Genomic evidence of genetic variation with pleiotropic effects on caterpillar fitness and plant traits in a model legume

Abstract

Plant–insect interactions are ubiquitous, and have been studied intensely because of their relevance to damage and pollination in agricultural plants, and to the ecology and evolution of biodiversity. Variation within species can affect the outcome of these interactions. Specific genes and chemicals that mediate these interactions have been identified, but genome‐ or metabolome‐scale studies might be necessary to better understand the ecological and evolutionary consequences of intraspecific variation for plant–insect interactions. Here, we present such a study. Specifically, we assess the consequences of genome‐wide genetic variation in the model plant Medicago truncatula for Lycaeides melissa caterpillar growth and survival (larval performance). Using a rearing experiment and a whole‐genome SNP data set (>5 million SNPs), we found that polygenic variation in M. truncatula explains 9%–41% of the observed variation in caterpillar growth and survival. Genetic correlations among caterpillar performance and other plant traits, including structural defences and some anonymous chemical features, suggest that multiple M. truncatula alleles have pleiotropic effects on plant traits and caterpillar performance (or that substantial linkage disequilibrium exists among distinct loci affecting subsets of these traits). A moderate proportion of the genetic effect of M. truncatula alleles on L. melissa performance can be explained by the effect of these alleles on the plant traits we measured, especially leaf toughness. Taken together, our results show that intraspecific genetic variation in M. truncatula has a substantial effect on the successful development of L. melissa caterpillars (i.e., on a plant–insect interaction), and further point toward traits potentially mediating this genetic effect.

Combining Experimental Evolution and Genomics to Understand How Seed Beetles Adapt to a Marginal Host Plant

“…Our results suggest that adult weight and development time had a polygenic basis in the BC mapping populations, with a lack of major effect loci, except perhaps for weight in BC-L2. A polygenic architecture is not unexpected given the quantitative and complex nature of weight and development time, and similar results have been observed in other systems (e.g., [13,85]). We found negative correlations between effect estimates for adult weight and development time, such that SNPs associated with increased weight were also associated with slower development (consistent with [62]).…”

“…All of these metrics integrate (via MCMC) over uncertainty in the effects of individual SNPs, including whether these are non-zero. Using this BSLMM approach, it is also possible to obtain genomic-estimated breeding values (GEBVs), that is, the expected trait value for an individual from the additive effects of their genes as captured by both β and the polygenic term [12,13].…”

“…These high genetic correlations suggest many causal variants with pleiotropic effects on both performance traits or tight linkage and high LD between variants affecting the two traits. Distinguishing between these two possibilities is difficult, and from a functional and analytical perspective, true pleiotropy is best viewed as an endpoint on a continuum from loose to tight linkage [13].…”

“…Examples include genes with large effects on defensive armor in sticklebacks (Eda and Pitx-1) [2,3], coat color in mice (Agouti and Mc1r) [4,5], wing pattern in Heliconius butterflies (Optix and WntA) [6][7][8], and diapause in Drosophila (the insulin-regulated PI3 kinase gene and Timeless) [9,10]. Although genomic approaches have been less successful at determining the specific genes affecting highly polygenic traits, these methods still can elucidate trait genetic architecture, such as the number of quantitative-trait loci (QTL) or causal variants, trait heritabilities, and genetic covariances among traits [11][12][13]. Summaries of trait genetic architecture can help explain patterns and dynamics of evolutionary change (e.g., [12,14,15]).…”

Caterpillars on a phytochemical landscape: The case of alfalfa and the Melissa blue butterfly

“…Predicting evolutionary response by M. sativa would of course depend on a genetic understanding of the relevant plant traits, which the present study does not include. However, a recent study of M. melissa performance on a related plant, Medicago truncatula , found that genetic variation in the plant explained a substantial proportion of phenotypic variation (between 8% and 57%) in phytochemical and structural traits but also in caterpillar performance (Gompert et al, 2019). Of course, most plants do not have the luxury of optimizing defense against a single herbivore, and it is easy to imagine that improvements in defense against one enemy could lead to increased attraction to another (Salazar et al, 2018), especially given the diversity of effects even within major classes studied here, including saponins and phenolic glycosides.…”