scholarly journals ARGON: Fast, Whole-Genome Simulation of the Discrete Time Wright-Fisher Process

2016 ◽  
Author(s):  
Pier Francesco Palamara
Keyword(s):  
Discrete Time ◽  
Demographic History ◽  
Memory Performance ◽  
Real Data ◽  
Supplementary Information ◽  
Human Populations ◽  
Data Sets ◽  
Recombination Rates ◽  
Coalescent Model ◽  
Fisher Model

AbstractMotivationSimulation under the coalescent model is ubiquitous in the analysis of genetic data. The rapid growth of real data sets from multiple human populations led to increasing interest in simulating very large sample sizes at whole-chromosome scales. When the sample size is large, the coalescent model becomes an increasingly inaccurate approximation of the discrete time Wright-Fisher model (DTWF). Analytical and computational treatment of the DTWF, however, is generally harder.ResultsWe present a simulator (ARGON) for the DTWF process that scales up to hundreds of thousands of samples and whole-chromosome lengths, with a time/memory performance comparable or superior to currently available methods for coalescent simulation. The simulator supports arbitrary demographic history, migration, Newick tree output, variable mutation/recombination rates and gene conversion, and efficiently outputs pairwise identical-bydescent (IBD) sharing data.AvailabilityARGON (version 0.1) is written in Java, open source, and freely available at https://github.com/pierpal/[email protected] informationSupplementary data are available online.

scholarly journals Inference and analysis of population-specific fine-scale recombination maps across 26 diverse human populations

2019 ◽  
Vol 5 (10) ◽  
pp. eaaw9206 ◽  
Author(s):  
Jeffrey P. Spence ◽  
Yun S. Song
Keyword(s):  
Binding Sites ◽  
Demographic History ◽  
Dna Binding Protein ◽  
Polycomb Group ◽  
Human Populations ◽  
Polycomb Group Proteins ◽  
Fine Scale ◽  
Population Differences ◽  
Recombination Rates ◽  
History Of

Fine-scale rates of meiotic recombination vary by orders of magnitude across the genome and differ between species and even populations. Studying cross-population differences has been stymied by the confounding effects of demographic history. To address this problem, we developed a demography-aware method to infer fine-scale recombination rates and applied it to 26 diverse human populations, inferring population-specific recombination maps. These maps recapitulate many aspects of the history of these populations including signatures of the trans-Atlantic slave trade and the Iberian colonization of the Americas. We also investigated modulators of the local recombination rate, finding further evidence that Polycomb group proteins and the trimethylation of H3K27 elevate recombination rates. Further differences in the recombination landscape across the genome and between populations are driven by variation in the gene that encodes the DNA binding protein PRDM9, and we quantify the weak effect of meiotic drive acting to remove its binding sites.

scholarly journals The impact of recent population history on the deleterious mutation load in humans and close evolutionary relatives

2016 ◽  
Author(s):  
Yuval B. Simons ◽  
Guy Sella
Keyword(s):  
Demographic History ◽  
Population History ◽  
Human Populations ◽  
Data Sets ◽  
Loss Of Function ◽  
Mutation Load ◽  
Synonymous Mutations ◽  
Show Evidence ◽  
Out Of Africa ◽  
The Impact

AbstractOver the past decade, there has been both great interest and confusion about whether recent demographic events—notably the Out-of-Africa-bottleneck and recent population growth—have led to differences in mutation load among human populations. The confusion can be traced to the use of different summary statistics to measure load, which lead to apparently conflicting results. We argue, however, that when statistics more directly related to load are used, the results of different studies and data sets consistently reveal little or no difference in the load of non-synonymous mutations among human populations. Theory helps to understand why no such differences are seen, as well as to predict in what settings they are to be expected. In particular, as predicted by modeling, there is evidence for changes in the load of recessive loss of function mutations in founder and inbred human populations. Also as predicted, eastern subspecies of gorilla, Neanderthals and Denisovans, who are thought to have undergone reductions in population sizes that exceed the human Out-of-Africa bottleneck in duration and severity, show evidence for increased load of non-synonymous mutations (relative to western subspecies of gorillas and modern humans, respectively). A coherent picture is thus starting to emerge about the effects of demographic history on the mutation load in populations of humans and close evolutionary relatives.

scholarly journals Complete deconvolution of DNA methylation signals from complex tissues: a geometric approach

2020 ◽  
Author(s):  
Weiwei Zhang ◽  
Hao Wu ◽  
Ziyi Li
Keyword(s):  
Dna Methylation ◽  
Geometric Approach ◽  
Real Data ◽  
Cell Types ◽  
Supplementary Information ◽  
Data Sets ◽  
Methylation Data ◽  
Cell Type ◽  
Tissue Samples ◽  
Different Cell Types

Abstract Motivation It is a common practice in epigenetics research to profile DNA methylation on tissue samples, which is usually a mixture of different cell types. To properly account for the mixture, estimating cell compositions has been recognized as an important first step. Many methods were developed for quantifying cell compositions from DNA methylation data, but they mostly have limited applications due to lack of reference or prior information. Results We develop Tsisal, a novel complete deconvolution method which accurately estimate cell compositions from DNA methylation data without any prior knowledge of cell types or their proportions. Tsisal is a full pipeline to estimate number of cell types, cell compositions, and identify cell-type-specific CpG sites. It can also assign cell type labels when (full or part of) reference panel is available. Extensive simulation studies and analyses of seven real data sets demonstrate the favorable performance of our proposed method compared with existing deconvolution methods serving similar purpose. Availability The proposed method Tsisal is implemented as part of the R/Bioconductor package TOAST at https://bioconductor.org/packages/TOAST. Contact [email protected] and [email protected]. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Inference and analysis of population-specific fine-scale recombination maps across 26 diverse human populations

2019 ◽  
Author(s):  
Jeffrey P. Spence ◽  
Yun S. Song
Keyword(s):  
Binding Sites ◽  
Demographic History ◽  
Dna Binding Protein ◽  
Polycomb Group ◽  
Human Populations ◽  
Polycomb Group Proteins ◽  
Fine Scale ◽  
Recombination Rates ◽  
Confounding Effect ◽  
History Of

AbstractFine-scale rates of meiotic recombination vary by several orders of magnitude across the genome, and are known to differ between species and even between populations. Studying the differences in recombination maps across populations has been stymied by the confounding effect of differences in demographic history. To address this problem, we developed a method that infers fine-scale recombination rates while taking demography into account and applied our method to infer population-specific recombination maps for each of 26 diverse human populations. These maps recapitulate many aspects of the history of these populations including signatures of the trans-Atlantic slave trade and the Iberian colonization of the Americas. We also investigated modulators of the local recombination rate, finding an unexpected role for Polycomb-group proteins and the tri-methylation of H3K27 in elevating recombination rates. Further differences in the recombination landscape across the genome and between populations are driven by variation in the gene that encodes the DNA-binding protein PRDM9, and we quantify the weak effect of meiotic drive acting to remove its binding sites.

scholarly journals CSHAP: efficient haplotype frequency estimation based on sparse representation

2018 ◽  
Vol 35 (16) ◽  
pp. 2827-2833
Author(s):  
Yinsheng Zhou ◽  
Han Zhang ◽  
Yaning Yang
Keyword(s):  
Compressive Sensing ◽  
Sparse Representation ◽  
Frequency Estimation ◽  
Haplotype Frequency ◽  
Supplementary Information ◽  
Human Populations ◽  
Genotype Data ◽  
Recombination Rates ◽  
Haplotype Frequency Estimation ◽  
Haplotype Frequencies

Abstract Motivation Estimating haplotype frequencies from genotype data plays an important role in genetic analysis. In silico methods are usually computationally involved since phase information is not available. Due to tight linkage disequilibrium and low recombination rates, the number of haplotypes observed in human populations is far less than all the possibilities. This motivates us to solve the estimation problem by maximizing the sparsity of existing haplotypes. Here, we propose a new algorithm by applying the compressive sensing (CS) theory in the field of signal processing, compressive sensing haplotype inference (CSHAP), to solve the sparse representation of haplotype frequencies based on allele frequencies and between-allele co-variances. Results Our proposed approach can handle both individual genotype data and pooled DNA data with hundreds of loci. The CSHAP exhibits the same accuracy compared with the state-of-the-art methods, but runs several orders of magnitude faster. CSHAP can also handle with missing genotype data imputations efficiently. Availability and implementation The CSHAP is implemented in R, the source code and the testing datasets are available at http://home.ustc.edu.cn/∼zhouys/CSHAP/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Transforming variables to central normality

2021 ◽  
Author(s):  
Jakob Raymaekers ◽  
Peter J. Rousseeuw
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimator ◽  
Simulation Study ◽  
Real Data ◽  
Data Sets ◽  
Transformation Parameter ◽  
Likelihood Estimator ◽  
Extensive Simulation ◽  
Highly Sensitive

AbstractMany real data sets contain numerical features (variables) whose distribution is far from normal (Gaussian). Instead, their distribution is often skewed. In order to handle such data it is customary to preprocess the variables to make them more normal. The Box–Cox and Yeo–Johnson transformations are well-known tools for this. However, the standard maximum likelihood estimator of their transformation parameter is highly sensitive to outliers, and will often try to move outliers inward at the expense of the normality of the central part of the data. We propose a modification of these transformations as well as an estimator of the transformation parameter that is robust to outliers, so the transformed data can be approximately normal in the center and a few outliers may deviate from it. It compares favorably to existing techniques in an extensive simulation study and on real data.

scholarly journals A New Extension of Thinning-Based Integer-Valued Autoregressive Models for Count Data

Entropy ◽  
2020 ◽  
Vol 23 (1) ◽  
pp. 62
Author(s):  
Zhengwei Liu ◽  
Fukang Zhu
Keyword(s):  
Likelihood Estimation ◽  
Real Data ◽  
Autoregressive Models ◽  
Superior Performance ◽  
Data Sets ◽  
Binomial Thinning ◽  
Free Case ◽  
Two Parameters ◽  
Conditional Maximum ◽  
Thinning Operator

The thinning operators play an important role in the analysis of integer-valued autoregressive models, and the most widely used is the binomial thinning. Inspired by the theory about extended Pascal triangles, a new thinning operator named extended binomial is introduced, which is a general case of the binomial thinning. Compared to the binomial thinning operator, the extended binomial thinning operator has two parameters and is more flexible in modeling. Based on the proposed operator, a new integer-valued autoregressive model is introduced, which can accurately and flexibly capture the dispersed features of counting time series. Two-step conditional least squares (CLS) estimation is investigated for the innovation-free case and the conditional maximum likelihood estimation is also discussed. We have also obtained the asymptotic property of the two-step CLS estimator. Finally, three overdispersed or underdispersed real data sets are considered to illustrate a superior performance of the proposed model.

scholarly journals Goodness-of-Fit Tests for Bivariate Time Series of Counts

Econometrics ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 10
Author(s):  
Šárka Hudecová ◽  
Marie Hušková ◽  
Simos G. Meintanis
Keyword(s):  
Goodness Of Fit ◽  
Probability Generating Function ◽  
Parametric Bootstrap ◽  
Real Data ◽  
Data Sets ◽  
Test Statistics ◽  
Finite Sample ◽  
Generalized Poisson ◽  
Goodness Of Fit Tests ◽  
Monte Carlo Experiments

This article considers goodness-of-fit tests for bivariate INAR and bivariate Poisson autoregression models. The test statistics are based on an L2-type distance between two estimators of the probability generating function of the observations: one being entirely nonparametric and the second one being semiparametric computed under the corresponding null hypothesis. The asymptotic distribution of the proposed tests statistics both under the null hypotheses as well as under alternatives is derived and consistency is proved. The case of testing bivariate generalized Poisson autoregression and extension of the methods to dimension higher than two are also discussed. The finite-sample performance of a parametric bootstrap version of the tests is illustrated via a series of Monte Carlo experiments. The article concludes with applications on real data sets and discussion.

scholarly journals TraceAll: A Real-Time Processing for Contact Tracing Using Indoor Trajectories

Information ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 202
Author(s):  
Louai Alarabi ◽  
Saleh Basalamah ◽  
Abdeltawab Hendawi ◽  
Mohammed Abdalla
Keyword(s):  
Infectious Diseases ◽  
Infected Patient ◽  
Public Health Problem ◽  
Real Data ◽  
Exposure Period ◽  
Contact Tracing ◽  
Data Sets ◽  
Major Public Health Problem ◽  
Real Time Processing ◽  
Recent Developments

The rapid spread of infectious diseases is a major public health problem. Recent developments in fighting these diseases have heightened the need for a contact tracing process. Contact tracing can be considered an ideal method for controlling the transmission of infectious diseases. The result of the contact tracing process is performing diagnostic tests, treating for suspected cases or self-isolation, and then treating for infected persons; this eventually results in limiting the spread of diseases. This paper proposes a technique named TraceAll that traces all contacts exposed to the infected patient and produces a list of these contacts to be considered potentially infected patients. Initially, it considers the infected patient as the querying user and starts to fetch the contacts exposed to him. Secondly, it obtains all the trajectories that belong to the objects moved nearby the querying user. Next, it investigates these trajectories by considering the social distance and exposure period to identify if these objects have become infected or not. The experimental evaluation of the proposed technique with real data sets illustrates the effectiveness of this solution. Comparative analysis experiments confirm that TraceAll outperforms baseline methods by 40% regarding the efficiency of answering contact tracing queries.

scholarly journals The Flexible Burr X-G Family: Properties, Inference, and Applications in Engineering Science

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 474
Author(s):  
Abdulhakim A. Al-Babtain ◽  
Ibrahim Elbatal ◽  
Hazem Al-Mofleh ◽  
Ahmed M. Gemeay ◽  
Ahmed Z. Afify ◽  
...  
Keyword(s):  
Numerical Simulations ◽  
Exponential Distribution ◽  
Real Data ◽  
Exponential Model ◽  
Statistical Properties ◽  
Engineering Science ◽  
Data Sets ◽  
Engineering Sciences ◽  
General Statistical ◽  
Anderson Darling

In this paper, we introduce a new flexible generator of continuous distributions called the transmuted Burr X-G (TBX-G) family to extend and increase the flexibility of the Burr X generator. The general statistical properties of the TBX-G family are calculated. One special sub-model, TBX-exponential distribution, is studied in detail. We discuss eight estimation approaches to estimating the TBX-exponential parameters, and numerical simulations are conducted to compare the suggested approaches based on partial and overall ranks. Based on our study, the Anderson–Darling estimators are recommended to estimate the TBX-exponential parameters. Using two skewed real data sets from the engineering sciences, we illustrate the importance and flexibility of the TBX-exponential model compared with other existing competing distributions.

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