Herman W. Raadsma

Genome-Wide Analysis of the World's Sheep Breeds Reveals High Levels of Historic Mixture and Strong Recent Selection

Genomic structure in a global collection of domesticated sheep reveals a history of artificial selection for horn loss and traits relating to pigmentation, reproduction, and body size.

A Genome Wide Survey of SNP Variation Reveals the Genetic Structure of Sheep Breeds

The genetic structure of sheep reflects their domestication and subsequent formation into discrete breeds. Understanding genetic structure is essential for achieving genetic improvement through genome-wide association studies, genomic selection and the dissection of quantitative traits. After identifying the first genome-wide set of SNP for sheep, we report on levels of genetic variability both within and between a diverse sample of ovine populations. Then, using cluster analysis and the partitioning of genetic variation, we demonstrate sheep are characterised by weak phylogeographic structure, overlapping genetic similarity and generally low differentiation which is consistent with their short evolutionary history. The degree of population substructure was, however, sufficient to cluster individuals based on geographic origin and known breed history. Specifically, African and Asian populations clustered separately from breeds of European origin sampled from Australia, New Zealand, Europe and North America. Furthermore, we demonstrate the presence of stratification within some, but not all, ovine breeds. The results emphasize that careful documentation of genetic structure will be an essential prerequisite when mapping the genetic basis of complex traits. Furthermore, the identification of a subset of SNP able to assign individuals into broad groupings demonstrates even a small panel of markers may be suitable for applications such as traceability.

A comparison of five methods to predict genomic breeding values of dairy bulls from genome-wide SNP markers

BackgroundGenomic selection (GS) uses molecular breeding values (MBV) derived from dense markers across the entire genome for selection of young animals. The accuracy of MBV prediction is important for a successful application of GS. Recently, several methods have been proposed to estimate MBV. Initial simulation studies have shown that these methods can accurately predict MBV. In this study we compared the accuracies and possible bias of five different regression methods in an empirical application in dairy cattle.MethodsGenotypes of 7,372 SNP and highly accurate EBV of 1,945 dairy bulls were used to predict MBV for protein percentage (PPT) and a profit index (Australian Selection Index, ASI). Marker effects were estimated by least squares regression (FR-LS), Bayesian regression (Bayes-R), random regression best linear unbiased prediction (RR-BLUP), partial least squares regression (PLSR) and nonparametric support vector regression (SVR) in a training set of 1,239 bulls. Accuracy and bias of MBV prediction were calculated from cross-validation of the training set and tested against a test team of 706 young bulls.ResultsFor both traits, FR-LS using a subset of SNP was significantly less accurate than all other methods which used all SNP. Accuracies obtained by Bayes-R, RR-BLUP, PLSR and SVR were very similar for ASI (0.39-0.45) and for PPT (0.55-0.61). Overall, SVR gave the highest accuracy.All methods resulted in biased MBV predictions for ASI, for PPT only RR-BLUP and SVR predictions were unbiased. A significant decrease in accuracy of prediction of ASI was seen in young test cohorts of bulls compared to the accuracy derived from cross-validation of the training set. This reduction was not apparent for PPT. Combining MBV predictions with pedigree based predictions gave 1.05 - 1.34 times higher accuracies compared to predictions based on pedigree alone. Some methods have largely different computational requirements, with PLSR and RR-BLUP requiring the least computing time.ConclusionsThe four methods which use information from all SNP namely RR-BLUP, Bayes-R, PLSR and SVR generate similar accuracies of MBV prediction for genomic selection, and their use in the selection of immediate future generations in dairy cattle will be comparable. The use of FR-LS in genomic selection is not recommended.

A primary assembly of a bovine haplotype block map based on a 15,036-single-nucleotide polymorphism panel genotyped in holstein-friesian cattle

Analysis of data on 1000 Holstein–Friesian bulls genotyped for 15,036 single-nucleotide polymorphisms (SNPs) has enabled genomewide identification of haplotype blocks and tag SNPs. A final subset of 9195 SNPs in Hardy–Weinberg equilibrium and mapped on autosomes on the bovine sequence assembly (release Btau 3.1) was used in this study. The average intermarker spacing was 251.8 kb. The average minor allele frequency (MAF) was 0.29 (0.05–0.5). Following recent precedents in human HapMap studies, a haplotype block was defined where 95% of combinations of SNPs within a region are in very high linkage disequilibrium. A total of 727 haplotype blocks consisting of ≥3 SNPs were identified. The average block length was 69.7 ± 7.7 kb, which is ∼5–10 times larger than in humans. These blocks comprised a total of 2964 SNPs and covered 50,638 kb of the sequence map, which constitutes 2.18% of the length of all autosomes. A set of tag SNPs, which will be useful for further fine-mapping studies, has been identified. Overall, the results suggest that as many as 75,000–100,000 tag SNPs would be needed to track all important haplotype blocks in the bovine genome. This would require ∼250,000 SNPs in the discovery phase.

Accuracy of direct genomic values in Holstein bulls and cows using subsets of SNP markers

BackgroundAt the current price, the use of high-density single nucleotide polymorphisms (SNP) genotyping assays in genomic selection of dairy cattle is limited to applications involving elite sires and dams. The objective of this study was to evaluate the use of low-density assays to predict direct genomic value (DGV) on five milk production traits, an overall conformation trait, a survival index, and two profit index traits (APR, ASI).MethodsDense SNP genotypes were available for 42,576 SNP for 2,114 Holstein bulls and 510 cows. A subset of 1,847 bulls born between 1955 and 2004 was used as a training set to fit models with various sets of pre-selected SNP. A group of 297 bulls born between 2001 and 2004 and all cows born between 1992 and 2004 were used to evaluate the accuracy of DGV prediction. Ridge regression (RR) and partial least squares regression (PLSR) were used to derive prediction equations and to rank SNP based on the absolute value of the regression coefficients. Four alternative strategies were applied to select subset of SNP, namely: subsets of the highest ranked SNP for each individual trait, or a single subset of evenly spaced SNP, where SNP were selected based on their rank for ASI, APR or minor allele frequency within intervals of approximately equal length.ResultsRR and PLSR performed very similarly to predict DGV, with PLSR performing better for low-density assays and RR for higher-density SNP sets. When using all SNP, DGV predictions for production traits, which have a higher heritability, were more accurate (0.52-0.64) than for survival (0.19-0.20), which has a low heritability. The gain in accuracy using subsets that included the highest ranked SNP for each trait was marginal (5-6%) over a common set of evenly spaced SNP when at least 3,000 SNP were used. Subsets containing 3,000 SNP provided more than 90% of the accuracy that could be achieved with a high-density assay for cows, and 80% of the high-density assay for young bulls.ConclusionsAccurate genomic evaluation of the broader bull and cow population can be achieved with a single genotyping assays containing ~ 3,000 to 5,000 evenly spaced SNP.

Strategies and utility of imputed SNP genotypes for genomic analysis in dairy cattle

BackgroundWe investigated strategies and factors affecting accuracy of imputing genotypes from lower-density SNP panels (Illumina 3K, 7K, Affymetrix 15K and 25K, and evenly spaced subsets) up to one medium (Illumina 50K) and one high-density (Illumina 800K) SNP panel. We also evaluated the utility of imputed genotypes on the accuracy of genomic selection using Australian Holstein-Friesian cattle data from 2727 and 845 animals genotyped with 50K and 800K SNP chip, respectively. Animals were divided into reference and test sets (genotyped with higher and lower density SNP panels, respectively) for evaluating the accuracies of imputation. For the accuracy of genomic selection, a comparison of direct genetic values (DGV) was made by dividing the data into training and validation sets under a range of imputation scenarios.ResultsOf the three methods compared for imputation, IMPUTE2 outperformed Beagle and fastPhase for almost all scenarios. Higher SNP densities in the test animals, larger reference sets and higher relatedness between test and reference animals increased the accuracy of imputation. 50K specific genotypes were imputed with moderate allelic error rates from 15K (2.85%) and 25K (2.75%) genotypes. Using IMPUTE2, SNP genotypes up to 800K were imputed with low allelic error rate (0.79% genome-wide) from 50K genotypes, and with moderate error rate from 3K (4.78%) and 7K (2.00%) genotypes. The error rate of imputing up to 800K from 3K or 7K was further reduced when an additional middle tier of 50K genotypes was incorporated in a 3-tiered framework. Accuracies of DGV for five production traits using imputed 50K genotypes were close to those obtained with the actual 50K genotypes and higher compared to using 3K or 7K genotypes. The loss in accuracy of DGV was small when most of the training animals also had imputed (50K) genotypes. Additional gains in DGV accuracies were small when SNP densities increased from 50K to imputed 800K.ConclusionPopulation-based genotype imputation can be used to predict and combine genotypes from different low, medium and high-density SNP chips with a high level of accuracy. Imputing genotypes from low-density SNP panels to at least 50K SNP density increases the accuracy of genomic selection.

Improving animal and human health through understanding liver fluke immunology

Sheep, goats and cattle represent the most numerous and economically important agricultural species worldwide used as sources for milk, fibre and red meat. In addition, in the developing world, these species often represent the sole asset base for small‐holder livestock farmers and cattle/buffaloes often provide the majority of draught power for crop production. Production losses caused by helminth diseases of these animals are a major factor in extending the cycle of poverty in developing countries and a major food security issue for developed economies. Fasciola spp. are one of the most important zoonotic diseases with a global economic impact in livestock production systems and a poorly defined but direct effect on human health. Improvements in human and animal health will require a concerted research effort into the development of new accurate and simple diagnostic tests and increased vaccine and drug development against Fasciola infections. Here, the use of definitive natural host breeds with contrasting resistance to Fasciola infections is discussed as a resource to contrast parasite–host interactions and identify parasite immune evasion strategies. Such studies are likely to boost the discovery of new vaccine, drug and diagnostic candidates and provide the foundation for future genetic selection of resistant animals.

Peritoneal Lavage Cells of Indonesian Thin-Tail Sheep Mediate Antibody-Dependent Superoxide Radical Cytotoxicity In Vitro against Newly Excysted Juvenile Fasciola gigantica but Not Juvenile Fasciola hepatica

ABSTRACT Indonesian thin-tail (ITT) sheep resist infection by Fasciola gigantica by an immunological mechanism within 2 to 4 weeks of infection yet are susceptible to F. hepatica infection. Studies of ITT sheep show that little liver damage occurs following F. gigantica infection, suggesting that the invading parasites are killed within the peritoneum or shortly after reaching the liver. We investigated whether cells isolated from the peritoneums of ITT sheep could kill newly excysted juvenile F. gigantica in vitro and act as a potential mechanism of resistance against F. gigantica infection. Peritoneal cells from F. gigantica-infected sheep, rich in macrophages and eosinophils, mediated antibody-dependent cytotoxicity against juvenile F. gigantica in vitro. Cytotoxicity was dependent on contact between the parasite and effector cells. Isolated mammary gland eosinophils of F. gigantica-infected sheep, or resident peritoneal monocytes/macrophages from uninfected sheep, also killed the juvenile parasites in vitro. By using inhibitors, we show that the molecular mechanism of killing in these assays was dependent on the production of superoxide radicals by macrophages and eosinophils. In contrast, this cytotoxic mechanism was ineffective against juvenile F. hepatica parasites in vitro. Analysis of superoxide dismutase activity and mRNA levels showed that activity and gene expression were higher in F. hepatica than in F. gigantica, suggesting a possible role for this enzyme in the resistance of F. hepatica to superoxide-mediated killing. We suggest that ovine macrophages and eosinophils, acting in concert with a specific antibody, may be important effector cells involved in the resistance of ITT sheep to F. gigantica.

Protective antibody titres and antigenic competition in multivalent Dichelobacter nodosus fimbrial vaccines using characterised rDNA antigens

The relationship between K-agglutination antibody titres and protection against experimental challenge with Dichelobacter nodosus, the effect of increasing the number of D. nodosus fimbrial antigens, and the importance of the nature of additional antigens in multivalent vaccines on antibody response and protection against experimental challenge with D. nodosus were examined in Merino sheep. A total of 204 Merino sheep were allocated to one of 12 groups, and vaccinated with preparations containing a variable number of rDNA D. nodosus fimbrial antigens. The most complex vaccine contained ten fimbrial antigens from all major D. nodosus serogroups, while the least complex contained a single fimbrial antigen. In addition to D. nodosus fimbrial antigens, other bacterial rDNA fimbrial antigens (Moraxella bovis Da12d and Escherichia coli K99), and bovine serum albumin (BSA) were used in some vaccines. Antibody titres to fimbrial antigens and BSA were measured by agglutination and ELISA tests, respectively. Antibody titres were determined on five occasions (Weeks 0, 3, 6, 8, and 11 after primary vaccination). All sheep were exposed to an experimental challenge with virulent isolates of D. nodosus from either serogroup A or B, 8 weeks after primary vaccination. For D. nodosus K-agglutinating antibody titres, a strong negative correlation between antibody titre and footrot lesion score was observed. This relationship was influenced by the virulence of the challenge strain. Increasing the number of fimbrial antigens in experimental rDNA D. nodosus fimbrial vaccines resulted in a linear decrease in K-agglutinating antibody titres to individual D. nodosus serogroups. Similarly, a linear decrease in protection to challenge with homologous serogroups was observed as the number of D. nodosus fimbrial antigens represented in the vaccine increased. The reduction in antibody titres in multicomponent vaccines is thought to be due to antigenic competition. The level of competition between individual antigens is not constant and appears to be related to the immunodominance (nature) of the competing antigens. Both BSA ELISA, and M. bovis K-agglutinating antibody titres were adversely affected by the presence of two D. nodosus fimbrial preparations, whereas the antigenicity of E. coli K99 was unchanged by the presence of two additional D. nodosus antigens. Further studies are required to determine the step(s) in the immune response which are influenced by antigenic competition. Our results suggest that antigen presentation, particularly following primary vaccination, is the step most strongly influenced by antigenic competition.

Bulldog dwarfism in Dexter cattle is caused by mutations in ACAN

Bulldog dwarfism in Dexter cattle is one of the earliest single-locus disorders described in animals. Affected fetuses display extreme disproportionate dwarfism, reflecting abnormal cartilage development (chondrodysplasia). Typically, they die around the seventh month of gestation, precipitating a natural abortion. Heterozygotes show a milder form of dwarfism, most noticeably having shorter legs. Homozygosity mapping in candidate regions in a small Dexter pedigree suggested aggrecan (ACAN) as the most likely candidate gene. Mutation screening revealed a 4-bp insertion in exon 11 (2266_2267insGGCA) (called BD1 for diagnostic testing) and a second, rarer transition in exon 1 (−198C>T) (called BD2) that cosegregate with the disorder. In chondrocytes from cattle heterozygous for the insertion, mutant mRNA is subject to nonsense-mediated decay, showing only 8% of normal expression. Genotyping in Dexter families throughout the world shows a one-to-one correspondence between genotype and phenotype at this locus. The heterozygous and homozygous-affected Dexter cattle could prove invaluable as a model for human disorders caused by mutations in ACAN.