Statistical Applications in Genetics and Molecular Biology最新文献_第3页

Comparisons of classification methods for viral genomes and protein families using alignment-free vectorization. 病毒基因组和蛋白家族分类方法的比较。

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2018-06-30 DOI: 10.1515/sagmb-2018-0004

Hsin-Hsiung Huang, Shuai Hao, Saul Alarcon, Jie Yang

引用次数: 4

A statistical method for measuring activation of gene regulatory networks. 一种测量基因调控网络激活的统计方法。

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2018-06-13 DOI: 10.1515/sagmb-2016-0059

Gustavo H Esteves, Luiz F L Reis

{"title":"A statistical method for measuring activation of gene regulatory networks.","authors":"Gustavo H Esteves, Luiz F L Reis","doi":"10.1515/sagmb-2016-0059","DOIUrl":"https://doi.org/10.1515/sagmb-2016-0059","url":null,"abstract":"Motivation: Gene expression data analysis is of great importance for modern molecular biology, given our ability to measure the expression profiles of thousands of genes and enabling studies rooted in systems biology. In this work, we propose a simple statistical model for the activation measuring of gene regulatory networks, instead of the traditional gene co-expression networks.Results: We present the mathematical construction of a statistical procedure for testing hypothesis regarding gene regulatory network activation. The real probability distribution for the test statistic is evaluated by a permutation based study. To illustrate the functionality of the proposed methodology, we also present a simple example based on a small hypothetical network and the activation measuring of two KEGG networks, both based on gene expression data collected from gastric and esophageal samples. The two KEGG networks were also analyzed for a public database, available through NCBI-GEO, presented as Supplementary Material.Availability: This method was implemented in an R package that is available at the BioConductor project website under the name maigesPack.","PeriodicalId":48980,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"17 3","pages":""},"PeriodicalIF":0.9,"publicationDate":"2018-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/sagmb-2016-0059","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36218900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Multi-locus data distinguishes between population growth and multiple merger coalescents. 多位点数据区分了人口增长和多重合并凝聚。

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2018-06-13 DOI: 10.1515/sagmb-2017-0011

Jere Koskela

{"title":"Multi-locus data distinguishes between population growth and multiple merger coalescents.","authors":"Jere Koskela","doi":"10.1515/sagmb-2017-0011","DOIUrl":"https://doi.org/10.1515/sagmb-2017-0011","url":null,"abstract":"We introduce a low dimensional function of the site frequency spectrum that is tailor-made for distinguishing coalescent models with multiple mergers from Kingman coalescent models with population growth, and use this function to construct a hypothesis test between these model classes. The null and alternative sampling distributions of the statistic are intractable, but its low dimensionality renders them amenable to Monte Carlo estimation. We construct kernel density estimates of the sampling distributions based on simulated data, and show that the resulting hypothesis test dramatically improves on the statistical power of a current state-of-the-art method. A key reason for this improvement is the use of multi-locus data, in particular averaging observed site frequency spectra across unlinked loci to reduce sampling variance. We also demonstrate the robustness of our method to nuisance and tuning parameters. Finally we show that the same kernel density estimates can be used to conduct parameter estimation, and argue that our method is readily generalisable for applications in model selection, parameter inference and experimental design.","PeriodicalId":48980,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"17 3","pages":""},"PeriodicalIF":0.9,"publicationDate":"2018-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/sagmb-2017-0011","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36218898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 17

Non-parametric estimation of population size changes from the site frequency spectrum. 非参数估计群体规模变化从站点频谱。

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2018-06-11 DOI: 10.1515/sagmb-2017-0061

Berit Lindum Waltoft, Asger Hobolth

{"title":"Non-parametric estimation of population size changes from the site frequency spectrum.","authors":"Berit Lindum Waltoft, Asger Hobolth","doi":"10.1515/sagmb-2017-0061","DOIUrl":"https://doi.org/10.1515/sagmb-2017-0061","url":null,"abstract":"Changes in population size is a useful quantity for understanding the evolutionary history of a species. Genetic variation within a species can be summarized by the site frequency spectrum (SFS). For a sample of size n, the SFS is a vector of length n - 1 where entry i is the number of sites where the mutant base appears i times and the ancestral base appears n - i times. We present a new method, CubSFS, for estimating the changes in population size of a panmictic population from an observed SFS. First, we provide a straightforward proof for the expression of the expected site frequency spectrum depending only on the population size. Our derivation is based on an eigenvalue decomposition of the instantaneous coalescent rate matrix. Second, we solve the inverse problem of determining the changes in population size from an observed SFS. Our solution is based on a cubic spline for the population size. The cubic spline is determined by minimizing the weighted average of two terms, namely (i) the goodness of fit to the observed SFS, and (ii) a penalty term based on the smoothness of the changes. The weight is determined by cross-validation. The new method is validated on simulated demographic histories and applied on unfolded and folded SFS from 26 different human populations from the 1000 Genomes Project.","PeriodicalId":48980,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"17 3","pages":""},"PeriodicalIF":0.9,"publicationDate":"2018-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/sagmb-2017-0061","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36209410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bayesian inference of selection in the Wright-Fisher diffusion model. Wright-Fisher扩散模型中贝叶斯选择推理。

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2018-06-06 DOI: 10.1515/sagmb-2017-0046

Jeffrey J Gory, Radu Herbei, Laura S Kubatko

{"title":"Bayesian inference of selection in the Wright-Fisher diffusion model.","authors":"Jeffrey J Gory, Radu Herbei, Laura S Kubatko","doi":"10.1515/sagmb-2017-0046","DOIUrl":"https://doi.org/10.1515/sagmb-2017-0046","url":null,"abstract":"The increasing availability of population-level allele frequency data across one or more related populations necessitates the development of methods that can efficiently estimate population genetics parameters, such as the strength of selection acting on the population(s), from such data. Existing methods for this problem in the setting of the Wright-Fisher diffusion model are primarily likelihood-based, and rely on numerical approximation for likelihood computation and on bootstrapping for assessment of variability in the resulting estimates, requiring extensive computation. Recent work has provided a method for obtaining exact samples from general Wright-Fisher diffusion processes, enabling the development of methods for Bayesian estimation in this setting. We develop and implement a Bayesian method for estimating the strength of selection based on the Wright-Fisher diffusion for data sampled at a single time point. The method utilizes the latest algorithms for exact sampling to devise a Markov chain Monte Carlo procedure to draw samples from the joint posterior distribution of the selection coefficient and the allele frequencies. We demonstrate that when assumptions about the initial allele frequencies are accurate the method performs well for both simulated data and for an empirical data set on hypoxia in flies, where we find evidence for strong positive selection in a region of chromosome 2L previously identified. We discuss possible extensions of our method to the more general settings commonly encountered in practice, highlighting the advantages of Bayesian approaches to inference in this setting.","PeriodicalId":48980,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"17 3","pages":""},"PeriodicalIF":0.9,"publicationDate":"2018-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/sagmb-2017-0046","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36198083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

A Bayesian hierarchical model for identifying significant polygenic effects while controlling for confounding and repeated measures. 一个贝叶斯层次模型，用于识别显著的多基因效应，同时控制混淆和重复测量。

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2017-11-27 DOI: 10.1515/sagmb-2017-0044

Christopher McMahan, James Baurley, William Bridges, Chase Joyner, Muhamad Fitra Kacamarga, Robert Lund, Carissa Pardamean, Bens Pardamean

{"title":"A Bayesian hierarchical model for identifying significant polygenic effects while controlling for confounding and repeated measures.","authors":"Christopher McMahan, James Baurley, William Bridges, Chase Joyner, Muhamad Fitra Kacamarga, Robert Lund, Carissa Pardamean, Bens Pardamean","doi":"10.1515/sagmb-2017-0044","DOIUrl":"https://doi.org/10.1515/sagmb-2017-0044","url":null,"abstract":"Genomic studies of plants often seek to identify genetic factors associated with desirable traits. The process of evaluating genetic markers one by one (i.e. a marginal analysis) may not identify important polygenic and environmental effects. Further, confounding due to growing conditions/factors and genetic similarities among plant varieties may influence conclusions. When developing new plant varieties to optimize yield or thrive in future adverse conditions (e.g. flood, drought), scientists seek a complete understanding of how the factors influence desirable traits. Motivated by a study design that measures rice yield across different seasons, fields, and plant varieties in Indonesia, we develop a regression method that identifies significant genomic factors, while simultaneously controlling for field factors and genetic similarities in the plant varieties. Our approach develops a Bayesian maximum a posteriori probability (MAP) estimator under a generalized double Pareto shrinkage prior. Through a hierarchical representation of the proposed model, a novel and computationally efficient expectation-maximization (EM) algorithm is developed for variable selection and estimation. The performance of the proposed approach is demonstrated through simulation and is used to analyze rice yields from a pilot study conducted by the Indonesian Center for Rice Research.","PeriodicalId":48980,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"16 5-6","pages":"407-419"},"PeriodicalIF":0.9,"publicationDate":"2017-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/sagmb-2017-0044","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35613840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 12

A smoothed EM-algorithm for DNA methylation profiles from sequencing-based methods in cell lines or for a single cell type. 一种平滑的em算法，用于细胞系或单个细胞类型中基于测序的方法的DNA甲基化谱。

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2017-11-27 DOI: 10.1515/sagmb-2016-0062

Lajmi Lakhal-Chaieb, Celia M T Greenwood, Mohamed Ouhourane, Kaiqiong Zhao, Belkacem Abdous, Karim Oualkacha

{"title":"A smoothed EM-algorithm for DNA methylation profiles from sequencing-based methods in cell lines or for a single cell type.","authors":"Lajmi Lakhal-Chaieb, Celia M T Greenwood, Mohamed Ouhourane, Kaiqiong Zhao, Belkacem Abdous, Karim Oualkacha","doi":"10.1515/sagmb-2016-0062","DOIUrl":"https://doi.org/10.1515/sagmb-2016-0062","url":null,"abstract":"We consider the assessment of DNA methylation profiles for sequencing-derived data from a single cell type or from cell lines. We derive a kernel smoothed EM-algorithm, capable of analyzing an entire chromosome at once, and to simultaneously correct for experimental errors arising from either the pre-treatment steps or from the sequencing stage and to take into account spatial correlations between DNA methylation profiles at neighbouring CpG sites. The outcomes of our algorithm are then used to (i) call the true methylation status at each CpG site, (ii) provide accurate smoothed estimates of DNA methylation levels, and (iii) detect differentially methylated regions. Simulations show that the proposed methodology outperforms existing analysis methods that either ignore the correlation between DNA methylation profiles at neighbouring CpG sites or do not correct for errors. The use of the proposed inference procedure is illustrated through the analysis of a publicly available data set from a cell line of induced pluripotent H9 human embryonic stem cells and also a data set where methylation measures were obtained for a small genomic region in three different immune cell types separated from whole blood.","PeriodicalId":48980,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"16 5-6","pages":"333-347"},"PeriodicalIF":0.9,"publicationDate":"2017-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/sagmb-2016-0062","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35628756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Modifying SAMseq to account for asymmetry in the distribution of effect sizes when identifying differentially expressed genes. 在鉴定差异表达基因时，修改SAMseq以解释效应大小分布的不对称性。

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2017-11-27 DOI: 10.1515/sagmb-2016-0037

Ekua Kotoka, Megan Orr

{"title":"Modifying SAMseq to account for asymmetry in the distribution of effect sizes when identifying differentially expressed genes.","authors":"Ekua Kotoka, Megan Orr","doi":"10.1515/sagmb-2016-0037","DOIUrl":"https://doi.org/10.1515/sagmb-2016-0037","url":null,"abstract":"RNA-Seq is a developing technology for generating gene expression data by directly sequencing mRNA molecules in a sample. RNA-Seq data consist of counts of reads recorded to a particular gene that are often used to identify differentially expressed (DE) genes. A common statistical method used to analyze RNA-Seq data is Significance Analysis of Microarray with emphasis on RNA-Seq data (SAMseq). SAMseq is a nonparametric method that uses a resampling technique to account for differences in sequencing depths when identifying DE genes. We propose a modification of this method that takes into account asymmetry in the distribution of the effect sizes by taking into account the sign of the test statistics. Through simulation studies, we showthat the proposed method, comparedwith the traditional SAMseqmethod and other existing methods provides better power for identifying truly DE genes or more sufficiently controls FDR in most settings where asymmetry is present. We illustrate the use of the proposed method by analyzing an RNA-Seq data set containing C57BL/6J (B6) and DBA/2J (D2) mouse strains samples.","PeriodicalId":48980,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"16 5-6","pages":"291-312"},"PeriodicalIF":0.9,"publicationDate":"2017-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/sagmb-2016-0037","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35645921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Approximate maximum likelihood estimation for population genetic inference. 群体遗传推断的近似最大似然估计。

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2017-11-27 DOI: 10.1515/sagmb-2017-0016

Johanna Bertl, Gregory Ewing, Carolin Kosiol, Andreas Futschik

{"title":"Approximate maximum likelihood estimation for population genetic inference.","authors":"Johanna Bertl, Gregory Ewing, Carolin Kosiol, Andreas Futschik","doi":"10.1515/sagmb-2017-0016","DOIUrl":"https://doi.org/10.1515/sagmb-2017-0016","url":null,"abstract":"In many population genetic problems, parameter estimation is obstructed by an intractable likelihood function. Therefore, approximate estimation methods have been developed, and with growing computational power, sampling-based methods became popular. However, these methods such as Approximate Bayesian Computation (ABC) can be inefficient in high-dimensional problems. This led to the development of more sophisticated iterative estimation methods like particle filters. Here, we propose an alternative approach that is based on stochastic approximation. By moving along a simulated gradient or ascent direction, the algorithm produces a sequence of estimates that eventually converges to the maximum likelihood estimate, given a set of observed summary statistics. This strategy does not sample much from low-likelihood regions of the parameter space, and is fast, even when many summary statistics are involved. We put considerable efforts into providing tuning guidelines that improve the robustness and lead to good performance on problems with high-dimensional summary statistics and a low signal-to-noise ratio. We then investigate the performance of our resulting approach and study its properties in simulations. Finally, we re-estimate parameters describing the demographic history of Bornean and Sumatran orang-utans.","PeriodicalId":48980,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"16 5-6","pages":"387-405"},"PeriodicalIF":0.9,"publicationDate":"2017-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/sagmb-2017-0016","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35215570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Multivariate association between single-nucleotide polymorphisms in Alzgene linkage regions and structural changes in the brain: discovery, refinement and validation. Alzgene连锁区域的单核苷酸多态性与大脑结构变化之间的多变量关联:发现、改进和验证。

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2017-11-27 DOI: 10.1515/sagmb-2016-0077

Elena Szefer, Donghuan Lu, Farouk Nathoo, Mirza Faisal Beg, Jinko Graham

{"title":"Multivariate association between single-nucleotide polymorphisms in Alzgene linkage regions and structural changes in the brain: discovery, refinement and validation.","authors":"Elena Szefer, Donghuan Lu, Farouk Nathoo, Mirza Faisal Beg, Jinko Graham","doi":"10.1515/sagmb-2016-0077","DOIUrl":"https://doi.org/10.1515/sagmb-2016-0077","url":null,"abstract":"Using publicly-available data from the Alzheimer's Disease Neuroimaging Initiative, we investigate the joint association between single-nucleotide polymorphisms (SNPs) in previously established linkage regions for Alzheimer's disease (AD) and rates of decline in brain structure. In an initial, discovery stage of analysis, we applied a weighted RV test to assess the association between 75,845 SNPs in the Alzgene linkage regions and rates of change in structural MRI measurements for 56 brain regions affected by AD, in 632 subjects. After confirming association, we selected refined lists of 1694 and 22 SNPs via a bootstrap-enhanced sparse canonical correlation analysis. In a final, validation stage, we confirmed association between the refined list of 1694 SNPs and the imaging phenotypes in an independent data set. Genes corresponding to priority SNPs having the highest contribution in the validation data have previously been implicated or hypothesized to be implicated in AD, including GCLC, IDE, and STAMBP1andFAS. Though the effect sizes of the 1694 SNPs in the priority set are likely small, further investigation within this set may advance understanding of the missing heritability in AD. Our analysis addresses challenges in current imaging-genetics studies such as biased sampling designs and high-dimensional data with low association signal.","PeriodicalId":48980,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"16 5-6","pages":"349-365"},"PeriodicalIF":0.9,"publicationDate":"2017-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/sagmb-2016-0077","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35211465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10