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Towards the identification of candidate gene nucleic polymorpibsms to predict the adaptedness of ugandense C. canephora populations to climate change

De Aquino Sinara Oliveira, Tournebize Rémi, Marraccini Pierre, Mariac Cédric, Bethune Kevin, Andrade Alan Carvalho, Kiwuka Catherine, Crouzillat Dominique, Anten Niels P.R., De Kochko Alexandre, Poncet Valérie. 2019. Towards the identification of candidate gene nucleic polymorpibsms to predict the adaptedness of ugandense C. canephora populations to climate change. In : 27th Biennial ASIC Conference, Portland, 16-20 September 2018. Book of abstracts 2019. ASIC, SCA. Portland : ASIC, Résumé, 1 p. Biennial ASIC Conference. 27, Portland, États-Unis, 16 September 2018/20 September 2018.

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Abstract : RATIONALE Testing whether and how natural populations are adapted to their local environment and predicting their response to future habitat alterations is of key importance in the face of future climate change. This is particulary the case for coffee trees for which the pace of climate change could be too fast and drastic for population adaptations. 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) identified as being involved in the adaptation of C. canephora populations to their local environment. By identifying environmental factors driving these processes we would predict the adaptedness of the populations to their future local climate. METHODS Based on the previous molecular studies (EMBRAPA/CIRAD/Nestle studies) and using whole coffee genome sequence annotation (Denoeud et al. 2014, Dereeper et al. 2015), a set of 324 CGs was selected, such as those coding for dehydrins, heat shock proteins, enzymes of sugar metabolism, as well as transcription factors like DREB/CBF (dehydration responsive element binding/cold-binding factor). Wild accessions of C. canephora from Uganda with recorded position (geo-localized samples) were used to assess the relationship between climate variation (www.worldclim.org/bioclirn) and CG nucleic diversity. We apply available statistical population genomic methods and model of allele distribution to detect CGSNPs correlated with climate parameters. The LFMM (Latent Factor Mixed Models) R package (Frichot et al. 2013) was used for screening sequences for signatures of environmental adaptation in coffee genomes. RESULTS The genotype-environment (GxE) association suggests regional adaptation with spatially varying environments. More specifically, we found selection signals tightly linked to several CGs involved in response to biotic and abiotic stress like MYB20 (coding a transcription factor involved in the response to drought stress) and DXMT (enzyme involved in caffeine biosynthesis) genes. CONCLUSIONS &PERSPECTIVES The selection signals detected support the hypothesis of present ecological gradient contributing to structure of the genetic diversity of U gandense C. canephora populations. The amount and character of genetic variations observed in genomic regions associated with climatic variables will help us to predict whether C. canephora species will be able to adapt quickly enough to track global warming.

Mots-clés Agrovoc : Coffea canephora, adaptation aux changements climatiques, Polymorphisme génétique, Intéraction génotype environnement

Mots-clés géographiques Agrovoc : Ouganda

Classification Agris : F30 - Plant genetics and breeding
F70 - Plant taxonomy and geography
P40 - Meteorology and climatology

Auteurs et affiliations

  • De Aquino Sinara Oliveira, UFLA (BRA)
  • Tournebize Rémi, IRD (FRA)
  • Marraccini Pierre, CIRAD-BIOS-UMR IPME (FRA) ORCID: 0000-0001-7637-6811
  • Mariac Cédric, IRD (FRA)
  • Bethune Kevin, IRD (FRA)
  • Andrade Alan Carvalho, EMBRAPA (BRA)
  • Kiwuka Catherine, NARO (UGA)
  • Crouzillat Dominique, Nestlé (FRA)
  • Anten Niels P.R., Wageningen University (NLD)
  • De Kochko Alexandre, IRD (FRA)
  • Poncet Valérie, IRD (FRA)

Autres liens de la publication

Source : Cirad-Agritrop (https://agritrop.cirad.fr/593456/)

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