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

Abstract

20Environmental association analyses (EAA) seek to identify genetic variants associated with local 21 adaptation by regressing local environmental conditions at collection sites on genome-wide 22 polymorphisms. The rationale is that environmental conditions impose selective pressure on trait(s), and 23 these traits are regulated in part by variation at a genomic level. Here, we present an alternative 24 multivariate genomic approach that can be utilized when both phenotypic and environmental data are 25 available for the population. This framework utilizes Bayesian networks (BN) to elucidate 26 interdependancies between local environmental conditions and empirical phenotypes, and jointly 27 estimates the direct and indirect genetic covariances between empirical phenotypes and environmental 28 conditions using a mixed-effects structural equation model (SEM). Direct genomic covariance between 29 empirical phenotypes and environmental conditions may provide insight into whether QTL that affect 30 adaptation to an environmental gradient also affects the observed phenotype. To demonstrate the utility 31of this approach, we leveraged two existing datasets consisting of 55 climate variables for 1,130 32 Arabidopsis accessions and empirical phenotypes for fitness and phenology collected on 515 accessions in 33 two common garden locations in Europe. BN showed that plasticity for fitness and phenology was highly 34 dependant on local environmental conditions. Moreover, genomic SEM revealed relatively high positive 35genomic correlation between plasticity in fitness and environmental variables that describe the 36 favorability of the local environment for plant growth, indicating the presence of common QTL or 37 independent QTL that are tightly linked. We believe the frameworks presented in this manuscript can 38 provide new insights into the genetic basis of local adaptation. 39 3/34 42 quantitative geneticists. In many cases, particularly those working with agronomic species, these studies 43 involve large-scale phenotypic evaluations in multiple environments, which are later integrated with 44 genomic data using quantitative genetic frameworks. However, when the population is composed of 45

Perspectives on Applications of Hierarchical Gene-To-Phenotype (G2P) Maps to Capture Non-stationary Effects of Alleles in Genomic Prediction

“…Valente et al (2013) provide an overview of the breeding applications of Structural Equation Models (Gianola and Sorensen, 2004;Pearl, 2012) and highlight their ability to allow prediction across a broader range of livestock and crop management practices than standard multi-trait models without requiring frequent re-estimation of the G2P map. Recently, there has been an increase in the use of Structural Equation Models for prediction and inference in both animal and plant breeding (Tiezzi et al, 2015;Momen et al, 2018;Campbell et al, 2019;Pegolo et al, 2020;Abdalla et al, 2021). However, due to a lack of prior knowledge of the underlying relationships, most studies have used Structural Equation Models to estimate linear relationships between traits.…”

Deciphering Cattle Temperament Measures Derived From a Four-Platform Standing Scale Using Genetic Factor Analytic Modeling

“…Phenotypes are often correlated at the genetic level due to the pleiotropic effect or the linkage disequilibrium among quantitative trait loci. The multivariate modeling has been widely used to model correlated structure by taking the advantage of the genetic or environmental covariance between phenotypes (Henderson and Quaas, 1976;Campbell et al, 2019). The standard multi-trait approach has been proven to be useful for a trait with low heritability or having scarce records (Mrode, 2014).…”

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

“…Phenotypes are often correlated at the genetic level due to the pleiotropic effect or the linkage disequilibrium among quantitative trait loci. The multivariate modeling has been widely used to model correlated structure by taking the advantage of the genetic or environmental covariance between phenotypes [36,37]. The standard multi-trait approach has been proven to be useful for a trait with low heritability or having scarce records [38].…”