Microbiability of milk composition and genetic control of microbiota effects in sheep.

Recently, high-dimensional omics data are becoming available in larger quantities, and models have been developed that integrate them with genomics to understand in finer detail the relationship between genotype and phenotype, and thus improve the performance of genetic evaluations. Our objectives are to quantify the effect of the inclusion of microbiome data in the genetic evaluation for dairy traits in sheep, through the estimation of the heritability, microbiability, and how the microbiome effect on dairy traits decomposes into genetic and nongenetic parts. In this study we analyzed milk and rumen samples of 795 Lacaune dairy ewes. We included, as phenotype, dairy traits and milk fatty acids and proteins composition; as omics measurements, 16S rRNA rumen bacterial abundances; and as genotyping, 54K SNP chip for all ewes. Two nested genomic models were used: a first model to predict the individual contributions of the genetic and microbial abundances to phenotypes, and a second model to predict the additive genetic effect of the microbial community. In addition, microbiome-wide association studies for all dairy traits were applied using the 2,059 rumen bacterial abundances, and the genetic correlations between microbiome principal components and dairy traits were estimated. Results showed that in general the inclusion of both genetic and microbiome effect did not improve the fit of the model compared with the model with the genetic effect only. In addition, for all dairy traits the total heritability was equal to the direct heritability after fitting microbiota effects, due to a microbiability being almost zero for most dairy traits and heritability of the microbial community was very close to zero. Microbiome-wide association studies did not show operational taxonomic units with major effect for any of the dairy traits evaluated, and the genetic correlations between the first 5 principal components and dairy traits were low to moderate. So far, we can conclude that, using a substantial data set of 795 Lacaune dairy ewes, rumen bacterial abundances do not provide improved genetic evaluation for dairy traits in sheep.

Bovine chromosomal regions affecting rheological traits in rennet-induced skim milk gels.

Optimizing cheese yield and quality is of central importance to cheese manufacturing. The yield is associated with the time it takes before the gel has an optimal consistency for further processing, and it is well known that gel formation differs between individual milk samples. By identifying genomic regions affecting traits related to rennet-induced gelation, the aim of this study was to identify potential candidate genes affecting these traits. Hence, rennet-induced gelation, including rennet coagulation time, gel strength, and yield stress, was measured in skim milk samples collected from 379 animals of the Swedish Red breed using low-amplitude oscillation measurements. All animals had genotypes for almost 621,000 segregating single nucleotide polymorphisms (SNP), identified using the Bovine HD SNPChip (Illumina Inc., San Diego, CA). The genome was scanned for associations, haplotypes based on SNP sets comprising highly associated SNP were inferred, and the effects of the 2 most common haplotypes within each region were analyzed using mixed models. Even though the number of animals was relatively small, a total of 21 regions were identified, with 4 regions showing association with more than one trait. A major quantitative trait locus for all traits was identified around the casein cluster explaining between 9.3 to 15.2% of the phenotypic variation of the different traits. In addition, 3 other possible candidate genes were identified; that is, UDP-N-acetyl-α-d-galactosamine:polypeptide N-acetylgalactosaminyl-transferase 1 (GALNT1), playing a role in O-glycosylation of κ-casein, and 2 cathepsins, CTSZ and CTSC, possibly involved in proteolysis of milk proteins. We have shown that other genes than the casein genes themselves may be involved in the regulation of gelation traits. However, additional analysis is needed to confirm these results. To our knowledge, this is the first study identifying quantitative trait loci affecting rennet-induced gelation of skim milk through a high-density genome-wide association study.

Short communication: alteration of priors for random effects in Gaussian linear mixed models.

Linear mixed models, for which the prior multivariate normal distributions of random effects are assumed to have a mean equal to 0, are commonly used in animal breeding. However, some statistical analyses (e.g., the consideration of a population under selection into a genomic scheme breeding, multiple-trait predictions of lactation yields, and Bayesian approaches integrating external information into genetic evaluations) need to alter both the mean and (co)variance of the prior distributions and, to our knowledge, most software packages available in the animal breeding community do not permit such alterations. Therefore, the aim of this study was to propose a method to alter both the mean and (co)variance of the prior multivariate normal distributions of random effects of linear mixed models while using currently available software packages. The proposed method was tested on simulated examples with 3 different software packages available in animal breeding. The examples showed the possibility of the proposed method to alter both the mean and (co)variance of the prior distributions with currently available software packages through the use of an extended data file and a user-supplied (co)variance matrix.

Comparison of genomic predictions using genomic relationship matrices built with different weighting factors to account for locus-specific variances.

Various models have been used for genomic prediction. Bayesian variable selection models often predict more accurate genomic breeding values than genomic BLUP (GBLUP), but GBLUP is generally preferred for routine genomic evaluations because of low computational demand. The objective of this study was to achieve the benefits of both models using results from Bayesian models and genome-wide association studies as weights on single nucleotide polymorphism (SNP) markers when constructing the genomic matrix (G-matrix) for genomic prediction. The data comprised 5,221 progeny-tested bulls from the Nordic Holstein population. The animals were genotyped using the Illumina Bovine SNP50 BeadChip (Illumina Inc., San Diego, CA). Weighting factors in this investigation were the posterior SNP variance, the square of the posterior SNP effect, and the corresponding minus base-10 logarithm of the marker association P-value [-log10(P)] of a t-test obtained from the analysis using a Bayesian mixture model with 4 normal distributions, the square of the estimated SNP effect, and the corresponding -log10(P) of a t-test obtained from the analysis using a classical genome-wide association study model (linear regression model). The weights were derived from the analysis based on data sets that were 0, 1, 3, or 5 yr before performing genomic prediction. In building a G-matrix, the weights were assigned either to each marker (single-marker weighting) or to each group of approximately 5 to 150 markers (group-marker weighting). The analysis was carried out for milk yield, fat yield, protein yield, fertility, and mastitis. Deregressed proofs (DRP) were used as response variables to predict genomic estimated breeding values (GEBV). Averaging over the 5 traits, the Bayesian model led to 2.0% higher reliability of GEBV than the GBLUP model with an original unweighted G-matrix. The superiority of using a GBLUP with weighted G-matrix over GBLUP with an original unweighted G-matrix was the largest when using a weighting factor of posterior variance, resulting in 1.7 percentage points higher reliability. The second best weighting factors were -log10 (P-value) of a t-test corresponding to the square of the posterior SNP effect from the Bayesian model and -log10 (P-value) of a t-test corresponding to the square of the estimated SNP effect from the linear regression model, followed by the square of estimated SNP effect and the square of the posterior SNP effect. In addition, group-marker weighting performed better than single-marker weighting in terms of reducing bias of GEBV, and also slightly increased prediction reliability. The differences between weighting factors and scenarios were larger in prediction bias than in prediction accuracy. Finally, weights derived from a data set having a lag up to 3 yr did not reduce reliability of GEBV. The results indicate that posterior SNP variance estimated from a Bayesian mixture model is a good alternative weighting factor, and common weights on group markers with a size of 30 markers is a good strategy when using markers of the 50,000-marker (50K) chip. In a population with gradually increasing reference data, the weights can be updated once every 3 yr.

Genomic evaluation methods to include intermediate correlated features such as high-throughput or omics phenotypes.

Gene expression is supposed to be an intermediate between DNA and the phenotype, and it can be measured. Thus, for a trait, we may have intermediate measures, which are in fact a series of genetically controlled traits. Similarly, several traits may be measured or predicted using infrared spectra, accelerometers, and similar high-throughput measures that we will call "omics." Although these measurements have errors, many of them are heritable, and they may be more accurate or easier to record than the trait of interest. It is therefore important to develop methods to use intermediate measurements in selection. Here, we present methods and perspectives for selection based on massively recorded intermediate traits (omics). Recent developments allow a hierarchical integrated framework for prediction, in which a trait is partially controlled by omics. In addition, the omics measures are themselves partly controlled by genetics ("mediated breeding values") and partly by environment or residual factors. Thus, a part of the genetic determinism of a trait is mediated by omics, whereas the remaining part is not mediated, which results in "residual breeding values." In such a framework, genetic evaluations consist of 2 nested genomic BLUP-based models. In the first, the effect of omics on the trait (which can be seen as an improved estimate of the phenotype) and the residual breeding values are estimated. The second model extracts the mediated breeding values from the improved estimate of the phenotype, considering that omics themselves are heritable. The whole procedure is called GOBLUP (genomics omics BLUP) and it allows measures in only some individuals; that is, it is a "single-step"-like method. In this model, heritability is split into "mediated" and "not mediated" parts. This decomposition allows us to predict how accurate the omics measure of the trait would be compared with the direct measure. The ideal omics measure is heritable and explains a large part of the phenotypic variation of the trait. Ideally, this could be the case for some traits with low heritability. However, even if the omics measure explains only a small part of the phenotypic variation, when omics measurement themselves are heritable, the use of such a model would lead to more accurate selection. Expressions for upper bounds of reliability given omics measurements are also presented. More studies are needed to confirm the usefulness of omics or high-throughput prediction. Usefulness of the technology likely needs to be checked on a case-by-case basis.

Genomic prediction for Nordic Red Cattle using one-step and selection index blending.

This study investigated the accuracy of direct genomic breeding values (DGV) using a genomic BLUP model, genomic enhanced breeding values (GEBV) using a one-step blending approach, and GEBV using a selection index blending approach for 15 traits of Nordic Red Cattle. The data comprised 6,631 bulls of which 4,408 bulls were genotyped using Illumina Bovine SNP50 BeadChip (Illumina, San Diego, CA). To validate reliability of genomic predictions, about 20% of the youngest genotyped bulls were taken as test data set. Deregressed proofs (DRP) were used as response variables for genomic predictions. Reliabilities of genomic predictions in the validation analyses were measured as squared correlations between DRP and genomic predictions corrected for reliability of DRP, based on the bulls in the test data sets. A set of weighting (scaling) factors was used to construct the combined relationship matrix among genotyped and nongenotyped bulls for one-step blending, and to scale DGV and its expected reliability in the selection index blending. Weighting (scaling) factors had a small influence on reliabilities of GEBV, but a large influence on the variation of GEBV. Based on the validation analyses, averaged over the 15 traits, the reliability of DGV for bulls without daughter records was 11.0 percentage points higher than the reliability of conventional pedigree index. Further gain of 0.9 percentage points was achieved by combining information from conventional pedigree index using the selection index blending, and gain of 1.3 percentage points was achieved by combining information of genotyped and nongenotyped bulls simultaneously applying the one-step blending. These results indicate that genomic selection can greatly improve the accuracy of preselection for young bulls in Nordic Red population, and the one-step blending approach is a good alternative to predict GEBV in practical genetic evaluation program.

Genomic regions associated with ventro-cranial chronic pleuritis in pig.

Ventro-cranial chronic pleuritis can be a result of pleuropneumonia and enzootic pneumonia. These diseases cause severe losses in intensive pig production worldwide, but host resistance is difficult to breed for. It could be beneficial to use marker-assisted selection, and a step towards this is to identify genomic regions associated with the trait. For this purpose, 7304 pigs from 11 boar families were analysed for associations between single nucleotide polymorphisms and ventro-cranial chronic pleuritis. The pigs were genotyped by the use of the iSelect Custom 7 K porcine SNP Chip. Quantitative trait loci (QTL), significant at the chromosome-wide level, were identified on Sus scrofa chromosomes (SSC) 2, 4, 11, 12 and 13 in four different boar families. The QTL on SSC 4 in family G was also significant at the genome-wide threshold according to Bonferroni correction. We have identified a number of candidate genes, but the causative mutations still need to be identified. Markers closely associated with the resistance traits have a strong potential for use in breeding towards animals with improved characteristics concerning ventro-cranial chronic pleuritis.

Identification of QTL for dorso-caudal chronic pleuritis in 12 crossbred porcine families.

Pleuropneumonia is a major problem in pig production. At the time of slaughter, chronic pleuritis (CP) developed from pleuropneumonia is a common finding, and breeding for a reduced incidence of CP using marker-assisted selection (MAS) would be advantageous. Before applying MAS, quantitative trait loci (QTL) or markers associated with the prevalence of CP should be identified. In this study, 7470 pigs from crosses between 12 Danish Duroc boars and 604 sows (Danish Landrace × Danish Large White) were evaluated for CP located on the dorso-caudal part of the lungs. Quantitative trait loci were identified within boar families using both a Binomial logistic regression method and a chi-square test of association. Significant QTL for CP were detected on Sus scrofa chromosomes (SSC) 2, 8, 12, 13, 14 and 18 using both methods. One QTL on SSC 8 was also detected across families. For the QTL identified within families, the odds-ratio of having CP was approximately twice as high for the unfavourable allele compared to the favourable one. These QTL and closely linked markers show promise for the development of gene-specific markers associated with a reduced incidence of CP located on the dorso-caudal part of the lungs.

Technical note: Genomic evaluation for crossbred performance in a single-step approach with metafounders.

A single-step genomic BLUP method (ssGBLUP) has been successfully developed and applied for purebred and crossbred performance in pigs. However, it requires phasing the genotypes and inferring the breed origin of alleles in crossbred animals, which is somewhat inconvenient. Recently, a new concept of metafounders that considers the relationship within and across base populations was developed. With this concept of metafounders, regular methods to build and invert the pedigree relationships matrix can be used with only minor modifications and, moreover, genomic relationships and pedigree-based relationships are automatically compatible in the ssGBLUP. In this study, data for the total number of piglets born in Danish Landrace, Yorkshire, and 2-way crossbred pigs and models for purebred and crossbred performance were revisited by use of ssGBLUP with 2 metafounders. Genetic variances and genetic correlations between purebred and crossbred performances were first reestimated. Then, model-based reliabilities of purebred boars for their crossbred performance and predictive abilities for crossbred animals were compared in different scenarios. Results in this study were compared to those in a previous study with identical data but with models that required known breed origin of crossbred genotypes. Results show that relationships for base individuals within Landrace and within Yorkshire are similar and that the ancestor populations for Landrace and Yorkshire are related. In terms of model-based reliabilities and predictive abilities, ssGBLUP with metafounders performs at least as well as the single-step method requiring phasing at a lower complexity.

Nurse capacity, fertility, and litter size in crossbred sows and genetic correlation to purebred sow information.

In pigs litter size has increased during the last decades and number of weaned piglets is an important issue. The aim of this study was to develop a new trait of nurse capacity (NC) of crossbred sows viewed as crossbred performances in the two purebred parent lines, and estimate the genetic correlation to fertility and litter size five days after birth. An experiment recording phenotypes of crossbred sows was conducted in three large production herds with 11,247 first litter Danish Landrace x Yorkshire sows. All terminal sires used were Duroc AI boars. The experiment was running from 2010 to 2013. At farrowing, the total number born (TNB) was recorded. Five days after farrowing the litter size of the biological mother (LS5) was recorded. During the first three days after farrowing the number of piglets at each nurse sow was equalized to 14 piglets and after three weeks the NC was recorded and defined as the number of piglets nursed. Additional records on TNB and LS5 from related sows in nucleus and multiplier herds were added to obtain a data set with both purebred and crossbred information. A reduced animal model including both purebred and crossbred records was used and parameters were estimated. The results show that NC recorded on crossbred first litter sows had heritabilities of 0.05 and 0.07 for crossbred performance in the purebred populations of Landrace and Yorkshire, respectively. Estimated genetic correlations between TNB in purebreds and crossbreds show that nearly 50% of genetic gain in the purebred populations was transferred to crossbreds. Unfavorable genetic correlations between TNB in purebreds and NC in crossbreds were observed. For LS5 the genetic (co)variances show that 61% of the genetic gain in the two purebred lines was transferred to the commercial pig production of crossbred first litter sows, but no statistically significant genetic correlation to NC was obtained.

Application of single-step genomic evaluation for crossbred performance in pig.

Crossbreding is predominant and intensively used in commercial meat production systems, especially in poultry and swine. Genomic evaluation has been successfully applied for breeding within purebreds but also offers opportunities of selecting purebreds for crossbred performance by combining information from purebreds with information from crossbreds. However, it generally requires that all relevant animals are genotyped, which is costly and presently does not seem to be feasible in practice. Recently, a novel single-step BLUP method for genomic evaluation of both purebred and crossbred performance has been developed that can incorporate marker genotypes into a traditional animal model. This new method has not been validated in real data sets. In this study, we applied this single-step method to analyze data for the maternal trait of total number of piglets born in Danish Landrace, Yorkshire, and two-way crossbred pigs in different scenarios. The genetic correlation between purebred and crossbred performances was investigated first, and then the impact of (crossbred) genomic information on prediction reliability for crossbred performance was explored. The results confirm the existence of a moderate genetic correlation, and it was seen that the standard errors on the estimates were reduced when including genomic information. Models with marker information, especially crossbred genomic information, improved model-based reliabilities for crossbred performance of purebred boars and also improved the predictive ability for crossbred animals and, to some extent, reduced the bias of prediction. We conclude that the new single-step BLUP method is a good tool in the genetic evaluation for crossbred performance in purebred animals.

Improving genetic evaluation of litter size and piglet mortality for both genotyped and nongenotyped individuals using a single-step method.

A single-step method allows genetic evaluation using information of phenotypes, pedigree, and markers from genotyped and nongenotyped individuals simultaneously. This paper compared genomic predictions obtained from a single-step BLUP (SSBLUP) method, a genomic BLUP (GBLUP) method, a selection index blending (SELIND) method, and a traditional pedigree-based method (BLUP) for total number of piglets born (TNB), litter size at d 5 after birth (LS5), and mortality rate before d 5 (Mort; including stillbirth) in Danish Landrace and Yorkshire pigs. Data sets of 778,095 litters from 309,362 Landrace sows and 472,001 litters from 190,760 Yorkshire sows were used for the analysis. There were 332,795 Landrace and 207,255 Yorkshire animals in the pedigree data, among which 3,445 Landrace pigs (1,366 boars and 2,079 sows) and 3,372 Yorkshire pigs (1,241 boars and 2,131 sows) were genotyped with the Illumina PorcineSNP60 BeadChip. The results showed that the 3 methods with marker information (SSBLUP, GBLUP, and SELIND) produced more accurate predictions for genotyped animals than the pedigree-based method. For genotyped animals, the average of reliabilities for all traits in both breeds using traditional BLUP was 0.091, which increased to 0.171 w+hen using GBLUP and to 0.179 when using SELIND and further increased to 0.209 when using SSBLUP. Furthermore, the average reliability of EBV for nongenotyped animals was increased from 0.091 for traditional BLUP to 0.105 for the SSBLUP. The results indicate that the SSBLUP is a good approach to practical genomic prediction of litter size and piglet mortality in Danish Landrace and Yorkshire populations.

Single-step methods for genomic evaluation in pigs.

Genetic evaluation based on information from phenotypes, pedigree and markers can be implemented using a recently developed single-step method. In this paper we compare accuracies of predicted breeding values for daily gain and feed conversion ratio (FCR) in Danish Duroc pigs obtained from different versions of single-step methods, the traditional pedigree-based method and the genomic BLUP (GBLUP) method. In particular, we present a single-step method with an adjustment of the genomic relationship matrix so that it is compatible to the pedigree-based relationship matrix. Comparisons are made for both genotyped and non-genotyped animals and univariate and bivariate models. The results show that the three methods with marker information (two single-step methods and GBLUP) produce more accurate predictions of genotyped animals than the pedigree-based method. In addition, single-step methods provide more accurate predictions for non-genotyped animals. The results also show that the single-step method with adjusted genomic relationship matrix produce more accurate predictions than the original single-step method. Finally, the results for the bivariate analyses show a somewhat improved accuracy and reduced inflation of predictions for FCR for the two single-step methods compared with the univariate analyses. The conclusions are: first, the methods with marker information improve prediction compared with the pedigree-based method; second, a single-step method, contrary to GBLUP, provides improved predictions for all animals compared to the pedigree-based method; and third, a single-step method should be used with an adjustment of the genomic relationship matrix.

Quantitative trait loci analysis of osteochondrosis traits in the elbow joint of pigs.

Osteochondrosis is a growth disorder in the cartilage of young animals and is characterised by lesions found in the cartilage and bone. This study identified quantitative trait loci (QTLs) associated with six osteochondrosis lesion traits in the elbow joint of finishing pigs. The traits were: thickening of the cartilage, lesion in the subchondral bone, irregular cartilage surface, fissure under the cartilage, an irregular sagittal central groove and depression of the proximal edge of the radius. The study comprised 7172 finishing pigs from crossing 12 Duroc boars with 600 crossbred Landrace × Large White sows and included 462 single nucleotide polymorphism markers. The results showed 18 QTLs exceeding the 5% genome-wide threshold. The QTLs associated with lesions in the medial part of the condylus humeri (assumed to be the four main osteochondrosis traits) were, in most cases, at common locations, whereas the QTLs associated with depression of the proximal edge of the radius in general were on the same chromosomes but at separate locations. The detected QTLs explain a large part of the genetic variation, which is promising for incorporating osteochondrosis into a breeding programme using marker-assisted selection.

Quantitative trait loci analysis of swine meat quality traits.

A QTL study was performed in large half-sib families to characterize the genetic background of variation in pork quality traits as well as to examine the possibilities of including QTL in a marker-assisted selection scheme. The quality traits included ultimate pH in LM and the semimembranosus, drip loss, and the Minolta color measurements L*, a*, and b* representing meat lightness, redness, and yellowness, respectively. The families consist of 3,883 progenies of 12 Duroc boars that were evaluated to identify the QTL. The linkage map consists of 462 SNP markers on 18 porcine autosomes. Quantitative trait loci were mapped using a linear mixed model with fixed factors (sire, sex, herd, month, sow age) and random factors (polygenic effect, QTL effects, and litter). Chromosome-wide and genome-wide significance thresholds were determined by Peipho's approach, and 95% Bayes credibility intervals were estimated from a posterior distribution of the QTL position. In total, 31 QTL for the 6 meat quality traits were found to be significant at the 5% chromosome-wide level, among which 11 QTL were significant at the 5% genome-wide level and 5 of these were significant at the 0.1% genome-wide level. Segregation of the identified QTL in different families was also investigated. Most of the identified QTL segregated in 1 or 2 families. For the QTL affecting ultimate pH in LM and semimembranosus and L* and b* value on SSC6, the positions of the QTL and the shapes of the likelihood curves were almost the same. In addition, a strong correlation of the estimated effects of these QTL was found between the 4 traits, indicating that the same genes control these traits. A similar pattern was seen on SSC15 for the QTL affecting ultimate pH in the 2 muscles and drip loss. The results from this study will be helpful for fine mapping and identifying genes affecting meat quality traits, and tightly linked markers may be incorporated into marker-assisted selection programs.

Spatial modelling and the prediction of Loa loa risk: decision making under uncertainty.

Health decision-makers working in Africa often need to act for millions of people over large geographical areas on little and uncertain information. Spatial statistical modelling and Bayesian inference have now been used to quantify the uncertainty in the predictions of a regional, environmental risk map for Loa loa (a map that is currently being used as an essential decision tool by the African Programme for Onchocerciasis Control). The methodology allows the expression of the probability that, given the data, a particular location does or does not exceed a predefined high-risk threshold for which a change in strategy for the delivery of the antihelmintic ivermectin is required.

Frequency and spatial distribution of environmental Campylobacter spp.

Humans are exposed to Campylobacter spp. in a range of sources via both food and environmental pathways. For this study, we explored the frequency and distribution of thermophilic Campylobacter spp. in a 10- by 10-km square rural area of Cheshire, United Kingdom. The area contains approximately 70, mainly dairy, farms and is used extensively for outdoor recreational activities. Campylobacter spp. were isolated from a range of environmental samples by use of a systematic sampling grid. Livestock (mainly cattle) and wildlife feces and environmental water and soil samples were cultured, and isolates were presumptively identified by standard techniques. These isolates were further characterized by PCR. Campylobacter jejuni was the most prevalent species in all animal samples, ranging from 11% in samples from nonavian wildlife to 36% in cattle feces, and was isolated from 15% of water samples. Campylobacter coli was commonly found in water (17%) and sheep (21%) samples, but rarely in other samples. Campylobacter lari was recovered from all sample types, with the exception of sheep feces, and was found in moderate numbers in birds (7%) and water (5%). Campylobacter hyointestinalis was only recovered from cattle (7%) and birds (1%). The spatial distribution and determinants of C. jejuni in cattle feces were examined by the use of model-based spatial statistics. The distribution was consistent with very localized within-farm or within-field transmission and showed little evidence of any larger-scale spatial dependence. We concluded that there is a potentially high risk of human exposure to Campylobacter spp., particularly C. jejuni, in the environment of our study area. The prevalence and likely risk posed by C. jejuni-positive cattle feces in the environment diminished as the fecal material aged. After we took into account the age of the fecal material, the absence or presence of rain, and the presence of bird feces, there was evidence of significant variation in the prevalence of C. jejuni-positive cattle feces between grazing fields but no evidence of spatial clustering beyond this resolution. The spatial pattern of C. jejuni is therefore consistent with that for an organism that is ubiquitous in areas contaminated with cattle feces, with a short-scale variation in infection intensity that cannot be explained solely by variations in the age of the fecal material. The observed pattern is not consistent with large-scale transmission attributable to watercourses, wildlife territories, or other geographical features that transcend field and farm boundaries.