bioRxivPlant BiologyPreprint
Contrasting levels of transcriptome-wide SNP diversity and decoupled patterns of molecular and functional adaptive variation in conifers
N. Pavy, s. gerardi, J. Prunier, P. Rigault, J. Laroche, G. Daigle, B. Boyle, J. J. MacKay, J. Bousquet
Abstract
Adaptive convergence can arise when response to natural selection involves shared molecular or functional mechanisms among multiple taxa. Conifers are of ancient origin with delayed sexual maturity related to their woody perennial nature. Thus, they represent a relevant plant group to assess if convergence from selection may have become disconnected between molecular and functional levels. In this purpose, transcriptome-wide SNP diversity was assessed in seven partially sympatric and reproductively isolated conifer species populating the temperate and boreal forests of northeastern North America. SNP diversity was found highly heterogeneous among species, which would relate to variation in species-specific demography and history. Rapidly evolving genes with signatures of positive selection were identified, and their relative abundance among species reflected differences in transcriptome-wide SNP diversity. Their analysis also revealed very limited convergence among taxa in spite of sampling same tissues at same age. However, convergence increased gradually at the levels of gene families and biological processes, which were largely related to stress response and regulatory mechanisms in all species. Given their multiple small to large gene families and long time since inception, conifers may have had sufficient gene network flexibility and gene functional redundancy for evolving alternative adaptive genes for similar metabolic responses to environmental selection pressures. Despite a long divergence time of [~]350 Mya between conifers and Angiosperms, we also uncovered a set of 20 key genes presumably under positive selection in both lineages.
- organism: plant (other)
- assay: DNA methylation
- assay: whole-genome sequencing
- research focus: fine-mapping
- research focus: gene regulatory network inference
- research focus: differential expression
- approach: statistical model
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