Statistical Applications in Genetics and Molecular Biology最新文献_第6页

Accuracy and sensitivity of different Bayesian methods for genomic prediction using simulation and real data. 不同贝叶斯方法在基因组预测中的准确性和敏感性。

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2020-08-10 DOI: 10.1515/sagmb-2019-0007

Saheb Foroutaifar

{"title":"Accuracy and sensitivity of different Bayesian methods for genomic prediction using simulation and real data.","authors":"Saheb Foroutaifar","doi":"10.1515/sagmb-2019-0007","DOIUrl":"https://doi.org/10.1515/sagmb-2019-0007","url":null,"abstract":"The main objectives of this study were to compare the prediction accuracy of different Bayesian methods for traits with a wide range of genetic architecture using simulation and real data and to assess the sensitivity of these methods to the violation of their assumptions. For the simulation study, different scenarios were implemented based on two traits with low or high heritability and different numbers of QTL and the distribution of their effects. For real data analysis, a German Holstein dataset for milk fat percentage, milk yield, and somatic cell score was used. The simulation results showed that, with the exception of the Bayes R, the other methods were sensitive to changes in the number of QTLs and distribution of QTL effects. Having a distribution of QTL effects, similar to what different Bayesian methods assume for estimating marker effects, did not improve their prediction accuracy. The Bayes B method gave higher or equal accuracy rather than the rest. The real data analysis showed that similar to scenarios with a large number of QTLs in the simulation, there was no difference between the accuracies of the different methods for any of the traits.","PeriodicalId":49477,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"19 3","pages":""},"PeriodicalIF":0.9,"publicationDate":"2020-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/sagmb-2019-0007","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38247369","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

Understanding hormonal crosstalk in Arabidopsis root development via emulation and history matching. 通过模拟和历史匹配了解拟南芥根系发育中的激素串扰。

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2020-07-13 DOI: 10.1515/sagmb-2018-0053

Samuel E Jackson, Ian Vernon, Junli Liu, Keith Lindsey

{"title":"Understanding hormonal crosstalk in Arabidopsis root development via emulation and history matching.","authors":"Samuel E Jackson, Ian Vernon, Junli Liu, Keith Lindsey","doi":"10.1515/sagmb-2018-0053","DOIUrl":"https://doi.org/10.1515/sagmb-2018-0053","url":null,"abstract":"A major challenge in plant developmental biology is to understand how plant growth is coordinated by interacting hormones and genes. To meet this challenge, it is important to not only use experimental data, but also formulate a mathematical model. For the mathematical model to best describe the true biological system, it is necessary to understand the parameter space of the model, along with the links between the model, the parameter space and experimental observations. We develop sequential history matching methodology, using Bayesian emulation, to gain substantial insight into biological model parameter spaces. This is achieved by finding sets of acceptable parameters in accordance with successive sets of physical observations. These methods are then applied to a complex hormonal crosstalk model for Arabidopsis root growth. In this application, we demonstrate how an initial set of 22 observed trends reduce the volume of the set of acceptable inputs to a proportion of 6.1 × 10-7 of the original space. Additional sets of biologically relevant experimental data, each of size 5, reduce the size of this space by a further three and two orders of magnitude respectively. Hence, we provide insight into the constraints placed upon the model structure by, and the biological consequences of, measuring subsets of observations.","PeriodicalId":49477,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"19 2","pages":""},"PeriodicalIF":0.9,"publicationDate":"2020-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/sagmb-2018-0053","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38140980","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: 4

Bivariate traits association analysis using generalized estimating equations in family data. 基于广义估计方程的家庭数据双变量性状关联分析。

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2020-05-05 DOI: 10.1515/sagmb-2019-0030

Mariza de Andrade, Mauricio A Mazo Lopera, Nubia E Duarte

{"title":"Bivariate traits association analysis using generalized estimating equations in family data.","authors":"Mariza de Andrade, Mauricio A Mazo Lopera, Nubia E Duarte","doi":"10.1515/sagmb-2019-0030","DOIUrl":"https://doi.org/10.1515/sagmb-2019-0030","url":null,"abstract":"Genome wide association study (GWAS) is becoming fundamental in the arduous task of deciphering the etiology of complex diseases. The majority of the statistical models used to address the genes-disease association consider a single response variable. However, it is common for certain diseases to have correlated phenotypes such as in cardiovascular diseases. Usually, GWAS typically sample unrelated individuals from a population and the shared familial risk factors are not investigated. In this paper, we propose to apply a bivariate model using family data that associates two phenotypes with a genetic region. Using generalized estimation equations (GEE), we model two phenotypes, either discrete, continuous or a mixture of them, as a function of genetic variables and other important covariates. We incorporate the kinship relationships into the working matrix extended to a bivariate analysis. The estimation method and the joint gene-set effect in both phenotypes are developed in this work. We also evaluate the proposed methodology with a simulation study and an application to real data.","PeriodicalId":49477,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"19 2","pages":""},"PeriodicalIF":0.9,"publicationDate":"2020-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/sagmb-2019-0030","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37905663","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 approach to discriminant problems for count data with application to multilocus short tandem repeat dataset. 计数数据判别问题的贝叶斯方法及其在多位点短串联重复数据集上的应用。

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2020-05-04 DOI: 10.1515/sagmb-2018-0044

Koji Tsukuda, Shuhei Mano, Toshimichi Yamamoto

{"title":"Bayesian approach to discriminant problems for count data with application to multilocus short tandem repeat dataset.","authors":"Koji Tsukuda, Shuhei Mano, Toshimichi Yamamoto","doi":"10.1515/sagmb-2018-0044","DOIUrl":"https://doi.org/10.1515/sagmb-2018-0044","url":null,"abstract":"Short Tandem Repeats (STRs) are a type of DNA polymorphism. This study considers discriminant analysis to determine the population of test individuals using an STR database containing the lengths of STRs observed at more than one locus. The discriminant method based on the Bayes factor is discussed and an improved method is proposed. The main issues are to develop a method that is relatively robust to sample size imbalance, identify a procedure to select loci, and treat the parameter in the prior distribution. A previous study achieved a classification accuracy of 0.748 for the g-mean (geometric mean of classification accuracies for two populations) and 0.867 for the AUC (area under the receiver operating characteristic curve). We improve the maximum values for the g-mean to 0.830 and the AUC to 0.935. Computer simulations indicate that the previous method is susceptible to sample size imbalance, whereas the proposed method is more robust while achieving almost identical classification accuracy. Furthermore, the results confirm that threshold adjustment is an effective countermeasure to sample size imbalance.","PeriodicalId":49477,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"19 2","pages":""},"PeriodicalIF":0.9,"publicationDate":"2020-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/sagmb-2018-0044","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37896963","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

Identification of supervised and sparse functional genomic pathways. 有监督和稀疏功能基因组通路的鉴定。

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2020-02-29 DOI: 10.1515/sagmb-2018-0026

Fan Zhang, Jeffrey C Miecznikowski, David L Tritchler

引用次数: 4

Joint variable selection and network modeling for detecting eQTLs. eqtl检测的联合变量选择与网络建模。

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2020-02-20 DOI: 10.1515/sagmb-2019-0032

Xuan Cao, Lili Ding, Tesfaye B Mersha

{"title":"Joint variable selection and network modeling for detecting eQTLs.","authors":"Xuan Cao, Lili Ding, Tesfaye B Mersha","doi":"10.1515/sagmb-2019-0032","DOIUrl":"https://doi.org/10.1515/sagmb-2019-0032","url":null,"abstract":"In this study, we conduct a comparison of three most recent statistical methods for joint variable selection and covariance estimation with application of detecting expression quantitative trait loci (eQTL) and gene network estimation, and introduce a new hierarchical Bayesian method to be included in the comparison. Unlike the traditional univariate regression approach in eQTL, all four methods correlate phenotypes and genotypes by multivariate regression models that incorporate the dependence information among phenotypes, and use Bayesian multiplicity adjustment to avoid multiple testing burdens raised by traditional multiple testing correction methods. We presented the performance of three methods (MSSL - Multivariate Spike and Slab Lasso, SSUR - Sparse Seemingly Unrelated Bayesian Regression, and OBFBF - Objective Bayes Fractional Bayes Factor), along with the proposed, JDAG (Joint estimation via a Gaussian Directed Acyclic Graph model) method through simulation experiments, and publicly available HapMap real data, taking asthma as an example. Compared with existing methods, JDAG identified networks with higher sensitivity and specificity under row-wise sparse settings. JDAG requires less execution in small-to-moderate dimensions, but is not currently applicable to high dimensional data. The eQTL analysis in asthma data showed a number of known gene regulations such as STARD3, IKZF3 and PGAP3, all reported in asthma studies. The code of the proposed method is freely available at GitHub (https://github.com/xuan-cao/Joint-estimation-for-eQTL).","PeriodicalId":49477,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"19 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2020-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/sagmb-2019-0032","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37660750","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

An extended model for phylogenetic maximum likelihood based on discrete morphological characters. 基于离散形态特征的系统发育最大似然扩展模型。

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2020-02-20 DOI: 10.1515/sagmb-2019-0029

David A Spade

引用次数: 1

Sparse latent factor regression models for genome-wide and epigenome-wide association studies 全基因组和表观全基因组关联研究的稀疏潜在因子回归模型

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2020-02-07 DOI: 10.1101/2020.02.07.938381

B. Jumentier, Kévin Caye, B. Heude, J. Lepeule, O. François

{"title":"Sparse latent factor regression models for genome-wide and epigenome-wide association studies","authors":"B. Jumentier, Kévin Caye, B. Heude, J. Lepeule, O. François","doi":"10.1101/2020.02.07.938381","DOIUrl":"https://doi.org/10.1101/2020.02.07.938381","url":null,"abstract":"Abstract Association of phenotypes or exposures with genomic and epigenomic data faces important statistical challenges. One of these challenges is to account for variation due to unobserved confounding factors, such as individual ancestry or cell-type composition in tissues. This issue can be addressed with penalized latent factor regression models, where penalties are introduced to cope with high dimension in the data. If a relatively small proportion of genomic or epigenomic markers correlate with the variable of interest, sparsity penalties may help to capture the relevant associations, but the improvement over non-sparse approaches has not been fully evaluated yet. Here, we present least-squares algorithms that jointly estimate effect sizes and confounding factors in sparse latent factor regression models. In simulated data, sparse latent factor regression models generally achieved higher statistical performance than other sparse methods, including the least absolute shrinkage and selection operator and a Bayesian sparse linear mixed model. In generative model simulations, statistical performance was slightly lower (while being comparable) to non-sparse methods, but in simulations based on empirical data, sparse latent factor regression models were more robust to departure from the model than the non-sparse approaches. We applied sparse latent factor regression models to a genome-wide association study of a flowering trait for the plant Arabidopsis thaliana and to an epigenome-wide association study of smoking status in pregnant women. For both applications, sparse latent factor regression models facilitated the estimation of non-null effect sizes while overcoming multiple testing issues. The results were not only consistent with previous discoveries, but they also pinpointed new genes with functional annotations relevant to each application.","PeriodicalId":49477,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"21 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2020-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46182011","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: 3

Fast approximate inference for variable selection in Dirichlet process mixtures, with an application to pan-cancer proteomics. Dirichlet过程混合物中变量选择的快速近似推断，并在泛癌症蛋白质组学中的应用。

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2019-12-12 DOI: 10.1515/sagmb-2018-0065

Oliver M Crook, Laurent Gatto, Paul D W Kirk

{"title":"Fast approximate inference for variable selection in Dirichlet process mixtures, with an application to pan-cancer proteomics.","authors":"Oliver M Crook, Laurent Gatto, Paul D W Kirk","doi":"10.1515/sagmb-2018-0065","DOIUrl":"10.1515/sagmb-2018-0065","url":null,"abstract":"The Dirichlet Process (DP) mixture model has become a popular choice for model-based clustering, largely because it allows the number of clusters to be inferred. The sequential updating and greedy search (SUGS) algorithm (Wang & Dunson, 2011) was proposed as a fast method for performing approximate Bayesian inference in DP mixture models, by posing clustering as a Bayesian model selection (BMS) problem and avoiding the use of computationally costly Markov chain Monte Carlo methods. Here we consider how this approach may be extended to permit variable selection for clustering, and also demonstrate the benefits of Bayesian model averaging (BMA) in place of BMS. Through an array of simulation examples and well-studied examples from cancer transcriptomics, we show that our method performs competitively with the current state-of-the-art, while also offering computational benefits. We apply our approach to reverse-phase protein array (RPPA) data from The Cancer Genome Atlas (TCGA) in order to perform a pan-cancer proteomic characterisation of 5157 tumour samples. We have implemented our approach, together with the original SUGS algorithm, in an open-source R package named sugsvarsel, which accelerates analysis by performing intensive computations in C++ and provides automated parallel processing. The R package is freely available from: https://github.com/ococrook/sugsvarsel.","PeriodicalId":49477,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"18 6","pages":""},"PeriodicalIF":0.9,"publicationDate":"2019-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7614016/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10481523","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: 0

AdaReg: data adaptive robust estimation in linear regression with application in GTEx gene expressions AdaReg：线性回归中的数据自适应稳健估计及其在GTEx基因表达中的应用

IF 0.9 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2019-12-10 DOI: 10.1101/869362

Meng Wang, Lihua Jiang, M. Snyder

{"title":"AdaReg: data adaptive robust estimation in linear regression with application in GTEx gene expressions","authors":"Meng Wang, Lihua Jiang, M. Snyder","doi":"10.1101/869362","DOIUrl":"https://doi.org/10.1101/869362","url":null,"abstract":"Abstract The Genotype-Tissue Expression (GTEx) project provides a valuable resource of large-scale gene expressions across multiple tissue types. Under various technical noise and unknown or unmeasured factors, how to robustly estimate the major tissue effect becomes challenging. Moreover, different genes exhibit heterogeneous expressions across different tissue types. Therefore, we need a robust method which adapts to the heterogeneities of gene expressions to improve the estimation for the tissue effect. We followed the approach of the robust estimation based on γ-density-power-weight in the works of Fujisawa, H. and Eguchi, S. (2008). Robust parameter estimation with a small bias against heavy contamination. J. Multivariate Anal. 99: 2053–2081 and Windham, M.P. (1995). Robustifying model fitting. J. Roy. Stat. Soc. B: 599–609, where γ is the exponent of density weight which controls the balance between bias and variance. As far as we know, our work is the first to propose a procedure to tune the parameter γ to balance the bias-variance trade-off under the mixture models. We constructed a robust likelihood criterion based on weighted densities in the mixture model of Gaussian population distribution mixed with unknown outlier distribution, and developed a data-adaptive γ-selection procedure embedded into the robust estimation. We provided a heuristic analysis on the selection criterion and found that our practical selection trend under various γ’s in average performance has similar capability to capture minimizer γ as the inestimable mean squared error (MSE) trend from our simulation studies under a series of settings. Our data-adaptive robustifying procedure in the linear regression problem (AdaReg) showed a significant advantage in both simulation studies and real data application in estimating tissue effect of heart samples from the GTEx project, compared to the fixed γ procedure and other robust methods. At the end, the paper discussed some limitations on this method and future work.","PeriodicalId":49477,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"20 1","pages":"51 - 71"},"PeriodicalIF":0.9,"publicationDate":"2019-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41415775","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: 3