Quantification of differences in resistance to gastrointestinal nematode infections in sheep using a multivariate blood parameter.

Breeding for resistance to gastrointestinal nematodes (GIN) in sheep relies largely on the use of worm egg counts (WEC) to identify animals that are able to resist infection. As an alternative to such measures of parasite load we aimed to develop a method to identify animals showing resistance to GIN infection based on the impact of the infection on blood parameters. We hypothesized that blood parameters may provide a measure of infection level with a blood-feeding parasite through perturbation of red blood cell parameters due to feeding behaviour of the parasite, and white blood cell parameters through the mounting of an immune response in the host animal. We measured a set of blood parameters in 390 sheep that had been exposed to an artificial regime of repeated challenges with Trichostrongylus colubriformis followed by Haemonchus contortus. A simple analysis revealed strong relationships between single blood parameters and WECs with correlation coefficients -0.54 to -0.60. We then used more complex multi-variate methods based on supervised classifier models (including Bayesian Network) as well as regression models (Lasso and Elastic Net) to study the relationships between WECs and blood parameters, and derived algorithms describing the relationships. The ability of these algorithms to classify sheep GIN resistance status was tested using the WEC and blood parameters collected from a different group of 418 sheep that had acquired natural infections of H. contortus from pasture. We identified the most resistant and most susceptible animals (10% percentiles) of this group based on WECs, and then compared the identities of these animals to the identities of animals that were predicted to be most resistant and most susceptible by our algorithms. The models showed varying abilities to predict susceptible and resistant sheep, with up to 65% of the most susceptible animals and 30% of the most resistant animals identified by the Elastic Net model algorithms. The prediction algorithms derived from female sheep data performed better than those for male sheep in some cases, with the predicted animals accounting for up to 50-60% of the actual resistant and susceptible female animals. Heritability values were calculated for blood parameters and the aggregate trait descriptions defined by the novel prediction algorithms. The aggregate trait descriptions were moderately heritable and may therefore be suitable for use in genetic selection strategies. The present study indicates that multivariate models based on blood parameter data showed some ability to predict the resistance status of sheep to infection with H. contortus.

Machine learning approach for pooled DNA sample calibration.

BACKGROUND: Despite ongoing reduction in genotyping costs, genomic studies involving large numbers of species with low economic value (such as Black Tiger prawns) remain cost prohibitive. In this scenario DNA pooling is an attractive option to reduce genotyping costs. However, genotyping of pooled samples comprising DNA from many individuals is challenging due to the presence of errors that exceed the allele frequency quantisation size and therefore cannot be simply corrected by clustering techniques. The solution to the calibration problem is a correction to the allele frequency to mitigate errors incurred in the measurement process. We highlight the limitations of the existing calibration solutions such as the fact they impose assumptions on the variation between allele frequencies 0, 0.5, and 1.0, and address a limited set of error types. We propose a novel machine learning method to address the limitations identified. RESULTS: The approach is tested on SNPs genotyped with the Sequenom iPLEX platform and compared to existing state of the art calibration methods. The new method is capable of reducing the mean square error in allele frequency to half that achievable with existing approaches. Furthermore for the first time we demonstrate the importance of carefully considering the choice of training data when using calibration approaches built from pooled data. CONCLUSION: This paper demonstrates that improvements in pooled allele frequency estimates result if the genotyping platform is characterised at allele frequencies other than the homozygous and heterozygous cases. Techniques capable of incorporating such information are described along with aspects of implementation.

Automated data quality assessment of marine sensors.

The automated collection of data (e.g., through sensor networks) has led to a massive increase in the quantity of environmental and other data available. The sheer quantity of data and growing need for real-time ingestion of sensor data (e.g., alerts and forecasts from physical models) means that automated Quality Assurance/Quality Control (QA/QC) is necessary to ensure that the data collected is fit for purpose. Current automated QA/QC approaches provide assessments based upon hard classifications of the gathered data; often as a binary decision of good or bad data that fails to quantify our confidence in the data for use in different applications. We propose a novel framework for automated data quality assessments that uses Fuzzy Logic to provide a continuous scale of data quality. This continuous quality scale is then used to compute error bars upon the data, which quantify the data uncertainty and provide a more meaningful measure of the data's fitness for purpose in a particular application compared with hard quality classifications. The design principles of the framework are presented and enable both data statistics and expert knowledge to be incorporated into the uncertainty assessment. We have implemented and tested the framework upon a real time platform of temperature and conductivity sensors that have been deployed to monitor the Derwent Estuary in Hobart, Australia. Results indicate that the error bars generated from the Fuzzy QA/QC implementation are in good agreement with the error bars manually encoded by a domain expert.