Bayesian Model-based Analysis Research Articles

Promiscuous potato: elucidating genetic identity and the complex genetic relationships of a cultivated potato germplasm collection.

A total of 3,860 accessions from the global in trust clonal potato germplasm collection w3ere genotyped with the Illumina Infinium SolCAP V2 12K potato SNP array to evaluate genetic diversity and population structure within the potato germplasm collection. Diploid, triploid, tetraploid, and pentaploid accessions were included representing the cultivated potato taxa. Heterozygosity ranged from 9.7% to 66.6% increasing with ploidy level with an average heterozygosity of 33.5%. Identity, relatedness, and ancestry were evaluated using hierarchal clustering and model-based Bayesian admixture analyses. Errors in genetic identity were revealed in a side-by-side comparison of in vitro clonal material with the original mother plants revealing mistakes putatively occurring during decades of processing and handling. A phylogeny was constructed to evaluate inter- and intraspecific relationships which together with a STRUCTURE analysis supported both commonly used treatments of potato taxonomy. Accessions generally clustered based on taxonomic and ploidy classifications with some exceptions but did not consistently cluster by geographic origin. STRUCTURE analysis identified putative hybrids and suggested six genetic clusters in the cultivated potato collection with extensive gene flow occurring among the potato populations, implying most populations readily shared alleles and that introgression is common in potato. Solanum tuberosum subsp. andigena (ADG) and S. curtilobum (CUR) displayed significant admixture. ADG likely has extensive admixture due to its broad geographic distribution. Solanum phureja (PHU), Solanum chaucha (CHA)/Solanum stenotomum subsp. stenotomum (STN), and Solanum tuberosum subsp. tuberosum (TBR) populations had less admixture from an accession/population perspective relative to the species evaluated. A core and mini core subset from the genebank material was also constructed. SNP genotyping was also carried out on 745 accessions from the Seed Savers potato collection which confirmed no genetic duplication between the two potato collections, suggesting that the collections hold very different genetic resources of potato. The Infinium SNP Potato Array is a powerful tool that can provide diversity assessments, fingerprint genebank accessions for quality management programs, use in research and breeding, and provide insights into the complex genetic structure and hybrid origin of the diversity present in potato genetic resource collections.

Deep Learning-Informed Bayesian Model-Based Analysis to Estimate Superspreading Events in Epidemic Outbreaks

Deep Learning-Informed Bayesian Model-Based Analysis to Estimate Superspreading Events in Epidemic Outbreaks

Accurately summarizing an outbreak using epidemiological models takes time.

Recent outbreaks of Mpox and Ebola, and worrying waves of COVID-19, influenza and respiratory syncytial virus, have all led to a sharp increase in the use of epidemiological models to estimate key epidemiological parameters. The feasibility of this estimation task is known as the practical identifiability (PI) problem. Here, we investigate the PI of eight commonly reported statistics of the classic susceptible-infectious-recovered model using a new measure that shows how much a researcher can expect to learn in a model-based Bayesian analysis of prevalence data. Our findings show that the basic reproductive number and final outbreak size are often poorly identified, with learning exceeding that of individual model parameters only in the early stages of an outbreak. The peak intensity, peak timing and initial growth rate are better identified, being in expectation over 20 times more probable having seen the data by the time the underlying outbreak peaks. We then test PI for a variety of true parameter combinations and find that PI is especially problematic in slow-growing or less-severe outbreaks. These results add to the growing body of literature questioning the reliability of inferences from epidemiological models when limited data are available.

Modeling Multivariate Spatial Dependencies Using Graphical Models.

Graphical models have witnessed significant growth and usage in spatial data science for modeling data referenced over a massive number of spatial-temporal coordinates. Much of this literature has focused on a single or relatively few spatially dependent outcomes. Recent attention has focused upon addressing modeling and inference for substantially large number of outcomes. While spatial factor models and multivariate basis expansions occupy a prominent place in this domain, this article elucidates a recent approach, graphical Gaussian Processes, that exploits the notion of conditional independence among a very large number of spatial processes to build scalable graphical models for fully model-based Bayesian analysis of multivariate spatial data.

Analysis of Genetic Diversity and Population Structure of Cowpea (Vigna unguiculata (L.) Walp) Genotypes Using Single Nucleotide Polymorphism Markers.

Cowpea (Vigna unguiculata (L.) Walp) is an important legume crop with immense potential for nutritional and food security, income generation, and livestock feed in Sub-Saharan Africa. The crop is highly tolerant to heat and drought stresses which makes it an extremely important crop for improving resilience in crop production in the face of climate change. This study was carried out to assess the genetic diversity and population structure of 90 cowpea accessions using single nucleotide polymorphism (SNP) markers. Out of 11,940 SNPs used, 5864 SNPs were polymorphic and maintained for genome diversity analysis. Polymorphic information content (PIC) values ranged from 0.22 to 0.32 with a mean value of 0.27. The model-based Bayesian STRUCTURE analysis classified 90 cowpea accessions into four subpopulations at K = 4, while the distance-based cluster analysis grouped the accessions into three distinct clusters. The analysis of molecular variance (AMOVA) revealed that 59% and 69% of the total molecular variation was attributed to among individual variation for model-based and distance-based populations, respectively, and 18% was attributed to within individual variations. Furthermore, the low heterozygosity among cowpea accessions and the high inbreeding coefficient observed in this study suggests that the accessions reached an acceptable level of homozygosity. This study would serve as a reference for future selection and breeding programs of cowpea with desirable traits and systematic conservation of these plant genetic resources.

Aspergillus flavus genetic structure at a turkey farm.

The ubiquitous environmental fungus Aspergillus flavus is also a life-threatening avian pathogen. This study aimed to assess the genetic diversity and population structure of A. flavus isolated from turkey lung biopsy or environmental samples collected in a poultry farm. A. flavus isolates were identified using both morphological and ITS sequence features. Multilocus microsatellite genotyping was performed by using a panel of six microsatellite markers. Population genetic indices were computed using FSTAT and STRUCTURE. A minimum-spanning tree (MST) and UPGMA dendrogram were drawn using BioNumerics and NTSYS-PC, respectively. The 63 environmental (air, surfaces, eggshells and food) A. flavus isolates clustered in 36 genotypes (genotypic diversity=0.57), and the 19 turkey lung biopsies isolates clustered in 17 genotypes (genotypic diversity=0.89). The genetic structure of environmental and avian A. flavus populations were clearly differentiated, according to both F-statistics and Bayesian model-based analysis' results. The Bayesian approach indicated gene flow between both A. flavus populations. The MST illustrated the genetic structure of this A. flavus population split in nine clusters, including six singletons. Our results highlight the distinct genetic structure of environmental and avian A. flavus populations, indicative of a genome-based adaptation of isolates involved in avian aspergillosis.

DArTSeq SNP-based genetic diversity and population structure studies among taro [(Colocasia esculenta (L.) Schott] accessions sourced from Nigeria and Vanuatu.

Taro is a valuable staple food crop among resource-poor rural people in countries such as Nigeria and Ghana, among others. Characterization of genetic diversity is a prerequisite for proper management of breeding programs and conservation of genetic resources. Two hundred seventy one taro accessions obtained from Nigeria and Vanuatu were genotyped using DArTseq-based SNP markers with the objectives of investigating the genetic diversity and population structure. In the analysis, 10,391 SNP markers were filtered from the sequence and used. The analysis revealed higher transition than transversion types of SNPs in the ratio of 1.43:1. The polymorphism ranged from 0.26 to 0.29 for the markers, indicating moderate genetic diversity. A model-based Bayesian clustering analysis of taro accessions yielded five subgroups and revealed the admixture situation in 19.19% of all accessions in the study. Vanuatu taro accessions exhibited more genetic diversity than Nigerian taro accessions. The population diversity estimate (PhiPt) was relatively higher (0.52) for accessions originating from Vanuatu than for Nigerian accessions. Analysis of molecular variance (AMOVA) revealed that most variation existed among individuals within a population at 52%. Nei's genetic distance showed that relatedness is based on geographical proximity. Collection of taro genetic resources should give more emphasis to within regions to utilize diversity in taro breeding program. This study also demonstrated the efficiency of DArTseq-based SNP genotyping for large-scale genome analysis in taro. The genotypic markers provided in this study are useful for association mapping studies.

Role of multiresolution vulnerability indices in COVID-19 spread in India: a Bayesian model-based analysis

ObjectivesCOVID-19 has differentially affected countries, with health infrastructure and other related vulnerability indicators playing a role in determining the extent of its spread. Vulnerability of a geographical region to COVID-19...

On Hierarchical Bayesian Spatial Small Area Model for Binary Data under Spatial Misalignment

Small area models have become popular methods for producing reliable estimates for sub-populations (small geographic areas in this study). Small area modeling may be carried out via model-assisted approaches within the model-based approaches or design-based paradigm. When there are medium or large samples, a model-assisted approach may be reliable. However, when data are scarce, a model-based technique may be required. Model-based Bayesian analysis is popular for its ability to combine information from several sources as well as taking account uncertainties in the analysis and spatial prediction of spatial data. Nevertheless, things become more complex when the geographic boundaries of interest are misaligned. Some authors have addressed the problem of misalignment under hierarchical Bayesian approach. In this study, we developed non-trivial extension of existing hierarchical Bayesian model for a binary outcome variable under spatial misalignment with three contributions. First, the model uses unit-level survey data and area-level auxiliary data to predict the posterior mean proportion spatially at the second geographic area level. Second, the linking model is changed to logit-normal model in the proposed model. Lastly, the mean process was considered to overcome the multicollinearity between the true predictors and the spatial random effect. Sensitivity analysis was also done via simulation.

Intra and between population genetic relationships among wild cherries [Prunus avium (L.) L.] based on molecular markers

Wild cherries [Prunus avium (L.) L.] are considered typical trees of the Slovenian landscape and often form vigorous natural populations. The determination of genetic relationships among cultivated and wild cherry tree genotypes is important for scientific and economic reasons. Both old trees and young plants were included in our genetic analysis. Sampling took place at two different locations near Maribor, NE Slovenia. The aim of the study was to determine the genetic relationships and variability among plants belonging to the studied populations. We focused on young trees and seedlings growing around old trees, which could be their hypothetical parents. We also analysed genetic uniformity within populations and identified genetically distant plants that had likely migrated as seeds from other populations. Three microsatellite loci were taken into account, 38 genotypes were examined, and 19 polymorphic alleles were detected, corresponding to an average of 6.3 per locus. Genetic distances between the studied genotypes were calculated using the Jaccard’s coefficient and a dendrogram was constructed. Genetic structure was analysed using both Principal Coordinate Analysis (PCoA) and Bayesian model-based analysis. Our study showed that there were differences between the two populations, however, there was some “communication” between them. We observed genetic relatedness as well as a relatively high level of uniformity within each of the wild cherry populations.

Bayesian modeling and simulation to inform rare disease drug development early decision-making: Application to Duchenne muscular dystrophy.

Rare disease clinical trials are constrained to small sample sizes and may lack placebo-control, leading to challenges in drug development. This paper proposes a Bayesian model-based framework for early go/no-go decision making in rare disease drug development, using Duchenne muscular dystrophy (DMD) as an example. Early go/no-go decisions were based on projections of long-term functional outcomes from a Bayesian model-based analysis of short-term trial data informed by prior knowledge based on 6MWT natural history literature data in DMD patients. Frequentist hypothesis tests were also applied as a reference analysis method. A number of combinations of hypothetical trial designs, drug effects and cohort comparison methods were assessed. The proposed Bayesian model-based framework was superior to the frequentist method for making go/no-go decisions across all trial designs and cohort comparison methods in DMD. The average decision accuracy rates across all trial designs for the Bayesian and frequentist analysis methods were 45.8 and 8.98%, respectively. A decision accuracy rate of at least 50% was achieved for 42 and 7% of the trial designs under the Bayesian and frequentist analysis methods, respectively. The frequentist method was limited to the short-term trial data only, while the Bayesian methods were informed with both the short-term data and prior information. The specific results of the DMD case study were limited due to incomplete specification of individual-specific covariates in the natural history literature data and should be reevaluated using a full natural history dataset. These limitations aside, the framework presented provides a proof of concept for the utility of Bayesian model-based methods for decision making in rare disease trials.

Genome –wide Allelic Variations and Population Structure Analysis of Cotton Genotypes using SSR Markers

The limited genetic base of modern cotton genotypes makes it difficult to improve cotton fibre yield and quality, emphasizing the necessity to study existing cotton germplasm resources. Therefore, this study was conducted to assess the genetic diversity and population structure of different cotton genotypes at DNA level. A total of 105 cotton genotypes were analyzed using 208 polymorphic SSR markers which were distributed on both A and D genomes. The total number of alleles in all genomes are 1382 Out of which 665 and 717 alleles were recorded in A and D genomes, respectively. In A genome, the maximum PIC values; 0.89 was observed for markers M92, M82 and M76 with the 10-total number of alleles exhibiting 100% polymorphism. These markers (M92, M82 and M76) located at the chromosomes 12A, 11A and 10A respectively with the estimation of 0.84 gene diversity. The highest PIC value 0.89 of D genome was detected for M191, M128 and M117 markers on 24D, 16D and 15D chromosomes with the 10 total numbers of alleles having the 0.84 values of gene diversity with 100% polymorphism. The UPGMA cluster DARWIN tree and Model-based Bayesian analysis clustered the studied cotton germplasm into four subpopulations, and the highest molecular variation revealed among subpopulations. Phylogenetic tree constructed for the whole germplasm population identified four major groups including; Group 1, Group 2, Group 3 and Group 4 consisting of 12, 21, 39 and 69 genotypes, respectively. Genetic distances among clusters clearly showed the variations among one another. SSR markers appear to be useful and reliable assets for future genetic and genomic analyses of cotton, such as genetic map construction, comparative genomic analyses, genetic diversity, and molecular marker-assisted breeding to select promising cotton genotypes, according to the current experiment.

A Model-Based Bayesian Framework for Pipeline Leakage Enumeration and Location Estimation

With the development of cities, the water resources loss and environmental pollution caused by pipeline leakage need to be solved urgently. In this paper, a probabilistic method of model-based Bayesian analysis is designed to solve the multi-leakage detection problem of reservoir pipeline valve system. Bayesian inference selects the model best suited to the measured data. This process estimates the number of leaks and then extracts the leak locations from a model that measures data preferences. In this paper, according to the characteristics of water head in pipeline, the Likelihood function of water head for Bayesian evidence calculation is given. It solves the problem that the location ability of recent research methods is limited by leakage location. The number and locations of leakages can be determined simultaneously. Different experimental Settings and scenarios are given to verify the effectiveness of the proposed method. For three leaks that do not contain tight leaks, the RMSE of each leak is 2.3068 m, and in the case of tight leaks, the average RMSE of each leak is 3.5011 m. The results demonstrating that this model-based Bayesian analysis is an accurate tool for leakage enumeration and location estimation.

Carnivorous mammals from the middle Eocene Washakie Formation, Wyoming, USA, and their diversity trajectory in a post-warming world

AbstractThe middle Eocene Washakie Formation of Wyoming, USA, provides a rare window, within a single depositional basin, into the faunal transition that followed the early Eocene warming events. Based on extensive examination, we report a minimum of 27 species of carnivorous mammals from this formation, more than doubling the previous taxic count. Included in this revised list are a new species of carnivoraform,Neovulpavus mccarrollin. sp., and up to ten other possibly new taxa. Our cladistic analysis of early Carnivoraformes incorporating new data clarified the array of middle Eocene taxa that are closely related to crown-group Carnivora. These anatomically relatively derived carnivoraforms collectively had an intercontinental distribution in North America and east Asia, exhibiting notable variations in body size and dental adaptation. This time period also saw parallel trends of increase in body size and dental sectoriality in distantly related lineages of carnivores spanning a wide range of body sizes. A new, model-based Bayesian analysis of diversity dynamics accounting for imperfect detection revealed a high probability of substantial loss of carnivore species between the late Bridgerian and early Uintan North American Land Mammal ‘Ages’, coinciding with the disappearance of formerly common mammals such as hyopsodontids and adapiform primates. Concomitant with this decline in carnivore diversity, the Washakie vertebrate fauna underwent significant disintegration, as measured by patterns of coordinated detection of taxa at the locality level. These observations are consistent with a major biomic transition in the region in response to climatically induced opening-up of forested habitats.UUID:http://zoobank.org/9162f1a6-a12c-4d55-ba1d-dc66e8cda261

Genetic Structure and Core Collection of Olive Germplasm from Albania Revealed by Microsatellite Markers.

Olive is considered one of the oldest and the most important cultivated fruit trees in Albania. In the present study, the genetic diversity and structure of Albanian olive germplasm is represented by a set of 194 olive genotypes collected in-situ in their natural ecosystems and in the ex-situ collection. The study was conducted using 26 microsatellite markers (14 genomic SSR and 12 Expressed Sequence Tag microsatellites). The identity analysis revealed 183 unique genotypes. Genetic distance-based and model-based Bayesian analyses were used to investigate the genetic diversity, relatedness, and the partitioning of the genetic variability among the Albanian olive germplasm. The genetic distance-based analysis grouped olives into 12 clusters, with an average similarity of 50.9%. Albanian native olives clustered in one main group separated from introduced foreign cultivars, which was also supported by Principal Coordinate Analysis (PCoA) and model-based methods. A core collection of 57 genotypes representing all allelic richness found in Albanian germplasm was developed for the first time. Herein, we report the first extended genetic characterization and structure of olive germplasm in Albania. The findings suggest that Albanian olive germplasm is a unique gene pool and provides an interesting genetic basis for breeding programs.

Towards Real-time, On-board, Hardware-supported Sensor and Software Health Management for Unmanned Aerial Systems

For unmanned aerial systems (UAS) to be successfully deployed and integrated within the national airspace, it is imperative that they possess the capability to effectively complete their missions without compromising the safety of other aircraft, as well as persons and property on the ground. This necessity creates a natural requirement for UAS that can respondto uncertain environmental conditions and emergent failures in real-time, with robustness and resilience close enough to those of manned systems. We introduce a system that meets this requirement with the design of a real-time onboard system health management (SHM) capability to continuously monitor sensors, software, and hardware components. This system can detect and diagnose failures and violations of safety or performance rules during the flight of a UAS. Our approach to SHM is three-pronged, providing: (1) real-time monitoring of sensor and software signals; (2) signal analysis, preprocessing, and advanced on-the-fly temporal and Bayesian probabilistic fault diagnosis; and (3) an unobtrusive, lightweight, read-only, low-power realization using Field Programmable Gate Arrays (FPGAs) that avoids overburdening limited computing resources or costly re-certification of flight software. We call this approach rt-R2U2, a name derived from its requirements. Our implementation provides a novel approach of combining modular building blocks, integrating responsive runtime monitoring of temporal logic system safety requirements with model-based diagnosis and Bayesian network-based probabilistic analysis. We demonstrate this approach using actual flight data from theNASA Swift UAS.

Model-based Bayesian analysis in acoustics-A tutorial.

Bayesian analysis has been increasingly applied in many acoustical applications. In these applications, prediction models are often involved to better understand the process under investigation by purposely learning from the experimental observations. When involving the model-based data analysis within a Bayesian framework, issues related to incorporating the experimental data and assigning probabilities into the inferential learning procedure need fundamental consideration. This paper introduces Bayesian probability theory on a tutorial level, including fundamental rules for manipulating the probabilities, and the principle of maximum entropy for assignment of necessary probabilities prior to the data analysis. This paper also employs a number of examples recently published in this journal to explain detailed steps on how to apply the model-based Bayesian inference to solving acoustical problems.

Selective kappa-opioid antagonism ameliorates anhedonic behavior: evidence from the Fast-fail Trial in Mood and Anxiety Spectrum Disorders (FAST-MAS).

Anhedonia remains a major clinical issue for which there is few effective interventions. Untreated or poorly controlled anhedonia has been linked to worse disease course and increased suicidal behavior across disorders. Taking a proof-of-mechanism approach under the auspices of the National Institute of Mental Health FAST-FAIL initiative, we were the first to show that, in a transdiagnostic sample screened for elevated self-reported anhedonia, 8 weeks of treatment with a kappa-opioid receptor (KOR) antagonist resulted in significantly higher reward-related activation in one of the core hubs of the brain reward system (the ventral striatum), better reward learning in the Probabilistic Reward Task (PRT), and lower anhedonic symptoms, relative to 8 weeks of placebo. Here, we performed secondary analyses of the PRT data to investigate the putative effects of KOR antagonism on anhedonic behavior with more precision by using trial-level model-based Bayesian computational modeling and probability analyses. We found that, relative to placebo, KOR antagonism resulted in significantly higher learning rate (i.e., ability to learn from reward feedback) and a more sustained preference toward the more frequently rewarded stimulus, but unaltered reward sensitivity (i.e., the hedonic response to reward feedback). Collectively, these findings provide novel evidence that in a transdiagnostic sample characterized by elevated anhedonia, KOR antagonism improved the ability to modulate behavior as a function of prior rewards. Together with confirmation of target engagement in the primary report (Krystal et al., Nat Med, 2020), the current findings suggest that further transdiagnostic investigation of KOR antagonism for anhedonia is warranted.

Appraisal of spatial and temporal behavior in monsoon precipitation series of Punjab-Pakistan using hierarchical Bayesian Models

Precipitation shows that structured variation across space and time and the pattern of this variation is changing round the world due to global warming. These changes affect the water resources and hydrological cycle. A spatio-temporal dataset of precipitation records together with data on temperature, wind speed and humidity is used to analyze changes in spatiotemporal pattern of precipitation across 17 major meteorological stations of Punjab-Pakistan for monsoon period during 2004–2012. A distinguishable trend in precipitation structure along spatial and temporal domains is noticed. We consider a model-based bayesian spatio-temporal analysis using dynamic linear model and gaussian process model. The meteorological variables (covariates) temperature, wind speed and humidity show varying effect on the precipitation at different locations and time points. Additionally, we construct the spatial and temporal boxplots of all meteorological stations and time points to observe the distribution of precipitation and covariates at micro level. Further to this, the interval plots also reveal the spatially varying and temporally dynamic pattern of covariates. The prediction results through predictive contour plots show high and low precipitation zones in the study area and are helpful for policy makers to identify the homogeneous climate.

New insights on spatial genetic structure and diversity of Coffea canephora (Rubiaceae) in Upper Guinea based on old herbaria

Backgrounds and aims – Previous studies showed that robusta coffee (Coffea canephora Pierre ex A.Froehner), one of the two cultivated coffee species worldwide, can be classified in two genetic groups: the Guinean group originating in Upper Guinea and the Congolese group in Lower Guinea and Congolia. Although C. canephora of the Guinean group is an important resource for genetic improvement of robusta coffee, its germplasm is under-represented in ex situ gene banks and its genetic diversity and population structure have not yet been investigated. Methods – To overcome the limitations of living collections, we explored old herbarium specimens collected in Guinea and Côte d’Ivoire and conserved at the Muséum National d’Histoire Naturelle, Paris. First, we reviewed the history of collection missions in both countries and how the C. canephora herbaria from the Muséum were assembled. Then, using 23 nuclear microsatellite markers, factorial and model-based Bayesian analyses, we investigated the genetic diversity of 126 specimens and 36 controls, analysed their distribution among the Congolese and Guinean groups, and estimated admixture proportions for each individual.Key results – For the first time, we detected population genetic structure within the Guinean group of C. canephora. The Guinean genotypes can be assigned to five sub-groups with distinct geographic distribution, especially in Guinea where two sub-groups (Maclaudii and Gamé) are characterized by a low level of admixture due to geographical isolation.Conclusions – We showed how combining a literature review and genetic data from old herbarium specimens can shed light on previous observations made by botanists and guide further actions to better preserve native coffee plants in forest remnants of West Africa.