Genomic Bayesian Confirmatory Factor Analysis and Bayesian Network To Characterize a Wide Spectrum of Rice Phenotypes

Abstract

With the advent of high-throughput phenotyping platforms, plant breeders have a means to assess many traits for large breeding populations. However, understanding the genetic interdependencies among high-dimensional traits in a statistically robust manner remains a major challenge. Since multiple phenotypes likely share mutual relationships, elucidating the interdependencies among economically important traits can better inform breeding decisions and accelerate the genetic improvement of plants. The objective of this study was to leverage confirmatory factor analysis and graphical modeling to elucidate the genetic interdependencies among a diverse agronomic traits in rice. We used a Bayesian network to depict conditional dependencies among phenotypes, which can not be obtained by standard multi-trait analysis. We utilized Bayesian confirmatory factor analysis which hypothesized that 48 observed phenotypes resulted from six latent variables including grain morphology, morphology, flowering time, physiology, yield, and morphological salt response. This was followed by studying the genetics of each latent variable, which is also known as factor, using single nucleotide polymorphisms. Bayesian network structures involving the genomic component of six latent variables were established by fitting four algorithms ( i.e. , Hill Climbing, Tabu, Max-Min Hill Climbing, and General 2-Phase Restricted Maximization algorithms). Physiological components influenced the flowering time and grain morphology, and morphology and grain morphology influenced yield. In summary, we show the Bayesian network coupled with factor analysis can provide an effective approach to understand the interdependence patterns among phenotypes and to predict the potential influence of external interventions or selection related to target traits in the interrelated complex traits systems.

A Multiple-trait Bayesian Variable Selection Regression Method for Integrating Phenotypic Causal Networks in Genome-Wide Association Studies

“…The causal structure IC1 was also inferred by Tabu, Hill Climbing, General 2-Phase Restricted Maximization and Max-Min Hill Climbing algorithm in Yu et al (2019) using a similar dataset. Thus, for the simplicity of our presentation, but without loss of generality, IC1 is used in this section to demonstrate the performance of SEM-BayesCΠ in capturing the causal relationship among traits and decomposing the total SNP effects.…”

Deciphering cattle temperament measures derived from a four-platform standing scale using genetic factor analytic modeling

“…Using results from the EFA as a prior, a confirmatory factor analysis (CFA) under the Bayesian framework was fitted following the procedure described in Yu et al [20] to obtain factor scores. We assigned the following priors for equations (1) and (2).…”

“…Penãgaricano et al [43] investigated the interrelationships of five latent variables extracted from 19 traits in swine using CFA. Similarly, a Bayesian CFA combined with Bayesian Network was employed to characterize the wide spectrum of 48 rice phenotypes in Yu et al [20]. These studies determined the latent structure by leveraging the prior biological knowledge between factors and phenotypes.…”

Examining the relationships between phenotypic plasticity and local environments with genomic structural equation models

“…The goal of FA is to define a reduced set of unobserved, latent variables that maximize the correlation among groups of related observed variables. In quantitative genetics, FA is routinely applied to multi-environmental trials and high-dimensional multi-trait applications (Kelly et al, 2007; Meyer, 2009; de Los Campos and Gianola, 2007; Runcie and Mukherjee, 2013; Yu et al,2019). Thus, when applied to high dimensional environmental data, FA may yield a reduced set of underlying variables that capture major patterns of local environments.…”