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

Statistical Applications in Genetics and Molecular Biology

Editor-in-Chief: Sanguinetti, Guido

6 Issues per year


IMPACT FACTOR 2017: 0.812
5-year IMPACT FACTOR: 1.104

CiteScore 2017: 0.86

SCImago Journal Rank (SJR) 2017: 0.456
Source Normalized Impact per Paper (SNIP) 2017: 0.527

Mathematical Citation Quotient (MCQ) 2016: 0.06

Online
ISSN
1544-6115
See all formats and pricing
More options …
Volume 11, Issue 4

Issues

Volume 10 (2011)

Volume 9 (2010)

Volume 6 (2007)

Volume 5 (2006)

Volume 4 (2005)

Volume 2 (2003)

Volume 1 (2002)

QTL Mapping Using a Memetic Algorithm with Modifications of BIC as Fitness Function

Florian Frommlet / Ivana Ljubic / Helga Björk Arnardóttir / Malgorzata Bogdan
Published Online: 2012-05-18 | DOI: https://doi.org/10.1515/1544-6115.1793

The problem of locating quantitative trait loci (QTL) for experimental populations can be approached by multiple regression analysis. In this context variable selection using a modification of the Bayesian Information Criterion (mBIC) has been well established in the past. In this article a memetic algorithm (MA) is introduced to find the model which minimizes the selection criterion. Apart from mBIC also a second modification (mBIC2) is considered, which has the property of controlling the false discovery rate. Given the Bayesian nature of our selection criteria, we are not only interested in finding the best model, but also in computing marker posterior probabilities using all models visited by MA. In a simulation study MA (with mBIC and mBIC2) is compared with a parallel genetic algorithm (PGA) which has been previously suggested for QTL mapping. It turns out that MA in combination with mBIC2 performs best, where determining QTL positions based on marker posterior probabilities yields even better results than using the best model selected by MA. Finally we consider a real data set from the literature and show that MA can also be extended to multiple interval mapping, which potentially increases the precision with which the exact location of QTLs can be estimated.

Keywords: QTL mapping; model selection; modifications of BIC; memetic algorithm; posterior probability

About the article

Published Online: 2012-05-18


Citation Information: Statistical Applications in Genetics and Molecular Biology, Volume 11, Issue 4, ISSN (Online) 1544-6115, DOI: https://doi.org/10.1515/1544-6115.1793.

Export Citation

©2012 Walter de Gruyter GmbH & Co. KG, Berlin/Boston.Get Permission

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]
Aliaksandr Hubin and Geir Storvik
Computational Statistics & Data Analysis, 2018
[2]
Florian Frommlet, Grégory Nuel, and Xiaofeng Wang
PLOS ONE, 2016, Volume 11, Number 2, Page e0148620

Comments (0)

Please log in or register to comment.
Log in