Effects of causal networks on the structure and stability of resource allocation trait correlations

Abstract

Abstract:Discovering the mechanisms by which genetic variation influences phenotypes is integral to understanding life-history evolution. Models describing causal relationships among traits in a developmental hierarchy provide a functional basis for understanding the correlations often observed among life-history traits. In this paper, we evaluate a developmental network model of life-history traits based on the perennial herb Arabidopsis lyrata, evaluate phenotypic, genetic, and environmental covariance matrices obtained under different scenarios of quantitative trait locus (QTL) effects in simulated crosses, test the efficacy of structural equation modeling to identify the correct basis for multiple-trait QTL effects, and compare model predictions with field data. We found that the trait network constrained the phenotypic covariance patterns to varying degrees, depending on which traits were directly affected by QTLs. Genetic and environmental covariance matrices were strongly correlated only when direct QTL effects were spread over many traits. Structural equation models that included all simulated traits correctly identified traits directly affected by QTLs, but heuristic search algorithms found several network structures other than the correct one that also fit the data closely. Estimated correlations among a subset of traits in F2 data from field studies corresponded closely to model predictions when simulated QTLs affected traits known to differ between the parental populations. Our results show that causal trait network models can unify several aspects of quantitative genetic theory with empirical observations on genetic and phenotypic covariance patterns, and that incorporating trait networks into genetic analysis offers promise for elucidating mechanisms of life history evolution.

The many faces of pleiotropy

“…There is pleiotropy if two effects contribute differently to selective value” (p. 61). Yet, the distinction between authentic/horizontal/mosaic/independent pleiotropy on the one hand and spurious/vertical/relational/reactive pleiotropy on the other hand may hold evolutionary relevance, insofar as the latter scenario produces phenotypic correlations between traits even in the absence of genetic variation (e.g., [14]).…”

Complex Genetic Effects on Early Vegetative Development Shape Resource Allocation Differences BetweenArabidopsis lyrataPopulations

“…However, in this article we include data for vegetative and seed-mass traits not addressed in the previous studies, except for comparison with simulated data in a theoretical study of developmental networks (Gove et al 2012). We also include data for additional populations planted at the North Carolina site.…”

“…SEM tests the extent to which a model of cause-effect relationships explains covariances between a set of variables; in this case QTL genotypes and traits involved in resource allocation. This approach is conceptually similar to systems genetics techniques used to evaluate networks of gene expression QTL (eQTL) at a single point in time (reviewed in Rockman 2008; Mackay et al 2009;Li et al 2010) but is also applicable to analysis of QTL for developmentally related traits expressed over time (Li et al 2006;Remington 2009;Gove et al 2012). Variance-covariance matrices were estimated for fall diameter, net winter diameter growth, net reproductive season diameter growth, number of reproductive shoots, siliques per shoot, the a, d, and i terms of each QTL peak location, and block mean productivity using the R cov function.…”

“…Single F 1 plants from each family were crossed reciprocally to generate two outcross F 2 families that differ only in their cytoplasmic origin. These crosses are the same as those described previously (Leinonen et al 2011(Leinonen et al , 2013Gove et al 2012). Data from the two F 2 reciprocals are combined for all analyses reported in this article.…”

Paths to selection on life history loci in different natural environments across the native range of Arabidopsis thaliana

“…However, it is often unclear whether the observed effect of a QTL on a fitness trait reflects a direct genetic effect on that trait or an indirect effect via correlated life history traits expressed earlier in life. Recent studies have addressed this issue using structural equation models (SEMs) to dissect indirect QTL effects on complex trait variation (Remington 2009;Wolf et al 2011), but few have focused on selection in natural environments (Gove et al 2012). Separately, several studies have extended the selection gradient approach of Lande & Arnold (1983) into SEMs, dissecting fitness through causal networks of correlated traits (Scheiner et al 2000;Latta & McCain 2009;Milla et al 2009), but these have not included specific loci in this framework.…”