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Utilizing trait networks and structural equation models as tools to interpret multi-trait genome-wide association studies

View ORCID ProfileMehdi Momen, View ORCID ProfileMalachy T. Campbell, View ORCID ProfileHarkamal Walia, View ORCID ProfileGota Morota
doi: https://doi.org/10.1101/553008
Mehdi Momen
1Department of Animal and Poultry Science, Virginia Polytechnic Institute and State University, Blacksburg VA 24061
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Malachy T. Campbell
1Department of Animal and Poultry Science, Virginia Polytechnic Institute and State University, Blacksburg VA 24061
2Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68583
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Harkamal Walia
2Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68583
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Gota Morota
1Department of Animal and Poultry Science, Virginia Polytechnic Institute and State University, Blacksburg VA 24061
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  • For correspondence: morota@vt.edu
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Abstract

Background Plant breeders seek to develop cultivars with maximal agronomic value. The merit of breeding material is often assessed using numerous, often genetically correlated traits. As intervention on one trait will affect the value of another, breeding decisions should consider the relationships among traits in the context of putative causal structures (i.e., trait networks). With the proliferation of multi-trait genome-wide association studies (MTM-GWAS), we can infer putative genetic signals at the multivariate scale. However, a standard MTM-GWAS does not accommodate the network structure of phenotypes, and therefore does not address how the traits are interrelated.

Results We extended the scope of MTM-GWAS by incorporating trait network structures into GWAS using structural equation models (SEM-GWAS). In this network GWAS model, the learned structure is used to define a set of explanatory variables that describe how other phenotypes may act on the focal trait. A salient feature of SEM-GWAS is that it can partition the total single nucleotide polymorphism (SNP) effects into direct and indirect effects. Here, we illustrate the utility of SEM-GWAS using a digital metric for shoot biomass, root biomass, water use, and water use efficiency in rice.

Conclusions We found that SNPs impacted water use efficiency directly as well as indirectly through shoot biomass and root biomass. In addition, SEM-GWAS partitioned significant SNP effects influencing water use efficiency into direct and indirect effects as a function of the other traits, providing further biological insights. These results suggest that the use of SEM may enhance our understanding of complex relationships among agronomic traits.

Footnotes

  • The title has been changed. The Results section has been updated.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted July 11, 2019.
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Utilizing trait networks and structural equation models as tools to interpret multi-trait genome-wide association studies
Mehdi Momen, Malachy T. Campbell, Harkamal Walia, Gota Morota
bioRxiv 553008; doi: https://doi.org/10.1101/553008
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Utilizing trait networks and structural equation models as tools to interpret multi-trait genome-wide association studies
Mehdi Momen, Malachy T. Campbell, Harkamal Walia, Gota Morota
bioRxiv 553008; doi: https://doi.org/10.1101/553008

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