Abstract
Pleiotropy, the control of multiple phenotypes by a single locus, is expected to slow the rate of adaptation by increasing the chance that beneficial alleles also have deleterious effects. However, a prediction arising from classical theory of quantitative trait evolution states that pleiotropic alleles may have a selective advantage when phenotypes are distant from their selective optima. We examine the role of pleiotropy in regulating adaptive differentiation among populations of common ragweed (Ambrosia artemisiifolia); a species that has recently expanded its North American range due to human-mediated habitat change. We employ a phenotype-free approach by using connectivity in gene networks as a proxy for pleiotropy. First, we identify loci bearing footprints of local adaptation, and then use genotype-expression mapping and co-expression networks to infer the connectivity of the genes. Our results indicate that the putatively adaptive loci are highly pleiotropic, as they are more likely than expected to affect the expression of other genes, and they reside in central positions within the gene networks. We propose that the conditionally advantageous alleles at these loci avoid the cost of pleiotropy by having large phenotypic effects that are beneficial when populations are far from their selective optima. We further use evolutionary simulations to show that these patterns are in agreement with a model where populations face novel selective pressures, as expected during a range expansion. Overall, our results suggest that highly connected genes may be targets of positive selection during environmental change, even though they likely experience strong purifying selection in stable selective environments.
Highlights
Theory by Fisher [1] suggests that adaptation mainly advances through the fixation of small-effect loci, because pleiotropy would cause large effect loci to move phenotypes away from their fitness optima
We investigate the relationship between local adaptation and network connectivity using transcriptome data collected from a widely-distributed wind-pollinated plant, Ambrosia artemisiifolia
We examined whether local adaptation outliers have higher connectivity than nonoutliers, as might be expected given that outliers had a high probability of being expression QTL (eQTL)
Summary
Theory by Fisher [1] suggests that adaptation mainly advances through the fixation of small-effect loci, because pleiotropy (the control of multiple phenotypes by a single locus) would cause large effect loci to move phenotypes away from their fitness optima. When populations adapt to new environments, phenotypes are initially far from their optimal values. This gives a selective advantage to loci with large phenotypic effects, as they can move phenotypes faster towards the optima. Experimental data indicates that the effect-sizes of loci increase with increasing level of pleiotropy [7,8,9], suggesting that highly pleiotropic loci may escape the “cost of complexity” [2] and be selectively advantageous if phenotypes are distant from their selective optima
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have