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

Details on availability and prices of recent back volumes and issues will be provided on request from service@degruyter.com. All volumes of the majority of our journals* with the noted exception of the current volume are being offered by

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Tel.: (+49) 80 64 – 221
Fax: (+49) 80 64 – 557
Email: schmidt@periodicals.com
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(* For periodicals not included in this agreement, please refer to the distribution address provided on the homepage of the periodical.)

Abstracting and Indexing

Statistical Applications in Genetics and Molecular Biology is covered by the following services:

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Web of Science - Biological Abstracts
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WorldCat (OCLC)
X-MOL
Yewno Discover
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Supplementary Materials

Topics

External Links

Online Submission of Manuscripts

Volume 22 Issue 1

January 2023

Research Article

February 1, 2023

A fast and efficient approach for gene-based association studies of ordinal phenotypes

Nanxing Li, Lili Chen, Yajing Zhou, Qianran Wei

Article number: 20210068

Abstract

Many human disease conditions need to be measured by ordinal phenotypes, so analysis of ordinal phenotypes is valuable in genome-wide association studies (GWAS). However, existing association methods for dichotomous or quantitative phenotypes are not appropriate to ordinal phenotypes. Therefore, based on an aggregated Cauchy association test, we propose a fast and efficient association method to test the association between genetic variants and an ordinal phenotype. To enrich association signals of rare variants, we first use the burden method to aggregate rare variants. Then we respectively test the significance of the aggregated rare variants and other common variants. Finally, the combination of transformed variant-level P values is taken as test statistic, that approximately follows Cauchy distribution under the null hypothesis. Extensive simulation studies and analysis of GAW19 show that our proposed method is powerful and computationally fast as a gene-based method. Especially, in the presence of an extremely low proportion of causal variants in a gene, our method has better performance.

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 10 (2011)

Issue 1

Volume 9 (2010)

Issue 1

Volume 8 (2009)

Issue 1

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

Submit

Submission
Online Submission of Manuscripts

Your benefits of publishing with us

Highly ranked statistics journal in the ISI Science Citation Index
International Editorial Board with experts from various fields in the relevant area
Decisions on your submission made as soon as possible
Every article easily discoverable because of Search Engine Optimization (SEO) and comprehensive abstracting and indexing services
Secure archiving by De Gruyter and the independent archiving service Portico
Professional sales and marketing activities
Ahead-of-print publishing ensures faster processing of fully proof-read, DOI-citable articles

Submission process

Submit your paper via our Scholar One submission management tool following the Instructions for Authors
After logging in you will be guided through every step of the submission process
In case of any problems the editorial staff will make its best to answer all your queries

Please note

Before submitting your article please have a look at our Copyright Transfer Agreement
Once your article is accepted you have the option to publish it open access
Our Repository Policy allows you to distribute 30 PDF copies of your published article to colleagues (the PDF has to include the information that it is an author's copy). Please also feel free to distribute the link to the online abstract
If you have any general questions please visit our FAQ for authors

We look forward to receiving your manuscript!

Hybrid Open Access

In this journal, authors have the option to publish their article under an open access license. Open Access allows you as an author to retain copyright and share your findings with colleagues and interested parties worldwide without any restraints.
Please note that authors from institutions with which we have a transformative agreement can publish open access without paying an article processing charge (APC). More information on the eligible institutions and articles can be found under the "Funding and Support" tab here.

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

SAGMB Editorial Office
Walter de Gruyter GmbH
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10785 Berlin
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Tel. +49-30-26005-176
Fax: +49-30-26005-352
E-mail: sagmb.editorial@degruyter.com

(Deutsch)

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