scholarly journals Probabilistic estimation of identity by descent segment endpoints and detection of recent selection

2020 ◽  
Author(s):  
Sharon R. Browning ◽  
Brian L. Browning
Keyword(s):  
Posterior Probability ◽  
Computationally Efficient ◽  
Identity By Descent ◽  
Posterior Probability Distribution ◽  
Fast Detection ◽  
Probabilistic Estimation ◽  
Unbiased Estimates ◽  
The Uk ◽  
Identity By State ◽  
Recent Selection

AbstractMost methods for fast detection of identity by descent (IBD) segments report identity by state segments without any quantification of the uncertainty in the endpoints and lengths of the IBD segments. We present a method for determining the posterior probability distribution of IBD segment endpoints. Our approach accounts for genotype errors, recent mutations, and gene conversions which disrupt DNA sequence identity within IBD segments. We find that our method’s estimates of uncertainty are well calibrated for homogeneous samples. We quantify endpoint uncertainty for 7.7 billion IBD segments from 408,883 individuals of White British ancestry in the UK Biobank, and use these IBD segments to find regions showing evidence of recent natural selection. We show that many spurious selection signals are eliminated by the use of unbiased estimates of IBD segment endpoints and a pedigree-based genetic map. Nine of the top ten regions with the greatest evidence for recent selection in our scan have been identified as selected in previous analyses using different approaches. Our computationally efficient method for quantifying IBD segment endpoint uncertainty is implemented in the open source ibd-ends software package.

scholarly journals Estimating relatedness between malaria parasites

2019 ◽  
Author(s):  
Aimee R. Taylor ◽  
Pierre E. Jacob ◽  
Daniel E. Neafsey ◽  
Caroline O. Buckee
Keyword(s):  
Genetic Epidemiology ◽  
Ad Hoc ◽  
Pathogen Transmission ◽  
Identity By Descent ◽  
Malaria Parasites ◽  
Data Set ◽  
Epidemiology Studies ◽  
Diverse Data ◽  
Prospective Study Design ◽  
Identity By State

1.AbstractUnderstanding the relatedness of individuals within or between populations is a common goal in biology. Increasingly, relatedness features in genetic epidemiology studies of pathogens. These studies are relatively new compared to those in humans and other organisms, but are important for designing interventions and understanding pathogen transmission. Only recently have researchers begun to routinely apply relatedness to apicomplexan eukaryotic malaria parasites, and to date have used a range of different approaches on an ad hoc basis. It remains unclear how to compare different studies, therefore, and which measures to use. Here, we systematically compare measures based on identity-by-state and identity-by-descent using a globally diverse data set of malaria parasites,Plasmodium falciparumandPlasmodium vivax, and provide marker requirements for estimates based on identity-by-descent. We formally show that the informativeness of polyallelic markers for relatedness inference is maximised when alleles are equifrequent. Estimates based on identity-by-state are sensitive to allele frequencies, which vary across populations and by experimental design. For portability across studies, we thus recommend estimates based on identity-by-descent. To generate reliable estimates, we recommend approximately 200 biallelic or 100 polyallelic markers. Confidence intervals illuminate inference across studies based on different sets of markers. These marker requirements, unlike many thus far reported, are immediately applicable to haploid malaria parasites and other haploid eukaryotes. This is the first attempt to provide rigorous analysis of the reliability of, and requirements for, relatedness inference in malaria genetic epidemiology, and will provide a basis for statistically informed prospective study design and surveillance strategies.

Estimation of Actual Dose Based On Bayesian Probabilistic Approach Using Personnel Monitoring Dose Records

2020 ◽  
Author(s):  
Munir S Pathan ◽  
S M Pradhan ◽  
T Palani Selvam
Keyword(s):  
Probability Distribution ◽  
Posterior Probability ◽  
Probabilistic Approach ◽  
Posterior Distributions ◽  
Uncertainty Distribution ◽  
Annual Dose ◽  
Monitoring Period ◽  
Posterior Probability Distribution ◽  
Dose Levels ◽  
The Bayesian Approach

Abstract In this study, the Bayesian probabilistic approach is applied for the estimation of the actual dose using personnel monitoring dose records of occupational workers. To implement the Bayesian approach, the probability distribution of the uncertainty in the reported dose as a function of the actual dose is derived. Using the uncertainty distribution function of reported dose and prior knowledge of dose levels generally observed in a monitoring period, the posterior probability distribution of the actual dose is estimated. The posterior distributions of each monitoring period in a year are convoluted to arrive at actual annual dose distribution. The estimated actual doses distributions show a significant deviation from reported annual doses particularly for low annual doses.

scholarly journals Distributed simultaneous localization and mapping for mobile robot networks via hybrid dynamic belief propagation

2017 ◽  
Vol 13 (8) ◽  
pp. 155014771772671
Author(s):  
Jiuqing Wan ◽  
Shaocong Bu ◽  
Jinsong Yu ◽  
Liping Zhong
Keyword(s):  
Mobile Robot ◽  
Posterior Probability ◽  
Belief Propagation ◽  
Nonlinear Models ◽  
Simultaneous Localization And Mapping ◽  
Factor Graph ◽  
Posterior Probability Distribution ◽  
Localization And Mapping ◽  
Nonparametric Techniques ◽  
Non Gaussian

This article proposes a hybrid dynamic belief propagation for simultaneous localization and mapping in the mobile robot network. The positions of landmarks and the poses of moving robots at each time slot are estimated simultaneously in an online and distributed manner, by fusing the odometry data of each robot and the measurements of robot–robot or robot–landmark relative distance and angle. The joint belief state of all robots and landmarks is encoded by a factor graph and the marginal posterior probability distribution of each variable is inferred by belief propagation. We show how to calculate, broadcast, and update messages between neighboring nodes in the factor graph. Specifically, we combine parametric and nonparametric techniques to tackle the problem arisen from non-Gaussian distributions and nonlinear models. Simulation and experimental results on publicly available dataset show the validity of our algorithm.

Performance Testing of a Large-Format X-ray Reflection Grating Prototype for a Suborbital Rocket Payload

2020 ◽  
Vol 09 (04) ◽  
pp. 2050017
Author(s):  
Benjamin D. Donovan ◽  
Randall L. McEntaffer ◽  
Casey T. DeRoo ◽  
James H. Tutt ◽  
Fabien Grisé ◽  
...  
Keyword(s):  
Probability Distribution ◽  
Confidence Level ◽  
Posterior Probability ◽  
Performance Testing ◽  
Test Facility ◽  
Finite Limit ◽  
Max Planck ◽  
X Ray ◽  
Posterior Probability Distribution ◽  
Reflection Grating

The soft X-ray grating spectrometer on board the Off-plane Grating Rocket Experiment (OGRE) hopes to achieve the highest resolution soft X-ray spectrum of an astrophysical object when it is launched via suborbital rocket. Paramount to the success of the spectrometer are the performance of the [Formula: see text] reflection gratings populating its reflection grating assembly. To test current grating fabrication capabilities, a grating prototype for the payload was fabricated via electron-beam lithography at The Pennsylvania State University’s Materials Research Institute and was subsequently tested for performance at Max Planck Institute for Extraterrestrial Physics’ PANTER X-ray Test Facility. Bayesian modeling of the resulting data via Markov chain Monte Carlo (MCMC) sampling indicated that the grating achieved the OGRE single-grating resolution requirement of [Formula: see text] at the 94% confidence level. The resulting [Formula: see text] posterior probability distribution suggests that this confidence level is likely a conservative estimate though, since only a finite [Formula: see text] parameter space was sampled and the model could not constrain the upper bound of [Formula: see text] to less than infinity. Raytrace simulations of the tested system found that the observed data can be reproduced with a grating performing at [Formula: see text]. It is therefore postulated that the behavior of the obtained [Formula: see text] posterior probability distribution can be explained by a finite measurement limit of the system and not a finite limit on [Formula: see text]. Implications of these results and improvements to the test setup are discussed.

scholarly journals A primer on Bayesian estimation of prevalence of COVID-19 patient outcomes

JAMIA Open ◽  
2020 ◽  
Author(s):  
Xiang Gao ◽  
Qunfeng Dong
Keyword(s):  
Patient Outcomes ◽  
Posterior Probability ◽  
The United States ◽  
Data Sets ◽  
Medical Outcomes ◽  
Posterior Probability Distribution ◽  
Prevalence Estimation ◽  
Point Estimates ◽  
Asymptomatic Children ◽  
Research Task

Abstract A common research task in COVID-19 studies often involves the prevalence estimation of certain medical outcomes. Although point estimates with confidence intervals are typically obtained, a better approach is to estimate the entire posterior probability distribution of the prevalence, which can be easily accomplished with a standard Bayesian approach using binomial likelihood and its conjugate beta prior distribution. Using two recently published COVID-19 data sets, we performed Bayesian analysis to estimate the prevalence of infection fatality in Iceland and asymptomatic children in the United States.

scholarly journals Bayesian Interpolation

1992 ◽  
Vol 4 (3) ◽  
pp. 415-447 ◽  
Author(s):  
David J. C. MacKay
Keyword(s):  
Posterior Probability ◽  
Bayesian Method ◽  
Model Comparison ◽  
Basis Sets ◽  
Effective Number ◽  
Noise Levels ◽  
Data Set ◽  
Inference Problem ◽  
Posterior Probability Distribution ◽  
Bayesian Interpolation

Although Bayesian analysis has been in use since Laplace, the Bayesian method of model-comparison has only recently been developed in depth. In this paper, the Bayesian approach to regularization and model-comparison is demonstrated by studying the inference problem of interpolating noisy data. The concepts and methods described are quite general and can be applied to many other data modeling problems. Regularizing constants are set by examining their posterior probability distribution. Alternative regularizers (priors) and alternative basis sets are objectively compared by evaluating the evidence for them. “Occam's razor” is automatically embodied by this process. The way in which Bayes infers the values of regularizing constants and noise levels has an elegant interpretation in terms of the effective number of parameters determined by the data set. This framework is due to Gull and Skilling.

Bayesian estimation of hyperparameters for indirect Fourier transformation in small-angle scattering

2000 ◽  
Vol 33 (6) ◽  
pp. 1415-1421 ◽  
Author(s):  
Steen Hansen
Keyword(s):  
Bayesian Methods ◽  
Small Angle ◽  
Fourier Transformation ◽  
Posterior Probability ◽  
Small Angle Scattering ◽  
Scattering Data ◽  
Distance Distribution Function ◽  
Posterior Probability Distribution ◽  
Angle Scattering ◽  
Real Experimental Data

Bayesian analysis is applied to the problem of estimation of hyperparameters, which are necessary for indirect Fourier transformation of small-angle scattering data. The hyperparameters most frequently needed are the overall noise level of the experiment and the maximum dimension of the scatterer. Bayesian methods allow the posterior probability distribution for the hyperparameters to be determined, making it possible to calculate the distance distribution function of interest as the weighted mean of all possible solutions to the indirect transformation problem. Consequently no choice of hyperparameters has to be made. The applicability of the method is demonstrated using simulated as well as real experimental data.

Sign in / Sign up

Export Citation Format

Share Document