Characterization of a Bayesian genetic clustering algorithm based on a Dirichlet process prior and comparison among Bayesian clustering methods

Recently developed Bayesian genotypic clustering methods for analysing genetic data offer a powerful tool to evaluate the genetic structure of domestic farm animal breeds. The unit of study with these approaches is the individual instead of the population. We aimed to empirically evaluate various individual-based population genetic statistical methods for characterization of genetic diversity and structure of livestock breeds. Eighteen British pig populations, comprising 819 individuals, were genotyped at 46 microsatellite markers. Three Bayesian genotypic clustering approaches, principle component analysis (PCA) and phylogenetic reconstruction were applied to individual multilocus genotypes to infer the genetic structure and diversity of the British pig breeds. Comparisons of the three Bayesian genotypic clustering methods (STRUCTURE, BAPS and STRUCTURAMA) revealed some broad similarities but also some notable differences. Overall, the methods agreed that majority of the British pig breeds are independent genetic units with little evidence of admixture. The three Bayesian genotypic clustering methods provided complementary, biologically credible clustering solutions but at different levels of resolution. BAPS detected finer genetic differentiation and in some cases, populations within breeds. Consequently, it estimated a greater number of underlying genetic populations (K, in the notation of Bayesian clustering methods). Two of the Bayesian methods (STRUCTURE and BAPS) and phylogenetic reconstruction provided similar success in assignment of individuals, supporting the use of these methods for breed assignment.

Microbial and epidemiological factors in early detection of esophageal squamous cell carcinoma and precancerous lesions.

Sampling across tree space is one of the major challenges in Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC) algorithms. Standard MCMC tree moves consider small random perturbations of the topology, and select from candidate trees at random or based on the distance between the old and new topologies. MCMC algorithms using such moves tend to get trapped in tree space, making them slow in finding the globally most probable trees (known as “convergence”) and in estimating the correct proportions of the different types of them (known as “mixing”). Here, we introduce a new class of moves, which propose trees based on their parsimony scores. The proposal distribution derived from the parsimony scores is a quickly computable albeit rough approximation of the conditional posterior distribution over candidate trees. We demonstrate with simulations that parsimony-guided moves correctly sample the uniform distribution of topologies from the prior. We then evaluate their performance against standard moves using six challenging empirical data sets, for which we were able to obtain accurate reference estimates of the posterior using long MCMC runs, a mix of topology proposals, and Metropolis coupling. On these data sets, ranging in size from 357 to 934 taxa and from 1740 to 5681 sites, we find that single chains using parsimony-guided moves usually converge an order of magnitude faster than chains using standard moves. They also exhibit better mixing, that is, they cover the most probable trees more quickly. Our results show that tree moves based on quick and dirty estimates of the posterior probability can significantly outperform standard moves. Future research will have to show to what extent the performance of such moves can be improved further by finding better ways of approximating the posterior probability, taking the trade-off between accuracy and speed into account. [Bayesian phylogenetic inference; MCMC; parsimony; tree proposal.]

In this study, we quantified the 3 pivotal genetic processes (i.e., genetic diversity, spatial genetic structuring, and migration) necessary for a better biological understanding and management of the singular "living-fossil" and near-threatened mouse opossum marsupial Dromiciops gliroides, the "Monito del Monte," in south-central Chile. We used 11 microsatellite loci to genotype 47 individuals distributed on the mainland and northern Chiloé Island. Allelic richness, observed and expected heterozygosity, inbreeding coefficient, and levels of genetic differentiation were estimated. The genetic structure was assessed based on Bayesian clustering methods. In addition, potential migration scenarios were evaluated based on a coalescent theory framework and Bayesian approach to parameter estimations. Microsatellites revealed moderate to high levels of genetic diversity across sampled localities. Moreover, such molecular markers suggested that at least 2 consistent genetic clusters could be identified along the D. gliroides distribution ("Northern" and "Southern" cluster). However, general levels of genetic differentiation observed among localities and between the 2 genetic clusters were relatively low. Migration analyses showed that the most likely routes of migration of D. gliroides occurred 1) from the Southern cluster to the Northern cluster and 2) from the Mainland to Chiloé Island. Our results could represent critical information for future conservation programs and for a recent proposal about the taxonomic status of this unique mouse opossum marsupial.

The small brown planthopper (SBPH), Laodelphax striatellus, is one of the most destructive agricultural pests that causes serious economic loss in the main rice‐producing areas of China. To clarify issues such as the genetic differentiation, gene flow and population genetic structure of SBPH populations, we investigated the genetic diversity, genetic structure and phylogeography of 27 SBPH populations at 23 sampling sites from three climatic zones of China using specific‐locus amplified fragment sequencing (SLAF‐seq) for large‐scale single nucleotide polymorphism (SNP) detection. In total, 115.95 M reads, 56,355 polymorphic specific‐locus amplified fragments were developed, and 32,556 reliable single nucleotides (SNPs) were detected. The results indicated that the genotypes of many polymorphism sites had low heterozygosity in every population. Overall, the pairwise FST values between the populations varied from 0.056 to 0.092; it suggested the lack of strong differentiation among three climatic zone groups, respectively. It also suggested a strong level of gene flow (Nm) among populations in different climate zones, ranging from 28.864 to 35.197. Phylogenetic analyses, principal component analysis (PCA), Bayesian clustering method and AMOVA revealed that there was no evidence genetic clustering in three main bioclimatic zones. Neutrality testing provided strong evidence for a recent rapid expansion without any recent genetic bottleneck in these populations. Accordingly, the results of the present study should be beneficial for SBPH management and provide insight into the genetics of SBPH.

Analysis of population genetic variation and structure is a common practice for genome-wide studies, including association mapping, ecology, and evolution studies in several crop species. In this study, machine learning (ML) clustering methods, K-means (KM), and hierarchical clustering (HC), in combination with non-linear and linear dimensionality reduction techniques, deep autoencoder (DeepAE) and principal component analysis (PCA), were used to infer population structure and individual assignment of maize inbred lines, i.e., dent field corn (n = 97) and popcorn (n = 86). The results revealed that the HC method in combination with DeepAE-based data preprocessing (DeepAE-HC) was the most effective method to assign individuals to clusters (with 96% of correct individual assignments), whereas DeepAE-KM, PCA-HC, and PCA-KM were assigned correctly 92, 89, and 81% of the lines, respectively. These findings were consistent with both Silhouette Coefficient (SC) and Davies–Bouldin validation indexes. Notably, DeepAE-HC also had better accuracy than the Bayesian clustering method implemented in InStruct. The results of this study showed that deep learning (DL)-based dimensional reduction combined with ML clustering methods is a useful tool to determine genetically differentiated groups and to assign individuals into subpopulations in genome-wide studies without having to consider previous genetic assumptions.

Genome-wide SNP discovery from Darjeeling tea cultivars - their functional impacts and application toward population structure and trait associations

▪ Abstract Bayesian data analysis relies on Bayes' Theorem, using data to update prior beliefs about parameters. In this review I introduce and contrast Bayesian analysis with conventional frequentist inference and then distinguish two types of Bayesian analysis in political science. First, Bayesian analysis is used to merge historical information with current data in an analysis of likely election outcomes in Florida in 2000; particular attention is paid to the sensitivity of the results to the choice of prior (i.e., how much confidence one places in the historical information). Second, a more “modern” style of Bayesian analysis is reviewed, relying on Markov chain Monte Carlo algorithms to generate computationally intensive “random tours” of the high dimensional posterior distributions that are the focus of many contemporary Bayesian analyses; the example used is a central problem in political science, the analysis of legislators' preferences using roll call data.

This paper presents a new Bayesian clustering method to analyse remote scenes sensed via multispectral Lidar measurements. To a first approximation, each Lidar waveform mainly consists of the temporal signature of the observed target, which depends on the wavelength of the laser source considered and which is corrupted by Poisson noise. By sensing the scene at several wavelengths, we expect a more accurate target range estimation and a more efficient spectral analysis of the scene. Thanks to its spectral classification capability, the proposed hierarchical Bayesian model, coupled with an efficient Markov chain Monte Carlo algorithm, allows the estimation of depth images together with reflectivity-based scene segmentation images. The proposed methodology is illustrated via experiments conducted with real multispectral Lidar data.

Maximum likelihood estimation techniques are widely used in twin and family studies, but soon reach computational boundaries when applied to highly complex models (e.g., models including gene-by-environment interaction and gene-environment correlation, item response theory measurement models, repeated measures, longitudinal structures, extended pedigrees). Markov Chain Monte Carlo (MCMC) algorithms are very well suited to fit complex models with hierarchically structured data. This article introduces the key concepts of Bayesian inference and MCMC parameter estimation and provides a number of scripts describing relatively simple models to be estimated by the freely obtainable BUGS software. In addition, inference using BUGS is illustrated using a data set on follicle-stimulating hormone and luteinizing hormone levels with repeated measures. The examples provided can serve as stepping stones for more complicated models, tailored to the specific needs of the individual researcher.

Spatial Poisson models for areal count data use nonstationary “intrinsic autoregressions,” also often referred to as “conditionally autoregressive” (CAR) models. Bayesian inference for these models has generally involved using single parameter updating Markov chain Monte Carlo algorithms, which often exhibit slow mixing (i.e., poor convergence) properties. These spatial models are richly parameterized and lend themselves to the structured Markov chain Monte Carlo (SMCMC) algorithms. SMCMC provides a simple, general, and flexible framework for accelerating convergence in an MCMC sampler by providing a systematic way to block groups of similar parameters while taking full advantage of the posterior correlation structure induced by the model and data. Among the SMCMC strategies considered here are blocking using different size blocks (grouping by geographical region), reparameterization, updating jointly with and without model hyperparameters, “oversampling” some of the model parameters, and “pilot adaptation” versus continuous tuning techniques for the proposal density. We apply the techniques presented here to datasets on cancer mortality and late detection in the state of Minnesota. We find that, compared to univariate sampling procedures, our techniques will typically lead to more accurate posterior estimates, and they are sometimes also far more efficient in terms of the number of effective samples generated per second.

Using Bayesian inference to calibrate constitutive model parameters has recently seen a rise in interest. The Markov chain Monte Carlo (MCMC) algorithm is one of the most commonly used methods to sample from the posterior. However, the choice of which MCMC algorithm to apply is typically pragmatic and based on considerations such as software availability and experience. We compare three commonly used MCMC algorithms: Metropolis-Hastings (MH), Affine Invariant Stretch Move (AISM) and No-U-Turn sampler (NUTS). For the comparison, we use the Kullback-Leibler (KL) divergence as a convergence criterion, which measures the statistical distance between the sampled and the ‘true’ posterior. We apply the Bayesian framework to a Newtonian squeeze flow problem, for which there exists an analytical model. Furthermore, we have collected experimental data using a tailored setup. The ground truth for the posterior is obtained by evaluating it on a uniform reference grid. We conclude that, for the same number of samples, the NUTS results in the lowest KL divergence, followed by the AISM sampler and last the MH sampler.

Parsimonious Bayesian model-based clustering with dissimilarities

The increasing use of electronic health records (EHRs) in medical research and AI-driven healthcare necessitates high-fidelity synthetic data that balances patient privacy with statistical and clinical utility. Traditional generative models, such as generative adversarial networks (GANs) and variational autoencoders (VAEs), struggle with mode collapse, limited sample diversity, and difficulties in modelling complex dependencies in high-dimensional tabular data. This study introduces a diffusion model-based approach for generating synthetic EHR data and evaluates its utility in clustering multimorbidity patterns using Dirichlet process mixture models (DPMMs). Denoising diffusion probabilistic models (DDPMs) iteratively refine noise through a structured denoising process, producing diverse, high-fidelity synthetic records. The DPMM framework, a Bayesian nonparametric clustering method, dynamically determines the number of clusters, effectively handling heterogeneous, imbalanced datasets. Model evaluation incorporates statistical similarity measures, feature correlation analysis, privacy risk assessments, and predictive performance metrics. Results demonstrate that DDPM-generated data surpasses GANs and VAEs in fidelity (Jensen–Shannon divergence (JSD) = 0.020, Pearson pairwise correlation (PPC) = 0.94), and privacy preservation (membership inference attack (MIA) Risk = 0.25). DPMM clustering reveals clinically meaningful disease patterns, outperforming traditional clustering models. These findings highlight the potential of diffusion models for privacy-preserving synthetic EHR generation and robust multimorbidity clustering in healthcare analytics.

Low levels of genetic variation are thought to contribute significantly to the higher extinction rates of endemic island populations compared to their mainland counterparts. We used six microsatellite loci to compare the genetic structure of the endangered silver rice rat (Oryzomys argentatus) population in Saddlebunch Key, Florida to the mainland population of the closely related marsh rice rat (Oryzomys palustris natator) in Everglades National Park. Allelic richness and gene diversity are significantly lower in Saddlebunch Key than in the larger mainland population, and the two populations are significantly differentiated as measured by both F-statistics and Bayesian clustering methods. These findings support the classification of the Keys population as a “distinct vertebrate population” by the U.S. Fish and Wildlife Service. Current gene diversity (H E) is higher than expected under mutation-drift equilibrium in Saddlebunch Key, indicating a genetic bottleneck. The Keys population also exhibits a mode shift in its allele frequency distribution which suggests a very recent bottleneck has occurred and is consistent with reports of recent population declines. Although habitat loss and exotic species pose a more immediate and serious threat to silver rice rats, the continued loss of genetic variation may contribute to their long-term extinction risk due to inbreeding or by lowering the population’s ability to adapt to future environmental changes. The protection of habitat and the removal of introduced predators and competitors may help increase the population size of silver rice rats and lower their risk of extinction, both from a demographic and a genetic perspective.