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ARS Home » Southeast Area » Florence, South Carolina » Coastal Plain Soil, Water and Plant Conservation Research » Research » Publications at this Location » Publication #252037
Title: Regression-based multi-trait QTL mapping using a structural equation model

Author
item MI, XIAOJUAN - University Of Nebraska
item ESKRIDGE, KENT - University Of Nebraska
item WANG, DONG - University Of Nebraska
item BAENZIGER, P - University Of Nebraska
item Campbell, Benjamin - Todd
item GILL, KULVINDER - Washington State University
item DWEIKAT, ISMAIL - University Of Nebraska
item BOVAIRD, JAMES - University Of Nebraska

Submitted to: Genetics Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/23/2010
Publication Date: 12/3/2010
Citation: Mi, X., Eskridge, K., Wang, D., Baenziger, P.S., Campbell, B.T., Gill, K.S., Dweikat, I., Bovaird, J. 2010. Regression-based multi-trait QTL mapping using a structural equation model. Genetics Research. 9(38):1-21.

Interpretive Summary: Complicated traits, such as grain yield, are often affected by the cumulative effects of related agronomic traits. Most often, the putative genes controlling grain yield and other complicated traits are mapped using quantitative trait locus methods focused on a single trait. More recently, multi-trait quantitative trait locus mapping methods that account for the correlation among multiple traits have been developed to improve the statistical power and the precision of parameter estimation. However none of these methods are capable of incorporating the causal structure (direct, indirect, and total effects) among the traits. In this paper, we developed a multiple-trait quantitative trait locus mapping method for causally related traits allowing for estimation of direct, indirect, and total effects. The performance of the proposed method was evaluated by simulation study and applied to data from a wheat experiment. Compared with single trait analysis, our proposed method not only improved the statistical power of quantitative trait locus detection, accuracy, and precision of parameter estimates but also provided important insight into how genes regulate traits directly and indirectly by fitting a more biologically sensible model.

Technical Abstract: Quantitative trait locus mapping often results in data on a number of traits that have well established causal relationships. Many multi-trait quantitative trait locus mapping methods that account for the correlation among the multiple traits have been developed to improve the statistical power and the precision of parameter estimation. However none of these methods are capable of incorporating the causal structure among the traits. Consequently, genetic functions of the quantitative trait locus may not be fully understood. In this paper, we developed a Bayesian multiple quantitative trait locus mapping method for causally related traits using a mixture structural equation model, which allows researchers to decompose quantitative trait locus effects into direct, indirect, and total effects. Parameters are estimated based on their marginal posterior distribution. The posterior distributions of parameters are estimated using Markov Chain Monte Carlo methods such as the Gibbs sampler and the Metropolis-Hasting algorithm. The number of quantitative trait loci affecting traits is determined by the Bayes factor. The performance of the proposed method is evaluated by simulation study and applied to data from a wheat experiment. Compared with single trait Bayesian analysis, our proposed method not only improved the statistical power of quantitative trait locus detection, accuracy, and precision of parameter estimates but also provided important insight into how genes regulate traits directly and indirectly by fitting a more biologically sensible model.