Adaptation of Coffea canephora to abiotic stress: search for candidate gene polymorphisms related to present and future climatic factors

Abstract

Testing whether and how natural populations are adapted to their local environment and predicting their responses to future habitat alterations is of key importance in the face of climate change. This is particulary the case for coffee trees for which the pace of climate change could be too fast and drastic for adaptation of populations. Using the geographic distribution of wild populations with contrasted habitats, the aim of the present study was to identify single-nucleotide polymorphisms (SNPs) in candidate genes (CGs) as being potentially involved in the adaptation of Coffea canephora (Cc) populations of Uganda to their local environment. In particular, modifications occurring in genes related to abiotic stress tolerance make these genes candidate for enhanced resilience to climate change. By identifying environmental factors driving these processes, we would predict the expected adaptedness of the populations to their future local climate. Based on the previous molecular studies and using whole coffee genome sequence annotation, a set of 323 CGs was selected. A targeted capture array was designed for these CGs and their flanking regions. Wild accessions of Cc from Uganda geo-localized were used to assess the relationship between climate variation and CG nucleic diversity. We have applied available statistical population genomic methods and model of allele distribution to detect CG-SNPs correlated with climate parameters. The LFMM (Latent Factor Mixed Models) package was used for screening sequences for signatures of environmental adaptation in the coffee genomes. The genotypeenvironment (GxE) association suggests regional adaptation with spatially varying environments. More specifically, we found selection signals tightly linked to several CGs involved in response to abiotic stress like ERF034 and DREB2A.3. The selection signals detected support the hypothesis of present ecological gradient contributing to structure of the genetic diversity of Ugandense Cc populations. To estimate the adaptedness of coffee populations to future local conditions, moving beyond characterization of variation, we explored how genomic information could be used to infer the potential for local adaptation of populations under climate change cenarios. We accessed the extent to which the present genotypic composition related to climate differ in average from those expected under modeled future climate, given the respective linear model of environmental association. This required average change in genotypic composition, here defined as the adaptive potential for the future. The linear model used in this study does not predict what population genotypic composition will be, but rather quantifies the theoretical change in genotypic composition “required” under projected climate change and uses this result to consider how feasible local adaptation may be. The primary aim of this study was to gain some initial insight into the pattern and magnitude of genotypic composition change that may be associated with projected climate change and how molecular information could be used to infer adaptive potential to projected future climate conditions.