Adaptive potential of Coffea canephora from Uganda in response to climate change

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.

A Quantitative Theory for Genomic Offset Statistics

“…RDA was performed by using principal components of fitted values of the GEA regression model. was computed as the average value of the absolute distance between predicted allelic frequencies across genomic loci ( Rellstab et al 2016 ; de Aquino et al 2022 ). GF computations were performed using the R package gradientForest version 0.1.…”

A quantitative theory for genomic offset statistics

“…RDA was performed by using principal components of fitted values of the GEA regression model. Rona was computed as the average value of the absolute distance between predicted allelic frequencies across genomic loci (de Aquino et al, 2022;Rellstab et al, 2016). GF computations were performed using the R package gradientForest version 0.1.…”

Ecological and genomic vulnerability to climate change across native populations of Robusta coffee (Coffea canephora)

“…One way these climate–genome associations can be used in this context is by contrasting the current versus future genomic composition change that would be required under future scenarios, assuming the climate–genome association should stay the same for the populations in order to remain adapted to their local environments. This has been called the risk of non‐adaptedness (RONA; Aquino et al, 2022; Rellstab et al, 2016), risk of (mal)adaptation (Capblancq, Fitzpatrick, et al, 2020; Jordan et al, 2017) or genomic vulnerability (Bay & Harrigan, 2018; Wood et al, 2021). For example, Bay and Harrigan (2018) examined associations between genomes and climatic gradients across the range of yellow warblers and showed that populations that are declining correspond to those that would require the greatest genomic changes to match associated climate changes, which have occurred in the same time period.…”

“…Climate change is predicted to negatively impact Coffea species, particularly because the rate at which change takes place might be too high for species to migrate in time or adapt via novel mutations (Bunn et al, 2015;Davis et al, 2012Davis et al, , 2019de Aquino et al, 2022;Moat et al, 2017Moat et al, , 2019Ovalle-Rivera et al, 2015). Coffee is the most important commodity crop globally, providing income to more than 125 million people, mostly in tropical nations, and having a retail market value of over 83 billion USD in 2017 alone (Voora et al, 2019).…”