Published since May 1, 2002

Statistical Applications in Genetics and Molecular Biology

ISSN: 1544-6115

Editor-in-chief: Guido Sanguinetti

Impact Factor: 0.676

About this journal

Objective
Statistical Applications in Genetics and Molecular Biology seeks to publish significant research on the application of statistical ideas to problems arising from computational biology. The focus of the papers should be on the relevant statistical issues but should contain a succinct description of the relevant biological problem being considered. The range of topics is wide and will include topics such as linkage mapping, association studies, gene finding and sequence alignment, protein structure prediction, design and analysis of microarray data, molecular evolution and phylogenetic trees, DNA topology, and data base search strategies. Both original research and review articles will be warmly received.

What scholars are saying about Statistical Applications in Genetics and Molecular Biology

“This journal will keep statisticians up to date on the thinking behind the development and validation of molecular biology based classifiers for diagnostic testing for use in such areas as early detection of disease or recurrence, risk stratification, prognosis, prediction of treatment response, monitoring, and drug dosing.”

Gene Pennello, Ph.D., Center for Devices and Radiological Health, FDA

“Statistical applications in Genetics and Genomics are very important areas of research. This journal is one of the high-quality journals that I use, and that my students use as well.”

Carl Lee, Professor of Mathematics, Central Michigan University

Topics

Linkage mapping
Association studies
Gene regulatory networks
Protein structure prediction
High-throughput data analysis
Molecular evolution and phylogenetic trees
Multi-omics data integration
Genetic and epigenetic data modelling

Article formats
Original Research Articles, Review Articles, Software Application Notes.

Your Benefits

Your benefits

Theoretical and computational innovations in the statistical analysis of biological data and systems
Practical applications of statistical ideas to computational biology, genomics, and systems biology and medicine
Specialized expertise in genomics, molecular, cellular and systems biology
Internationally recognized authors and editorial board
Rapid online publication of contributions as “ahead-of-print” versions
High quality peer review
Rapid “ahead-of-print” online publication of accepted papers
Open Access publication option available

History

Content available since 2002 (Volume 1, Issue 1)

Previously published by Berkeley Electronic Press

Other publications in the field

Previous Titles

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Supplementary Materials

Topics

External Links

Online Submission of Manuscripts

Volume 21 Issue 1

January 2022

Review Article

May 2, 2022

Challenges for machine learning in RNA-protein interaction prediction

Viplove Arora, Guido Sanguinetti

Article number: 20210087

Abstract

RNA-protein interactions have long being recognised as crucial regulators of gene expression. Recently, the development of scalable experimental techniques to measure these interactions has revolutionised the field, leading to the production of large-scale datasets which offer both opportunities and challenges for machine learning techniques. In this brief note, we will discuss some of the major stumbling blocks towards the use of machine learning in computational RNA biology, focusing specifically on the problem of predicting RNA-protein interactions from next-generation sequencing data.

Research Articles

October 10, 2022

pwrBRIDGE: a user-friendly web application for power and sample size estimation in batch-confounded microarray studies with dependent samples

Qing Xia, Jeffrey A. Thompson, Devin C. Koestler

Article number: 20220003

Abstract

B atch effect R eduction of m I croarray data with D ependent samples usin G E mpirical Bayes ( BRIDGE ) is a recently developed statistical method to address the issue of batch effect correction in batch-confounded microarray studies with dependent samples. The key component of the BRIDGE methodology is the use of samples run as technical replicates in two or more batches, “bridging samples”, to inform batch effect correction/attenuation. While previously published results indicate a relationship between the number of bridging samples, M , and the statistical power of downstream statistical testing on the batch-corrected data, there is of yet no formal statistical framework or user-friendly software, for estimating M to achieve a specific statistical power for hypothesis tests conducted on the batch-corrected data. To fill this gap, we developed pwrBRIDGE , a simulation-based approach to estimate the bridging sample size, M , in batch-confounded longitudinal microarray studies. To illustrate the use of pwrBRIDGE , we consider a hypothetical, longitudinal batch-confounded study whose goal is to identify Alzheimer’s disease (AD) progression-associated genes from amnestic mild cognitive impairment (aMCI) to AD in human blood after a 5-year follow-up. pwrBRIDGE helps researchers design and plan batch-confounded microarray studies with dependent samples to avoid over- or under-powered studies.

July 14, 2022

Use of SVM-based ensemble feature selection method for gene expression data analysis

Shizhi Zhang, Mingjin Zhang

Article number: 20220002

Abstract

Gene selection is one of the key steps for gene expression data analysis. An SVM-based ensemble feature selection method is proposed in this paper. Firstly, the method builds many subsets by using Monte Carlo sampling. Secondly, ranking all the features on each of the subsets and integrating them to obtain a final ranking list. Finally, the optimum feature set is determined by a backward feature elimination strategy. This method is applied to the analysis of 4 public datasets: the Leukemia, Prostate, Colorectal, and SMK_CAN, resulting 7, 10, 13, and 32 features. The AUC obtained from independent test sets are 0.9867, 0.9796, 0.9571, and 0.9575, respectively. These results indicate that the features selected by the proposed method can improve sample classification accuracy, and thus be effective for gene selection from gene expression data.

June 6, 2022

A robust association test with multiple genetic variants and covariates

Jen-Yu Lee, Pao-Sheng Shen, Kuang-Fu Cheng

Article number: 20210029

Abstract

Due to the advancement of genome sequencing techniques, a great stride has been made in exome sequencing such that the association study between disease and genetic variants has become feasible. Some powerful and well-known association tests have been proposed to test the association between a group of genes and the disease of interest. However, some challenges still remain, in particular, many factors can affect the performance of testing power, e.g., the sample size, the number of causal and non-causal variants, and direction of the effect of causal variants. Recently, a powerful test, called T REM , is derived based on a random effects model. T REM has the advantages of being less sensitive to the inclusion of non-causal rare variants or low effect common variants or the presence of missing genotypes. However, the testing power of T REM can be low when a portion of causal variants has effects in opposite directions. To improve the drawback of T REM , we propose a novel test, called T ROB , which keeps the advantages of T REM and is more robust than T REM in terms of having adequate power in the case of variants with opposite directions of effect. Simulation results show that T ROB has a stable type I error rate and outperforms T REM when the proportion of risk variants decreases to a certain level and its advantage over T REM increases as the proportion decreases. Furthermore, T ROB outperforms several other competing tests in most scenarios. The proposed methodology is illustrated using the Shanghai Breast Cancer Study.

May 26, 2022

Estimation of the covariance structure from SNP allele frequencies

Jan van Waaij, Zilong Li, Carsten Wiuf

Article number: 20220005

Abstract

We propose two new statistics, V ̂ $\hat{V}$ and S ̂ $\hat{S}$ , to disentangle the population history of related populations from SNP frequency data. If the populations are related by a tree, we show by theoretical means as well as by simulation that the new statistics are able to identify the root of a tree correctly, in contrast to standard statistics, such as the observed matrix of F 2 -statistics (distances between pairs of populations). The statistic V ̂ $\hat{V}$ is obtained by averaging over all SNPs (similar to standard statistics). Its expectation is the true covariance matrix of the observed population SNP frequencies, offset by a matrix with identical entries. In contrast, the statistic S ̂ $\hat{S}$ is put in a Bayesian context and is obtained by averaging over pairs of SNPs, such that each SNP is only used once. It thus makes use of the joint distribution of pairs of SNPs. In addition, we provide a number of novel mathematical results about old and new statistics, and their mutual relationship.

May 2, 2022

GMEPS: a fast and efficient likelihood approach for genome-wide mediation analysis under extreme phenotype sequencing

Janaka S. S. Liyanage, Jeremie H. Estepp, Kumar Srivastava, Yun Li, Motomi Mori, Guolian Kang

Article number: 20210071

Abstract

Due to many advantages such as higher statistical power of detecting the association of genetic variants in human disorders and cost saving, extreme phenotype sequencing (EPS) is a rapidly emerging study design in epidemiological and clinical studies investigating how genetic variations associate with complex phenotypes. However, the investigation of the mediation effect of genetic variants on phenotypes is strictly restrictive under the EPS design because existing methods cannot well accommodate the non-random extreme tails sampling process incurred by the EPS design. In this paper, we propose a likelihood approach for testing the mediation effect of genetic variants through continuous and binary mediators on a continuous phenotype under the EPS design (GMEPS). Besides implementing in EPS design, it can also be utilized as a general mediation analysis procedure. Extensive simulations and two real data applications of a genome-wide association study of benign ethnic neutropenia under EPS design and a candidate-gene study of neurocognitive performance in patients with sickle cell disease under random sampling design demonstrate the superiority of GMEPS under the EPS design over widely used mediation analysis procedures, while demonstrating compatible capabilities under the general random sampling framework.

March 7, 2022

Sparse latent factor regression models for genome-wide and epigenome-wide association studies

Basile Jumentier, Kevin Caye, Barbara Heude, Johanna Lepeule, Olivier François

Article number: 20210035

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.

Ahead of Print / Just Accepted

Ahead of Print / Just Accepted

Volume 21 (2022)

Issue 1

Volume 20 (2021)

Volume 19 (2020)

Volume 18 (2019)

Volume 17 (2018)

Volume 16 (2017)

Volume 15 (2016)

Volume 14 (2015)

Volume 13 (2014)

Volume 12 (2013)

Volume 11 (2012)

Volume 7 (2008)

Volume 6 (2007)

Issue 1

Volume 5 (2006)

Issue 1

Volume 4 (2005)

Issue 1

Volume 3 (2004)

Issue 1

Volume 2 (2003)

Issue 1

Volume 1 (2002)

Issue 1

5-year Impact Factor	1.063
Cite Score	1.4
Impact Factor	0.676
Journal Citation Indicator	0.17
Mathematical Citation Quotient	0.06
SCImago Journal Rank	0.404
Source Normalized Impact per Paper	0.455

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Editorial

Editor-in-Chief
Guido Sanguinetti, International School of Advanced Studies Trieste

Founding Editors
Nicholas P. Jewell, University of California Berkeley
Gary Churchill, The Jackson Laboratory
Elizabeth Thompson, Johns Hopkins University of Baltimore

Associate Editors
Mark Beaumont, University of Bristol
Tim Beißbarth, University of Göttingen
Harald Binder, Universitty of Freiburg
Giulio Caravagna, University of Trieste
Frank Dondelinger, Lancaster University
Antti Honkela, University of Helsinki
Alan Hubbard, University of California
Dirk Husmeier, University of Glasgow
Hongkai Ji, Johns Hopkins University
Sunduz Keles, University of Wisconsin
Shili Lin, Ohio State University
Ping Ma, University of Georgia
Paul Marjoram, University of Southern California
Bart Mertens, Leiden University Medical Centre
Sach Mukherjee, German Center for Neurodegenerative Diseases Bonn
Olle Nerman, Chalmers University of Technology
Enrico G. Petretto, Imperial College London
John Pinney, Imperial College London
Magnus Rattray, University of Manchester
Andrey Rzhetsky, University of Chicago
Nicolas Salamin, University of Lausanne
Michael Stumpf, University of Melbourne
Catalina Vallejos, University of Edinburgh
Mark van der Laan, University of California Berkeley
Arndt von Haeseler, Center for Integrative Bioinformatics Vienna
Ernst Wit, University of Groningen
Christopher Yau, University of Birmingham

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(Deutsch)

Online ISSN: 1544-6115
Type: Journal
Language: English
Publisher: De Gruyter
First published: May 1, 2002
Publication Frequency: 6 Issues per Year
Audience: Researchers and practitioners interested in statistical applications in genetics and molecular biology