Adaptive potential of Coffea canephora from Uganda in response to climate change. Issue 6 (1st February 2022)

• Record Type: Journal Article
• Authors: de Aquino, Sinara Oliveira; Kiwuka, Catherine; Tournebize, Rémi; Gain, Clément; Marraccini, Pierre; Mariac, Cédric; Bethune, Kévin; Couderc, Marie; Cubry, Philippe; Andrade, Alan C.; Lepelley, Maud; Darracq, Olivier; Crouzillat, Dominique; Anten, Niels; Musoli, Pascal; Vigouroux, Yves; de Kochko, Alexandre; Manel, Stéphanie; François, Olivier; Poncet, Valérie
• Is Part Of: Molecular ecology. Volume 31:Issue 6(2022)
• Page Start: 1800
• Page End: 1819
• Publication Date: 2022-02-01
• Subjects: candidate genes -- climate change -- environmental association -- landscape genomics -- target capture -- wild coffee; Molecular ecology -- Periodicals; Molecular population biology -- Periodicals; 576
• DOI: 10.1111/mec.16360
• Languages: English
• ISSNs: 0962-1083

Abstract

Abstract: Understanding vulnerabilities of plant populations to climate change could help preserve their biodiversity and reveal new elite parents for future breeding programmes. To this end, landscape genomics is a useful approach for assessing putative adaptations to future climatic conditions, especially in long‐lived species such as trees. We conducted a population genomics study of 207 Coffea canephora trees from seven forests along different climate gradients in Uganda. For this, we sequenced 323 candidate genes involved in key metabolic and defence pathways in coffee. Seventy‐one single nucleotide polymorphisms (SNPs) were found to be significantly associated with bioclimatic variables, and were thereby considered as putatively adaptive loci. These SNPs were linked to key candidate genes, including transcription factors, like DREB ‐like and MYB family genes controlling plant responses to abiotic stresses, as well as other genes of organoleptic interest, such as the DXMT gene involved in caffeine biosynthesis and a putative pest repellent. These climate‐associated genetic markers were used to compute genetic offsets, predicting population responses to future climatic conditions based on local climate change forecasts. Using these measures of maladaptation to future conditions, substantial levels of genetic differentiation between present and future diversity were estimated for all populations and scenarios considered. The populations from the forests Zoka and Budongo, in the northernmost zone of Uganda, appeared to have the lowest genetic offsets under all predicted climate change patterns, while populations from Kalangala and Mabira, in the Lake Victoria region, exhibited the highest genetic offsets. The potential of these findings in terms of ex situ conservation strategies are discussed.