrome (PMS) is a novel developmental disorder occurring in the progeny of a single bull. Its clinical spectrum includes polledness (complete agenesis of horns), facial dysmorphism, growth delay, chronic diarrhea, premature ovarian failure, and variable neurological and cardiac anomalies. PMS is also characterized by a deviation of the sex-ratio, suggesting male lethality during pregnancy. Using Mendelian error mapping and whole-genome sequencing, we identified a 3.7 Mb deletion on the paternal bovine chromosome 2 encompassing ARHGAP15, GTDC1 and ZEB2 genes. We then produced control and affected 90-day old fetuses to characterize this syndrome by histological and expression analyses. Compared to wild type individuals, affected animals showed a decreased expression of the three deleted genes. Based on a comparison with human Mowat-Wilson syndrome, we suggest that deletion of ZEB2, is responsible for most of the effects of the mutation. Finally sperm-FISH, embryo genotyping and analysis of reproduction records confirmed somatic mosaicism in the founder bull and male-specific lethality during the first third of gestation. In conclusion, we identified a novel locus involved in bovid horn ontogenesis and suggest that epithelial-to-mesenchymal transition plays a critical role in horn bud differentiation. We also provide new insights into the pathogenicity of ZEB2 loss of heterozygosity in bovine and humans and describe the first case of male-specific lethality associated with an autosomal locus in a non-murine mammalian species. This result sets PMS as a unique model to study sex-specific gene expression/regulation.
The objective of the study was to develop a genomic evaluation for French beef cattle breeds and assess accuracy and bias of prediction for different genomic selection strategies. Based on a reference population of 2,682 Charolais bulls and cows, genotyped or imputed to a high-density SNP panel (777K SNP), we tested the influence of different statistical methods, marker densities (50K versus 777K), and training population sizes and structures on the quality of predictions. Four different training sets containing up to 1,979 animals and a unique validation set of 703 young bulls only known on their individual performances were formed. BayesC method had the largest average accuracy compared to genomic BLUP or pedigree-based BLUP. No gain of accuracy was observed when increasing the density of markers from 50K to 777K. For a BayesC model and 777K SNP panels, the accuracy calculated as the correlation between genomic predictions and deregressed EBV (DEBV) divided by the square root of heritability was 0.42 for birth weight, 0.34 for calving ease, 0.45 for weaning weight, 0.52 for muscular development, and 0.27 for skeletal development. Half of the training set constituted animals having only their own performance recorded, whose contribution only represented 5% of the accuracy. Using DEBV as a response brought greater accuracy than using EBV (+5% on average). Considering a residual polygenic component strongly reduced bias for most of the traits. The optimal percentage of polygenic variance varied across traits. Among the methodologies tested to implement genomic selection in the French Charolais beef cattle population, the most accurate and less biased methodology was to analyze DEBV under a BayesC strategy and a residual polygenic component approach. With this approach, a 50K SNP panel performed as well as a 777K panel.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.