Search

Unfavorable genetic correlations between milk production, fertility, and urea traits have been reported. However, knowledge of the genomic regions associated with these unfavorable correlations is limited. Here, we used the correlation scan method to identify and investigate the regions driving or antagonizing the genetic correlations between production vs. fertility, urea vs. fertility, and urea vs. production traits. Driving regions produce an estimate of correlation that is in the same direction as the global correlation. Antagonizing regions produce an estimate in the opposite direction of the global estimates. Our dataset comprised 6567, 4700, and 12,658 Holstein cattle with records of production traits (milk yield, fat yield, and protein yield), fertility (calving interval) and urea traits (milk urea nitrogen and blood urea nitrogen predicted using milk-mid-infrared spectroscopy), respectively. Several regions across the genome drive the correlations between production, fertility, and urea traits. Antagonizing regions were confined to certain parts of the genome and the genes within these regions were mostly involved in preventing metabolic dysregulation, liver reprogramming, metabolism remodeling, and lipid homeostasis. The driving regions were enriched for QTL related to puberty, milk, and health-related traits. Antagonizing regions were mostly related to muscle development, metabolic body weight, and milk traits. In conclusion, we have identified genomic regions of potential importance for dairy cattle breeding. Future studies could investigate the antagonizing regions as potential genomic regions to break the unfavorable correlations and improve milk production as well as fertility and urea traits., (© 2024 The Author(s). Animal Genetics published by John Wiley & Sons Ltd on behalf of Stichting International Foundation for Animal Genetics.)

Economically important reproduction traits in sheep, such as number of lambs weaned and litter size, are expressed only in females and later in life after most selection decisions are made, which makes them ideal candidates for genomic selection. Accurate genomic predictions would lead to greater genetic gain for these traits by enabling accurate selection of young rams with high genetic merit. The aim of this study was to design and evaluate the accuracy of a genomic prediction method for female reproduction in sheep using daughter trait deviations (DTD) for sires and ewe phenotypes (when individual ewes were genotyped) for three reproduction traits: number of lambs born (NLB), litter size (LSIZE) and number of lambs weaned. Genomic best linear unbiased prediction (GBLUP), BayesR and pedigree BLUP analyses of the three reproduction traits measured on 5340 sheep (4503 ewes and 837 sires) with real and imputed genotypes for 510 174 SNPs were performed. The prediction of breeding values using both sire and ewe trait records was validated in Merino sheep. Prediction accuracy was evaluated by across sire family and random cross-validations. Accuracies of genomic estimated breeding values (GEBVs) were assessed as the mean Pearson correlation adjusted by the accuracy of the input phenotypes. The addition of sire DTD into the prediction analysis resulted in higher accuracies compared with using only ewe records in genomic predictions or pedigree BLUP. Using GBLUP, the average accuracy based on the combined records (ewes and sire DTD) was 0.43 across traits, but the accuracies varied by trait and type of cross-validations. The accuracies of GEBVs from random cross-validations (range 0.17-0.61) were higher than were those from sire family cross-validations (range 0.00-0.51). The GEBV accuracies of 0.41-0.54 for NLB and LSIZE based on the combined records were amongst the highest in the study. Although BayesR was not significantly different from GBLUP in prediction accuracy, it identified several candidate genes which are known to be associated with NLB and LSIZE. The approach provides a way to make use of all data available in genomic prediction for traits that have limited recording., (© 2017 Stichting International Foundation for Animal Genetics.)

Polyceraty (presence of multiple horns) is rare in modern day ungulates. Although not found in wild sheep, polyceraty does occur in a small number of domestic sheep breeds covering a wide geographical region. Damara are fat-tailed hair sheep, from the south-western region of Africa, which display polyceraty, with horn number ranging from zero to four. We conducted a genome-wide association study for horn number with 43 Damara genotyped with 606 006 SNP markers. The analysis revealed a region with multiple significant SNPs on ovine chromosome 2, in a location different from the mutation for polled in sheep on chromosome 10. The causal mutation for polyceraty was not identified; however, the region associated with polyceraty spans nine HOXD genes, which are critical in embryonic development of appendages. Mutations in HOXD genes are implicated in polydactly phenotypes in mice and humans. There was no evidence for epistatic interactions contributing to polyceraty. This is the first report on the genetic mechanisms underlying polyceraty in the under-studied Damara., (© 2016 Stichting International Foundation for Animal Genetics.)

Genotyping sheep for genome-wide SNPs at lower density and imputing to a higher density would enable cost-effective implementation of genomic selection, provided imputation was accurate enough. Here, we describe the design of a low-density (12k) SNP chip and evaluate the accuracy of imputation from the 12k SNP genotypes to 50k SNP genotypes in the major Australian sheep breeds. In addition, the impact of imperfect imputation on genomic predictions was evaluated by comparing the accuracy of genomic predictions for 15 novel meat traits including carcass and meat quality and omega fatty acid traits in sheep, from 12k SNP genotypes, imputed 50k SNP genotypes and real 50k SNP genotypes. The 12k chip design included 12 223 SNPs with a high minor allele frequency that were selected with intermarker spacing of 50-475 kb. SNPs for parentage and horned or polled tests also were represented. Chromosome ends were enriched with SNPs to reduce edge effects on imputation. The imputation performance of the 12k SNP chip was evaluated using 50k SNP genotypes of 4642 animals from six breeds in three different scenarios: (1) within breed, (2) single breed from multibreed reference and (3) multibreed from a single-breed reference. The highest imputation accuracies were found with scenario 2, whereas scenario 3 was the worst, as expected. Using scenario 2, the average imputation accuracy in Border Leicester, Polled Dorset, Merino, White Suffolk and crosses was 0.95, 0.95, 0.92, 0.91 and 0.93 respectively. Imputation scenario 2 was used to impute 50k genotypes for 10 396 animals with novel meat trait phenotypes to compare genomic prediction accuracy using genomic best linear unbiased prediction (GBLUP) with real and imputed 50k genotypes. The weighted mean imputation accuracy achieved was 0.92. The average accuracy of genomic estimated breeding values (GEBVs) based on only 12k data was 0.08 across traits and breeds, but accuracies varied widely. The mean GBLUP accuracies with imputed 50k data more than doubled to 0.21. Accuracies of genomic prediction were very similar for imputed and real 50k genotypes. There was no apparent impact on accuracy of GEBVs as a result of using imputed rather than real 50k genotypes, provided imputation accuracy was >90%., (© 2015 Stichting International Foundation for Animal Genetics.)

The extent of linkage disequilibrium (LD) between genetic loci has implications for both association studies and the accuracy of genomic prediction. To characterise the persistence of LD in diverse sheep breeds, two SNP genotyping platforms were used. First, existing SNP genotypes from 63 breeds obtained using the ovine SNP50 BeadChip (49,034 loci) were used to estimate LD decay in populations with contrasting levels of genetic diversity. Given the paucity of marker pairs separated by short physical distances on the SNP50 BeadChip, genotyping was subsequently performed for four breeds using the recently developed ovine HD BeadChip that assays approximately 600,000 SNPs with an average genomic spacing of 5 kb. This facilitated a highly accurate estimate of LD over short genomic distances (<30 kb) and revealed LD varies considerably between sheep breeds. Further, sheep appear to contain generally lower levels of LD than do other domestic species, likely a reflection of aspects of their past population history., (© 2014 Stichting International Foundation for Animal Genetics.)

The aim of this study was to fine map the genomic location of the Horns locus in the Australian Merino sheep population and to identify markers that can be used to predict the horn phenotype. A linkage disequilibrium analysis of horn data from Australian Merino sheep mapped the Horns locus to a small region on chromosome 10. A single nucleotide polymorphism in the region was found to be highly predictive for the polled phenotype in an experimental population of Merino sheep. This was owing to a dominance effect of one of the alleles when inherited maternally. It was suggested that a genetic test would provide a good predictor of the polled phenotype. Finally, an evaluation of industry data showed that the SNP is at very different frequencies in Poll Merino sheep that have been bred for polledness (based on phenotype alone) compared with the Merino sheep breed., (© 2011 The Authors, Animal Genetics © 2011 Stichting International Foundation for Animal Genetics.)

Although genomic selection offers the prospect of improving the rate of genetic gain in meat, wool and dairy sheep breeding programs, the key constraint is likely to be the cost of genotyping. Potentially, this constraint can be overcome by genotyping selection candidates for a low density (low cost) panel of SNPs with sparse genotype coverage, imputing a much higher density of SNP genotypes using a densely genotyped reference population. These imputed genotypes would then be used with a prediction equation to produce genomic estimated breeding values. In the future, it may also be desirable to impute very dense marker genotypes or even whole genome re-sequence data from moderate density SNP panels. Such a strategy could lead to an accurate prediction of genomic estimated breeding values across breeds, for example. We used genotypes from 48 640 (50K) SNPs genotyped in four sheep breeds to investigate both the accuracy of imputation of the 50K SNPs from low density SNP panels, as well as prospects for imputing very dense or whole genome re-sequence data from the 50K SNPs (by leaving out a small number of the 50K SNPs at random). Accuracy of imputation was low if the sparse panel had less than 5000 (5K) markers. Across breeds, it was clear that the accuracy of imputing from sparse marker panels to 50K was higher if the genetic diversity within a breed was lower, such that relationships among animals in that breed were higher. The accuracy of imputation from sparse genotypes to 50K genotypes was higher when the imputation was performed within breed rather than when pooling all the data, despite the fact that the pooled reference set was much larger. For Border Leicesters, Poll Dorsets and White Suffolks, 5K sparse genotypes were sufficient to impute 50K with 80% accuracy. For Merinos, the accuracy of imputing 50K from 5K was lower at 71%, despite a large number of animals with full genotypes (2215) being used as a reference. For all breeds, the relationship of individuals to the reference explained up to 64% of the variation in accuracy of imputation, demonstrating that accuracy of imputation can be increased if sires and other ancestors of the individuals to be imputed are included in the reference population. The accuracy of imputation could also be increased if pedigree information was available and was used in tracking inheritance of large chromosome segments within families. In our study, we only considered methods of imputation based on population-wide linkage disequilibrium (largely because the pedigree for some of the populations was incomplete). Finally, in the scenarios designed to mimic imputation of high density or whole genome re-sequence data from the 50K panel, the accuracy of imputation was much higher (86-96%). This is promising, suggesting that in silico genome re-sequencing is possible in sheep if a suitable pool of key ancestors is sequenced for each breed., (© 2011 The Authors, Animal Genetics © 2011 Stichting International Foundation for Animal Genetics.)

Mastitis is the most frequent and costly disease in dairy production and solutions leading to a reduction in the incidence of mastitis are highly demanded. Here a genome-wide association study was performed to identify polymorphisms affecting susceptibility to mastitis. Genotypes for 17 349 SNPs distributed across the 29 bovine autosomal chromosomes from a total of 2589 sires with 1 389 776 daughters with records on clinical mastitis were included in the analysis. Records of occurrence of clinical mastitis were divided into seven time periods in the first three lactations in order to identify quantitative trait loci affecting mastitis susceptibility in particular phases of lactation. The most convincing results from the association mapping were followed up and validated by a combined linkage disequilibrium and linkage analysis. The study revealed quantitative trait loci affecting occurrence of clinical mastitis in the periparturient period on chromosomes 2, 6 and 20 and a quantitative trait locus affecting occurrence of clinical mastitis in late lactation on chromosome 14. None of the quantitative trait loci for clinical mastitis detected in the study seemed to affect lactation average of somatic cell score. The SNPs highly associated with clinical mastitis lie near both the gene encoding interleukin 8 on chromosome 6 and the genes encoding the two interleukin 8 receptors on chromosome 2., (© 2011 The Authors, Animal Genetics © 2011 Stichting International Foundation for Animal Genetics.)

Reproductive performance is a critical trait in dairy cattle. Poor reproductive performance leads to prolonged calving intervals, higher culling rates and extra expenses related to multiple inseminations, veterinary treatments and replacements. Genetic gain for improved reproduction through traditional selection is often slow because of low heritability and negative correlations with production traits. Detection of DNA markers associated with improved reproductive performance through genome-wide association studies could lead to genetic gain that is more balanced between fertility and production. Norwegian Red cattle are well suited for such studies, as very large numbers of detailed reproduction records are available. We conducted a genome-wide association study for non-return rate, fertility treatments and retained placenta using almost 1 million records on these traits and 17 343 genome-wide single-nucleotide polymorphisms. Genotyping costs were minimized by genotyping the sires of the cows recorded and by using daughter averages as phenotypes. The genotyped sires were assigned to either a discovery or a validation population. Associations were only considered to be validated if they were significant in both groups. Strong associations were found and validated on chromosomes 1, 5, 8, 9, 11 and 12. Several of these were highly supported by findings in other studies. The most important result was an association for non-return rate in heifers in a region of BTA12 where several associations for milk production traits have previously been found. Subsequent fine-mapping verified the presence of a quantitative trait loci (QTL) having opposing effects on non-return rate and milk production at 18 Mb. The other reproduction QTL did not have pleiotropic effects on milk production, and these are therefore of considerable interest for use in marker-assisted selection., (© 2011 Norwegian University of Life Science, Dept. of Animal and Aquacultural Sciences, Animal Genetics. © 2011 Stichting International Foundation for Animal Genetics.)

Dystocia and stillbirth are significant causes of female and neonatal death in many species and there is evidence for a genetic component to both traits. Identifying causal mutations affecting these traits through genome wide association studies could reveal the genetic pathways involved and will be a step towards targeted interventions. Norwegian Red cattle are an ideal model breed for such studies as very large numbers of records are available. We conducted a genome wide association study for direct and maternal effects of dystocia and stillbirth using almost 1 million records of these traits. Genotyping costs were minimized by genotyping the sires of the recorded cows, and using daughter averages as phenotypes. A dense marker map containing 17,343 single nucleotide polymorphisms covering all autosomal chromosomes was utilized. The genotyped sires were assigned to one of two groups in an attempt to ensure independence between the groups. Associations were only considered validated if they occurred in both groups. Strong associations were found and validated on chromosomes 4, 5, 6, 9, 12, 20, 22 and 28. The QTL region on chromosome 6 was refined using LDLA analysis. The results showed that this chromosome most probably contains two QTL for direct effect on dystocia and one for direct effect on stillbirth. Several candidate genes may be identified close to these QTL. Of these, a cluster of genes expected to affect bone and cartilage formation (i.e. SPP1, IBSP and MEPE) are of particular interest and we suggest that these genes are screened in candidate gene studies for dystocia and stillbirth in cattle as well as other species.

A number of cattle breeds have become highly specialized for milk or beef production, following strong artificial selection for these traits. In this paper, we compare allele frequencies from 9323 single nucleotide polymorphism (SNP) markers genotyped in dairy and beef cattle breeds averaged in sliding windows across the genome, with the aim of identifying divergently selected regions of the genome between the production types. The value of the method for identifying selection signatures was validated by four sources of evidence. First, differences in allele frequencies between dairy and beef cattle at individual SNPs were correlated with the effects of those SNPs on production traits. Secondly, large differences in allele frequencies generally occurred in the same location for two independent data sets (correlation 0.45) between sliding window averages. Thirdly, the largest differences in sliding window average difference in allele frequencies were found on chromosome 20 in the region of the growth hormone receptor gene, which carries a mutation known to have an effect on milk production traits in a number of dairy populations. Finally, for the chromosome tested, the location of selection signatures between dairy and beef cattle was correlated with the location of selection signatures within dairy cattle.

The extent and pattern of linkage disequilibrium (LD) between closely spaced markers contain information about population history, including past population size and selection history. Selection signatures can be identified by comparing the LD surrounding a putative selected allele at a locus to the putative non-selected allele. In livestock populations, locations of selection signatures identified in this way should be correlated with QTL affecting production traits, as the populations have been under strong artificial selection for these traits. We used a dense SNP map of bovine chromosome 6 to characterize the pattern of LD on this chromosome in Norwegian Red cattle, a breed which has been strongly selected for milk production. The pattern of LD was generally consistent with strong selection in regions containing QTL affecting milk production traits, including a strong selection signature in a region containing a mutation known to affect milk production. The results demonstrate that in livestock populations, the origin of selection signatures will often be QTL for livestock production traits, and illustrate the value of selection signatures in uncovering new mutations with potential effects on quantitative traits.