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

Statistical Applications in Genetics and Molecular Biology

Editor-in-Chief: Sanguinetti, Guido


IMPACT FACTOR 2018: 0.536
5-year IMPACT FACTOR: 0.764

CiteScore 2018: 0.49

SCImago Journal Rank (SJR) 2018: 0.316
Source Normalized Impact per Paper (SNIP) 2018: 0.342

Mathematical Citation Quotient (MCQ) 2018: 0.02

Online
ISSN
1544-6115
See all formats and pricing
More options …
Volume 9, Issue 1

Issues

Volume 10 (2011)

Volume 9 (2010)

Volume 6 (2007)

Volume 5 (2006)

Volume 4 (2005)

Volume 2 (2003)

Volume 1 (2002)

Regression-Based Multi-Trait QTL Mapping Using a Structural Equation Model

Xiaojuan Mi / Kent Eskridge / Dong Wang / P. Stephen Baenziger / B. Todd Campbell / Kulvinder S. Gill / Ismail Dweikat / James Bovaird
Published Online: 2010-10-19 | DOI: https://doi.org/10.2202/1544-6115.1552

Quantitative trait loci (QTL) mapping often results in data on a number of traits that have well-established causal relationships. Many multi-trait QTL mapping methods that account for the correlation among multiple traits have been developed to improve the statistical power and the precision of QTL parameter estimation. However, none of these methods are capable of incorporating the causal structure among the traits. Consequently, genetic functions of the QTL may not be fully understood. Structural equation modeling (SEM) allows researchers to explicitly characterize the causal structure among the variables and to decompose effects into direct, indirect, and total effects. In this paper, we developed a multi-trait SEM method of QTL mapping that takes into account the causal relationships among traits related to grain yield. Performance of the proposed method is evaluated by simulation study and applied to data from a wheat experiment. Compared with single trait analysis and the multi-trait least-squares analysis, our multi-trait SEM improves statistical power of QTL detection and provides important insight into how QTLs regulate traits by investigating the direct, indirect, and total QTL effects. The approach also helps build biological models that more realistically reflect the complex relationships among QTL and traits and is more precise and efficient in QTL mapping than single trait analysis.

Keywords: QTL mapping; multiple traits; structural equation model; least squares

About the article

Published Online: 2010-10-19


Citation Information: Statistical Applications in Genetics and Molecular Biology, Volume 9, Issue 1, ISSN (Online) 1544-6115, DOI: https://doi.org/10.2202/1544-6115.1552.

Export Citation

©2011 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]
Mehdi Momen, Ahmad Ayatollahi Mehrgardi, Mahmoud Amiri Roudbar, Andreas Kranis, Renan Mercuri Pinto, Bruno D. Valente, Gota Morota, Guilherme J. M. Rosa, and Daniel Gianola
Frontiers in Genetics, 2018, Volume 9
[2]
A. A. Igolkina and M. G. Samsonova
Biophysics, 2018, Volume 63, Number 2, Page 139
[3]
Anna A. Igolkina, Chris Armoskus, Jeremy R. B. Newman, Oleg V. Evgrafov, Lauren M. McIntyre, Sergey V. Nuzhdin, and Maria G. Samsonova
Frontiers in Molecular Neuroscience, 2018, Volume 11
[4]
Charlotte Trontin, Sébastien Tisné, Liên Bach, and Olivier Loudet
Current Opinion in Plant Biology, 2011, Volume 14, Number 3, Page 225

Comments (0)

Please log in or register to comment.
Log in