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

Mi Xiaojuan, Eskridge Kent, Wang Dong, Baenziger P. Stephen, Campbell B. Todd, Gill Kulvinder S., Dweikat Ismail and Bovaird James
Additional contact information
Mi Xiaojuan: University of Nebraska–Lincoln
Eskridge Kent: University of Nebraska–Lincoln
Wang Dong: University of Nebraska–Lincoln
Baenziger P. Stephen: University of Nebraska–Lincoln
Campbell B. Todd: USDA-ARS Coastal Plains Soil, Water, and Plant Research Center
Gill Kulvinder S.: Washington State University
Dweikat Ismail: University of Nebraska–Lincoln
Bovaird James: University of Nebraska–Lincoln

Statistical Applications in Genetics and Molecular Biology, 2010, vol. 9, issue 1, 23

Abstract: 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 (search for similar items in EconPapers)
Date: 2010
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://doi.org/10.2202/1544-6115.1552 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:sagmbi:v:9:y:2010:i:1:n:38

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/sagmb/html

DOI: 10.2202/1544-6115.1552

Access Statistics for this article

Statistical Applications in Genetics and Molecular Biology is currently edited by Michael P. H. Stumpf

More articles in Statistical Applications in Genetics and Molecular Biology from De Gruyter
Bibliographic data for series maintained by Peter Golla ().