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PubMed1999

Genetic determination of individual birth weight and its association with sow productivity traits using Bayesian analyses.

Genetic association between individual birth weight (IBW) and litter birth weight (LBW) was analyzed on records of 14,950 individual pigs born alive between 1988 and 1994 at the pig breeding farm of the University of Kiel. Dams were from three purebred lines (German Landrace, German Edelschwein, and Large White) and their crosses. Phenotypically, preweaning mortality of pigs decreased substantially from 40% for pigs with < or = 1 kg weight to less than 7% for pigs with > 1.6 kg. For these low to high birth weight categories, preweaning growth (d 21 of age) and early postweaning growth (weaning to 25 kg) increased by more than 28 and 8% per day, respectively. Bayesian analysis was performed based on direct-maternal effects models for IBW and multiple-trait direct effects models for number of pigs born in total (NOBT) and alive (NOBA) and LBW. Bayesian posterior means for direct and maternal heritability and litter proportion of variance in IBW were .09, .26, and .18, respectively. After adjustment for NOBT, these changed to .08, .22, and .09, respectively. Adjustment for NOBT reduced the direct and maternal genetic correlation from -.41 to -.22. For these direct-maternal correlations, the 95% highest posterior density intervals were -.75 to -.07, and -.58 to .17 before and after adjustment for NOBT. Adjustment for NOBT was found to be necessary to obtain unbiased estimates of genetic effects for IBW. The relationship between IBW and NOBT, and thus the adjustment, was linear with a decrease in IBW of 44 g per additionally born pig. For litter traits, direct heritabilities were .10, .08, and .08 for NOBT, NOBA, and LBW, respectively. After adjustment of LBW for NOBA the heritability changed to .43. Expected variance components for LBW derived from estimates of IBW revealed that genetic and environmental covariances between full-sibs and variation in litter size resulted in the large deviation of maternal heritability for IBW and its equivalent estimate for LBW. These covariances among full-sibs could not be estimated if only LBW were recorded. Therefore, selection for increased IBW is recommended, with the opportunity to improve both direct and maternal genetic effects of birth weight of pigs and, thus, their vitality and pre- and postnatal growth.

Animals↗
PubMed2001

Evaluation of Bayesian predictability of vancomycin concentration in patients with various degrees of renal function.

To assess the usefulness of the population pharmacokinetic parameters of vancomycin (VCM) based on a two-compartment model in Japanese adult patients, predictability by a Bayesian method was evaluated using a concentration time course after single dosing to 22 patients with various degrees of renal function. Using one or two points from the observed data for each patient, the concentrations predicted by a Bayesian method were compared with the observed data for each sampling time. The patients were separated into five groups based on their renal functions indicated by creatinine clearance, and the mean prediction error (MPE) and root mean squared error (RMSE) were calculated for each group as measures of accuracy and precision, respectively. In both one- and two-point methods, the absolute MPE values at each sampling time in the elimination phase were less than 2.5 microg/ml, and the RMSE values were also small. No clear differences were found in MPE and RMSE among the groups. In the distribution phase, the MPE and RMSE were somewhat greater, and RMSE in some groups was around 15 microg/ml when trough data was used to predict the peak concentration. Also, the theoretical RMSE using this population parameter setting could well explain the observed RMSE. These results confirmed this population parameter setting is useful for at least predicting concentration in the elimination phase after single dosing, and the predictability was independent of renal function.

Adult↗
PubMed2005

Informative structure priors: joint learning of dynamic regulatory networks from multiple types of data.

We present a method for jointly learning dynamic models of transcriptional regulatory networks from gene expression data and transcription factor binding location data. Models are automatically learned using dynamic Bayesian network inference algorithms; joint learning is accomplished by incorporating evidence from gene expression data through the likelihood, and from transcription factor binding location data through the prior. We propose a new informative structure prior with two advantages. First, the prior incorporates evidence from location data probabilistically, allowing it to be weighed against evidence from expression data. Second, the prior takes on a factorable form that is computationally efficient when learning dynamic regulatory networks. Results obtained from both simulated and experimental data from the yeast cell cycle demonstrate that this joint learning algorithm can recover dynamic regulatory networks from multiple types of data that are more accurate than those recovered from each type of data in isolation.

Bayes Theorem↗
PubMed2005

Impact of Federal drug law enforcement on the supply of heroin in Australia.

AIMS: To conduct an empirical investigation of the efficacy of law enforcement in reducing heroin supply in Australia. Specifically, this paper addresses the question of whether heroin purity levels in the Australian Capital Territory (ACT) could be predicted by heroin seizures at the national level by the Australian Federal Police (AFP) in the preceding year. DESIGN: We considered two forms of evidence. First, a Bayesian Markov Chain Monte Carlo (MCMC) change-point model was used to discover (a) if there was a substantial increase in heroin seizures by the AFP, (b) when the increase began and (c) whether it occurred after increased funding to the Australian Federal Police for the purpose of drug law enforcement. Second, standard time-series methods were used to ascertain whether fluctuations in heroin seizure weights or the frequency of large-scale seizures after the aforementioned changes in seizure levels predicted fluctuations in heroin purity levels in the ACT after autocorrelation had been removed from the purity series. FINDINGS: A Bayesian MCMC change-point model supported the hypothesis that heroin seizures rapidly increased about a year before the estimated decline in heroin purity and after the increased funding of AFP. The autoregression models suggested that 10-20% of the variance in the residuals of the heroin purity series was predicted by appropriately lagged residuals of the seizure-number and log-weight series, after autocorrelation had been removed. CONCLUSION: The overall results are consistent with the hypothesis that large-scale heroin seizures by the AFP reduce street-level heroin supply a year or so later, although the short-term dynamics suggest an 'opponent' response to residual fluctuations in seizures. To our knowledge, this is first time a connection has been identified between large-scale heroin seizures and street-level supply.

Australia↗
PubMedDate not supplied

Comparison between estimation of breeding values and fixed effects using Bayesian and empirical BLUP estimation under selection on parents and missing pedigree information.

Bayesian (via Gibbs sampling) and empirical BLUP (EBLUP) estimation of fixed effects and breeding values were compared by simulation. Combinations of two simulation models (with or without effect of contemporary group (CG)), three selection schemes (random, phenotypic and BLUP selection), two levels of heritability (0.20 and 0.50) and two levels of pedigree information (0% and 15% randomly missing) were considered. Populations consisted of 450 animals spread over six discrete generations. An infinitesimal additive genetic animal model was assumed while simulating data. EBLUP and Bayesian estimates of CG effects and breeding values were, in all situations, essentially the same with respect to Spearman's rank correlation between true and estimated values. Bias and mean square error (MSE) of EBLUP and Bayesian estimates of CG effects and breeding values showed the same pattern over the range of simulated scenarios. Methods were not biased by phenotypic and BLUP selection when pedigree information was complete, albeit MSE of estimated breeding values increased for situations where CG effects were present. Estimation of breeding values by Bayesian and EBLUP was similarly affected by joint effect of phenotypic or BLUP selection and randomly missing pedigree information. For both methods, bias and MSE of estimated breeding values and CG effects substantially increased across generations.

Alleles↗
PubMed2007

Random changepoint modelling of HIV immunologic responses.

We propose a changepoint model for the analysis of longitudinal CD4 T-cell counts for HIV infected subjects following highly active antiretroviral treatment. The profile of CD4 counts for each subject follows a simple, 'broken stick' changepoint model, with random subject-specific parameters, including the changepoint. The model accounts for baseline covariates. The longitudinal CD4 records are censored at the time of the subject going off-study-treatment. This is a potentially informative drop-out mechanism, which we address by modelling it jointly with the CD4 count outcome. The drop-out model incorporates terms from the CD4 model, including the changepoint. The estimation is done in a Bayesian framework, with implementation via Markov chain Monte Carlo methods in the WinBUGS software. Model selection using DIC indicates that the data support the complex random changepoint and informative censoring model.

Antiretroviral Therapy, Highly Active↗
PubMed1999

Genetic variance components analysis for binary phenotypes using generalized linear mixed models (GLMMs) and Gibbs sampling.

The common complex diseases such as asthma are an important focus of genetic research, and studies based on large numbers of simple pedigrees ascertained from population-based sampling frames are becoming commonplace. Many of the genetic and environmental factors causing these diseases are unknown and there is often a strong residual covariance between relatives even after all known determinants are taken into account. This must be modelled correctly whether scientific interest is focused on fixed effects, as in an association analysis, or on the covariances themselves. Analysis is straightforward for multivariate Normal phenotypes, but difficulties arise with other types of trait. Generalized linear mixed models (GLMMs) offer a potentially unifying approach to analysis for many classes of phenotype including multivariate Normal traits, binary traits, and censored survival times. Markov Chain Monte Carlo methods, including Gibbs sampling, provide a convenient framework within which such models may be fitted. In this paper, Bayesian inference Using Gibbs Sampling (a generic Gibbs sampler; BUGS) is used to fit GLMMs for multivariate Normal and binary phenotypes in nuclear families. BUGS is easy to use and readily available. We motivate a suitable model structure for Normal phenotypes and show how the model extends to binary traits. We discuss parameter interpretation and statistical inference and show how to circumvent a number of important theoretical and practical problems that we encountered. Using simulated data we show that model parameters seem consistent and appear unbiased in smaller data sets. We illustrate our methods using data from an ongoing cohort study.

Binomial Distribution↗
PubMed2003

Latent class model diagnosis from a frequentist point of view.

This is in response to Garrett and Zeger (2000, Biometrics 56, 1055-1067) who, within the Bayesian framework, developed mainly graphical methods for latent class model diagnosis. Possible problems with this approach, and with its application to both generated and empirical data, are pointed out. The impact of the proposed tools cannot be understood by their reader, as no comparisons are made to results obtainable using established methods for latent class model diagnosis; this applies especially to overall goodness-of-fit tests, for which alternatives (bootstrap, Rudas-Clogg-Lindsay index of fit) are mentioned. Further, in one case of generated data, the methods proposed by Garrett and Zeger seem to give problematic results as to identifiability; in the case of the empirical data on major depression, they lead to accepting a suboptimal three-class model. In the latter case, one can be rather sure that an identifiable, well-fitting latent class model could have been identified--if Garrett and Zeger had also considered restricted latent class models.

Biometry↗
PubMed2007

Prediction methods for nicotine clearance using cotinine and 3-hydroxy-cotinine spot saliva samples II. Model application.

To develop and compare methods that predict individual nicotine (NIC) clearance, which reflects CYP2A6 activity, using random saliva cotinine (COT) and trans 3'-hydroxycotinine (3HC) measurements. COT and 3HC saliva concentrations in smokers were simulated utilizing a mechanistic population pharmacokinetic model of NIC metabolism that was adapted from the one described in a companion paper. Four methods to predict NIC clearance using the metabolites concentrations were compared. The precision bias, and the fraction of predictions that are made with an absolute error below 25% were the performance measures evaluated. Four prediction methods were compared: (M1) reference method, an intercept slope model of the metabolite concentration ratios ([3HC]/[COT]) (M2) an intercept slope model of the natural logarithm of the metabolite ratios (M3) a spline of the logarithm of the metabolite ratios (M4) Maximal Posteriori Bayesian estimate of NIC clearance conditioned on the model, COT and 3HC concentrations. In addition, the effect of smoking patterns on the concentrations of COT and 3HC was evaluated. The precision, accuracy, and the fraction of predictions with an absolute error below 25%, were higher for methods M2-M4 compared to method M1. However, the differences between M2 and M4 were small. Additionally, smoking pattern did not affect the metabolite concentration profiles. Predicting NIC clearance using an intercept slope model of the natural logarithm of the ratio of 3HC to COT appears to be a relatively simple method that is better than using the metabolite ratio directly. This method has a bias of approximately -10%, precision of approximately 60%. The fraction of estimates below an absolute error of 25% is 43%. These results support use of M2 to estimate CYP2A6 activity in smokers in the clinical setting.

Aryl Hydrocarbon Hydroxylases↗
PubMed2004

Effects of age at first substance use and psychiatric comorbidity on the development of substance use disorders.

In this paper, we examine the effects of age at first substance use, and history of psychiatric disorders, on the development of substance use disorder (SUD) by age 16. We use a prospective, longitudinal design to disaggregate the effects of age at first use and time since first use on the development of adolescent SUD. Second, we test the hypothesis that adolescent SUD is an unlikely progression from early substance use unless children also show other early conduct problems. A population sample of 1,420 children from the Great Smoky Mountains Study (GSMS) was assessed annually between ages 9 and 16. Logistic regression models were applied within the hierarchical Bayesian framework, where the covariate effects were described by time-varying parameters having a first-order auto-regressive prior distribution. Posterior analyses based on a Gibbs sampling approach revealed that, controlling for years of exposure, the risk of transition to SUD increased with age at onset for onsets before age 13, but began to fall for onset at 14. Among users, use alone, without early conduct problems, led to a 11% prevalence of SUD by age 16. Past conduct disorder (CD) had a strong additive effect at ages 13-15, but at age 16, when substance use and abuse became more normative, the excess risk from prior CD decreased. Boys, but not girls, with a history of depression were at increased risk of SUD. Anxiety increased the risk of SUD in girls at age 16, but not before that. Results only partially support the study hypothesis; early use was a major predictor of adolescent SUD even in the absence of CD.

Adolescent↗
PubMed2006

Bayesian filtering of myoelectric signals.

Surface electromyography is used in research, to estimate the activity of muscle, in prosthetic design, to provide a control signal, and in biofeedback, to provide subjects with a visual or auditory indication of muscle contraction. Unfortunately, successful applications are limited by the variability in the signal and the consequent poor quality of estimates. I propose to use a nonlinear recursive filter based on Bayesian estimation. The desired filtered signal is modeled as a combined diffusion and jump process and the measured electromyographic (EMG) signal is modeled as a random process with a density in the exponential family and rate given by the desired signal. The rate is estimated on-line by calculating the full conditional density given all past measurements from a single electrode. The Bayesian estimate gives the filtered signal that best describes the observed EMG signal. This estimate yields results with very low short-time variability but also with the capability of very rapid response to change. The estimate approximates isometric joint torque with lower error and higher signal-to-noise ratio than current linear methods. Use of the nonlinear filter significantly reduces noise compared with current algorithms, and it may therefore permit more effective use of the EMG signal for prosthetic control, biofeedback, and neurophysiology research.

Adult↗
PubMed2003

Performance-based selection of likelihood models for phylogeny estimation.

Phylogenetic estimation has largely come to rely on explicitly model-based methods. This approach requires that a model be chosen and that that choice be justified. To date, justification has largely been accomplished through use of likelihood-ratio tests (LRTs) to assess the relative fit of a nested series of reversible models. While this approach certainly represents an important advance over arbitrary model selection, the best fit of a series of models may not always provide the most reliable phylogenetic estimates for finite real data sets, where all available models are surely incorrect. Here, we develop a novel approach to model selection, which is based on the Bayesian information criterion, but incorporates relative branch-length error as a performance measure in a decision theory (DT) framework. This DT method includes a penalty for overfitting, is applicable prior to running extensive analyses, and simultaneously compares all models being considered and thus does not rely on a series of pairwise comparisons of models to traverse model space. We evaluate this method by examining four real data sets and by using those data sets to define simulation conditions. In the real data sets, the DT method selects the same or simpler models than conventional LRTs. In order to lend generality to the simulations, codon-based models (with parameters estimated from the real data sets) were used to generate simulated data sets, which are therefore more complex than any of the models we evaluate. On average, the DT method selects models that are simpler than those chosen by conventional LRTs. Nevertheless, these simpler models provide estimates of branch lengths that are more accurate both in terms of relative error and absolute error than those derived using the more complex (yet still wrong) models chosen by conventional LRTs. This method is available in a program called DT-ModSel.

Bayes Theorem↗
PubMed2005

NovoHMM: a hidden Markov model for de novo peptide sequencing.

De novo sequencing of peptides poses one of the most challenging tasks in data analysis for proteome research. In this paper, a generative hidden Markov model (HMM) of mass spectra for de novo peptide sequencing which constitutes a novel view on how to solve this problem in a Bayesian framework is proposed. Further extensions of the model structure to a graphical model and a factorial HMM to substantially improve the peptide identification results are demonstrated. Inference with the graphical model for de novo peptide sequencing estimates posterior probabilities for amino acids rather than scores for single symbols in the sequence. Our model outperforms state-of-the-art methods for de novo peptide sequencing on a large test set of spectra.

Amino Acid Sequence↗
PubMed2005

Spatiotemporal Bayesian inference dipole analysis for MEG neuroimaging data.

Recently, we described a Bayesian inference approach to the MEG/EEG inverse problem that used numerical techniques to estimate the full posterior probability distributions of likely solutions upon which all inferences were based [Schmidt, D.M., George, J.S., Wood, C.C., 1999. Bayesian inference applied to the electromagnetic inverse problem. Human Brain Mapping 7, 195; Schmidt, D.M., George, J.S., Ranken, D.M., Wood, C.C., 2001. Spatial-temporal bayesian inference for MEG/EEG. In: Nenonen, J., Ilmoniemi, R. J., Katila, T. (Eds.), Biomag 2000: 12th International Conference on Biomagnetism. Espoo, Norway, p. 671]. Schmidt et al. (1999) focused on the analysis of data at a single point in time employing an extended region source model. They subsequently extended their work to a spatiotemporal Bayesian inference analysis of the full spatiotemporal MEG/EEG data set. Here, we formulate spatiotemporal Bayesian inference analysis using a multi-dipole model of neural activity. This approach is faster than the extended region model, does not require use of the subject's anatomical information, does not require prior determination of the number of dipoles, and yields quantitative probabilistic inferences. In addition, we have incorporated the ability to handle much more complex and realistic estimates of the background noise, which may be represented as a sum of Kronecker products of temporal and spatial noise covariance components. This reduces the effects of undermodeling noise. In order to reduce the rigidity of the multi-dipole formulation which commonly causes problems due to multiple local minima, we treat the given covariance of the background as uncertain and marginalize over it in the analysis. Markov Chain Monte Carlo (MCMC) was used to sample the many possible likely solutions. The spatiotemporal Bayesian dipole analysis is demonstrated using simulated and empirical whole-head MEG data.

Algorithms↗
PubMed2006

Revised assessment of cancer risk to dichloromethane II. Application of probabilistic methods to cancer risk determinations.

An updated PBPK model of methylene chloride (DCM, dichloromethane) carcinogenicity in mice was recently published using Bayesian statistical methods (Marino et al., 2006). In this work, this model was applied to humans, as recommended by Sweeney et al.(2004). Physiological parameters for input into the MCMC analysis were selected from multiple sources reflecting, in each case, the source that was considered to represent the most current scientific evidence for each parameter. Metabolic data for individual subjects from five human studies were combined into a single data set and population values derived using MCSim. These population values were used for calibration of the human model. The PBPK model using the calibrated metabolic parameters was used to perform a cancer risk assessment for DCM, using the same tumor incidence and exposure concentration data relied upon in the current IRIS entry. Unit risks, i.e., the risk of cancer from exposure to 1 microg/m3 over a lifetime, for DCM were estimated using the calibrated human model. The results indicate skewed distributions for liver and lung tumor risks, alone or in combination, with a mean unit risk (per microg/m3) of 1.05 x 10(-9), considering both liver and lung tumors. Adding the distribution of genetic polymorphisms for metabolism to the ultimate carcinogen, the unit risks range from 0 (which is expected given that approximately 20% of the US population is estimated to be nonconjugators) up to a unit risk of 2.70 x 10(-9) at the 95th percentile. The median, or 50th percentile, is 9.33 x 10(-10), which is approximately a factor of 500 lower than the current EPA unit risk of 4.7 x 10(-7) using a previous PBPK model. These values represent the best estimates to date for DCM cancer risk because all available human data sets were used, and a probabilistic methodology was followed.

Carcinogens↗
PubMed2025

Towards improved fine-mapping of candidate causal variants.

Fine-mapping in genome-wide association studies aims to identify potentially causal genetic variants among a set of candidate variants that are often highly correlated with each other owing to linkage disequilibrium. A variety of statistical approaches are used in fine-mapping, almost all of which are based on a multiple regression framework to model the relationship between genotype and phenotype, while accommodating specific assumptions about the distribution of variant effect sizes and using different inference algorithms. Owing to their modelling flexibility and the ease of making inferential statements, these approaches are predominantly Bayesian in nature. Recently, these approaches have been improved by refining modelling assumptions, integrating additional information, accommodating summary statistics, and developing scalable computational algorithms that improve computation efficiency and fine-mapping resolution.

Humans↗
PubMed2025

Bayesian inference of fitness landscapes via tree-structured branching processes.

MOTIVATION: The complex dynamics of cancer evolution, driven by mutation and selection, underlies the molecular heterogeneity observed in tumors. The evolutionary histories of tumors of different patients can be encoded as mutation trees and reconstructed in high resolution from single-cell sequencing data, offering crucial insights for studying fitness effects of and epistasis among mutations. Existing models, however, either fail to separate mutation and selection or neglect the evolutionary histories encoded by the tumor phylogenetic trees. RESULTS: We introduce FiTree, a tree-structured multi-type branching process model with epistatic fitness parameterization and a Bayesian inference scheme to learn fitness landscapes from single-cell tumor mutation trees. Through simulations, we demonstrate that FiTree outperforms state-of-the-art methods in inferring the fitness landscape underlying tumor evolution. Applying FiTree to a single-cell acute myeloid leukemia dataset, we identify epistatic fitness effects consistent with known biological findings and quantify uncertainty in predicting future mutational events. The new model unifies probabilistic graphical models of cancer progression with population genetics, offering a principled framework for understanding tumor evolution and informing therapeutic strategies. AVAILABILITY AND IMPLEMENTATION: The Python package FiTree and the analysis workflows are available at https://github.com/cbg-ethz/FiTree.

Bayes Theorem↗
PubMed1999

Autoregressive models for describing non-linear changes in biological parameters fitted using BUGS.

Many biological processes give outcome data which show a curvilinear association with time which tends to an asymptote. We show how autoregressive models can be used to describe this association within individual subjects. We also present a Bayesian approach implemented using statistical software, BUGS, to fit these models in a multi-level (hierarchical) setting that describes variation in the association between subjects. Peak expiratory flow data from a clinical trial involving subjects with asthma are used to illustrate the methods.

Anti-Inflammatory Agents↗

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