|Campbell, Benjamin - Todd|
Submitted to: Genetics Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/1/2010
Publication Date: 12/3/2010
Publication URL: http://hdl.handle.net/10113/47710
Citation: Mi, X., Eskridge, K.M., Wang, D., Baenziger, P.S., Campbell, B.T., Gill, K.S., Dweikat, I. 2010. Bayesian mixture structural equation modelling in multiple-trait QTL mapping. Genetics Research. 92:239-250. Interpretive Summary: Quantitative trait loci (QTL) mapping experiments are conducted to identify the putative genes controlling agronomically important traits such as grain yield. Often times, yield component traits and other grain yield correlated traits are also evaluated in these experiments. However, statistical methods used to detect QTL are not available that account for the causal relationships among grain yield and correlated traits. In this paper, we developed a multi-trait structural equation modeling method (SEM) of QTL mapping that takes into account the causal relationships among traits and their relationship to grain yield. Performance of the proposed method was evaluated by simulation study and applied to data from a wheat experiment. Results suggest that complex QTL and trait relationships can be explained more precisely and efficiently using our multi-trait SEM model. This approach provides potential biological models that more realistically reflect the complex relationships among QTL and traits.
Technical 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 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.