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11 results for “Coffea canephora”
Genetic diversity of wild and cultivated Coffea canephora in northeastern DR Congo and the implications for conservation - Additional Data
<p>List of wild and cultivated <em>Coffea canephora </em>accessions from northeastern Democratic Republic of the Congo included in Vanden Abeele et al. 2021 - American Journal of Botany, and the corresponding alleles for each of the 18 microsatellite markers (0 indicates missing alleles).</p>
Adaptive potential of Coffea canephora from Uganda in response to climate change
<p>Understanding vulnerabilities of plant populations to climate change could help preserve their biodiversity and reveal new elite parents for future breeding programs. 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 <i>Coffea canephora</i> trees from seven forests along different climate gradients in Uganda. For this, we sequenced 323 candidate genes involved in key metabolic and defense pathways in coffee. Seventy-one 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 <i>DREB</i>-like and <i>MYB</i> family genes controlling plant responses to abiotic stresses, as well as other genes of organoleptic interest, like the <i>DXMT</i> 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 <i>ex-situ</i> conservation strategies are discussed.</p>
Adaptive potential of Coffea canephora from Uganda in response to climate change
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Coffea canephora lipid profile
<p>Coffee (<i>Coffea</i> spp.) is one of the most popular refreshing beverage globally. Coffee lipid diversity has untapped potential for improving coffee marketability because lipids contribute significantly to both the health benefits and cup quality of coffee. However, there have not been extensive studies of lipids of <i>C. canephora</i> genotypes. In this study, Ultra-performance liquid chromatography coupled with mass spectrometry (UPLC–MS) profiling of lipid molecules was performed for 30 genotypes consisting of 15 cultivated and 15 conserved genotypes of <i>C. canephora</i> in Southwestern Nigeria. We identified nine classes of lipids in the 30 genotypes which belong to the 'Niaouli', 'Kouillou' and 'Java Robusta' group: among these, the most abundant lipid class was the triacylglycerols, followed by the fatty acyls group. Although 'Niaouli' diverged from the 'Kouillou' and 'Java Robusta' genotypes when their lipid profiles were compared, there was greater similarity in their lipid composition by multivariate analysis, compared to that observed when their primary metabolites and especially their secondary metabolite profiles were examined. However, distinctions could be made among genotypes. Members of the fatty acyls group had the greatest power to discriminate among genotypes, however, lipids that were low in abundance e.g. a cholesterol ester (20:3), and phosphotidylethanolamine (34:0) were also helpful to understand the relationships among <i>C. canephora</i> genotypes. The two lipid diversity identified among the <i>C. canephora</i> genotypes examined correlated with their overall Single Nucleotide Polymorphism diversity assessed by genotype-by-sequencing, will be exploited, and included in coffee cup quality improvement.</p>
Data from: Accurate genomic prediction of Coffea canephora in multiple environments using whole-genome statistical models
Genomic selection have been proposed as the standard method to predict breeding values in animal and plant breeding. Although some crops have benefited from this methodology, studies in Coffea are still emerging. To date, there have been no studies of how well genomic prediction models work across populations and environments for different complex traits in coffee. Considering that predictive models are based on biological and statistical assumptions, it is expected that their performance vary depending on how well these assumptions align with the true genetic architecture of the phenotype. To investigate this, we used data from two recurrent selection populations of Coffea canephora, evaluated in two locations, and single nucleotide polymorphisms identified by Genotyping-by-Sequencing. In particular, we evaluated the performance of 13 statistical approaches to predict three important traits in the coffee — production of coffee beans, leaf rust incidence and yield of green beans. Analyses were performed for predictions within-environment, across locations and across populations to assess the reliability of genomic selection. Overall, differences in the prediction accuracy of the competing models were small, although the Bayesian methods showed a modest improvement over other methods, at the cost of more computation time. As expected, predictive accuracy for within-environment analysis, on average, were higher than predictions across locations and across populations. Our results support the potential of genomic selection to reshape traditional plant breeding schemes. In practice, we expect to increase the genetic gain per unit of time by reducing the length cycle of recurrent selection in coffee.
Data from: Accurate genomic prediction of Coffea canephora in multiple environments using whole-genome statistical models
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Coffea canephora lipid profile
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Occurence data for species distribution modelling of wild Coffea canephora
<p><span>The assessment of population vulnerability under climate change is crucial for planning conservation as well as for ensuring food security. <em>Coffea canephora</em> is, in its native habitat, an understory tree that is mainly distributed in the lowland rainforests of tropical Africa. Also known as Robusta, its commercial value constitutes a significant revenue for many human populations in tropical countries. Comparing ecological and genomic vulnerabilities within the species' native range can provide valuable insights about habitat loss and the species' adaptive potential, allowing to identify genotypes that may be act as a resource for varietal improvement. By applying species distribution models, we assessed ecological vulnerability as the decrease in climatic suitability under future climatic conditions from 492 occurrences. We then quantified genomic vulnerability (or risk of maladaptation) as the allelic composition change required to keep pace with predicted climate change. Genomic vulnerability was estimated from genomic environmental correlations throughout the native range. Suitable habitat was predicted to diminish to half its size by 2050, with populations near coastlines and around the Congo River being the most vulnerable. Whole-genome sequencing revealed 165 candidate SNPs associated to climatic adaptation in <em>C. canephora</em>, which were located in genes involved in plant response to biotic and abiotic stressors. Genomic vulnerability was higher for populations in West Africa and in the region at the border between DRC and Uganda. Despite an overall low correlation between genomic and ecological vulnerability at broad scale, these two components of vulnerability overlap spatially in ways that may become damaging. Genomic vulnerability was estimated to be 23% higher in populations where habitat will be lost in 2050 compared to regions where habitat will remain suitable. These results highlight how ecological and genomic vulnerabilities are relevant when planning on how to cope with climate change regarding an economically important species.</span></p>
Occurence data for species distribution modelling of wild Coffea canephora
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Coffea canephora sRNA profiling
GEO Series GSE46617. Coffea canephora. 1 samples. Type: Non-coding RNA profiling by high throughput sequencing.
Transcriptome analysis of ARF and Aux/IAA expression during the induction of somatic embryogenesis in Coffea canephora
GEO Series GSE128888. Coffea canephora. 12 samples. Type: Expression profiling by high throughput sequencing.
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