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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Mi Xiaojuan & Eskridge Kent & Wang Dong & Baenziger P. Stephen & Campbell B. Todd & Gill Kulvinder S. & Dweikat Ismail & Bovaird James, 2010. "Regression-Based Multi-Trait QTL Mapping Using a Structural Equation Model," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 9(1), pages 1-23, October.
    • Handle: RePEc:bpj:sagmbi:v:9:y:2010:i:1:n:38
    DOI: 10.2202/1544-6115.1552
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    References listed on IDEAS

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    1. Dag Sörbom, 1989. "Model modification," Psychometrika, Springer;The Psychometric Society, vol. 54(3), pages 371-384, September.
    2. Hongying Li & Zhongwen Huang & Junyi Gai & Song Wu & Yanru Zeng & Qin Li & Rongling Wu, 2007. "A Conceptual Framework for Mapping Quantitative Trait Loci Regulating Ontogenetic Allometry," PLOS ONE, Public Library of Science, vol. 2(11), pages 1-10, November.
    3. Renhua Li & Shirng-Wern Tsaih & Keith Shockley & Ioannis M Stylianou & Jon Wergedal & Beverly Paigen & Gary A Churchill, 2006. "Structural Model Analysis of Multiple Quantitative Traits," PLOS Genetics, Public Library of Science, vol. 2(7), pages 1-12, July.
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    Cited by:

    1. Xiaodong Cai & Juan Andrés Bazerque & Georgios B Giannakis, 2013. "Inference of Gene Regulatory Networks with Sparse Structural Equation Models Exploiting Genetic Perturbations," PLOS Computational Biology, Public Library of Science, vol. 9(5), pages 1-13, May.

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