Luqman, H. | bioRxiv Science

bioRxiv2021

Polygenic routes lead to parallel altitudinal adaptation in Heliosperma pusillum (Caryophyllaceae)

Understanding how organisms adapt to the environment is a major goal of modern biology. Parallel evolution - the independent evolution of similar phenotypes in different populations - provides a powerful framework to investigate the evolutionary potential of populations, the constraints of evolution, its repeatability and therefore its predictability. Here, we quantified the degree of gene expression and functional parallelism across replicated ecotype formation in Heliosperma pusillum (Caryophyllaceae), and gained insights into the architecture of adaptive traits. Population structure analyses and demographic modelling support a previously formulated hypothesis of parallel polytopic divergence of montane and alpine ecotypes. We detect a large proportion of differentially expressed genes (DEGs) underlying divergence within each replicate ecotype pair, with a strikingly low amount of shared DEGs across pairs. Functional enrichment of DEGs reveals that the traits affected by significant expression divergence are largely consistent across ecotype pairs, in strong contrast to the non-shared genetic basis. The remarkable redundancy of differential gene expression indicates a polygenic architecture for the diverged adaptive traits. We conclude that polygenic traits appear key to opening multiple routes for adaptation, widening the adaptive potential of organisms.

evolutionary biology↗

bioRxiv2020

Identifying loci under selection via explicit demographic models

Adaptive genetic variation is a function of both selective and neutral forces. To accurately identify adaptive loci, it is thus critical to account for demographic history. Theory suggests that signatures of selection can be inferred using the coalescent, following the premise that genealogies of selected loci deviate from neutral expectations. Here, we build on this theory to develop an analytical framework to identify Loci under Selection via explicit Demographic models (LSD). Under this framework, signatures of selection are inferred through deviations in demographic parameters, rather than through summary statistics directly, and demographic history is accounted for explicitly. Leveraging on the property of demographic models to incorporate directionality, we show that LSD can provide information on the environment in which selection acts on a population. This can prove useful in elucidating the selective processes underlying local adaptation, by characterising genetic trade-offs and extending the concepts of antagonistic pleiotropy and conditional neutrality from ecological theory to practical application in genomic data. We implement LSD via Approximate Bayesian Computation and demonstrate, via simulations, that LSD has i) high power to identify selected loci across a large range of demographic-selection regimes, ii) outperforms commonly applied genome-scan methods under complex demographies, and iii) accurately infers the directionality of selection for identified candidates. Using the same simulations, we further characterise the behaviour of isolation-with-migration models conducive to the study of local adaptation under regimes of selection. Finally, we demonstrate an application of LSD by detecting loci and characterising genetic trade-offs underlying flower colour in Antirrhinum majus.

evolutionary biology↗