High-resolution haplotype block structure in the cattle genome

The contribution of dominance to the understanding of quantitative genetic variation

Knowledge of the genetic architecture of a quantitative trait is useful to adjust methods for the prediction of genomic breeding values and to discover the extent to which common assumptions in quantitative trait locus (QTL) mapping experiments and breeding value estimation are violated. It also affects our ability to predict the long-term response of selection. In this paper, we focus on additive and dominance effects of QTL. We derive formulae that can be used to estimate the number of QTLs that affect a quantitative trait and parameters of the distribution of their additive and dominance effects from variance components, inbreeding depression and results from QTL mapping experiments. It is shown that a lower bound for the number of QTLs depends on the ratio of squared inbreeding depression to dominance variance. That is, high inbreeding depression must be due to a sufficient number of QTLs because otherwise the dominance variance would exceed the true value. Moreover, the second moment of the dominance coefficient depends only on the ratio of dominance variance to additive variance and on the dependency between additive effects and dominance coefficients. This has implications on the relative frequency of overdominant alleles. It is also demonstrated how the expected number of large QTLs determines the shape of the distribution of additive effects. The formulae are applied to milk yield and productive life in Holstein cattle. Possible sources for a potential bias of the results are discussed.

Tracing cattle breeds with principal components analysis ancestry informative SNPs

The recent release of the Bovine HapMap dataset represents the most detailed survey of bovine genetic diversity to date, providing an important resource for the design and development of livestock production. We studied this dataset, comprising more than 30,000 Single Nucleotide Polymorphisms (SNPs) for 19 breeds (13 taurine, three zebu, and three hybrid breeds), seeking to identify small panels of genetic markers that can be used to trace the breed of unknown cattle samples. Taking advantage of the power of Principal Components Analysis and algorithms that we have recently described for the selection of Ancestry Informative Markers from genomewide datasets, we present a decision-tree which can be used to accurately infer the origin of individual cattle. In doing so, we present a thorough examination of population genetic structure in modern bovine breeds. Performing extensive cross-validation experiments, we demonstrate that 250-500 carefully selected SNPs suffice in order to achieve close to 100% prediction accuracy of individual ancestry, when this particular set of 19 breeds is considered. Our methods, coupled with the dense genotypic data that is becoming increasingly available, have the potential to become a valuable tool and have considerable impact in worldwide livestock production. They can be used to inform the design of studies of the genetic basis of economically important traits in cattle, as well as breeding programs and efforts to conserve biodiversity. Furthermore, the SNPs that we have identified can provide a reliable solution for the traceability of breed-specific branded products.

A combined long-range phasing and long haplotype imputation method to impute phase for SNP genotypes

BACKGROUND

Knowing the phase of marker genotype data can be useful in genome-wide association studies, because it makes it possible to use analysis frameworks that account for identity by descent or parent of origin of alleles and it can lead to a large increase in data quantities via genotype or sequence imputation. Long-range phasing and haplotype library imputation constitute a fast and accurate method to impute phase for SNP data.

METHODS

A long-range phasing and haplotype library imputation algorithm was developed. It combines information from surrogate parents and long haplotypes to resolve phase in a manner that is not dependent on the family structure of a dataset or on the presence of pedigree information.

RESULTS

The algorithm performed well in both simulated and real livestock and human datasets in terms of both phasing accuracy and computation efficiency. The percentage of alleles that could be phased in both simulated and real datasets of varying size generally exceeded 98% while the percentage of alleles incorrectly phased in simulated data was generally less than 0.5%. The accuracy of phasing was affected by dataset size, with lower accuracy for dataset sizes less than 1000, but was not affected by effective population size, family data structure, presence or absence of pedigree information, and SNP density. The method was computationally fast. In comparison to a commonly used statistical method (fastPHASE), the current method made about 8% less phasing mistakes and ran about 26 times faster for a small dataset. For larger datasets, the differences in computational time are expected to be even greater. A computer program implementing these methods has been made available.

CONCLUSIONS

The algorithm and software developed in this study make feasible the routine phasing of high-density SNP chips in large datasets.

Genomic evaluations with many more genotypes

BACKGROUND

Genomic evaluations in Holstein dairy cattle have quickly become more reliable over the last two years in many countries as more animals have been genotyped for 50,000 markers. Evaluations can also include animals genotyped with more or fewer markers using new tools such as the 777,000 or 2,900 marker chips recently introduced for cattle. Gains from more markers can be predicted using simulation, whereas strategies to use fewer markers have been compared using subsets of actual genotypes. The overall cost of selection is reduced by genotyping most animals at less than the highest density and imputing their missing genotypes using haplotypes. Algorithms to combine different densities need to be efficient because numbers of genotyped animals and markers may continue to grow quickly.

METHODS

Genotypes for 500,000 markers were simulated for the 33,414 Holsteins that had 50,000 marker genotypes in the North American database. Another 86,465 non-genotyped ancestors were included in the pedigree file, and linkage disequilibrium was generated directly in the base population. Mixed density datasets were created by keeping 50,000 (every tenth) of the markers for most animals. Missing genotypes were imputed using a combination of population haplotyping and pedigree haplotyping. Reliabilities of genomic evaluations using linear and nonlinear methods were compared.

RESULTS

Differing marker sets for a large population were combined with just a few hours of computation. About 95% of paternal alleles were determined correctly, and > 95% of missing genotypes were called correctly. Reliability of breeding values was already high (84.4%) with 50,000 simulated markers. The gain in reliability from increasing the number of markers to 500,000 was only 1.6%, but more than half of that gain resulted from genotyping just 1,406 young bulls at higher density. Linear genomic evaluations had reliabilities 1.5% lower than the nonlinear evaluations with 50,000 markers and 1.6% lower with 500,000 markers.

CONCLUSIONS

Methods to impute genotypes and compute genomic evaluations were affordable with many more markers. Reliabilities for individual animals can be modified to reflect success of imputation. Breeders can improve reliability at lower cost by combining marker densities to increase both the numbers of markers and animals included in genomic evaluation. Larger gains are expected from increasing the number of animals than the number of markers.

Marker imputation with low-density marker panels in Dutch Holstein cattle

The availability of high-density bovine genotyping arrays made implementation of genomic selection possible in dairy cattle. Development of low-density single nucleotide polymorphism (SNP) panels will allow the extension of genomic selection to a larger portion of the population. Prediction of ungenotyped markers, called imputation, is a strategy that allows using the same low-density chips for all traits (and for different breeds). In the present study, we evaluated the accuracy of imputation with low-density genotyping arrays in the Dutch Holstein population. Five different sizes of genotyping arrays were tested, from 384 to 6,000 SNP. According to marker density, the overall allelic imputation error rate obtained with the program DAGPHASE, which relies on linkage disequilibrium and linkage, ranged from 11.7 to 2.0%, and that obtained with the program CHROMIBD, which relies on linkage and the set of all genotyped ancestors, ranged from 10.7 to 3.3%. However, imputation efficiency was influenced by the relationship between low-density and high-density genotyped animals. Animals with both parents genotyped had particularly low imputation error rates: <1% with 1,500 SNP or more. In summary, missing marker alleles can be predicted with 3 to 4% errors with approximately 1 SNP/Mb (approximately 3,000 markers). The CHROMIBD program proved more efficient than DAGPHASE only at lower marker densities or when several genotyped ancestors were available. Future studies are required to measure the effect of these imputation error rates on accuracy of genomic selection with low-density SNP panels.

Quantitative trait loci for variation in immune response to a Foot-and-Mouth Disease virus peptide

Background

Infectious disease of livestock continues to be a cause of substantial economic loss and has adverse welfare consequences in both the developing and developed world. New solutions to control disease are needed and research focused on the genetic loci determining variation in immune-related traits has the potential to deliver solutions. However, identifying selectable markers and the causal genes involved in disease resistance and vaccine response is not straightforward. The aims of this study were to locate regions of the bovine genome that control the immune response post immunisation. 195 F2 and backcross Holstein Charolais cattle were immunised with a 40-mer peptide derived from foot-and-mouth disease virus (FMDV). T cell and antibody (IgG1 and IgG2) responses were measured at several time points post immunisation. All experimental animals (F0, F1 and F2, n = 982) were genotyped with 165 microsatellite markers for the genome scan.

Results

Considerable variability in the immune responses across time was observed and sire, dam and age had significant effects on responses at specific time points. There were significant correlations within traits across time, and between IgG1 and IgG2 traits, also some weak correlations were detected between T cell and IgG2 responses. The whole genome scan detected 77 quantitative trait loci (QTL), on 22 chromosomes, including clusters of QTL on BTA 4, 5, 6, 20, 23 and 25. Two QTL reached 5% genome wide significance (on BTA 6 and 24) and one on BTA 20 reached 1% genome wide significance.

Conclusions

A proportion of the variance in the T cell and antibody response post immunisation with an FDMV peptide has a genetic component. Even though the antigen was relatively simple, the humoral and cell mediated responses were clearly under complex genetic control, with the majority of QTL located outside the MHC locus. The results suggest that there may be specific genes or loci that impact on variation in both the primary and secondary immune responses, whereas other loci may be specifically important for early or later phases of the immune response. Future fine mapping of the QTL clusters identified has the potential to reveal the causal variations underlying the variation in immune response observed.

Prediction of unobserved single nucleotide polymorphism genotypes of Jersey cattle using reference panels and population-based imputation algorithms

The availability of dense single nucleotide polymorphism (SNP) genotypes for dairy cattle has created exciting research opportunities and revolutionized practical breeding programs. Broader application of this technology will lead to situations in which genotypes from different low-, medium-, or high-density platforms must be combined. In this case, missing SNP genotypes can be imputed using family- or population-based algorithms. Our objective was to evaluate the accuracy of imputation in Jersey cattle, using reference panels comprising 2,542 animals with 43,385 SNP genotypes and study samples of 604 animals for which genotypes were available for 1, 2, 5, 10, 20, 40, or 80% of loci. Two population-based algorithms, fastPHASE 1.2 (P. Scheet and M. Stevens; University of Washington TechTransfer Digital Ventures Program, Seattle, WA) and IMPUTE 2.0 (B. Howie and J. Marchini; Department of Statistics, University of Oxford, UK), were used to impute genotypes on Bos taurus autosomes 1, 15, and 28. The mean proportion of genotypes imputed correctly ranged from 0.659 to 0.801 when 1 to 2% of genotypes were available in the study samples, from 0.733 to 0.964 when 5 to 20% of genotypes were available, and from 0.896 to 0.995 when 40 to 80% of genotypes were available. In the absence of pedigrees or genotypes of close relatives, the accuracy of imputation may be modest (generally <0.80) when low-density platforms with fewer than 1,000 SNP are used, but population-based algorithms can provide reasonably good accuracy (0.80 to 0.95) when medium-density platforms of 2,000 to 4,000 SNP are used in conjunction with high-density genotypes (e.g., >40,000 SNP) from a reference population. Accurate imputation of high-density genotypes from inexpensive low- or medium-density platforms could greatly enhance the efficiency of whole-genome selection programs in dairy cattle.

Bovine tuberculosis: the genetic basis of host susceptibility

The prevalence of bovine tuberculosis (BTB) in the UK remains a significant economic burden and problem for the agri-food industry. Much effort has been directed towards improving diagnostics, finding vaccine candidates and assessing the usefulness of badger culling. The contribution that host genotype makes to disease outcome has, until recently, been overlooked; yet, it is biologically untenable that genetic variation does not play a role. In this review, we highlight the evidence, past and present, for a role of host genetics in determining susceptibility to BTB in livestock. We then address some of the major issues surrounding the design of future studies tasked with finding the exact causative genetic variation underpinning the TB susceptibility phenotype. Finally, we discuss some of the potential future benefits, and problems, that a knowledge of the genetic component to BTB resistance/susceptibility may bring to the agricultural industries and the wider scientific community.

GSEA-SNP identifies genes associated with Johne's disease in cattle

SNP-based gene-set enrichment analysis from single nucleotide polymorphisms, or GSEA-SNP, is a tool to identify candidate genes based on enrichment analysis of sets of genes rather than single SNP associations. The objective of this study was to identify modest-effect genes associated with Mycobacterium avium subsp. paratuberculosis (Map) tissue infection or fecal shedding using GSEA-SNP applied to KEGG pathways or Gene Ontology (GO) gene sets. The Illumina Bovine SNP50 BeadChip was used to genotype 209 Holstein cows for the GSEA-SNP analyses. For each of 13,744 annotated genes genome-wide located within 50 kb of a Bovine SNP50 SNP, the single SNP with the highest Cochran-Armitage Max statistic was used as a proxy statistic for that gene's strength of affiliation with Map. Gene-set enrichment was tested using a weighted Kolmogorov-Smirnov-like running sum statistic with data permutation to adjust for multiple testing. For tissue infection and fecal shedding, no gene sets in KEGG pathways or in GO sets for molecular function or cellular component were enriched for signal. The GO biological process gene set for positive regulation of cell motion (GO:0051272, q = 0.039, 5/11 genes contributing to the core enrichment) was enriched for Map tissue infection, while no GO biological process gene sets were enriched for fecal shedding. GSEA-SNP complements traditional SNP association approaches to identify genes of modest effects as well as genes with larger effects as demonstrated by the identification of one locus that we previously found to be associated with Map tissue infection using a SNP-by-SNP genome-wide association study.

Copy number variation in the bovine genome

Background

Copy number variations (CNVs), which represent a significant source of genetic diversity in mammals, have been shown to be associated with phenotypes of clinical relevance and to be causative of disease. Notwithstanding, little is known about the extent to which CNV contributes to genetic variation in cattle.

Results

We designed and used a set of NimbleGen CGH arrays that tile across the assayable portion of the cattle genome with approximately 6.3 million probes, at a median probe spacing of 301 bp. This study reports the highest resolution map of copy number variation in the cattle genome, with 304 CNV regions (CNVRs) being identified among the genomes of 20 bovine samples from 4 dairy and beef breeds. The CNVRs identified covered 0.68% (22 Mb) of the genome, and ranged in size from 1.7 to 2,031 kb (median size 16.7 kb). About 20% of the CNVs co-localized with segmental duplications, while 30% encompass genes, of which the majority is involved in environmental response. About 10% of the human orthologous of these genes are associated with human disease susceptibility and, hence, may have important phenotypic consequences.

Conclusions

Together, this analysis provides a useful resource for assessment of the impact of CNVs regarding variation in bovine health and production traits.

The pattern of linkage disequilibrium in German Holstein cattle

This study presents a second generation of linkage disequilibrium (LD) map statistics for the whole genome of the Holstein-Friesian population, which has a four times higher resolution compared with that of the maps available so far. We used DNA samples of 810 German Holstein-Friesian cattle genotyped by the Illumina Bovine SNP50K BeadChip to analyse LD structure. A panel of 40 854 (75.6%) markers was included in the final analysis. The pairwise r(2) statistic of SNPs up to 5 Mb apart across the genome was estimated. A mean value of r(2) = 0.30 +/- 0.32 was observed in pairwise distances of <25 kb and it dropped to 0.20 +/- 0.24 at 50-75 kb, which is nearly the average inter-marker space in this study. The proportion of SNPs in useful LD (r(2) > or = 0.25) was 26% for the distance of 50 and 75 kb between SNPs. We found a lower level of LD for SNP pairs at the distance < or =100 kb than previously thought. Analysis revealed 712 haplo-blocks spanning 4.7% of the genome and containing 8.0% of all SNPs. Mean and median block length were estimated as 164 +/- 117 kb and 144 kb respectively. Allele frequencies of the SNPs have a considerable and systematic impact on the estimate of r(2). It is shown that minimizing the allele frequency difference between SNPs reduces the influence of frequency on r(2) estimates. Analysis of past effective population size based on the direct estimates of recombination rates from SNP data showed a decline in effective population size to N(e) = 103 up to approximately 4 generations ago. Systematic effects of marker density and effective population size on observed LD and haplotype structure are discussed.

Comparison of linkage disequilibrium and haplotype diversity on macro- and microchromosomes in chicken

Background

The chicken (Gallus gallus), like most avian species, has a very distinct karyotype consisting of many micro- and a few macrochromosomes. While it is known that recombination frequencies are much higher for micro- as compared to macrochromosomes, there is limited information on differences in linkage disequilibrium (LD) and haplotype diversity between these two classes of chromosomes. In this study, LD and haplotype diversity were systematically characterized in 371 birds from eight chicken populations (commercial lines, fancy breeds, and red jungle fowl) across macro- and microchromosomes. To this end we sampled four regions of ~1 cM each on macrochromosomes (GGA1 and GGA2), and four 1.5 -2 cM regions on microchromosomes (GGA26 and GGA27) at a high density of 1 SNP every 2 kb (total of 889 SNPs).

Results

At a similar physical distance, LD, haplotype homozygosity, haploblock structure, and haplotype sharing were all lower for the micro- as compared to the macrochromosomes. These differences were consistent across populations. Heterozygosity, genetic differentiation, and derived allele frequencies were also higher for the microchromosomes. Differences in LD, haplotype variation, and haplotype sharing between populations were largely in line with known demographic history of the commercial chicken. Despite very low levels of LD, as measured by r² for most populations, some haploblock structure was observed, particularly in the macrochromosomes, but the haploblock sizes were typically less than 10 kb.

Conclusion

Differences in LD between micro- and macrochromosomes were almost completely explained by differences in recombination rate. Differences in haplotype diversity and haplotype sharing between micro- and macrochromosomes were explained by differences in recombination rate and genotype variation. Haploblock structure was consistent with demography of the chicken populations, and differences in recombination rates between micro- and macrochromosomes. The limited haploblock structure and LD suggests that future whole-genome marker assays will need 100+K SNPs to exploit haplotype information. Interpretation and transferability of genetic parameters will need to take into account the size of chromosomes in chicken, and, since most birds have microchromosomes, in other avian species as well.

Diversity and evolution of 11 innate immune genes in Bos taurus taurus and Bos taurus indicus cattle

The Toll-like receptor (TLR) and peptidoglycan recognition protein 1 (PGLYRP1) genes play key roles in the innate immune systems of mammals. While the TLRs recognize a variety of invading pathogens and induce innate immune responses, PGLYRP1 is directly microbicidal. We used custom allele-specific assays to genotype and validate 220 diallelic variants, including 54 nonsynonymous SNPs in 11 bovine innate immune genes (TLR1-TLR10, PGLYRP1) for 37 cattle breeds. Bayesian haplotype reconstructions and median joining networks revealed haplotype sharing between Bos taurus taurus and Bos taurus indicus breeds at every locus, and we were unable to differentiate between the specialized B. t. taurus beef and dairy breeds, despite an average polymorphism density of one locus per 219 bp. Ninety-nine tagSNPs and one tag insertion-deletion polymorphism were sufficient to predict 100% of the variation at all 11 innate immune loci in both subspecies and their hybrids, whereas 58 tagSNPs captured 100% of the variation at 172 loci in B. t. taurus. PolyPhen and SIFT analyses of nonsynonymous SNPs encoding amino acid replacements indicated that the majority of these substitutions were benign, but up to 31% were expected to potentially impact protein function. Several diversity-based tests provided support for strong purifying selection acting on TLR10 in B. t. taurus cattle. These results will broadly impact efforts related to bovine translational genomics.

Genetic heterogeneity at the bovine KIT gene in cattle breeds carrying different putative alleles at the spotting locus

According to classical genetic studies, piebaldism in cattle is largely influenced by the allelic series at the spotting locus (S), which includes the S(H) (Hereford pattern), S(+) (non-spotted) and s (spotted) alleles. The S locus was mapped on bovine chromosome 6 in the region containing the KIT gene. We investigated the KIT gene, analysing its variability and haplotype distribution in cattle of three breeds (Angus, Hereford and Holstein) with different putative alleles (S(+), S(H) and s respectively) at the S locus. Resequencing of a whole of 0.485 Mb revealed 111 polymorphisms. The global nucleotide diversity was 0.087%. Tajima's D-values were negative for all breeds, indicating putative directional selection. Of the 28 inferred haplotypes, only five were observed in the Hereford breed, in which one was the most frequent. Coalescent simulation showed that it is highly unlikely (P < 10E-6) to obtain this low number of haplotypes conditionally on the observed number of segregating SNPs. Therefore, the neutral model could be rejected for the Hereford breed, suggesting that a selection sweep occurred at the KIT locus. Twelve haplotypes were inferred in Holstein and Angus. For these two breeds, the neutral model could not be rejected. High heterogeneity of the KIT gene was confirmed from a phylogenetic analysis. Our results suggest a role of the KIT gene in determining the S(H) allele(s) in the Hereford, but no evidence of selective sweep was obtained in Holstein, suggesting that complex mechanisms (or other genes) might be the cause of the spotted phenotype in this breed.

An enhanced single base extension technique for the analysis of complex viral populations

Many techniques for the study of complex populations provide either specific information on a small number of variants or general information on the entire population. Here we describe a powerful new technique for elucidating mutation frequencies at each genomic position in a complex population. This single base extension (SBE) based microarray platform was designed and optimized using poliovirus as the target genotype, but can be easily adapted to assay populations derived from any organism. The sensitivity of the method was demonstrated by accurate and consistent readouts from a controlled population of mutant genotypes. We subsequently deployed the technique to investigate the effects of the nucleotide analog ribavirin on a typical poliovirus population through two rounds of passage. Our results show that this economical platform can be used to investigate dynamic changes occurring at frequencies below 1% within a complex nucleic acid population. Given that many key aspects of the study and treatment of disease are intimately linked to population-level genomic diversity, our SBE-based technique provides a scalable and cost-effective complement to both traditional and next generation sequencing methodologies.

Unlocking the bovine genome

The draft genome sequence of cattle (Bos taurus) has now been analyzed by the Bovine Genome Sequencing and Analysis Consortium and the Bovine HapMap Consortium, which together represent an extensive collaboration involving more than 300 scientists from 25 different countries.