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

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.