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Structural Model Analysis of Multiple Quantitative Traits

Author

Listed:
  • Renhua Li
  • Shirng-Wern Tsaih
  • Keith Shockley
  • Ioannis M Stylianou
  • Jon Wergedal
  • Beverly Paigen
  • Gary A Churchill

Abstract

We introduce a method for the analysis of multilocus, multitrait genetic data that provides an intuitive and precise characterization of genetic architecture. We show that it is possible to infer the magnitude and direction of causal relationships among multiple correlated phenotypes and illustrate the technique using body composition and bone density data from mouse intercross populations. Using these techniques we are able to distinguish genetic loci that affect adiposity from those that affect overall body size and thus reveal a shortcoming of standardized measures such as body mass index that are widely used in obesity research. The identification of causal networks sheds light on the nature of genetic heterogeneity and pleiotropy in complex genetic systems.Synopsis: Disease states are often associated with multiple, correlated traits that may result from shared genetic and nongenetic factors. Genetic analysis of multiple traits can reveal a network of effects in which each trait is influenced by more than one genetic locus (heterogeneity) and different traits share one or more loci in common (pleiotropy). Physiological interactions independent of genetic factors may also contribute to the observed correlations. Structural equation modeling is proposed as a statistical method to characterize the architecture of multiple trait genetic systems. Application of structural equation modeling to body size, adiposity, and bone geometry traits illustrates how the effects of a genetic locus can be decomposed along direct and indirect paths that may be mediated through interactions with other traits. Using this technique the authors identify adiposity loci that act independently of loci affecting overall body size.

Suggested Citation

  • 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.
    • Handle: RePEc:plo:pgen00:0020114
    DOI: 10.1371/journal.pgen.0020114
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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.

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