IDEAS home Printed from https://ideas.repec.org/a/bpj/sagmbi/v9y2010i1n38.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://doi.org/10.2202/1544-6115.1552
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.2202/1544-6115.1552?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Moharil Janhavi & May Paul & Gaile Daniel P. & Blair Rachael Hageman, 2016. "Belief propagation in genotype-phenotype networks," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 15(1), pages 39-53, March.
    2. Gefen, David, 2000. "E-commerce: the role of familiarity and trust," Omega, Elsevier, vol. 28(6), pages 725-737, December.
    3. Jiguo Cao & Liangliang Wang & Zhongwen Huang & Junyi Gai & Rongling Wu, 2017. "Functional Mapping of Multiple Dynamic Traits," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(1), pages 60-75, March.
    4. Benjamin A Logsdon & Jason Mezey, 2010. "Gene Expression Network Reconstruction by Convex Feature Selection when Incorporating Genetic Perturbations," PLOS Computational Biology, Public Library of Science, vol. 6(12), pages 1-13, December.
    5. 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.
    6. Kausel, Edgar E. & Slaughter, Jerel E., 2011. "Narrow personality traits and organizational attraction: Evidence for the complementary hypothesis," Organizational Behavior and Human Decision Processes, Elsevier, vol. 114(1), pages 3-14, January.
    7. Torbjørn Torsheim & Oddrun Samdal & Mette Rasmussen & John Freeman & Robert Griebler & Wolfgang Dür, 2012. "Cross-National Measurement Invariance of the Teacher and Classmate Support Scale," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 105(1), pages 145-160, January.
    8. Huange Wang & Fred A van Eeuwijk, 2014. "A New Method to Infer Causal Phenotype Networks Using QTL and Phenotypic Information," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-13, August.
    9. Zijian Dong & Tiecheng Song & Chuang Yuan, 2013. "Inference of Gene Regulatory Networks from Genetic Perturbations with Linear Regression Model," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-9, December.
    10. Kaido Lepik & Tarmo Annilo & Viktorija Kukuškina & eQTLGen Consortium & Kai Kisand & Zoltán Kutalik & Pärt Peterson & Hedi Peterson, 2017. "C-reactive protein upregulates the whole blood expression of CD59 - an integrative analysis," PLOS Computational Biology, Public Library of Science, vol. 13(9), pages 1-20, September.
    11. Anthony Papadopoulos, 2011. "Stochastic Ontogenetic Allometry: The Statistical Dynamics of Relative Growth," PLOS ONE, Public Library of Science, vol. 6(9), pages 1-8, September.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bpj:sagmbi:v:9:y:2010:i:1:n:38. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.