Bayesian survival analysis in genetic association studies

Motivation: Large-scale genetic association studies are carried out with the hope of discovering single nucleotide polymorphisms involved in the etiology of complex diseases. There are several existing methods in the literature for performing this kind of analysis for case-control studies, but less work has been done for prospective cohort studies. We present a Bayesian method for linking markers to censored survival outcome by clustering haplotypes using gene trees. Coalescent-based approaches are promising for LD mapping, as the coalescent offers a good approximation to the evolutionary history of mutations

Defining the True Sensitivity of Culture for the Diagnosis of Melioidosis Using Bayesian Latent Class Models

BACKGROUND: Culture remains the diagnostic gold standard for many bacterial infections, and the method against which other tests are often evaluated. Specificity of culture is 100% if the pathogenic organism is not found in healthy subjects, but the sensitivity of culture is more difficult to determine and may be low. Here, we apply Bayesian latent class models (LCMs) to data from patients with a single Gram-negative bacterial infection and define the true sensitivity of culture together with the impact of misclassification by culture on the reported accuracy of alternative diagnostic tests. METHODS/PRINCIPAL FINDINGS: Data from published studies describing the application of five diagnostic tests (culture and four serological tests) to a patient cohort with suspected melioidosis were re-analysed using several Bayesian LCMs. Sensitivities, specificities, and positive and negative predictive values (PPVs and NPVs) were calculated. Of 320 patients with suspected melioidosis, 119 (37%) had culture confirmed melioidosis. Using the final model (Bayesian LCM with conditional dependence between serological tests), the sensitivity of culture was estimated to be 60.2%. Prediction accuracy of the final model was assessed using a classification tool to grade patients according to the likelihood of melioidosis, which indicated that an estimated disease prevalence of 61.6% was credible. Estimates of sensitivities, specificities, PPVs and NPVs of four serological tests were significantly different from previously published values in which culture was used as the gold standard. CONCLUSIONS/SIGNIFICANCE: Culture has low sensitivity and low NPV for the diagnosis of melioidosis and is an imperfect gold standard against which to evaluate alternative tests. Models should be used to support the evaluation of diagnostic tests with an imperfect gold standard. It is likely that the poor sensitivity/specificity of culture is not specific for melioidosis, but rather a generic problem for many bacterial and fungal infections

Comparative analysis of genome-wide association studies signals for lipids, diabetes, and coronary heart disease: Cardiovascular Biomarker Genetics Collaboration

To evaluate the associations of emergent genome-wide-association study-derived coronary heart disease (CHD)-associated single nucleotide polymorphisms (SNPs) with established and emerging risk factors, and the association of genome-wide-association study-derived lipid-associated SNPs with other risk factors and CHD events

Method for the analysis of incomplete longitudinal data

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A Monte Carlo EM algorithm for random-coefficient-based dropout models

Longitudinal studies of neurological disorders suffer almost inevitably from non-compliance, which is likely to be non-ignorable. It is important in these cases to model the response variable and the dropout mechanism jointly. In this article we propose a Monte Carlo version of the EM algorithm that can be used to fit random-coefficient-based dropout models. A linear mixed model is assumed for the response variable and a discrete-time proportional hazards model for the dropout mechanism; these share a common set of random coefficients. The ideas are illustrated using data from a five-year trial assessing the efficacy of two drugs in the treatment of patients in the early stages of Parkinson's disease.

Genetic association mapping via evolution-based clustering of haplotypes.

Multilocus analysis of single nucleotide polymorphism haplotypes is a promising approach to dissecting the genetic basis of complex diseases. We propose a coalescent-based model for association mapping that potentially increases the power to detect disease-susceptibility variants in genetic association studies. The approach uses Bayesian partition modelling to cluster haplotypes with similar disease risks by exploiting evolutionary information. We focus on candidate gene regions with densely spaced markers and model chromosomal segments in high linkage disequilibrium therein assuming a perfect phylogeny. To make this assumption more realistic, we split the chromosomal region of interest into sub-regions or windows of high linkage disequilibrium. The haplotype space is then partitioned into disjoint clusters, within which the phenotype-haplotype association is assumed to be the same. For example, in case-control studies, we expect chromosomal segments bearing the causal variant on a common ancestral background to be more frequent among cases than controls, giving rise to two separate haplotype clusters. The novelty of our approach arises from the fact that the distance used for clustering haplotypes has an evolutionary interpretation, as haplotypes are clustered according to the time to their most recent common ancestor. Our approach is fully Bayesian and we develop a Markov Chain Monte Carlo algorithm to sample efficiently over the space of possible partitions. We compare the proposed approach to both single-marker analyses and recently proposed multi-marker methods and show that the Bayesian partition modelling performs similarly in localizing the causal allele while yielding lower false-positive rates. Also, the method is computationally quicker than other multi-marker approaches. We present an application to real genotype data from the CYP2D6 gene region, which has a confirmed role in drug metabolism, where we succeed in mapping the location of the susceptibility variant within a small error

Assessing uncertainty about parameter estimates with incomplete repeated ordinal data

Data collected in clinical trials involving follow-up of patients over a period of time will almost inevitably be incomplete. Patients will fail to turn up at some of the intended measurement times or will not complete the study, giving rise to various patterns of missingness. In these circumstances, the validity of the conclusions drawn from an analysis of available cases depends crucially on the mechanism driving the missing data process; this in turn cannot be known for certain. For incomplete categorical data, various authors have recently proposed taking into account in a systematic way the ignorance caused by incomplete data. In particular, the idea of intervals of ignorance has been introduced, whereby point estimates for parameters of interest are replaced by intervals or regions of ignorance (Vansteelandt and Goetghebeur, 2001; Kenward et al., 2001; Molenberghs et al., 2001). These are identified by the set of estimates corresponding to possible outcomes for the missing data under little or no assumptions about the missing data mechanism. Here we extend this idea to incomplete repeated ordinal data. We describe a modified version of standard algorithms used for fitting marginal models to longitudinal categorical data, which enables calculation of intervals of ignorance for the parameters of interest. The ideas are illustrated using dental pain measurements from a longitudinal clinical trial. </jats:p

A novel protective prion protein variant that colocalizes with kuru exposure.

BACKGROUND: Kuru is a devastating epidemic prion disease that affected a highly restricted geographic area of the Papua New Guinea highlands; at its peak, it predominantly affected adult women and children of both sexes. Its incidence has steadily declined since the cessation of its route of transmission, endocannibalism. METHODS: We performed genetic and selected clinical and genealogic assessments of more than 3000 persons from Eastern Highland populations, including 709 who participated in cannibalistic mortuary feasts, 152 of whom subsequently died of kuru. RESULTS: Persons who were exposed to kuru and survived the epidemic in Papua New Guinea are predominantly heterozygotes at the known resistance factor at codon 129 of the prion protein gene (PRNP). We now report a novel PRNP variant--G127V--that was found exclusively in people who lived in the region in which kuru was prevalent and that was present in half of the otherwise susceptible women from the region of highest exposure who were homozygous for methionine at PRNP codon 129. Although this allele is common in the area with the highest incidence of kuru, it is not found in patients with kuru and in unexposed population groups worldwide. Genealogic analysis reveals a significantly lower incidence of kuru in pedigrees that harbor the protective allele than in geographically matched control families. CONCLUSIONS: The 127V polymorphism is an acquired prion disease resistance factor selected during the kuru epidemic, rather than a pathogenic mutation that could have triggered the kuru epidemic. Variants at codons 127 and 129 of PRNP demonstrate the population genetic response to an epidemic of prion disease and represent a powerful episode of recent selection in humans