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PubMed2002

Multivariate analysis of litter size for multiple parities with production traits in pigs: II. Response to selection for litter size and correlated response to production traits.

Litter size and production trait responses to experimental selection for increased litter size in a Landrace pig population are reported. The numbers of sows and litters available for the first cycle of selection were 3,034 and 961, respectively. Selection was carried out using a BLUP repeatability animal model for number of piglets born alive (NBA). The experiment included one selection and one control line, each with three nonoverlapping generations. The selection line (H) consisted of the 160 sows with the highest breeding values and one boar from each of 25 full-sib families with the highest breeding values. The control line (C) consisted of 160 sows and 25 boars randomly chosen. The two subsequent generations in each line were obtained by random selection. A Bayesian analysis of genetic response using a multivariate model was carried out by Gibbs sampler. Marginal posterior distributions were obtained for direct response in NBA, and for correlated response in weight (WT), and backfat thickness (BT) at 175 d of age. The posterior means and posterior standard deviation (PSD) for direct genetic response of NBA ranged from 0.32 (PSD 0.08) in the first parity to 0.64 (PSD 0.08) in the fourth. The posterior means for correlated genetic response in WT and BT were -0.66 kg (PSD 0.36) and 0.20 mm (PSD 0.10), respectively. For WT and BT, the 95% highest posterior density regions (HPD) contain zero-correlated genetic response. Marginal posterior distributions of selection differentials were investigated. The posterior means for standardized selection differentials for NBA in different parities ranged from 0.70 (PSD 0.12) to 0.94 (PSD 0.06) in females for line H, from 0.22 (PSD 0.19) to 0.34 (PSD 0.10) in males for line H, and from 0.08 (PSD 0.08) to 0.13 (PSD 0.07) in females for line C. All available males were used in line C. Results from this experiment showed that selection for increased litter size is effective. Responses to selection were heterogeneous across parities, suggesting that litter size in each parity may have a different genetic background. No correlated genetic response to growth and backfat thickness was observed.

Adipose Tissue↗

PubMed2006

Genomic-assisted prediction of genetic value with semiparametric procedures.

Semiparametric procedures for prediction of total genetic value for quantitative traits, which make use of phenotypic and genomic data simultaneously, are presented. The methods focus on the treatment of massive information provided by, e.g., single-nucleotide polymorphisms. It is argued that standard parametric methods for quantitative genetic analysis cannot handle the multiplicity of potential interactions arising in models with, e.g., hundreds of thousands of markers, and that most of the assumptions required for an orthogonal decomposition of variance are violated in artificial and natural populations. This makes nonparametric procedures attractive. Kernel regression and reproducing kernel Hilbert spaces regression procedures are embedded into standard mixed-effects linear models, retaining additive genetic effects under multivariate normality for operational reasons. Inferential procedures are presented, and some extensions are suggested. An example is presented, illustrating the potential of the methodology. Implementations can be carried out after modification of standard software developed by animal breeders for likelihood-based or Bayesian analysis.

Algorithms↗

PubMed2000

Baseline risk as predictor of treatment benefit: three clinical meta-re-analyses.

A relationship between baseline risk and treatment effect is increasingly investigated as a possible explanation of between-study heterogeneity in clinical trial meta-analysis. An approach that is still often applied in the medical literature is to plot the estimated treatment effects against the estimated measures of risk in the control groups (as a measure of baseline risk), and to compute the ordinary weighted least squares regression line. However, it has been pointed out by several authors that this approach can be seriously flawed. The main problem is that the observed treatment effect and baseline risk measures should be viewed as estimates rather than the true values. In recent years several methods have been proposed in the statistical literature to potentially deal with the measurement errors in the estimates. In this article we propose a vague priors Bayesian solution to the problem which can be carried out using the 'Bayesian inference using Gibbs sampling' (BUGS) implementation of Markov chain Monte Carlo numerical integration techniques. Different from other proposed methods, it uses the exact rather than an approximate likelihood, while it can handle many different treatment effect measures and baseline risk measures. The method differs from a recently proposed Bayesian method in that it explicitly models the distribution of the underlying baseline risks. We apply the method to three meta-analyses published in the medical literature and compare the results with the outcomes of the other recently proposed methods. In particular we compare our approach to McIntosh's method, for which we show how it can be carried out using standard statistical software. We conclude that our proposed method offers a very general and flexible solution to the problem, which can be carried out relatively easily with existing Bayesian analysis software. A confidence band for the underlying relationship between true effect measure and baseline risk and a confidence interval for the value of the baseline risk measure for which there is no treatment effect are easily obtained by-products of our approach.

Bayes Theorem↗

PubMed2004

When can a veterinarian be expected to detect classical swine fever virus among breeding sows in a herd during an outbreak?

The herd sensitivity (HSe) and herd specificity (Hsp) of clinical diagnosis of an infection with classical swine fever (CSF) virus during veterinary inspection of breeding sows in a herd was evaluated. Data gathered from visits to herds during the CSF outbreak in 1997-1998 in The Netherlands were used for the analysis. Herds were visited one or more times by the same or by different veterinarians. On the basis of the veterinarians' reports, each visit was coded as 0 (negative clinical diagnosis) or 1 (positive clinical diagnosis). The HSe for clinical diagnosis of CSF was modelled as a function of days elapsed since introduction of the virus. The moment of introduction of the CSF virus in the CSF-positive herds was unknown, so for each herd, a probability distribution for the unknown number of days since introduction was derived from serum samples collected at depopulation. The information from the reports of the veterinarians and from the test results of the serum samples at depopulation was combined in a Bayesian analysis. Data from CSF-negative herds were analysed to estimate HSp of clinical diagnosis of CSF. The HSe of clinical diagnosis was 0.5 at 37 days after virus introduction (95% CI: 31, 45) and reached 0.9 at 47 days after virus introduction (95% CI: 41, 54). The estimated herd specificity was 0.72 (95% CI: 0.64, 0.79). Dependence of HSe and HSp on characteristics of the veterinarians and the herds also was studied. Specialisation of the veterinarian significantly, although not markedly, affected the HSe.

Animal Husbandry↗

PubMed2003

Shrinkage-based similarity metric for cluster analysis of microarray data.

The current standard correlation coefficient used in the analysis of microarray data was introduced by M. B. Eisen, P. T. Spellman, P. O. Brown, and D. Botstein [(1998) Proc. Natl. Acad. Sci. USA 95, 14863-14868]. Its formulation is rather arbitrary. We give a mathematically rigorous correlation coefficient of two data vectors based on James-Stein shrinkage estimators. We use the assumptions described by Eisen et al., also using the fact that the data can be treated as transformed into normal distributions. While Eisen et al. use zero as an estimator for the expression vector mean mu, we start with the assumption that for each gene, mu is itself a zero-mean normal random variable [with a priori distribution N(0,tau 2)], and use Bayesian analysis to obtain a posteriori distribution of mu in terms of the data. The shrunk estimator for mu differs from the mean of the data vectors and ultimately leads to a statistically robust estimator for correlation coefficients. To evaluate the effectiveness of shrinkage, we conducted in silico experiments and also compared similarity metrics on a biological example by using the data set from Eisen et al. For the latter, we classified genes involved in the regulation of yeast cell-cycle functions by computing clusters based on various definitions of correlation coefficients and contrasting them against clusters based on the activators known in the literature. The estimated false positives and false negatives from this study indicate that using the shrinkage metric improves the accuracy of the analysis.

Algorithms↗

PubMed2003

Association of tumor necrosis factor gene polymorphisms and prolonged mechanical ventilation after coronary artery bypass surgery.

OBJECTIVE: Prolonged mechanical ventilation is a common complication after coronary artery bypass graft surgery. Tumor necrosis factor alpha is an important proinflammatory mediator in the post-coronary artery bypass graft inflammatory cascade. We attempted to study the effect of polymorphisms at the -308 site in the promoter region of the tumor necrosis factor gene (TNF-308) and the +250 site within the lymphotoxin-alpha gene (LT alpha+250) on the risk of prolonged mechanical ventilation after coronary artery bypass grafting. DESIGN: Prospective observational study. SETTING: Tertiary care center. PATIENTS: A total of 400 patients undergoing coronary artery bypass grafting were enrolled. MEASUREMENTS: The primary end point was time to extubate. Secondary end points were the percentages of patients extubated at 8, 24, and 48 hrs; the length of intensive care unit and hospital stay; the need for a rehabilitation facility; and 30-day mortality. Precollected blood was used for gene analysis. Genotyping was performed by polymerase chain reaction and restriction enzyme digestion. MAIN RESULTS: Patients with an AA genotype at the LT alpha+250 site and those without the LT alpha+250G/-308TNFG haplotype had a shorter duration of mechanical ventilation (11.5 vs. 27.8 hrs and 11.2 vs. 29.4 hrs; =.039 and.01, respectively). The risk of prolonged mechanical ventilation at 8, 24, and 48 hrs was higher for patients with a GA or GG genotype at the LT alpha+250 site and the LT alpha+250G/TNF-308G haplotype. This association between genotype and duration of mechanical ventilation was more dramatic in patients undergoing conventional coronary artery bypass grafting than in those undergoing off-pump coronary artery bypass grafting. With Bayesian analysis, clinical criteria and genotype can be used sequentially to predict the risk of prolonged mechanical ventilation. CONCLUSIONS: The LT alpha+250 and LT alpha+250G/TNF-308G haplotypes are associated with prolonged mechanical ventilation after coronary artery bypass graft. Preoperative genetic screening may guide intraoperative management to reduce postoperative complications.

Bayes Theorem↗

PubMed2006

Complete genome analysis of 33 ecologically and biologically diverse Rift Valley fever virus strains reveals widespread virus movement and low genetic diversity due to recent common ancestry.

Rift Valley fever (RVF) virus is a mosquito-borne RNA virus responsible for large explosive outbreaks of acute febrile disease in humans and livestock in Africa with significant mortality and economic impact. The successful high-throughput generation of the complete genome sequence was achieved for 33 diverse RVF virus strains collected from throughout Africa and Saudi Arabia from 1944 to 2000, including strains differing in pathogenicity in disease models. While several distinct virus genetic lineages were determined, which approximately correlate with geographic origin, multiple exceptions indicative of long-distance virus movement have been found. Virus strains isolated within an epidemic (e.g., Mauritania, 1987, or Egypt, 1977 to 1978) exhibit little diversity, while those in enzootic settings (e.g., 1970s Zimbabwe) can be highly diverse. In addition, the large Saudi Arabian RVF outbreak in 2000 appears to have involved virus introduction from East Africa, based on the close ancestral relationship of a 1998 East African virus. Virus genetic diversity was low (approximately 5%) and primarily involved accumulation of mutations at an average of 2.9 x 10(-4) substitutions/site/year, although some evidence of RNA segment reassortment was found. Bayesian analysis of current RVF virus genetic diversity places the most recent common ancestor of these viruses in the late 1800s, the colonial period in Africa, a time of dramatic changes in agricultural practices and introduction of nonindigenous livestock breeds. In addition to insights into the evolution and ecology of RVF virus, these genomic data also provide a foundation for the design of molecular detection assays and prototype vaccines useful in combating this important disease.

Animals↗

PubMed2005

Two phylogenetically highly distinct beta-tubulin genes of the basidiomycete Suillus bovinus.

Genes tubb1 and tubb2 which encode beta-tubulins 1 and 2, respectively, were characterised from the ectomycorrhizal basidiomycete Suillus bovinus. The two beta-tubulins are surprisingly divergent, with the lowest known sequence identity (60%) in any single fungal species. Comparative analysis showed that beta-tubulin 1 and the intron distribution within the tubb1 gene resemble the other beta-tubulins. beta-Tubulin 2, in contrast, is the most divergent fully described fungal beta-tubulin and the gene contains at least 21 introns, which is the largest amount known for any beta-tubulin gene. Despite this divergence, both genes are constitutively expressed in the functional compartments of the mycorrhizosphere and in pure cultures. Transcription of tubb1 is about 2.4 times higher than that of tubb2; and this difference is also seen at the translation level. Evidence suggested that phosphorylation may be the main post-translational modification of both beta-tubulins. The putative GTP-binding site residues of beta-tubulin 1 match crystallised pig beta-tubulin residues, while five of the nine differences in beta-tubulin 2 match the pig alpha-tubulin GTP-site, suggesting the presence of adaptive sequence evolution. In a Bayesian analysis, beta-tubulin 1 joins the other basidiomycete sequences, while beta-tubulin 2 loosely associates with the group of divergent ascomycete sequences without any clear relative among the known full-length fungal beta-tubulin sequences.

Amino Acid Sequence↗

PubMed2006

A molecular assessment of phylogenetic relationships and lineage accumulation rates within the family Salamandridae (Amphibia, Caudata).

We examine phylogenetic relationships among salamanders of the family Salamandridae using approximately 2700 bases of new mtDNA sequence data (the tRNALeu, ND1, tRNAIle, tRNAGln, tRNAMet, ND2, tRNATrp, tRNAAla, tRNAAsn, tRNACys, tRNATyr, and COI genes and the origin for light-strand replication) collected from 96 individuals representing 61 of the 66 recognized salamandrid species and outgroups. Phylogenetic analyses using maximum parsimony and Bayesian analysis are performed on the new data alone and combined with previously reported sequences from other parts of the mitochondrial genome. The basal phylogenetic split is a polytomy of lineages ancestral to (1) the Italian newt Salamandrina terdigitata, (2) a strongly supported clade comprising the "true" salamanders (genera Chioglossa, Mertensiella, Lyciasalamandra, and Salamandra), and (3) a strongly supported clade comprising all newts except S. terdigitata. Strongly supported clades within the true salamanders include monophyly of each genus and grouping Chioglossa and Mertensiella as the sister taxon to a clade comprising Lyciasalamandra and Salamandra. Among newts, genera Echinotriton, Pleurodeles, and Tylototriton form a strongly supported clade whose sister taxon comprises the genera Calotriton, Cynops, Euproctus, Neurergus, Notophthalmus, Pachytriton, Paramesotriton, Taricha, and Triturus. Our results strongly support monophyly of all polytypic newt genera except Paramesotriton and Triturus, which appear paraphyletic, and Calotriton, for which only one of the two species is sampled. Other well-supported clades within newts include (1) Asian genera Cynops, Pachytriton, and Paramesotriton, (2) North American genera Notophthalmus and Taricha, (3) the Triturus vulgaris species group, and (4) the Triturus cristatus species group; some additional groupings appear strong in Bayesian but not parsimony analyses. Rates of lineage accumulation through time are evaluated using this nearly comprehensive sampling of salamandrid species-level lineages. Rate of lineage accumulation appears constant throughout salamandrid evolutionary history with no obvious fluctuations associated with origins of morphological or ecological novelties.

Animals↗

PubMed2004

Accuracy of cDNA microarray methods to detect small gene expression changes induced by neuregulin on breast epithelial cells.

BACKGROUND: cDNA microarrays are a powerful means to screen for biologically relevant gene expression changes, but are often limited by their ability to detect small changes accurately due to "noise" from random and systematic errors. While experimental designs and statistical analysis methods have been proposed to reduce these errors, few studies have tested their accuracy and ability to identify small, but biologically important, changes. Here, we have compared two cDNA microarray experimental design methods with northern blot confirmation to reveal changes in gene expression that could contribute to the early antiproliferative effects of neuregulin on MCF10AT human breast epithelial cells. RESULTS: We performed parallel experiments on identical samples using a dye-swap design with ANOVA and an experimental design that excludes systematic biases by "correcting" experimental/control hybridization ratios with control/control hybridizations on a spot-by-spot basis. We refer to this approach as the "control correction method" (CCM). Using replicate arrays, we identified a decrease in proliferation genes and an increase in differentiation genes. Using an arbitrary cut-off of 1.7-fold and p values <0.05, we identified a total of 32 differentially expressed genes, 9 with the dye-swap method, 18 with the CCM, and 5 genes with both methods. 23 of these 32 genes were subsequently verified by northern blotting. Most of these were <2-fold changes. While the dye-swap method (using either ANOVA or Bayesian analysis) detected a smaller number of genes (14-16) compared to the CCM (46), it was more accurate (89-92% vs. 75%). Compared to the northern blot results, for most genes, the microarray results underestimated the fold change, implicating the importance of detecting these small changes. CONCLUSIONS: We validated two experimental design paradigms for cDNA microarray experiments capable of detecting small (<2-fold) changes in gene expression with excellent fidelity that revealed potentially important genes associated with the anti-proliferative effects of neuregulin on MCF10AT breast epithelial cells.

Blotting, Northern↗

PubMed1998

More powerful randomization-based p-values in double-blind trials with non-compliance.

Standard randomization-based tests of sharp null hypotheses in randomized clinical trials, that is, intent-to-treat analyses, are valid without extraneous assumptions, but generally can be appropriately powerful only with alternative hypotheses that involve treatment assignment having an effect on outcome. In the context of clinical trials with non-compliance, other alternative hypotheses can be more natural. In particular, when a trial is double-blind, it is often reasonable for the alternative hypothesis to exclude any effect of treatment assignment on outcome for a unit unless the assignment affected which treatment that unit actually received. Bayesian analysis under this alternative 'exclusion' hypothesis leads to new estimates of the effect of receipt of treatment, and to a new randomization-based procedure that has frequentist validity yet can be substantially more powerful than the standard intent-to-treat procedure. The key idea is to obtain a p-value using a posterior predictive check distribution, which includes a model for non-compliance behaviour, although only under the standard sharp null hypothesis of no effect of assignment (or receipt) of treatment on outcome. It is important to note that these new procedures are distinctly different from 'as treated' and 'per protocol' analyses, which are not only badly biased in general, but generally have very low power.

Data Interpretation, Statistical↗

PubMed1999

Applying Bayesian ideas to the development of medical guidelines.

Measurements of the quality of health care, in particular the underuse and overuse of medical therapies and diagnostic tests, often involve employment of medical practice guidelines to assess the appropriateness of treatments. This paper presents a case study of a Bayesian analysis for the development of medical guidelines based on expert opinion, using ordinal categorical rater data. We develop guidelines for the use of coronary angiography following an acute myocardial infarction (AMI) for 890 clinical indications using statistical models fit to appropriateness ratings obtained from a nine-member expert panel. The main foci of our analyses were on the estimation of an appropriateness score for each of the clinical indications, an associated measure of precision, and functions of the underlying score. We considered two classes of models that assume the ratings are either in the form of grouped normal data or are ungrouped variables arising from a normal distribution, while permitting rater effects and indication heterogeneity in both. We estimated models using Markov chain Monte Carlo methods and constructed indices quantifying appropriateness based on posterior probabilities of selected model parameters. We compared our model-based approach to the standard approach currently employed in medical guideline development and found that the standard approach correctly identified 99 per cent of the appropriate indications while overestimating appropriateness 18 per cent of the time compared to our model-based approach.

Bayes Theorem↗

PubMedDate not supplied

Bayesian modelling of inseparable space-time variation in disease risk.

This paper proposes a unified framework for a Bayesian analysis of incidence or mortality data in space and time. We introduce four different types of prior distributions for space x time interaction in extension of a model with only main effects. Each type implies a certain degree of prior dependence for the interaction parameters, and corresponds to the product of one of the two spatial with one of the two temporal main effects. The methodology is illustrated by an analysis of Ohio lung cancer data 1968-1988 via Markov chain Monte Carlo simulation. We compare the fit and the complexity of several models with different types of interaction by means of quantities related to the posterior deviance. Our results confirm an epidemiological hypothesis about the temporal development of the association between urbanization and risk factors for cancer.

Bayes Theorem↗

PubMed2000

A Bayesian approach to economic analyses of clinical trials: the case of stenting versus balloon angioplasty.

New results about the costs and effects of a new therapy may be weighted with prior information. As such, classical confidence intervals surrounding the costs and effects of a therapy may not reflect the real uncertainties. Bayesian techniques may improve this by formalizing the way that prior information is taken into account in assessing the new evidence. Costs and effects can be analysed separately, but also, when considering the balance between costs and effects, they can be analysed simultaneously. Here, an example is given using data from two trials that compared costs and effectiveness of stent implantation with balloon angioplasty. The Bayesian results make it clear that different prior distributions may lead to different decisions, and it is concluded that even Bayesian analysis may not always reflect the process of capturing the remaining uncertainties.

Angioplasty, Balloon↗

PubMed1986

The ear lobe crease sign and coronary artery disease in aortic stenosis.

Ear lobe creases have been proposed as useful indirect markers of coronary artery disease. To test such a hypothesis, this physical sign was evaluated in 100 patients with symptomatic aortic stenosis undergoing cardiac catheterization to establish the hemodynamic severity of the obstruction and the degree of coronary artery involvement. This is a disorder where the coexistence of cardiac ischemia may play an important part in diagnosis and management. Criteria were established for the degree of ear lobe involvement with a grading of mild (Grade 1), moderate (Grade 2), and severe (Grade 3). Significant coronary artery disease was defined as narrowing greater than or equal to 50% and a coronary score was established. Sensitivity, specificity, positive and negative predictive values were calculated, using Bayesian analysis for three levels of assumed coronary artery disease prevalence. An ear lobe crease score was correlated with a coronary artery disease score, taking into account the variables of age, sex, and body mass index. No useful statistical correlations were found and it is concluded that this physical sign is of little practical value in this clinical setting.

Adult↗

PubMed2006

A Bayesian toolkit for genetic association studies.

We present a range of modelling components designed to facilitate Bayesian analysis of genetic-association-study data. A key feature of our approach is the ability to combine different submodels together, almost arbitrarily, for dealing with the complexities of real data. In particular, we propose various techniques for selecting the "best" subset of genetic predictors for a specific phenotype (or set of phenotypes). At the same time, we may control for complex, non-linear relationships between phenotypes and additional (non-genetic) covariates as well as accounting for any residual correlation that exists among multiple phenotypes. Both of these additional modelling components are shown to potentially aid in detecting the underlying genetic signal. We may also account for uncertainty regarding missing genotype data. Indeed, at the heart of our approach is a novel method for reconstructing unobserved haplotypes and/or inferring the values of missing genotypes. This can be deployed independently or, alternatively, it can be fully integrated into arbitrary genotype- or haplotype-based association models such that the missing data and the association model are "estimated" simultaneously. The impact of such simultaneous analysis on inferences drawn from the association model is shown to be potentially significant. Our modelling components are packaged as an "add-on" interface to the widely used WinBUGS software, which allows Markov chain Monte Carlo analysis of a wide range of statistical models. We illustrate their use with a series of increasingly complex analyses conducted on simulated data based on a real pharmacogenetic example.

Bayes Theorem↗

PubMed2004

Estimating the demand for health care with panel data: a semiparametric Bayesian approach.

This paper is concerned with the problem of estimating the demand for health care with panel data. A random effects model is specified within a semiparametric Bayesian approach using a Dirichlet process prior. This results in a very flexible distribution for both the random effects and the count variable. In particular, the model can be seen as a mixture distribution with a random number of components, and is therefore a natural extension of prevailing latent class models. A full Bayesian analysis using Markov chain Monte Carlo simulation methods is proposed. The methodology is illustrated with an application using data from Germany.

Aged↗

PubMed1983

Bayesian approach to bioequivalence assessment: an example.

The statistical methods required for a Bayesian analysis of bioequivalence are outlined and numerically illustrated. The analysis consists of the calculation of the posterior probability, given the experimental results, that the ratio of true means of a new and a standard formulation of a drug with respect to some biological response lies in a given interval. Nomograms helpful for the calculation of these probabilities are provided.

Bayes Theorem↗