Biomedical subjects

J M Henshall

Publications and source records attributed to J M Henshall.

5 recordsLinked to original sources

PubMed2006

Adjusting worm egg counts for faecal moisture in sheep.

The number of eggs from gastrointestinal nematodes per gram of faeces (worm egg count WEC) is commonly used to determine the need for anti-parasite treatments and the breeding value of animals when selecting for worm resistance. Diarrhoea increases faecal moisture and may dilute the number of worm eggs observed. To quantify this effect, egg counts in sheep at pasture were simulated by dosing 15 animals with chromic oxide particles. The simulated WEC diminished as faecal moisture increased. When faeces were dried, simulated WEC per unit dry matter was not influenced by the amount of faecal moisture present prior to drying. The results suggest that adjustment for faecal moisture may provide an improved estimate of FEC. Drying faeces to calculate the WEC per unit dry matter would provide such an adjustment but may not be practical for industry application. In the past, the CSIRO McMaster Laboratory has used an adjustment factor developed by Gordon based on the classification of faecal consistency derived from the morphology of faeces. To examine the utility of an adjustment factor based on faecal consistency score (FCS), the relationships between FCS and simulated WEC and dry matter were examined. Dry matter and simulated WEC exhibited an exponential decline as FCS increased. The relationship between FCS and dry matter was further examined in 368 samples collected over 12 months from sheep at pasture, where it was observed that dry matter showed a linear decline as FCS increased. Adjustment factors based on dry matter were similar to those proposed by Gordon however adjustment factors predicted from simulated WEC diverged from the remainder for FCS>4. As no samples scored FCS 5 in the study of simulated FEC, the adjustment factors based on the larger study that included samples with FCS 5 was therefore considered more robust. Adjustment factors were given by the equation: WEC(estimated)=(WEC(observed)/(34.21-5.15 FCS))x29.06. This equation estimates for samples with FCS>1 the WEC that would be expected if the samples were FCS 1, the faecal consistency score for normal faeces. The impact of adjustment of observed WEC for faecal moisture predicted by FCS on decision points for treatment and on estimated breeding values requires further examination.

Animals↗

PubMed2001

Estimating genotypes with independently sampled descent graphs.

A method for estimating genotypic and identity-by-descent probabilities in complex pedigrees is described. The method consists of an algorithm for drawing independent genotype samples which are consistent with the pedigree and observed genotype. The probability distribution function for samples obtained using the algorithm can be evaluated up to a normalizing constant, and combined with the likelihood to produce a weight for each sample. Importance sampling is then used to estimate genotypic and identity-by-descent probabilities. On small but complex pedigrees, the genotypic probability estimates are demonstrated to be empirically unbiased. On large complex pedigrees, while the algorithm for obtaining genotype samples is feasible, importance sampling may require an infeasible number of samples to estimate genotypic probabilities with accuracy.

Algorithms↗

PubMed1999

Multiple-trait mapping of quantitative trait loci after selective genotyping using logistic regression.

Experiments to map QTL usually measure several traits, and not uncommonly genotype only those animals that are extreme for some trait(s). Analysis of selectively genotyped, multiple-trait data presents special problems, and most simple methods lead to biased estimates of the QTL effects. The use of logistic regression to estimate QTL effects is described, where the genotype is treated as the dependent variable and the phenotype as the independent variable. In this way selection on phenotype does not bias the results. If normally distributed errors are assumed, the logistic-regression analysis is almost equivalent to a maximum-likelihood analysis, but can be carried out with standard statistical packages. Analysis of a simulated half-sib experiment shows that logistic regression can estimate the effect and position of a QTL without bias and confirms the increased power achieved by multiple-trait analysis.

Genotype↗

PubMed1991

Effects of Cordyceps sinensis on murine T lymphocyte subsets.

It was shown by flow cytometry analysis that crystalized preparation of Cordyceps sinensis (Cs-Cr) caused significant elevation of the number of T helper cells and Lyt-1/Lyt-2 (T helper to T suppressor cell) ratio both in peripheral blood and the treated mice spleen. The spleen weight, phagocyte counts and phagocytic activity were also elevated in the treated group. In addition, Cs-Cr could protect T helper cells from the immunosuppressive effects of prednisolone acetate and cyclophosphamide. These results further substantiate the fact that Cs-Cr is an immunoregulator/biological response modifier of cellular immunity and may be potentially useful in handling immunodeficient or immunosuppressed patients.

Animals↗

PubMedDate not supplied

Use of the EM algorithm to detect QTL affecting multiple-traits in an across half-sib family analysis.

QTL detection experiments in livestock species commonly use the half-sib design. Each male is mated to a number of females, each female producing a limited number of progeny. Analysis consists of attempting to detect associations between phenotype and genotype measured on the progeny. When family sizes are limiting experimenters may wish to incorporate as much information as possible into a single analysis. However, combining information across sires is problematic because of incomplete linkage disequilibrium between the markers and the QTL in the population. This study describes formulae for obtaining MLEs via the expectation maximization (EM) algorithm for use in a multiple-trait, multiple-family analysis. A model specifying a QTL with only two alleles, and a common within sire error variance is assumed. Compared to single-family analyses, power can be improved up to fourfold with multi-family analyses. The accuracy and precision of QTL location estimates are also substantially improved. With small family sizes, the multi-family, multi-trait analyses reduce substantially, but not totally remove, biases in QTL effect estimates. In situations where multiple QTL alleles are segregating the multi-family analysis will average out the effects of the different QTL alleles.

Algorithms↗