bioRxivEvolutionary BiologyPreprint
Lineage-specific positive selection on stress-response genes in wild wheat relatives
Manuel Barrientos TecuniD, Vincent RanweziD, Nathalie ChantretiD, Concetta BurgarellaiD
Abstract
Understanding how crop wild relatives (CWR) adapt to diverse environments is important both for evolutionary biology and for the development of climate-resilient agriculture. CWR represent an important reservoir of adaptive variation that can be exploited for future crop improvement. To investigate how natural selection has shaped adaptation in these species, we analysed whole-transcriptome data from four diploid wild wheat relatives with contrasting mating systems: the outcrossing species Aegilops speltoides and Aegilops mutica, and the selfing species Aegilops tauschii and Triticum urartu. We combined the imputed McDonald--Kreitman test (MKT) with lineage-specific estimates of divergence obtained from branch-specific models of molecular evolution. This framework reduces biases caused by slightly deleterious mutations and enables adaptive substitutions to be assigned to individual evolutionary lineages. We detected marked differences among species in the prevalence of positive selection. Evidence of adaptive evolution was concentrated in the outcrossing species, whereas few positively selected genes were identified in the selfing species. Comparisons between standard and branch-specific MKT approaches showed that some signals initially inferred as shared among species were reassigned to individual lineages when divergence was estimated on a branch-specific basis, emphasizing the importance of accurately localizing adaptive substitutions. Functional annotation revealed several candidate genes containing domains associated with abiotic and biotic stress responses. Our study provides a framework for detecting positive selection in wild wheat relatives and identifies candidate genes that may contribute to adaptation and represent valuable targets for future functional analyses and wheat improvement programs.
- assay: whole-genome sequencing
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
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