Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Statistical Applications in Genetics and Molecular Biology

Editor-in-Chief: Stumpf, Michael P.H.

6 Issues per year


IMPACT FACTOR 2016: 0.646
5-year IMPACT FACTOR: 1.191

CiteScore 2016: 0.94

SCImago Journal Rank (SJR) 2016: 0.625
Source Normalized Impact per Paper (SNIP) 2016: 0.596

Mathematical Citation Quotient (MCQ) 2016: 0.06

Online
ISSN
1544-6115
See all formats and pricing
More options …
Volume 11, Issue 1 (Jan 2012)

Issues

Volume 10 (2011)

Volume 9 (2010)

Volume 6 (2007)

Volume 5 (2006)

Volume 4 (2005)

Volume 2 (2003)

Volume 1 (2002)

Fast Identification of Biological Pathways Associated with a Quantitative Trait Using Group Lasso with Overlaps

Matt Silver / Giovanni Montana / Alzheimer's Disease Neuroimaging Initiative
Published Online: 2012-01-06 | DOI: https://doi.org/10.2202/1544-6115.1755

Where causal SNPs (single nucleotide polymorphisms) tend to accumulate within biological pathways, the incorporation of prior pathways information into a statistical model is expected to increase the power to detect true associations in a genetic association study. Most existing pathways-based methods rely on marginal SNP statistics and do not fully exploit the dependence patterns among SNPs within pathways.We use a sparse regression model, with SNPs grouped into pathways, to identify causal pathways associated with a quantitative trait. Notable features of our “pathways group lasso with adaptive weights” (P-GLAW) algorithm include the incorporation of all pathways in a single regression model, an adaptive pathway weighting procedure that accounts for factors biasing pathway selection, and the use of a bootstrap sampling procedure for the ranking of important pathways. P-GLAW takes account of the presence of overlapping pathways and uses a novel combination of techniques to optimise model estimation, making it fast to run, even on whole genome datasets.In a comparison study with an alternative pathways method based on univariate SNP statistics, our method demonstrates high sensitivity and specificity for the detection of important pathways, showing the greatest relative gains in performance where marginal SNP effect sizes are small.

Keywords: pathways; GWAs; quantitative traits; group lasso; penalised regression; Alzheimer's disease; imaging genetics

About the article

Published Online: 2012-01-06


Citation Information: Statistical Applications in Genetics and Molecular Biology, ISSN (Online) 1544-6115, DOI: https://doi.org/10.2202/1544-6115.1755.

Export Citation

©2012 Walter de Gruyter GmbH & Co. KG, Berlin/Boston. Copyright Clearance Center

Citing Articles

Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.

[1]
Kanchana Padmanabhan, Kelly Nudelman, Steve Harenberg, Gonzalo Bello, Dongwha Sohn, Katie Shpanskaya, Priyanka Tiwari Dikshit, Pallavi Yerramsetty, Rudolph Tanzi, Andrew Saykin, Jeffrey Petrella, P. Doraiswamy, Nagiza Samatova, and Alzheimer’s Disease Neuroimaging Initiative
Processes, 2017, Volume 5, Number 3, Page 47
[2]
Runqing Yang, Hongwang Li, Lina Fu, and Yongxin Liu
Briefings in Bioinformatics, 2014, Volume 15, Number 5, Page 814
[3]
Benjamin Lehne and Thomas Schlitt
Pharmacogenomics, 2012, Volume 13, Number 16, Page 1967
[4]
Matt Silver, Eva Janousova, Xue Hua, Paul M. Thompson, and Giovanni Montana
NeuroImage, 2012, Volume 63, Number 3, Page 1681
[5]
Matt Silver, Peng Chen, Ruoying Li, Ching-Yu Cheng, Tien-Yin Wong, E-Shyong Tai, Yik-Ying Teo, Giovanni Montana, and Scott M. Williams
PLoS Genetics, 2013, Volume 9, Number 11, Page e1003939
[6]
Michael W. Weiner, Dallas P. Veitch, Paul S. Aisen, Laurel A. Beckett, Nigel J. Cairns, Jesse Cedarbaum, Robert C. Green, Danielle Harvey, Clifford R. Jack, William Jagust, Johan Luthman, John C. Morris, Ronald C. Petersen, Andrew J. Saykin, Leslie Shaw, Li Shen, Adam Schwarz, Arthur W. Toga, and John Q. Trojanowski
Alzheimer's & Dementia, 2015, Volume 11, Number 6, Page e1
[7]
Keisuke Nagata, Yoshinobu Kawahara, Takashi Washio, and Akira Unami
Fundamental Toxicological Sciences, 2015, Volume 2, Number 4, Page 161
[8]
Maria Vounou, Eva Janousova, Robin Wolz, Jason L. Stein, Paul M. Thompson, Daniel Rueckert, and Giovanni Montana
NeuroImage, 2012, Volume 60, Number 1, Page 700
[9]
Li Shen, Paul M. Thompson, Steven G. Potkin, Lars Bertram, Lindsay A. Farrer, Tatiana M. Foroud, Robert C. Green, Xiaolan Hu, Matthew J. Huentelman, Sungeun Kim, John S. K. Kauwe, Qingqin Li, Enchi Liu, Fabio Macciardi, Jason H. Moore, Leanne Munsie, Kwangsik Nho, Vijay K. Ramanan, Shannon L. Risacher, David J. Stone, Shanker Swaminathan, Arthur W. Toga, Michael W. Weiner, and Andrew J. Saykin
Brain Imaging and Behavior, 2014, Volume 8, Number 2, Page 183

Comments (0)

Please log in or register to comment.
Log in