Abstract

Research on mapping quantitative trait loci (QTL) often results in data on a number of traits that have well established causal relationships. Many multi-trait QTL mapping methods, taking into account the correlation among the multiple traits, have been developed to improve the statistical power of the test for QTL and the precision of parameter estimation. However none of these methods are capable of incorporating the causal structure among the traits with the consequence that 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 the 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. The performance of the proposed method is evaluated by simulation study. Compared with single trait analysis and the multi-trait least-squares analysis, our proposed model (Multitrait SEM) provides important insight into how QTLs regulate traits by investigating the direct, indirect, and total QTL effects, which is generally not possible with other methods. The approach also helps with building models that more realistically reflect complex relationships among QTL and traits, and is more precise and efficient in QTL mapping than single trait analysis.

Keywords

QTL mapping; multiple traits; SEM; least squares

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Apr 27th, 9:30 AM

MULTI-TRAIT QTL MAPPING USING A STRUCTURAL EQUATION MODEL

Research on mapping quantitative trait loci (QTL) often results in data on a number of traits that have well established causal relationships. Many multi-trait QTL mapping methods, taking into account the correlation among the multiple traits, have been developed to improve the statistical power of the test for QTL and the precision of parameter estimation. However none of these methods are capable of incorporating the causal structure among the traits with the consequence that 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 the 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. The performance of the proposed method is evaluated by simulation study. Compared with single trait analysis and the multi-trait least-squares analysis, our proposed model (Multitrait SEM) provides important insight into how QTLs regulate traits by investigating the direct, indirect, and total QTL effects, which is generally not possible with other methods. The approach also helps with building models that more realistically reflect complex relationships among QTL and traits, and is more precise and efficient in QTL mapping than single trait analysis.