Detection of selection signatures in dairy and beef cattle using high-density genomic information

Background

Artificial selection for economically important traits in cattle is expected to have left distinctive selection signatures on the genome. Access to high-density genotypes facilitates the accurate identification of genomic regions that have undergone positive selection. These findings help to better elucidate the mechanisms of selection and to identify candidate genes of interest to breeding programs.

Results

Information on 705 243 autosomal single nucleotide polymorphisms (SNPs) in 3122 dairy and beef male animals from seven cattle breeds (Angus, Belgian Blue, Charolais, Hereford, Holstein-Friesian, Limousin and Simmental) were used to detect selection signatures by applying two complementary methods, integrated haplotype score (iHS) and global fixation index (F_ST). To control for false positive results, we used false discovery rate (FDR) adjustment to calculate adjusted iHS within each breed and the genome-wide significance level was about 0.003. Using the iHS method, 83, 92, 91, 101, 85, 101 and 86 significant genomic regions were detected for Angus, Belgian Blue, Charolais, Hereford, Holstein-Friesian, Limousin and Simmental cattle, respectively. None of these regions was common to all seven breeds. Using the F_ST approach, 704 individual SNPs were detected across breeds. Annotation of the regions of the genome that showed selection signatures revealed several interesting candidate genes i.e. DGAT1, ABCG2, MSTN, CAPN3, FABP3, CHCHD7, PLAG1, JAZF1, PRKG2, ACTC1, TBC1D1, GHR, BMP2, TSG1, LYN, KIT and MC1R that play a role in milk production, reproduction, body size, muscle formation or coat color. Fifty-seven common candidate genes were found by both the iHS and global F_ST methods across the seven breeds. Moreover, many novel genomic regions and genes were detected within the regions that showed selection signatures; for some candidate genes, signatures of positive selection exist in the human genome. Multilevel bioinformatic analyses of the detected candidate genes suggested that the PPAR pathway may have been subjected to positive selection.

Conclusions

This study provides a high-resolution bovine genomic map of positive selection signatures that are either specific to one breed or common to a subset of the seven breeds analyzed. Our results will contribute to the detection of functional candidate genes that have undergone positive selection in future studies.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-015-0127-3) contains supplementary material, which is available to authorized users.     

International genomic evaluation methods for dairy cattle

Background

Genomic evaluations are rapidly replacing traditional evaluation systems used for dairy cattle selection. Higher reliabilities from larger genotype files promote cooperation across country borders. Genomic information can be exchanged across countries using simple conversion equations, by modifying multi-trait across-country evaluation (MACE) to account for correlated residuals originating from the use of foreign evaluations, or by multi-trait analysis of genotypes for countries that use the same reference animals.

Methods

Traditional MACE assumes independent residuals because each daughter is measured in only one country. Genomic MACE could account for residual correlations using daughter equivalents from genomic data as a fraction of the total in each country and proportions of bulls shared. MACE methods developed to combine separate within-country genomic evaluations were compared to direct, multi-country analysis of combined genotypes using simulated genomic and phenotypic data for 8,193 bulls in nine countries.

Results

Reliabilities for young bulls were much higher for across-country than within-country genomic evaluations as measured by squared correlations of estimated with true breeding values. Gains in reliability from genomic MACE were similar to those of multi-trait evaluation of genotypes but required less computation. Sharing of reference genotypes among countries created large residual correlations, especially for young bulls, that are accounted for in genomic MACE.

Conclusions

International genomic evaluations can be computed either by modifying MACE to account for residual correlations across countries or by multi-trait evaluation of combined genotype files. The gains in reliability justify the increased computation but require more cooperation than in previous breeding programs.     

Individualized mating system estimation using genomic data

The estimation of outcrossing rates in hermaphroditic species has been a major focus in the evolutionary study of reproductive strategies, and is also essential for plant breeding and conservation. Surprisingly, genomics has thus far minimally influenced outcrossing rate studies. In this article, we generalize a Bayesian inference method (BORICE) to accommodate genomic data from multiple subpopulations of a species. As an empirical demonstration, BORICE is applied to 115 maternal families of Mimulus guttatus. The analysis shows that low‐level whole genome sequencing of parents and offspring is sufficient for individualized mating system estimation: 208 offspring (88.5%) were definitively called as outcrossed, 23 (9.8%) as selfed. After mating system parameters are established (each offspring as outcrossed or selfed and the inbreeding level of maternal plants), BORICE outputs posterior genotype probabilities for each SNP genomewide. Individual SNP calls are often burdened with considerable uncertainty and distilling information from closely linked sites (within genomic windows) can be a useful strategy. For the Mimulus data, principal components based on window statistics were sufficient to diagnose inversion polymorphisms and estimate their effects on spatial structure, phenotypic and fitness measures. More generally, mating system estimation with BORICE can set the stage for population and quantitative genomic analyses, particularly researchers collect phenotypic or fitness data from maternal individuals.     

Accuracy of genomic breeding values in multi-breed dairy cattle populations

Background

Two key findings from genomic selection experiments are 1) the reference population used must be very large to subsequently predict accurate genomic estimated breeding values (GEBV), and 2) prediction equations derived in one breed do not predict accurate GEBV when applied to other breeds. Both findings are a problem for breeds where the number of individuals in the reference population is limited. A multi-breed reference population is a potential solution, and here we investigate the accuracies of GEBV in Holstein dairy cattle and Jersey dairy cattle when the reference population is single breed or multi-breed. The accuracies were obtained both as a function of elements of the inverse coefficient matrix and from the realised accuracies of GEBV.

Methods

Best linear unbiased prediction with a multi-breed genomic relationship matrix (GBLUP) and two Bayesian methods (BAYESA and BAYES_SSVS) which estimate individual SNP effects were used to predict GEBV for 400 and 77 young Holstein and Jersey bulls respectively, from a reference population of 781 and 287 Holstein and Jersey bulls, respectively. Genotypes of 39,048 SNP markers were used. Phenotypes in the reference population were de-regressed breeding values for production traits. For the GBLUP method, expected accuracies calculated from the diagonal of the inverse of coefficient matrix were compared to realised accuracies.

Results

When GBLUP was used, expected accuracies from a function of elements of the inverse coefficient matrix agreed reasonably well with realised accuracies calculated from the correlation between GEBV and EBV in single breed populations, but not in multi-breed populations. When the Bayesian methods were used, realised accuracies of GEBV were up to 13% higher when the multi-breed reference population was used than when a pure breed reference was used. However no consistent increase in accuracy across traits was obtained.

Conclusion

Predicting genomic breeding values using a genomic relationship matrix is an attractive approach to implement genomic selection as expected accuracies of GEBV can be readily derived. However in multi-breed populations, Bayesian approaches give higher accuracies for some traits. Finally, multi-breed reference populations will be a valuable resource to fine map QTL.     

Optimising multistage dairy cattle breeding schemes including genomic selection using decorrelated or optimum selection indices

Background

The prediction of the outcomes from multistage breeding schemes is especially important for the introduction of genomic selection in dairy cattle. Decorrelated selection indices can be used for the optimisation of such breeding schemes. However, they decrease the accuracy of estimated breeding values and, therefore, the genetic gain to an unforeseeable extent and have not been applied to breeding schemes with different generation intervals and selection intensities in each selection path.

Methods

A grid search was applied in order to identify optimum breeding plans to maximise the genetic gain per year in a multistage, multipath dairy cattle breeding program. In this program, different values of the accuracy of estimated genomic breeding values and of their costs per individual were applied, whereby the total breeding costs were restricted. Both decorrelated indices and optimum selection indices were used together with fast multidimensional integration algorithms to produce results.

Results

In comparison to optimum indices, the genetic gain with decorrelated indices was up to 40% less and the proportion of individuals undergoing genomic selection was different. Additionally, the interaction between selection paths was counter-intuitive and difficult to interpret. Independent of using decorrelated or optimum selection indices, genomic selection replaced traditional progeny testing when maximising the genetic gain per year, as long as the accuracy of estimated genomic breeding values was ≥ 0.45. Overall breeding costs were mainly generated in the path "dam-sire". Selecting males was still the main source of genetic gain per year.

Conclusion

Decorrelated selection indices should not be used because of misleading results and the availability of accurate and fast algorithms for exact multidimensional integration. Genomic selection is the method of choice when maximising the genetic gain per year but genotyping females may not allow for a reduction in overall breeding costs. Furthermore, the economic justification of genotyping females remains questionable.     

Genotyping strategies for genomic selection in small dairy cattle populations

This study evaluated different female-selective genotyping strategies to increase the predictive accuracy of genomic breeding values (GBVs) in populations that have a limited number of sires with a large number of progeny. A simulated dairy population was utilized to address the aims of the study. The following selection strategies were used: random selection, two-tailed selection by yield deviations, two-tailed selection by breeding value, top yield deviation selection and top breeding value selection. For comparison, two other strategies, genotyping of sires and pedigree indexes from traditional genetic evaluation, were included in the analysis. Two scenarios were simulated, low heritability (h² = 0.10) and medium heritability (h² = 0.30). GBVs were estimated using the Bayesian Lasso. The accuracy of predicted GBVs using the two-tailed strategies was better than the accuracy obtained using other strategies (0.50 and 0.63 for the two-tailed selection by yield deviations strategy and 0.48 and 0.63 for the two-tailed selection by breeding values strategy in low- and medium-heritability scenarios, respectively, using 1000 genotyped cows). When 996 genotyped bulls were used as the training population, the sire’ strategy led to accuracies of 0.48 and 0.55 for low- and medium-heritability traits, respectively. The Random strategies required larger training populations to outperform the accuracies of the pedigree index; however, selecting females from the top of the yield deviations or breeding values of the population did not improve accuracy relative to that of the pedigree index. Bias was found for all genotyping strategies considered, although the Top strategies produced the most biased predictions. Strategies that involve genotyping cows can be implemented in breeding programs that have a limited number of sires with a reliable progeny test. The results of this study showed that females that exhibited upper and lower extreme values within the distribution of yield deviations may be included as training population to increase reliability in small reference populations. The strategies that selected only the females that had high estimated breeding values or yield deviations produced suboptimal results.     

Effectiveness of genomic prediction on milk flow traits in dairy cattle

Background

Milkability, primarily evaluated by measurements of milking speed and time, has an economic impact in milk production of dairy cattle. Recently the Italian Brown Swiss Breeders Association has included milking speed in genetic evaluations. The main objective of this study was to investigate the possibility of implementing genomic selection for milk flow traits in the Italian Brown Swiss population and thereby evaluate the potential of genomic selection for novel traits in medium-sized populations. Predicted breeding values and reliabilities based on genomic information were compared with those obtained from traditional breeding values.

Methods

Milk flow measures for total milking time, ascending time, time of plateau, descending time, average milk flow and maximum milk flow were collected on 37 213 Italian Brown Swiss cows. Breeding values for genotyped sires (n = 1351) were obtained from standard BLUP and genome-enhanced breeding value techniques utilizing two-stage and single-step methods. Reliabilities from a validation dataset were estimated and used to compare accuracies obtained from parental averages with genome-enhanced predictions.

Results

Genome-enhanced breeding values evaluated using two-stage methods had similar reliabilities with values ranging from 0.34 to 0.49 for the different traits. Across two-stage methods, the average increase in reliability from parental average was approximately 0.17 for all traits, with the exception of descending time, for which reliability increased to 0.11. Combining genomic and pedigree information in a single-step produced the largest increases in reliability over parent averages: 0.20, 0.24, 0.21, 0.14, 0.20 and 0.21 for total milking time, ascending time, time of plateau, descending time, average milk flow and maximum milk flow, respectively.

Conclusions

Using genomic models increased the accuracy of prediction compared to traditional BLUP methods. Our results show that, among the methods used to predict genome-enhanced breeding values, the single-step method was the most successful at increasing the reliability for most traits. The single-step method takes advantage of all the data available, including phenotypes from non-genotyped animals, and can easily be incorporated into current breeding evaluations.     

Accounting for genomic pre-selection in national BLUP evaluations in dairy cattle

Background

In future Best Linear Unbiased Prediction (BLUP) evaluations of dairy cattle, genomic selection of young sires will cause evaluation biases and loss of accuracy once the selected ones get progeny.

Methods

To avoid such bias in the estimation of breeding values, we propose to include information on all genotyped bulls, including the culled ones, in BLUP evaluations. Estimated breeding values based on genomic information were converted into genomic pseudo-performances and then analyzed simultaneously with actual performances. Using simulations based on actual data from the French Holstein population, bias and accuracy of BLUP evaluations were computed for young sires undergoing progeny testing or genomic pre-selection. For bulls pre-selected based on their genomic profile, three different types of information can be included in the BLUP evaluations: (1) data from pre-selected genotyped candidate bulls with actual performances on their daughters, (2) data from bulls with both actual and genomic pseudo-performances, or (3) data from all the genotyped candidates with genomic pseudo-performances. The effects of different levels of heritability, genomic pre-selection intensity and accuracy of genomic evaluation were considered.

Results

Including information from all the genotyped candidates, i.e. genomic pseudo-performances for both selected and culled candidates, removed bias from genetic evaluation and increased accuracy. This approach was effective regardless of the magnitude of the initial bias and as long as the accuracy of the genomic evaluations was sufficiently high.

Conclusions

The proposed method can be easily and quickly implemented in BLUP evaluations at the national level, although some improvement is necessary to more accurately propagate genomic information from genotyped to non-genotyped animals. In addition, it is a convenient method to combine direct genomic, phenotypic and pedigree-based information in a multiple-step procedure.     

Integrating genomic selection into dairy cattle breeding programmes: a review

Extensive genetic progress has been achieved in dairy cattle populations on many traits of economic importance because of efficient breeding programmes. Success of these programmes has relied on progeny testing of the best young males to accurately assess their genetic merit and hence their potential for breeding. Over the last few years, the integration of dense genomic information into statistical tools used to make selection decisions, commonly referred to as genomic selection, has enabled gains in predicting accuracy of breeding values for young animals without own performance. The possibility to select animals at an early stage allows defining new breeding strategies aimed at boosting genetic progress while reducing costs. The first objective of this article was to review methods used to model and optimize breeding schemes integrating genomic selection and to discuss their relative advantages and limitations. The second objective was to summarize the main results and perspectives on the use of genomic selection in practical breeding schemes, on the basis of the example of dairy cattle populations. Two main designs of breeding programmes integrating genomic selection were studied in dairy cattle. Genomic selection can be used either for pre-selecting males to be progeny tested or for selecting males to be used as active sires in the population. The first option produces moderate genetic gains without changing the structure of breeding programmes. The second option leads to large genetic gains, up to double those of conventional schemes because of a major reduction in the mean generation interval, but it requires greater changes in breeding programme structure. The literature suggests that genomic selection becomes more attractive when it is coupled with embryo transfer technologies to further increase selection intensity on the dam-to-sire pathway. The use of genomic information also offers new opportunities to improve preservation of genetic variation. However, recent simulation studies have shown that putting constraints on genomic inbreeding rates for defining optimal contributions of breeding animals could significantly reduce achievable genetic gain. Finally, the article summarizes the potential of genomic selection to include new traits in the breeding goal to meet societal demands regarding animal health and environmental efficiency in animal production.     

Serological diagnosis of neosporosis in a herd of dairy cattle in southern Brazil.

Neospora caninum-specific antibodies were detected in 60 of 172 (34.8%) dairy cattle by enzyme-linked immunosorbent assay (ELISA) in a herd from Parana State, Brazil. The seropositive animals included 47 of 126 (37.3%) adult cows, 7 of 29 (24%) heifers (1-2 yr), 4 of 15 (27%) heifers (5 mo-1 yr), and 2 precolostral samples. Data collected over a 9-yr follow-up period revealed that the proportion of pregnancies ending in abortion was 20% (31/154) among ELISA seropositive cows and 8% (15/193) among seronegative cows. The farm recorded 46 abortions, of which 31 (67.3%) were from seropositive cows. All sera positive by ELISA (n = 60) and sera from cows (n = 11) that were ELISA-negative but that had aborted were tested by the indirect fluorescent antibody test (IFAT) at dilutions from 1:25 to 1:200. All sera from ELISA-positive cows (n = 47) had an IFAT titer of 1:25:35 (74%) of these sera were also seropositive at a dilution of 1:200 (IFAT). Cows seropositive by ELISA had a 4-fold increased risk of having aborted at least once, compared to ELISA-seronegative cows. This association was statistically significant (P = 0.0016). The attributable fraction for this association indicated that approximately 76% of the risk for a cow having a history of abortion was attributable to seroconversion to N. caninum.     

A mating method accounting for inbreeding and multi-trait selection in dairy cattle populations

Selection in dairy cattle populations usually takes into account both the breed profiles for many traits and their overall estimated breeding values (EBV). This can result in effective contributions of breeding animals departing substantially from contributions optimised for saving future genetic variability. In this work, we propose a mating method that considers not only inbreeding but also the detailed EBV of progeny or the EBV of sires in reference to acceptance thresholds. Penalties were defined for inbreeding and for inadequate EBV profiles. Relative reductions of penalties yielded by any mating design were expressed on a scale ranging from 0 to 1. A value of 0 represented the average performance of random matings and a value of 1 represented the maximal reduction allowed by a specialized, single-penalty, mating design. The core of the method was an adaptative simulated annealing, where the maximized function was the average of both ratios, under the constraints that both relative penalty reductions should be equal and that the within-herd concentration criterion should be equal to a predefined reasonable value. The method was tested on two French dairy cattle populations originating from the same AI organization. The optimised mating design allowed substantial reductions of penalty: 70% and 64% for the Holstein and the Normandy populations, respectively. Thus, this mating method decreased inbreeding and met various demands from breeders.     

A simple method for genomic selection of moderately sized dairy cattle populations

An efficient algorithm for genomic selection of moderately sized populations based on single nucleotide polymorphism chip technology is described. A total of 995 Israeli Holstein bulls with genetic evaluations based on daughter records were genotyped for either the BovineSNP50 BeadChip or the BovineSNP50 v2 BeadChip. Milk, fat, protein, somatic cell score, female fertility, milk production persistency and herd-life were analyzed. The 400 markers with the greatest effects on each trait were first selected based on individual analysis of each marker with the genetic evaluations of the bulls as the dependent variable. The effects of all 400 markers were estimated jointly using a 'cow model,' estimated from the data truncated to exclude lactations with freshening dates after September 2006. Genotype probabilities for each locus were computed for all animals with missing genotypes. In Method I, genetic evaluations were computed by analysis of the truncated data set with the sum of the marker effects subtracted from each record. Genomic estimated breeding values for the young bulls with genotypes, but without daughter records, were then computed as their parent averages combined with the sum of each animal's marker effects. Method II genomic breeding values were computed based on regressions of estimated breeding values of bulls with daughter record on their parent averages, sum of marker effects and birth year. Method II correlations of the current breeding values of young bulls without daughter records in the truncated data set were higher than the correlations of the current breeding values with the parent averages for fat and protein production, persistency and herd-life. Bias of evaluations, estimated as a difference between the mean of current breeding values of the young bulls and their genomic evaluations, was reduced for milk production traits, persistency and herd-life. Bias for milk production traits was slightly negative, as opposed to the positive bias of parent averages. Correlations of Method II with the means of daughter records adjusted for fixed effects were higher than parent averages for fat, protein, fertility, persistency and herd-life. Reducing the number of markers included in the analysis from 400 to 300 did not reduce correlations of genomic breeding values for protein with current breeding values, but did slightly reduce correlations with means of daughter records. Method II has the advantages as compared with the method of VanRaden in that genotypes of cows can be readily incorporated into the Method II analysis, and it is more effective for moderately sized populations.     

Review: How to improve genomic predictions in small dairy cattle populations

This paper reviews strategies and methods to improve accuracies of genomic predictions from the perspective of a numerically small population. Improvements are realized by influencing one or both of the main factors: (1) improve or increase genomic connections to phenotypic records in training data. (2) Models and strategies to focus genomic predictions on markers closer to the causative variants. Combining populations into a joint reference population results in high improvements when combining populations of the same breed and diminishes as the genetic distance between populations increases. For distantly related breeds sophisticated Bayesian variable selection models in combination with denser markers sets or functional subsets of markers is needed. This is expected to be further improved by the efficient use of sequence information. In addition predictions can be improved by the use of phenotypes of genotyped and non-genotyped cows directly. For a small population the optimal approach will combine the above components.     

Identification of genomic regions associated with inbreeding depression in Holstein and Jersey dairy cattle

Background

Inbreeding reduces the fitness of individuals by increasing the frequency of homozygous deleterious recessive alleles. Some insight into the genetic architecture of fitness, and other complex traits, can be gained by using single nucleotide polymorphism (SNP) data to identify regions of the genome which lead to reduction in performance when identical by descent (IBD). Here, we compared the effect of genome-wide and location-specific homozygosity on fertility and milk production traits in dairy cattle.

Methods

Genotype data from more than 43 000 SNPs were available for 8853 Holstein and 4138 Jersey dairy cows that were part of a much larger dataset that had pedigree records (338 696 Holstein and 64 049 Jersey animals). Measures of inbreeding were based on: (1) pedigree data; (2) genotypes to determine the realised proportion of the genome that is IBD; (3) the proportion of the total genome that is homozygous and (4) runs of homozygosity (ROH) which are stretches of the genome that are homozygous.

Results

A 1% increase in inbreeding based either on pedigree or genomic data was associated with a decrease in milk, fat and protein yields of around 0.4 to 0.6% of the phenotypic mean, and an increase in calving interval (i.e. a deterioration in fertility) of 0.02 to 0.05% of the phenotypic mean. A genome-wide association study using ROH of more than 50 SNPs revealed genomic regions that resulted in depression of up to 12.5 d and 260 L for calving interval and milk yield, respectively, when completely homozygous.

Conclusions

Genomic measures can be used instead of pedigree-based inbreeding to estimate inbreeding depression. Both the diagonal elements of the genomic relationship matrix and the proportion of homozygous SNPs can be used to measure inbreeding. Longer ROH (>3 Mb) were found to be associated with a reduction in milk yield and captured recent inbreeding independently and in addition to overall homozygosity. Inbreeding depression can be reduced by minimizing overall inbreeding but maybe also by avoiding the production of offspring that are homozygous for deleterious alleles at specific genomic regions that are associated with inbreeding depression.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-014-0071-7) contains supplementary material, which is available to authorized users.     

Predicted accuracy of and response to genomic selection for new traits in dairy cattle

Genomic selection relaxes the requirement of traditional selection tools to have phenotypic measurements on close relatives of all selection candidates. This opens up possibilities to select for traits that are difficult or expensive to measure. The objectives of this paper were to predict accuracy of and response to genomic selection for a new trait, considering that only a cow reference population of moderate size was available for the new trait, and that selection simultaneously targeted an index and this new trait. Accuracy for and response to selection were deterministically evaluated for three different breeding goals. Single trait selection for the new trait based only on a limited cow reference population of up to 10 000 cows, showed that maximum genetic responses of 0.20 and 0.28 genetic standard deviation (s.d.) per year can be achieved for traits with a heritability of 0.05 and 0.30, respectively. Adding information from the index based on a reference population of 5000 bulls, and assuming a genetic correlation of 0.5, increased genetic response for both heritability levels by up to 0.14 genetic s.d. per year. The scenario with simultaneous selection for the new trait and the index, yielded a substantially lower response for the new trait, especially when the genetic correlation with the index was negative. Despite the lower response for the index, whenever the new trait had considerable economic value, including the cow reference population considerably improved the genetic response for the new trait. For scenarios with a zero or negative genetic correlation with the index and equal economic value for the index and the new trait, a reference population of 2000 cows increased genetic response for the new trait with at least 0.10 and 0.20 genetic s.d. per year, for heritability levels of 0.05 and 0.30, respectively. We conclude that for new traits with a very small or positive genetic correlation with the index, and a high positive economic value, considerable genetic response can already be achieved based on a cow reference population with only 2000 records, even when the reliability of individual genomic breeding values is much lower than currently accepted in dairy cattle breeding programs. New traits may generally have a negative genetic correlation with the index and a small positive economic value. For such new traits, cow reference populations of at least 10 000 cows may be required to achieve acceptable levels of genetic response for the new trait and for the whole breeding goal.     

Field crickets change mating preferences using remembered social information

Plasticity in female mate choice can fundamentally alter selection on male ornaments, but surprisingly few studies have examined the role of social learning in shaping female mating decisions in invertebrates. We used the field cricket Teleogryllus oceanicus to show that females retain information about the attractiveness of available males based on previous social experience, compare that information with incoming signals and then dramatically reverse their preferences to produce final, predictable, mating decisions. Male ornament evolution in the wild may depend much more on the social environment and behavioural flexibility through learning than was previously thought for non-social invertebrates. The predictive power of these results points to a pressing need for theoretical models of sexual selection that incorporate effects of social experience.     

A stochastic model for disease transmission in a managed herd, motivated by Neospora caninum amongst dairy cattle

A stochastic model for the spread of Neospora caninum infection within a herd of dairy cattle is studied, in particular the long-term (equilibrium) behaviour of the model. The model incorporates the interesting feature that total herd size is constrained to lie within a fairly small interval, but not held exactly constant. Approximations for the joint distribution of numbers of susceptible and infected individuals present in equilibrium are derived based upon a diffusion approximation to the infection process. The effect of both 'typical herd size' and 'the amount of permitted variation in herd size' upon disease prevalence in equilibrium are considered using both the exact equilibrium distribution of the process and our approximations.     

Clustering of diverse genomic data using information fusion

MOTIVATION: Genome sequencing projects and high-through-put technologies like DNA and Protein arrays have resulted in a very large amount of information-rich data. Microarray experimental data are a valuable, but limited source for inferring gene regulation mechanisms on a genomic scale. Additional information such as promoter sequences of genes/DNA binding motifs, gene ontologies, and location data, when combined with gene expression analysis can increase the statistical significance of the finding. This paper introduces a machine learning approach to information fusion for combining heterogeneous genomic data. The algorithm uses an unsupervised joint learning mechanism that identifies clusters of genes using the combined data. RESULTS: The correlation between gene expression time-series patterns obtained from different experimental conditions and the presence of several distinct and repeated motifs in their upstream sequences is examined here using publicly available yeast cell-cycle data. The results show that the combined learning approach taken here identifies correlated genes effectively. The algorithm provides an automated clustering method, but allows the user to specify apriori the influence of each data type on the final clustering using probabilities. AVAILABILITY: Software code is available by request from the first author. CONTACT: jkasturi@cse.psu.edu.