Three-point appraisal of genetic linkage maps.

This paper develops a simple diagnostic for the investigation of uncertainty within genetic linkage maps using a Bayesian procedure. The method requires only the genotyping data and the proposed genetic map, and calculates the posterior probability for the possible orders of any set of three markers, accounting for the presence of genotyping error (mistyping) and for missing genotype data. The method uses a Bayesian approach to give insight into conflicts between the order in the proposed map and the genotype scores. The method can also be used to assess the accuracy of a genetic map at different genomic scales and to assess alternative potential marker orders. Simulation and two case studies were used to illustrate the method. In the first case study, the diagnostic revealed conflicts in map ordering for short inter-marker distances that were resolved at a distance of 8-12 cM, except for a set of markers at the end of the linkage group. In the second case study, the ordering did not resolve as distances increase, which could be attributed to regions of the map where many individuals were untyped.

Estimating the incidence of an epidemic when it is first discovered and the design of early detection monitoring.

The early detection of an invading epidemic is crucial for successful disease control. Although models have been used extensively to test control strategies following the first detection of an epidemic, few studies have addressed the issue of how to achieve early detection in the first place. Moreover, sampling theory has made great progress in understanding how to estimate the incidence or spatial distribution of an epidemic but how to sample for early detection has been largely ignored. Using a simple epidemic model we demonstrate a method to calculate the incidence of an epidemic when it is discovered for the first time (given a monitoring programme taking samples at regular intervals). We use the method to explore how the intensity and frequency of sampling influences early detection. In particular, we find that for epidemics characterised by high population growth rates it is most effective to spread sampling resources evenly in time. In addition we derive a useful approximation to our method which results in a simple equation capturing the relation between monitoring and epidemic dynamics. Not only does this provide valuable new insight but it provides a simple rule of thumb for the design of monitoring programmes in practice.

Statistical information characterization of conserved non-coding elements in vertebrates.

Recently, a set of highly conserved non-coding elements (CNEs) has been derived from a comparison between the genomes of the puffer fish, Takifugu or Fugu rubripes, and man. In order to facilitate the identification of these conserved elements in silico, we characterize them by a number of statistical features. We found a pronounced information pattern around CNE borders; although the CNEs themselves are AT rich and have high entropy (complexity), they are flanked by GC-rich regions of low entropy (complexity). We also identified the most abundant motifs within and around of CNEs, and identified those that group around their borders. Like in human promoter regions, the TBP, NF-Y and some other binding motifs are clustered around CNE boundaries, which may suggest a possible transcription regulatory function of CNEs.

Population genetic approaches to neurological disease: Parkinson's disease as an example.

Parkinson's disease (PD) is a common, progressive, incurable disabling condition. The cause is unknown but over the past few years tremendous progress in our understanding of the genetic bases of this condition has been made. To date, this has almost exclusively come from the study of relatively rare Mendelian forms of the disease and there are no currently, widely accepted common variants known to increase susceptibility. The role that the "Mendelian" genes play in common sporadic forms of PD is unknown. Moreover, most studies in PD can really be described as candidate polymorphism studies rather than true and complete assessments of the genes themselves. We provide a model of how one might tackle some of these issues using Parkinson's disease as an illustration. One of the emerging hypotheses of gene environment interaction in Parkinson's disease is based on drug metabolizing (or xenobiotic) enzymes and their interaction with putative environmental toxins. This motivated us to describe a tagging approach for an extensive but not exhaustive list of 55 drug metabolizing enzyme genes. We use these data to illustrate the power, and some of the limitations of a haplotype tagging approach. We show that haplotype tagging is extremely efficient and works well with only a modest increase in effort through different populations. The tagging approach works much less well if the minor allele frequency is below 5%. However, it will now be possible using these tags to evaluate these genes comprehensively in PD and other neurodegenerative conditions.

DJ-1 mutations in Parkinson's disease.

Mutations in the DJ-1 gene have recently been shown to cause autosomal recessive Parkinson's disease. To estimate the prevalence of this mutation, an analysis was undertaken of 39 index cases of Parkinson's disease in whom a family history suggested autosomal recessive inheritance. No DJ-1 mutations were found in these patients, indicating that this gene is unlikely to be of numerical significance in clinical practice. The hypothesis was also tested that young onset Parkinson's disease patients in whom, despite extensive analysis, only a single heterozygous parkin mutation was found, might harbour a second mutation in the DJ-1 gene--that is, digenic inheritance. No patient was found with a single mutation in both DJ-1 and parkin genes, making this mode of inheritance unlikely. Finally it was confirmed that PARK6 and PARK7 (DJ-1), despite being phenotypically similar and mapping to the same small chromosomal region of 1p36, are caused by mutations in separate genes.

Diagnostic boundaries, reasoning and depressive disorder, I. Development of a probabilistic morbidity model for public health psychiatry.

BACKGROUND: In recent years diagnostic practice in psychiatry has become increasingly structured in an attempt to standardize definitions of disorders and improve reliability. At the same time there has been an increasing recognition of the need to take account of uncertainty in the process of diagnostic decision making. For the most part, diagnosis is still represented by a binary outcome while this is known to entail a substantial loss of information. Many diagnostic schemes involve, in part, taking thresholds on the numbers of symptoms required from symptom lists. METHODS: A model is proposed here, using ideas derived from latent class analysis to permit generalization from these schemes through moving from a binary to a probabilistic measure of psychiatric case status and replacing thresholds with smoothed transitions. RESULTS: An outcome measure is produced where disorder status is expressed in terms of probabilities without changing the meaning of the original measure. Prevalence estimates (using ICD-10 Depressive Episode criteria) are more stable and can be given with increased precision. CONCLUSIONS: Disorder status when expressed in this way retains more diagnostic information and provides a useful extension to traditional binary analyses when looking at prevalence and risk factor estimation.

Diagnostic boundaries, reasoning and depressive disorder, II. Application of a probabilistic model to the OPCS general population survey of psychiatric morbidity in Great Britain.

BACKGROUND: Reliable prevalence and risk estimation of psychiatric disorder is a cornerstone to achieving objectives in public health psychiatry. Research strategies have increasingly depended, therefore, upon the progressive evolution and refinement of diagnostic approaches designed to reflect better current knowledge concerning prognosis, course and outcome but essentially the need to improve agreement between users of the various schemes. METHODS: This paper contrasts a conventional with a probabilistic approach to the diagnosis of depression based upon the OPCS United Kingdom National survey of psychiatric morbidity. The probabilistic approach, while designed to mimic current diagnostic practice in relation to the depressive disorders, naturally includes provision for the allocation of respondents on a scale of diagnostic uncertainty according to the severity of their presenting condition. RESULTS: Findings are reported arising from the application of the probabilistic method to three areas of research interest in public health psychiatry, namely; an evaluation of additivity of event exposure and depressive morbidity, secondly use of the approach for investigating psychosocial models of depressive disorder and thirdly for assessing the agreement between depressive disorder when classified according to competing diagnostic schemes. CONCLUSIONS: The results show application of the probabilistic approach to provide a firm basis for achieving gains in both the stability and precision of risk profile estimation for depressive conditions.

Disease mapping with errors in covariates.

We describe Bayesian hierarchical-spatial models for disease mapping with imprecisely observed ecological covariates. We posit smoothing priors for both the disease submodel and the covariate submodel. We apply the models to an analysis of insulin Dependent Diabetes Mellitus incidence in Sardinia, with malaria prevalence as a covariate.

Evidence for interrupted bone resorption in human iliac cancellous bone.

Bone resorption and formation are coupled both in time and space and may occur simultaneously in the same remodeling unit. A number of studies have shown that the formative phase of the remodeling sequence may undergo temporary interruptions prior to completion and it is possible that bone resorption may be subject to similar interruptions. We have investigated this hypothesis by studying the distribution of eroded depth in resorption cavities in human cancellous bone. Eroded depth was assessed in iliac crest cancellous bone from 41 normal healthy subjects using a cubic spline curve fitting technique. The distribution of mean eroded depths was skewed to the right. Comparison of the observed distribution with an expected distribution, which was calculated from previously published data and assumes resorption begins rapidly and slows as it approaches completion, showed a significantly greater proportion of shallower cavities than expected (p<0001). Similarly, comparison of observed and uniform distributions, which assumes a constant rate of resorption throughout the erosion period, also showed a significantly greater proportion of smaller cavities (p<0.01). In subjects aged less than 39 years, there were fewer small cavities than in those aged 40-59 years. In addition, there was some evidence that females of 40-59 years had a proportionately greater number of smaller cavities than males; however, there were no differences in other age groups. Our results demonstrate a significantly greater proportion of smaller resorption cavities than would be expected from current models of bone remodeling and are consistent with the hypothesis that resorption undergoes temporary interruptions and/or permanent arrest during the process of bone remodeling.

Estimation of population pharmacokinetics using the Gibbs sampler.

Quantification of the average and interindividual variation in pharmacokinetic behavior within the patient population is an important aspect of drug development. Population pharmacokinetic models typically involve large numbers of parameters related nonlinearly to sparse, observational data, which creates difficulties for conventional methods of analysis. The nonlinear mixed-effects method implemented in the computer program NONMEM is a widely used approach to the estimation of population parameters. However, the method relies on somewhat restrictive modeling assumptions to enable efficient parameter estimation. In this paper we describe a Bayesian approach to population pharmacokinetic analysis which used a technique known as Gibbs sampling to simulate values for each model parameter. We provide details of how to implement the method in the context of population pharmacokinetic analysis, and illustrate this via an application to gentamicin population pharmacokinetics in neonates.

Statistical analysis of data from the Third International IALSC Workshop on Lung Tumor and Differentiation Antigens.

The main aim of the statistical analysis of data collected in the Third International IALSC Workshop on Lung Tumor and Differentiation Antigens, was to identify groups of monoclonal antibodies (MAbs) having similar profiles of reactivity against a variety of cell types in flow cytometry, histology, immunofluorescence and immunocytochemistry experiments. This was achieved through cluster analysis. We describe the methods used in the cluster analysis, and in the data processing leading to it.

The ontogeny of immune responses.

It has been known for some years that a "window of opportunity" exists for transplantation in neonates. Patients who undergo transplantation during the first weeks of life usually have a very quiescent postoperative course. During fetal development a process of building tolerance to self-antigens occurs. It is now known that this "recognition of self" process is restricted by the major histocompatibility complex. Data will be presented that demonstrate that this major histocompatibility complex-restricted self-tolerance is the cause of the allogeneic effect. Thus when a transplantation is performed antigen presenting cells of the donor stimulate the T cells of the recipient to a very high degree. This stimulation is caused by presentation of monomorphic antigens in an inappropriate major histocompatibility complex environment. Loss of these antigen presenting cells can result in a quiescent transplantation course that represents the "window of opportunity" phenomenon. During the immediate postnatal period the infant possesses a naive immune system. One of the characteristics of this naiveté is a lack of class II expressing cells and a failure of appropriate antigen presentation. It will be suggested that this failure could contribute to the ease with which neonatal transplants can be immunosuppressed.

Conditional independence models for epidemiological studies with covariate measurement error.

We construct a unifying representation of the structure of measurement error problems with particular reference to situations commonly encountered in epidemiological studies, and outline how estimation of the parameters of interest can be carried out in a Bayesian framework using Gibbs sampling. We show how this approach can be implemented for designs involving continuous measurement errors assessed through a validation substudy, and discuss our results on simulated data.