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

Authors

Mehdi Momen, Virginia Polytechnic Institute and State University
Malachy T. Campbell, Virginia Polytechnic Institute and State UniversityFollow
Harkamal Walia, University of Nebraska-LincolnFollow
Gota Morota, Virginia Polytechnic Institute and State UniversityFollow

ORCID IDs

Mehdi Momen

Malachy T. Campbell

Harkamal Walia

Gota Morota

Date of this Version

2019

Citation

Plant Methods (2019) 15:107

Comments

© The Author(s) 2019.

Open access

https://doi.org/10.1186/s13007-019-0493-x

Abstract

Background: Plant breeders seek to develop cultivars with maximal agronomic value, which 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). While multi-trait genome-wide association studies (MTM-GWAS) can infer putative genetic signals at the multivariate scale, standard MTM-GWAS does not accommodate the network structure of phenotypes, and therefore does not address how the traits are interrelated. We extended the scope of MTM-GWAS by incorporating trait network structures into GWAS using structural equation models (SEM-GWAS). 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.

Results: A salient feature of SEM-GWAS is that it can partition the total single nucleotide polymorphism (SNP) effects acting on a trait into direct and indirect effects. Using this novel approach, we show that for most QTL associated with water use, total SNP effects were driven by genetic effects acting directly on water use rather that genetic effects originating from upstream traits. Conversely, total SNP effects for water use efficiency were largely due to indirect effects originating from the upstream trait, projected shoot area.

Conclusions: We describe a robust framework that can be applied to multivariate phenotypes to understand the interrelationships between complex traits. This framework provides novel insights into how QTL act within a phenotypic network that would otherwise not be possible with conventional multi-trait GWAS approaches. Collectively, these results suggest that the use of SEM may enhance our understanding of complex relationships among agronomic traits.