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PubMed2000

Bayesian mapping of quantitative trait loci for complex binary traits.

A complex binary trait is a character that has a dichotomous expression but with a polygenic genetic background. Mapping quantitative trait loci (QTL) for such traits is difficult because of the discrete nature and the reduced variation in the phenotypic distribution. Bayesian statistics are proved to be a powerful tool for solving complicated genetic problems, such as multiple QTL with nonadditive effects, and have been successfully applied to QTL mapping for continuous traits. In this study, we show that Bayesian statistics are particularly useful for mapping QTL for complex binary traits. We model the binary trait under the classical threshold model of quantitative genetics. The Bayesian mapping statistics are developed on the basis of the idea of data augmentation. This treatment allows an easy way to generate the value of a hypothetical underlying variable (called the liability) and a threshold, which in turn allow the use of existing Bayesian statistics. The reversible jump Markov chain Monte Carlo algorithm is used to simulate the posterior samples of all unknowns, including the number of QTL, the locations and effects of identified QTL, genotypes of each individual at both the QTL and markers, and eventually the liability of each individual. The Bayesian mapping ends with an estimation of the joint posterior distribution of the number of QTL and the locations and effects of the identified QTL. Utilities of the method are demonstrated using a simulated outbred full-sib family. A computer program written in FORTRAN language is freely available on request.

Algorithms↗
PubMed2002

Fine-scale mapping of disease loci via shattered coalescent modeling of genealogies.

We present a Bayesian, Markov-chain Monte Carlo method for fine-scale linkage-disequilibrium gene mapping using high-density marker maps. The method explicitly models the genealogy underlying a sample of case chromosomes in the vicinity of a putative disease locus, in contrast with the assumption of a star-shaped tree made by many existing multipoint methods. Within this modeling framework, we can allow for missing marker information and for uncertainty about the true underlying genealogy and the makeup of ancestral marker haplotypes. A crucial advantage of our method is the incorporation of the shattered coalescent model for genealogies, allowing for multiple founding mutations at the disease locus and for sporadic cases of disease. Output from the method includes approximate posterior distributions of the location of the disease locus and population-marker haplotype proportions. In addition, output from the algorithm is used to construct a cladogram to represent genetic heterogeneity at the disease locus, highlighting clusters of case chromosomes sharing the same mutation. We present detailed simulations to provide evidence of improvements over existing methodology. Furthermore, inferences about the location of the disease locus are shown to remain robust to modeling assumptions.

Algorithms↗
PubMed2000

Statistical models for longitudinal biomarkers of disease onset.

We consider the analysis of serial biomarkers to screen and monitor individuals in a given population for onset of a specific disease of interest. The biomarker readings are subject to error. We survey some of the existing literature and concentrate on two recently proposed models. The first is a fully Bayesian hierarchical structure for a mixed effects segmented regression model. Posterior estimates of the changepoint (onset time) distribution are obtained by Gibbs sampling. The second is a hidden changepoint model in which the onset time distribution is estimated by maximum likelihood using the EM algorithm. Both methods lead to a dynamic index that represents a strength of evidence that onset has occurred by the current time in an individual subject. The methods are applied to some large data sets concerning prostate specific antigen (PSA) as a serial marker for prostate cancer. Rules based on the indices are compared to standard diagnostic criteria through the use of ROC curves adapted for longitudinal data.

Algorithms↗
PubMed2000

Modelling the cumulative risk for a false-positive under repeated screening events.

Screening examinations are widely utilized in detecting the presence of medical disorders, for instance, screening mammograms and clinical breast examinations for detection of breast cancer. Such procedures are invaluable in enabling early treatment but produce the possibilities of false-positive and false-negative diagnoses. Focusing on false-positive results, with increasing number of screening events, it is clear that the risk of a false-positive increases. The objective of this paper is to quantify the cumulative risk associated with repeated screening. We provide a very general framework within which to investigate this risk, both at the population and the individual level. The latter allows incorporation of evolving patient medical history to permit individualized assessment of risk. We model cumulative risk in terms of the number of screening events until first false-positive. We develop models which are essentially familiar actuarial models for life table data adding a Cox regression to enable individual level modelling. Because it offers several advantages, we employ a Bayesian inference framework and apply our modelling to the analysis of 9773 screening mammograms collected from 2227 women at an HMO serving nearly 300000 adults in and around Boston, MA.

Adult↗
PubMed2006

Bayesian inference in a hidden stochastic two-compartment model for feline hematopoiesis.

In this paper, we describe a hidden two-compartment stochastic process used to model the kinetics of feline hematopoietic stem cells (HSCs) in continuous time. Because of the experimental design and data collection scheme, the inferential task presents numerous challenges. While the hematopoietic process evolves in continuous time, the observations are collected only at discrete irregular times and are a probabilistic function of the state of the process. In addition, the animals go through an experimental procedure such that their reserve of HSCs is severely depleted at the start of the observation period. This impedes any approximation of the hematopoietic process with a continuous state-space process (normal approximation of the transition probabilities would be inaccurate when the state of the process, i.e. the number of stem cells, is small). We implement a Markov chain Monte Carlo algorithm that allows us to estimate the posterior distribution of the parameters of the hematopoietic process while maintaining its state-space discrete (i.e. without using any approximation). We show the performance of the algorithm on simulated data. Finally, we apply the algorithm to data on multiple experimental cats and provide estimates of the rates of the fates of feline HSCs. The obtained estimates are in agreement with the estimates obtained with different methods published in the medical literature. However, the proposed approach makes a more efficient use of the data and hence the parameter estimates are much more accurate than the one obtained with the methods previously proposed.

Algorithms↗
PubMed2005

Using the quantitative genetic threshold model for inferences between and within species.

Sewall Wright's threshold model has been used in modelling discrete traits that may have a continuous trait underlying them, but it has proven difficult to make efficient statistical inferences with it. The availability of Markov chain Monte Carlo (MCMC) methods makes possible likelihood and Bayesian inference using this model. This paper discusses prospects for the use of the threshold model in morphological systematics to model the evolution of discrete all-or-none traits. There the threshold model has the advantage over 0/1 Markov process models in that it not only accommodates polymorphism within species, but can also allow for correlated evolution of traits with far fewer parameters that need to be inferred. The MCMC importance sampling methods needed to evaluate likelihood ratios for the threshold model are introduced and described in some detail.

Bayes Theorem↗
PubMed2003

Bayesian approaches to joint cure-rate and longitudinal models with applications to cancer vaccine trials.

Complex issues arise when investigating the association between longitudinal immunologic measures and time to an event, such as time to relapse, in cancer vaccine trials. Unlike many clinical trials, we may encounter patients who are cured and no longer susceptible to the time-to-event endpoint. If there are cured patients in the population, there is a plateau in the survival function, S(t), after sufficient follow-up. If we want to determine the association between the longitudinal measure and the time-to-event in the presence of cure, existing methods for jointly modeling longitudinal and survival data would be inappropriate, since they do not account for the plateau in the survival function. The nature of the longitudinal data in cancer vaccine trials is also unique, as many patients may not exhibit an immune response to vaccination at varying time points throughout the trial. We present a new joint model for longitudinal and survival data that accounts both for the possibility that a subject is cured and for the unique nature of the longitudinal data. An example is presented from a cancer vaccine clinical trial.

Bayes Theorem↗
PubMed2002

Generalised additive models and hierarchical logistic regression of lameness in dairy cows.

We examined the relationship between lameness (defined by locomotion score) and four time-related variables using data collected from a study of cattle lameness conducted in the UK from 1998 to 1992. The data were 19,667 locomotion scores for 1790 cows from 27 dairy herds; the four variables were time-from-calving, time of year, parity and time spent in the study. The shape of the relationships between calving and temporal variables and lameness were assessed using loess smoothed terms in a multivariable logistic generalised additive model (GAM). Polynomial relationships derived from the GAM then were included in a Bayesian hierarchical logistic-regression model incorporating between-herd, between-cow and within-cow random effects. The final hierarchical multivariable model showed that the most important variable influencing the probability of lameness was the time of scoring in the study; but, parity, time of year and time-from-calving also were significant. Between-herd and between-cow effects were of roughly equal importance.

Animals↗
PubMed2005

Comparison of statistical analysis and Bayesian Networks in the evaluation of dissolution performance of BCS Class II model drugs.

This project compared the effect of formulation variables on the dissolution performance of model Biopharmaceutics Classification System (BCS) Class II drugs from hard gelatin capsules using statistical analysis and Bayesian networks. The drugs chosen for this study were carbamazepine (CAR), chlorpropamide (CHL), diazepam (DIA), ketoprofen (KET), and naproxen (NAP). Formulations contained anhydrous lactose, microcrystalline cellulose, sodium stearyl fumerate, sodium lauryl sulfate, and croscarmellose sodium. A Box-Behnken experimental design was used in the statistical analysis. The weakly acidic drugs were tested using USP apparatus II with capsule sinkers in 0.1M pH 6.8 Potassium Phosphate buffer. The weakly basic drugs were tested using USP apparatus I in 0.1N HCl buffer. Mean dissolution profiles were compared via calculation of the similarity factor. The Box-Behnken experimental design was found to be useful in assessing primary and secondary excipient effects on dissolution. The Bayesian Network developed for the dataset mirrored the key excipient effects on dissolution performance.

Bayes Theorem↗
PubMed2003

Toward better outcomes with tacrolimus therapy: population pharmacokinetics and individualized dosage prediction in adult liver transplantation.

Patient outcomes in transplantation would improve if dosing of immunosuppressive agents was individualized. The aim of this study is to develop a population pharmacokinetic model of tacrolimus in adult liver transplant recipients and test this model in individualizing therapy. Population analysis was performed on data from 68 patients. Estimates were sought for apparent clearance (CL/F) and apparent volume of distribution (V/F) using the nonlinear mixed effects model program (NONMEM). Factors screened for influence on these parameters were weight, age, sex, transplant type, biliary reconstructive procedure, postoperative day, days of therapy, liver function test results, creatinine clearance, hematocrit, corticosteroid dose, and interacting drugs. The predictive performance of the developed model was evaluated through Bayesian forecasting in an independent cohort of 36 patients. No linear correlation existed between tacrolimus dosage and trough concentration (r(2) = 0.005). Mean individual Bayesian estimates for CL/F and V/F were 26.5 +/- 8.2 (SD) L/hr and 399 +/- 185 L, respectively. CL/F was greater in patients with normal liver function. V/F increased with patient weight. CL/F decreased with increasing hematocrit. Based on the derived model, a 70-kg patient with an aspartate aminotransferase (AST) level less than 70 U/L would require a tacrolimus dose of 4.7 mg twice daily to achieve a steady-state trough concentration of 10 ng/mL. A 50-kg patient with an AST level greater than 70 U/L would require a dose of 2.6 mg. Marked interindividual variability (43% to 93%) and residual random error (3.3 ng/mL) were observed. Predictions made using the final model were reasonably nonbiased (0.56 ng/mL), but imprecise (4.8 ng/mL). Pharmacokinetic information obtained will assist in tacrolimus dosing; however, further investigation into reasons for the pharmacokinetic variability of tacrolimus is required.

Adult↗
PubMed2001

The relatively small decline in orientation acuity as stimulus size decreases.

Orientation acuity was measured with circular patches of sinusoidal gratings of various sizes. Threshold estimates were lowest (acuity highest) for the largest size patch, and increased as the stimulus size was reduced, consistent with the results of many researchers using line stimuli. These results are compared with the predictions of a simple and widely accepted model of spatial vision whereby the output of independent feed-forward filters are combined to produce threshold estimates. Specifically, the rectified output of a number of independent filters (i.e. Gabors) spanning the stimulus space (i.e. orientation) are combined via Bayesian decision theory. This model cannot account quantitatively for the relatively low thresholds estimated for the small sized stimuli when compared to the thresholds measured with larger patches. Application of a comparable analysis, with preliminary measurements of neuronal responses from primary visual cortex replacing the response rectified Gabor filter's responses, provides a more reasonable account of behavioral acuity. This indicates a fundamental inadequacy of the feed-forward filter model in accounting for V1 neurons' role in perception.

Adult↗
PubMed2005

Mixture models with adaptive spatial regularization for segmentation with an application to FMRI data.

Mixture models are often used in the statistical segmentation of medical images. For example, they can be used for the segmentation of structural images into different matter types or of functional statistical parametric maps (SPMs) into activations and nonactivations. Nonspatial mixture models segment using models of just the histogram of intensity values. Spatial mixture models have also been developed which augment this histogram information with spatial regularization using Markov random fields. However, these techniques have control parameters, such as the strength of spatial regularization, which need to be tuned heuristically to particular datasets. We present a novel spatial mixture model within a fully Bayesian framework with the ability to perform fully adaptive spatial regularization using Markov random fields. This means that the amount of spatial regularization does not have to be tuned heuristically but is adaptively determined from the data. We examine the behavior of this model when applied to artificial data with different spatial characteristics, and to functional magnetic resonance imaging SPMs.

Algorithms↗
PubMed2000

Multiple-objective designs in a dose-response experiment.

This article is an extension of the work of Huang and Wong (1), who found dual-objective designs for models with a continuous outcome. We consider quantal dose-response experiments with a binary outcome and develop multiple-objective designs for two or more Bayesian optimality criteria. Using the logit model as an illustrative example, we construct numerically optimal designs for estimating model parameters and percentiles, with possibly unequal interest in each of the objectives. We also show that the popular equal dosage assignment rule can be a rather inefficient design for estimating model parameters under a Bayesian setup.

Bayes Theorem↗
PubMed2026

Insights Into the Structural Features, Codon Usage Patterns, and Phylogenetic Analysis in Neoniphon argenteus (Teleostei: Holocentriformes) Based on Complete Mitochondrial Genome.

Neoniphon argenteus, a widely distributed nocturnal coral reef fish in the family Holocentridae, plays an important role in maintaining coral reef ecosystem health, yet its phylogenetic position remains poorly resolved. To bridge this gap, we sequenced and analyzed the complete mitochondrial genome of a specimen from the South China Sea to characterize its structural features, codon usage patterns, and phylogenetic relationships. The 16,569 bp mitogenome (GenBank: PP190474.1) encodes 13 protein-coding genes (PCGs), 22 tRNAs, two rRNAs, and two non-coding regions, exhibiting a distinct A + T bias. All tRNAs fold into typical cloverleaf secondary structures except tRNA-Ser (AGN), which lacks the dihydrouridine (DHU) arm. The control region contains palindromic motifs (TACAT/ATGTA) capable of forming hairpin structures and five conserved sequence blocks, whereas the OL region harbors a conserved 5'-GCCGG-3' motif. RSCU analysis revealed 31 frequently used codons (RSCU > 1) with a pronounced preference for A/C-ending codons. The ΔRSCU method identified 10 candidate optimal codons (GCA, CAA, GAA, GGA, AUU, CUA, CCA, CGA, ACA, and GUC). Selection pressure analysis using EasyCodeML and site-specific models indicated that all PCGs are predominantly under purifying selection, with no significant evidence of pervasive positive selection. ND6 exhibited elevated pairwise Ka/Ks ratios (mean = 1.209 ± 0.047), consistent with reduced selective constraint rather than adaptive evolution. Phylogenetic analysis of 19 Holocentriformes species using maximum likelihood and Bayesian inference with partitioned models based on 13 PCGs and two rRNA genes (12S and 16S) assigned all taxa to two well-supported subfamilies (Holocentrinae and Myripristinae). Within Holocentrinae, Neoniphon species form a monophyletic clade nested within a paraphyletic Sargocentron, suggesting that the genus Sargocentron as currently defined is not monophyletic. This study provides useful baseline molecular data for further exploration of the evolutionary history of N. argenteus and other members of Holocentriformes.

Holocentridae↗
PubMed2005

Quantitative risk modelling for new pharmaceutical compounds.

The process of discovering and developing new drugs is long, costly and risk-laden. Faced with a wealth of newly discovered compounds, industrial scientists need to target resources carefully to discern the key attributes of a drug candidate and to make informed decisions. Here, we describe a quantitative approach to modelling the risk associated with drug development as a tool for scenario analysis concerning the probability of success of a compound as a potential pharmaceutical agent. We bring together the three strands of manufacture, clinical effectiveness and financial returns. This approach involves the application of a Bayesian Network. A simulation model is demonstrated with an implementation in MS Excel using the modelling engine Crystal Ball.

Algorithms↗
PubMed2003

Quantitative comparison of FBP, EM, and Bayesian reconstruction algorithms for the IndyPET scanner.

We quantitatively compare filtered backprojection (FBP), expectation-maximization (EM), and Bayesian reconstruction algorithms as applied to the IndyPET scanner--a dedicated research scanner which has been developed for small and intermediate field of view imaging applications. In contrast to previous approaches that rely on Monte Carlo simulations, a key feature of our investigation is the use of an empirical system kernel determined from scans of line source phantoms. This kernel is incorporated into the forward model of the EM and Bayesian algorithms to achieve resolution recovery. Three data sets are used, data collected on the IndyPET scanner using a bar phantom and a Hoffman three-dimensional brain phantom, and simulated data containing a hot lesion added to a uniform background. Reconstruction quality is analyzed quantitatively in terms of bias-variance measures (bar phantom) and mean square error (lesion phantom). We observe that without use of the empirical system kernel, the FBP, EM, and Bayesian algorithms give similar performance. However, with the inclusion of the empirical kernel, the iterative algorithms provide superior reconstructions compared with FBP, both in terms of visual quality and quantitative measures. Furthermore, Bayesian methods outperform EM. We conclude that significant improvements in reconstruction quality can be realized by combining accurate models of the system response with Bayesian reconstruction algorithms.

Algorithms↗
PubMed2004

A phylogenetic mixture model for detecting pattern-heterogeneity in gene sequence or character-state data.

We describe a general likelihood-based 'mixture model' for inferring phylogenetic trees from gene-sequence or other character-state data. The model accommodates cases in which different sites in the alignment evolve in qualitatively distinct ways, but does not require prior knowledge of these patterns or partitioning of the data. We call this qualitative variability in the pattern of evolution across sites "pattern-heterogeneity" to distinguish it from both a homogenous process of evolution and from one characterized principally by differences in rates of evolution. We present studies to show that the model correctly retrieves the signals of pattern-heterogeneity from simulated gene-sequence data, and we apply the method to protein-coding genes and to a ribosomal 12S data set. The mixture model outperforms conventional partitioning in both these data sets. We implement the mixture model such that it can simultaneously detect rate- and pattern-heterogeneity. The model simplifies to a homogeneous model or a rate-variability model as special cases, and therefore always performs at least as well as these two approaches, and often considerably improves upon them. We make the model available within a Bayesian Markov-chain Monte Carlo framework for phylogenetic inference, as an easy-to-use computer program.

Base Sequence↗