Genetic differentiation of wild and cultivatedpopulations: diversity of Coffea canephora Pierrein Uganda

P. Musoli, P. Cubry, P. Aluka, C. Billot, M. Dufour, F. De Bellis, D. Pot, D. Bieysse,A. Charrier, and T. Leroy

Abstract: Coffea canephora Pierre ex Frohener is a perennial plant originated from Africa. Two main groups, Guineanand Congolese, have already been identified within this species. They correspond to main refugia in western and centralAfrica. In this paper we present the analysis of a region that has not yet been studied, Uganda. Two wild, one feral (oncecultivated but abandoned for many years), and two cultivated populations of C. canephora from Uganda were evaluatedusing 24 microsatellite markers. Basic diversity, dissimilarity and genetic distances between individuals, genetic differen-tiation between populations, and structure within populations were analysed. Expected heterozygosity was high for wildcompartments (0.48 to 0.54) and for cultivated and feral ones (0.57 to 0.59), with the number of private alleles rangingfrom 12 for cultivated genotypes to 37 for a wild compartment. The Ugandan samples show significant population struc-turing. We compared the Ugandan populations with a representative sample of known genetic diversity groups within thespecies using 18 markers. Coffea canephora of Ugandan origin was found to be genetically different from previously iden-tified diversity groups, implying that it forms another diversity group within the species. Given its large distribution andextremely recent domestication, C. canephora can be used to understand the effect of refugia colonization on genetic di-versity.

Key words: Coffea canephora Pierre, genetic diversity, microsatellites, differentiation, Uganda.

Resume : Coffea canephora Pierre ex Frohener est une plante perenne originaire d’Afrique. Deux groupes de diversite, lesGuineens et les Congolais, ont deja ete identifies chez cette espece. Ils correspondent a des zones refuges importantes enAfrique occidentale et centrale. Dans cet article, nous presentons l’analyse d’une region jamais etudiee auparavant, l’Ou-ganda. Deux populations sauvages, une ferale et deux populations cultivees ont ete analysees avec 24 marqueurs microsa-tellites. L’heterozygotie attendue etait forte, de 0,48 a 0,54 pour les populations sauvages, et de 0,57 a 0,59 pour lescultivees et les ferales, avec un nombre d’alleles specifiques de 12 pour les genotypes cultives a 37 pour un compartimentsauvage. Les echantillons d’Ouganda montrent une forte structuration en populations. Dix-huit marqueurs ont ete utilisespour comparer les populations de cafeiers ougandais a celles des autres groupes de diversite deja identifies chez l’espece.Les genotypes de C. canephora d’Ouganda s’averent differents de ceux des autres groupes prealablement identifies, ils for-ment donc un nouveau groupe de diversite de l’espece. Compte tenu de sa grande zone de repartition et de sa domestica-tion extremement recente, cette espece nous permet d’avoir une bonne image de la dynamique des refuges et de sesconsequences sur la diversite genetique.

Mots-cles : Coffea canephora Pierre, diversite genetique, microsatellites, differenciation, Ouganda.

Introduction

Knowledge of genetic diversity in crops is necessary forboth conservation and breeding purposes. Coffee is an ex-tremely important crop, with nearly 7 million tons of greenbeans produced yearly in about 80 tropical countries. Interms of economic importance on international markets, it is

second only to oil. The genus Coffea (Rubiaceae) is endemicto the tropical forests of Africa, from Guinea to easternAfrica and Madagascar. This genus includes about 100 spe-cies. There are two main types of cultivated coffee, Arabicaand Robusta. Arabica is produced from Coffea arabica L.,which grows in highlands, while Robusta is produced fromCoffea canephora Pierre ex Frohener, which is cultivated at

Received 26 September 2008. Accepted 12 April 2009. Published on the NRC Research Press Web site at genome.nrc.ca on 30 June2009.

Corresponding Editor: A. Civetta.

P. Musoli and P. Aluka. Coffee Research Institute, P.O. Box 185, Mukono, Uganda.P. Cubry, C. Billot, M. Dufour, F. De Bellis, D. Pot, D. Bieysse, and T. Leroy.1 CIRAD, UMR DAP, TA A-96/03, avenue Agropolis,34398 Montpellier CEDEX 5, France.A. Charrier. Montpellier SupAgro, 2 place Viala, Montpellier, F-34060, France.

1Corresponding author (e-mail: thierry.leroy@cirad.fr).

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Genome 52: 634–646 (2009) doi:10.1139/G09-037 Published by NRC Research Press

low to medium altitudes. Coffea canephora, which accountsfor 35% to 40% of world production (International CoffeeOrganization, www.ico.org), is self-incompatible and dip-loid, while C. arabica (60%–65%) is tetraploid and self-compatible (Charrier and Berthaud 1985).

Coffea canephora is indigenous to the tropical Africanforest and covers a large area stretching from West Africathrough Cameroon, Central African Republic, Congo, theDemocratic Republic of Congo, Uganda, and northern Tan-zania up to northern Angola. Coffea canephora populationsare generally small, disconnected populations with a smallnumber of mother trees and a few offspring scattered overareas smaller than 1 ha. For this long-lived outcrossing per-ennial plant, seed dispersal is assured by gravity and inges-tion by birds and mammals, while insects and wind are themain pollinators. For this perennial plant, a comprehensivestrategy for conservation of genetic resources is of great im-portance owing to (i) the rapid extinction of primary forestsin Africa and (ii) climatic changes.

The genetic diversity of C. canephora was first describedby Berthaud in 1986 using isozymes and studying wild andcultivated genotypes from western and central Africa. Heidentified 2 diversity groups: a Congolese group, whichcomprised genotypes from Central African Republic and Ca-meroon, and a Guinean group, which consisted of genotypesof wild origin from Ivory Coast. Montagnon et al. (1992),also using isozymes, proposed a substructure in the Congo-lese group with 2 subdivisions, SG1 and SG2. Dussert et al.(2003), using RFLP (restriction fragment length polymor-phism) molecular markers, placed C. canephora genotypesof cultivated and wild origins into 5 diversity groups, adding2 subgroups, B and C, to the Congolese group. Montagnon(2000) analysed 120 genotypes from 50 populations usingRFLP markers. He found a high FST coefficient within thespecies (0.47) and pairwise FST between the 5 previouslyidentified diversity groups ranging from 0.35 to 0.69. Ex-pected heterozygosity ranged from 0.19 to 0.52 for the 5groups. Recent studies using microsatellites on C. canephora(Cubry et al. 2005 for 210 genotypes using 34 loci; Cubry etal. 2008 for 9 genotypes using 60 loci) confirmed the pre-vious findings of 5 diversity groups: SG1, SG2, B, C, andGuinean. Expected heterozygosity for the 5 groups withinthe species, calculated using microsatellites, ranged from0.37 to 0.69 (Cubry et al. 2005).

According to the literature (Thomas 1935), wild and do-mesticated C. canephora were identified in Uganda andnorthern Tanzania long before colonization in the early1900s. Then, C. canephora was developed as a plantationcrop from the end of the 19th century by introducing seeds,mainly from the Belgian Congo (Thomas 1947). However,self-incompatibility of the species and the proximity of cul-tivated fields to the forest might have led to admixture be-tween various origins, both in the wild and in coffeeplantations, therefore obscuring any opportunity to uncoversources of colonization. Previous studies of samples fromUganda (Nyakaana 2007; Cubry et al. 2008) have been lim-ited to a single sample of wild origin or a few genotypes andno study has examined the genetic structure of Ugandanwild and cultivated populations.

Three centres of diversity have been identified as likely

refugia during the Last Glacial Maximum in Africa, namelythe Guinean region, the central continental region, and thecoastal Atlantic region (Hamilton 1976; White 1979). Someauthors contend that there is another ‘‘east Central Africa’’refugium located in the interlacustrine highlands borderingthe Albertine Rift and including western Uganda (Jolly etal. 1997; Anhuf et al. 2006). In this context, consideringthat C. canephora is a perennial species found across differ-ent hypothetical refugia, including the east Central Africarefugium, this species is likely to provide significant insightsinto the understanding of refugium locations in Africa. It isimportant to determine the diversity and uniqueness ofC. canephora genotypes in Uganda, as these can have con-sequences for the identification of a possible east CentralAfrica refugium and its potential use in coffee breeding.

This paper presents the results of a study of Ugandanwild, feral, and cultivated C. canephora genotypes, andcompares them with genotypes from known diversitygroups. The feral genotypes are located on islands. Theywere cultivated and then abandoned for more than 50 years,so they could be considered semi-wild types. In this paperwe address 3 questions: (i) What are the genetic diversityand population structure of C. canephora within Uganda?(ii) How do Ugandan genotypes compare with global esti-mates of C. canephora genetic diversity? (iii) What can wetell about coffee history and spreading from refugia inAfrica?

Materials and methods

Population samplingFor the purpose of studying Ugandan populations and

testing relationships between cultivated and wild popula-tions, hierarchical sampling was performed, covering 3 re-gions and 2 cultivated types.

Wild and feral compartmentsKibale and Itwara are primary forests considered to be a

natural home for wild C. canephora (Maitland 1926; Tho-mas 1944). The two forests once formed a continuum but inthe early 1900s areas in between were allocated to humansettlement, and they now lay 30 km apart. The first wild re-gion, Itwara forest, covers an area of 100 km2. Five sitesseparated by distances of 0.6 to 10 km were sampled. Ateach site, 7 to 18 individuals were sampled, for a total of55 from the region. The second wild region, Kibale forest,covers 500 km2. Four to 30 individuals per site weresampled at 4 sites separated by distances of 7 to 19 km; 54individuals were sampled in that region. In both the Kibaleand Itwara regions, sampled trees were healthy looking andestimated to be 40 to 70 years old. This sampling strategywas chosen according to the spatial dispersal of wild coffeepopulations: a few adult trees and volunteers scattered allaround the mother trees. Volunteers are plantlets originatedfrom seeds spread from the mother trees. Sampling sites inthese regions were located throughout the forests and 1 to5 km from the edges of the forests. A third region, Kalan-gala, consisted of 5 sites separated by at least 10 km on twoislands in Lake Victoria (3 sites on one island and 2 sites onthe other). Sites were located 0.5 to 2 km from the edges ofthe islands. Three to 12 healthy-looking trees over 40 years

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old were sampled per site, for a total of 35 individualssampled in this region. Kalangala consists of a secondaryforest regenerated from former cultivated coffee areas, andcoffee populations are thus considered feral (Thomas 1935).

Cultivated compartmentUgandan coffee farmers consider two main cultivated cof-

fee ideotypes, Nganda and Erect. Nganda has a spreadinggrowth habit and develops many secondary stems fromsuckers growing on the main stems. Erect has upright mainstems. It is often believed that the latter type was introducedfrom the Congo Basin, whereas the former may originatefrom wild coffee sampled from the local forests (Thomas1935). However, this hypothesis is not substantiated. Indi-viduals of both the Nganda (31 individuals) and Erect (21individuals) types were sampled from the Kawanda collec-tion in Uganda. Individuals of each ideotype were selected

on the basis of historical records in addition to their pheno-typic appearance. In our study, Nganda and Erect genotypeswere considered separate groups equivalent to regions.

Sampling outside UgandaTo assign Ugandan C. canephora to a diversity group

within the species, we selected controls from the Guineanand Congolese groups (i.e., Guinean, SG1, SG2, B, and C)(Fig. 1). Based on the results of a previous study (Cubry etal. 2005), a subset of 36 genotypes encompassing knownC. canephora diversity was selected. A random representa-tive sample was selected from each group on the basis of di-versity within each group (Cubry et al. 2005), as follows:SG1, 2 genotypes; B, 7 genotypes; C, 5 genotypes; SG2, 7genotypes (2 sites); and Guinean, 15 genotypes (2 sites).Guinean and SG1, B, and C genotypes are of wild origin,while SG2 genotypes are cultivated (Fig. 2).

Fig. 1. Geographical distribution of Coffea canephora Pierre diversity groups. The Guinean region covers Ivory Coast and Guinea. TheCongolese group (B, C, SG1, and SG2) is located in the Central African Republic, Cameroon, and the Congo Basin. The locations ofUgandan surveyed populations are indicated by small white circles.

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DNA extractionGenomic DNA was extracted from ground frozen leaves

using a MATAB buffer–based extraction method adaptedfrom a procedure used for cocoa (Risterucci et al. 2000).The extracts were purified using the solution-based PromegaWizard Genomic DNA Purification Kit. The concentrationof the extracts was estimated with a spectrophotometer andstandardized at 0.5 ng/mL (final working solution).

SSR genotypingTwenty-four polymorphic microsatellite markers (SSRs)

mapped on the C. canephora genome were used to genotypethe 196 individuals from Uganda (Table 1). Markers DL013and DL025 were previously designed from a BAC librarydeveloped for studying sugar metabolism in coffee (Leroyet al. 2005). The other markers came from an SSR-enrichedlibrary of C. canephora clone 126 (Dufour et al. 2002) andfrom enriched libraries of C. arabica ‘Caturra’ (Combes etal. 2000; Rovelli et al. 2000). These markers have been as-sessed for their amplification in C. canephora and relatedspecies (Poncet et al. 2007; Cubry et al. 2008). To avoidany redundancy in diversity information, the chosen markerscovered 9 of the 11 linkage groups (n = 11) and were lo-cated at a distance of at least 50 cM from each other. Thisset of markers allows differentiation of genetic groupswithin the species (Cubry 2009).

A subset of 18 of the 24 SSR markers used in a previousstudy (Cubry et al. 2005) was considered for the compara-tive analyses between Ugandan genotypes and other diver-sity groups (Table 1).

PCR amplification and visualization of themicrosatellites

PCR reactions were performed in 10 mL containing 2.5 ngof DNA, 1 mmol/L Tris-HCl, 5 mmol/L KCl, 2 mmol/LMgCl2, 200 mmol/L dNTPs, 0.10 mmol/L reverse primer,0.08 mmol/L forward primer tailed with M13 sequence,0.10 mmol/L infrared fluorescently labelled M13 primer,and 0.1 U of Taq DNA polymerase. PCR amplificationswere run in an Eppendorf Ep384 thermocycler. The amplifi-cation programme consisted of an initial denaturation cycleof 4 min at 94 8C, followed by 10 ‘‘touch-down’’ cycles(45 s at 94 8C; 1 min at 60 8C to 55 8C, decreasing by0.5 8C per cycle; and 1 min 30 s at 72 8C), 25 cycles (45 sat 94 8C, 1 min at 55 8C, and 1 min 30 s at 72 8C), and afinal elongation step at 72 8C for 5 min.

Fluorescently labelled PCR products were analysed byelectrophoresis on a LI-COR 4300 automated sequencerwith a 6.5% acrylamide gel. The gel images were retrievedand annotated with the manufacturer’s program, SAGAGT.Allele sizes were evaluated on the basis of allelic controlspreviously defined by Cubry et al. (2005). A data matrix

Fig. 2. Hierarchical representation of sampling for Coffea canephora diversity: country, regions, and sites are identified. The number ofindividuals per region and site (N) is indicated in the boxes. The sampling area for Ugandan forests is indicated in parentheses.

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Table 1. Microsatellite markers used for genotyping 196 Coffea canephora samples from Uganda.

Markercode LG

Repeatsequence (SSR)

Allelesize (bp) Forward primer Reverse primer

EMBLdatabase No. Reference

351* B (GT)13 304 AAGGATGGCAAGTGGATTTCT GCAGCTCTTGATTGTAGTTTCGT AM231551 Dufour et al. 2002355* D (TG)15 177 CTATGATGTCTTCCAACCTTCTAAC GGTCCAATTCTGTTTCAATTTC AM231552 Dufour et al. 2002358* G (CA)11 248 CATGCACTATTATGTTTGTGTTTT TCTCGTCATATTTACAGGTAGGTT AM231554 Dufour et al. 2002364* A A21 90 AGAAGAATGAAGACGAAACACA TAACGCCTGCCATCG AM231556 Dufour et al. 2002368* H (TG)13 160 CACATCTCCATCCATAACCATTT TCCTACCTACTTGCCTGTGCT AM231558 Dufour et al. 2002384* B (AC)10 255 ACGCTATGACAAGGCAATGA TGCAGTAGTTTCACCCTTTATCC AM231560 Dufour et al. 2002394* A (TG)9 124 GCCGTCTCGTATCCCTCA GAAGCCAGAAAGTCAGTCACATAG AM231563 Dufour et al. 2002429* D A13 175 CATTCGATGCCAACAACCT GGGTCAACGCTTCTCCTG AM231565 Dufour et al. 2002442 (CA)19 227 CGCAAATCTGAGTATCCCAAC TGGATCAACACTGCCCTTC AM231566 Dufour et al. 2002445* I (AC)10 274 CCACAGCTTGAATGACCAGA AATTGACCAAGTAATCACCGACT AM231567 Dufour et al. 2002456* H (AC)14 297 TGGTTGTTTTCTTCCATCAATC TCCAGTTTCCCACGCTCT AM231568 Dufour et al. 2002461* D (AC)9 461 CGGCTGTGACTGATGTG AATTGCTAAGGGTCGAGAA AM231570 Dufour et al. 2002463 C (AC)8 227 CATTCTTCCCACGATTCTATCTC GTGACTTTCGGTTGAAATACTGG AM231571 Dufour et al. 2002471* D (CT)12 301 TTACCTCCCGGCCAGAC CAGGAGACCAAGACCTTAGCA AM231572 Dufour et al. 2002501* J (TG)8 343 CACCACCATCTAATGCACCT CTGCACCAGCTAATTCAAGC AM231576 Dufour et al. 2002753* A (CA)15 294 GGAGACGCAGGTGGTAGAAG TCGAGAAGTCTTGGGGTGTT AJ308753 Rovelli et al. 2000755 D (CA)20 184 CCCTCCCTCTTTCTCCTCTC TCTGGGTTTTCTGTGTTCTCG AJ250258 Combes et al. 2000774* B (CT)5(CA)7 228 GCCACAAGTTTCGTGCTTTT GGGTGTCGGTGTAGGTGTATG AJ308774 Rovelli et al. 2000779 J (TG)17 116 TCCCCCATCTTTTTCTTTCC GGGAGTGTTTTTGTGTTGCTT AJ308779 Rovelli et al. 2000782* F (GT)15 114 AAAGGAAAATTGTTGGCTCTGA TCCACATACATTTCCCAGCA AJ308782 Rovelli et al. 2000790 D (GT)21 134 TTTTCTGGGTTTTCTGTGTTCTC TAACTCTCCATTCCCGCATT AJ308790 Rovelli et al. 2000837 D (TG)16GA 102 CTCGCTTTCACGCTCTCTCT CGGTATGTTCCTCGTTCCTC AJ308837 Rovelli et al. 2000DL013* D (CA)6(CT)8 267 AGAGGGATGTCAGCATAA ATTTGTGTTTGGTAGATGTG AJ871892 Leroy et al. 2005DL025* D C17 197 TTGTTGAGAGTGGAGGA CCAAAGACAGTGCAGTAA AJ871902 Leroy et al. 2005

Note: LG, linkage group of the genetic map.*Loci used for the comparison between Ugandan genotypes and other diversity groups.

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was used for further analyses. All genotyping experimentswere performed on the Genopole Genotyping Platform,Montpellier, Languedoc-Roussillon, France.

Genetic variability within UgandaGlobal summary statistics, including the mean number of

alleles and the mean observed and expected heterozygosity(calculated as gene diversity), were calculated for eachUgandan region using PowerMarker (Liu and Muse 2005).Significance was tested using a bootstrap procedure (1000repetitions) to determine standard deviation. CONVERTsoftware (Glaubitz 2004) was used to identify private allelesin all regions and sites. Allelic richness and private allelicrichness are highly dependent on sample size; large samplesare expected to contain more alleles than small samples (Ka-linowski 2004). As a consequence, allelic richness and pri-vate allelic richness were calculated with the rarefactionmethod to compensate for the differences in sample size, us-ing HP-RARE 1.0 software (Kalinowski 2005). The CON-VERT software was also used to format the data for othersoftware analyses (see below), including Fstat (Goudet2001), Arlequin (Excoffier et al. 2005), and Structure(Pritchard et al. 2000) analyses.

Summary statistics were also calculated for each of the 24loci analysed. The number of alleles and the expected andobserved heterozygosity were calculated. The effective num-ber of alleles, ne, was estimated using the formula ne = 1/(1 – He). The proportion of null alleles was estimated usingthe nullIIM coefficient as proposed by Chybicki and Burczyk(2009). This coefficient is based on an individual inbreedingmodel. The INEST software presented by the authors wasused to evaluate the null allele frequency.

Population structure and differentiationThe dissimilarity matrix between Ugandan individuals,

based on a simple matching index, was computed withDARwin 5 (Perrier et al. 2003), taking into account missingdata. This index is calculated as dij ¼ 1� 1

LSLI¼1

mlp

, where dij

is the dissimilarity between units i and j, L is the number ofloci, p is the ploidy, and ml is the number of matching al-leles for locus l. A factorial representation of the dissimilar-ities was obtained with the same software by principalcoordinate analysis.

For the different sampling levels within Uganda, Hardy–Weinberg equilibrium was tested using fixation indices(FIS) within sites, regions, or country. Genetic differentiationbetween sites or regions was estimated with FST for all re-gions. All F-statistics were estimated with Fstat software(Goudet 2001) using estimations proposed by Weir and

Cockerham (1984), since Nei’s F-statistics (Nei 1973) areless convenient when sample sizes vary substantially.

Although SSR markers might not follow assumptionsneeded for F-statistics, especially in terms of mutation rate,these statistics were preferred to RST (Slatkin 1995). In ourcase (small number of individuals in some cases, and smallnumber of loci), F-statistics appeared to be more robust andconservative (Gaggiotti et al. 1999). The significance of allstatistics was tested with 1000 permutations of the geno-types (Goudet et al. 1996).

Finally, the fine structure and relationships of Ugandanpopulations were analysed using the Bayesian method im-plemented in structure 2.1 software (Pritchard et al. 2000).This software was used to infer the probability of ancestryamong the best identified number of ancestral populations(K) for each genotype within Uganda, and discover moreabout admixtures between defined populations. A 50 000burn-in period and 100 000 Markov chain Monte Carlo iter-ations were used for each run. We chose to take the defaultparameters with no prior information about the populations’origins to reallocate our individuals to groups. Simulationswere made for a number of groups, K varying from 1 to 20with 5 repetitions for each value of K. Transformation of theresulting data using the method proposed by Evanno et al.(2005), which took into account the rate of change in logprobability of data between successive K values, enhanceddetection of the optimal K value.

Genetic diversity analysisTo study the place of Ugandan populations within the

whole diversity of the species, a pairwise genetic distancematrix of FST was built using 18 markers and the previouslydescribed procedure using Fstat. The level at which geneticdifferentiation occurred was estimated by hierarchical analy-ses of molecular variance (AMOVA) using Arlequin 3.0software (Excoffier et al. 2005). Analyses were performedat region, population, and individual levels, with an acceptedlevel of missing data of 0.2, and tested with 1000 permuta-tions. Molecular distance was computed using the number ofdifferent alleles. We performed an AMOVA at the specieslevel, considering groups SG1, SG2, B, C, Guinean, andUgandan.

Results

Genetic variability within UgandaTable 2 presents a summary of basic diversity statistics

for Ugandan populations. Among the Ugandan samples,those from the Kibale and Itwara regions gave lower valuesfor observed and expected heterozygosity than cultivated

Table 2. Basic diversity statistics for Coffea canephora regions in Uganda for 24 microsatellite markers.

RegionNo. ofindividuals

Total no.of alleles

Mean no. ofalleles (SD)

Allelicrichness (SD) He (SD) Ho (SD)

Inbreedingcoefficient

No. of privatealleles

Private allelicrichness (SD)

Itwara 55 193 8.04 (0.93) 4.56 (2.38) 0.54 (0.05) 0.43 (0.05) 0.21 37 1.06 (0.96)Kibale 54 177 7.38 (0.83) 4.33 (2.41) 0.48 (0.05) 0.30 (0.05) 0.37 19 0.57 (0.56)Kalangala 35 206 8.58 (0.92) 5.71 (3.09) 0.59 (0.05) 0.44 (0.05) 0.28 34 1.21 (0.91)Erect 21 172 7.17 (0.76) 5.53 (3.05) 0.58 (0.05) 0.44 (0.05) 0.27 12 0.84 (0.84)Nganda 31 194 8.08 (0.77) 5.64 (2.94) 0.57 (0.06) 0.45 (0.05) 0.23 12 0.64 (0.49)

Note: Mean values are means over 1000 repetitions of a bootstrap procedure (PowerMarker 3.24). SD, standard deviation; He, expected heterozygosity;Ho, observed heterozygosity. Allelic richness and private allelic richness are expressed as mean allele number per locus corrected for sampling disequili-brium (HP-RARE 1.1).

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populations and the population from the Kalangala region.The Itwara region had higher values for observed hetero-zygosity and gene diversity than the Kibale region. For thecultivated and feral Ugandan populations, observed and ex-pected heterozygosities are close to values for the cultivatedSG2 group (Cubry et al. 2005). For all regions, observedheterozygosity was lower than expected heterozygosity. Ki-bale and Itwara had heterozygosity values close to those ob-tained for other known wild populations: Guinean,Congolese B and C (Cubry et al. 2005). Among the Ugan-dan regions, Itwara and Kalangala had the highest private al-lelic richness (1.06 and 1.21 private alleles per locus,respectively). It was found that fewer than 10% of the indi-viduals harboured those private alleles, except for one locusin the Itwara region. The number of private alleles and theprivate allelic richness were higher in the Itwara populationthan in the Kibale population. The inbreeding coefficientwas highest in Kibale and lowest in Itwara. Summary statis-tics for the 24 loci are presented in Table 3 for the 5 Ugan-dan populations. The number of alleles ranged from 1 to 19and the number of effective alleles from 1.02 (locus 774 inItwara population) to 12.24 (locus 355 in Kalangala popula-tion). The mean number of effective alleles ranged from1.91 (Kibale) to 2.45 (Kalangala). The estimated proportionof null alleles was high for most loci in most populations(from 0.02 to 0.40). This high proportion of null allelescould contribute also to the high FIS values observed withinour populations.

A factorial representation of the diversity for 194 individ-uals from Uganda is presented in Fig. 3. Four groups of gen-

otypes were identified, discriminating between Kibale,Itwara, Kalangala, and a group that included Erect andNganda individuals. One Nganda genotype was locatedclose to the wild genotypes. The apparent sharing betweengroups has to be carefully considered, since the first twoaxes represent 17% of the global diversity, and we couldidentify that as a genetic proximity.

Population structure and differentiationDifferentiation was significant among the 5 Ugandan re-

gions, with an FST of 0.16 (Fig. 4). In all the Ugandan sites,FIS values were significantly different from zero, except for2 sites with a small number of individuals (7), one in Kibaleand one in Itwara (Fig. 4). To assess population differentia-tion, FST coefficients were estimated between sites for eachregion and between regions for Uganda. For differentiationbetween sites within regions, the results were different forthe Kibale, Itwara, and Kalangala regions. The Kibale re-gion exhibited a highly significant between-site FST coeffi-cient (FST = 0.17). This was probably due to large distancesbetween sites and thereby reduced genetic exchange. Theseresults are consistent with other analyses of populationsfrom the same forest (Nyakaana 2007). FST coefficients be-tween sites were not significant for the Itwara forest (FST =0.017) and Kalangala region (FST = 0.039). This result isprobably due to shorter distances between sites and therebymore genetic exchange when compared with sites in the Ki-bale region. For the Kalangala region, successive introduc-tions of cultivated genotypes might also explain the lack ofbetween-site differentiation. Pairwise FST values among

Table 3. Characterization of 24 microsatellites for Coffea canephora regions in Uganda.

Itwara Kibale Kalangala

Locus Na ne He Ho NullIIM Na ne He Ho NullIIM Na ne

DL013 2 1.84 0.46 0.41 0.08 2 1.52 0.34 0.28 0.09 4 2.56DL025 6 3.09 0.68 0.10 0.35 5 3.94 0.75 0.06 0.40 4 1.71351 6 2.04 0.51 0.45 0.09 10 3.96 0.75 0.37 0.23 11 3.74355 9 1.69 0.41 0.22 0.17 5 1.25 0.20 0.02 0.21 17 12.24358 2 1.38 0.28 0.00 0.26 3 1.33 0.25 0.00 0.25 3 1.53364 2 1.89 0.47 0.29 0.13 4 2.15 0.54 0.17 0.25 7 3.90368 9 3.98 0.75 0.65 0.07 14 5.18 0.81 0.58 0.13 14 6.82384 3 1.83 0.45 0.41 0.08 1 na na na na 3 1.52394 4 2.24 0.55 0.19 0.25 2 1.02 0.02 0.02 0.08 5 2.67429 10 3.67 0.73 0.56 0.15 9 3.53 0.72 0.35 0.22 8 2.59442 11 2.24 0.55 0.67 0.02 9 2.10 0.52 0.67 0.02 9 1.48445 7 2.88 0.65 0.55 0.08 5 1.73 0.42 0.38 0.08 3 1.13456 7 2.31 0.57 0.60 0.05 7 1.77 0.44 0.31 0.12 7 3.05461 9 2.73 0.63 0.67 0.04 11 2.87 0.65 0.43 0.13 12 6.19463 15 3.45 0.71 0.68 0.05 11 2.39 0.58 0.40 0.13 9 3.63471 8 3.28 0.69 0.53 0.13 3 1.80 0.44 0.43 0.06 8 3.59501 2 1.02 0.02 0.02 0.08 3 1.08 0.08 0.00 0.16 9 2.45753 4 2.39 0.58 0.59 0.05 4 2.46 0.59 0.34 0.19 3 2.14755 19 4.45 0.78 0.78 0.03 10 3.61 0.72 0.65 0.08 11 6.85774 2 1.02 0.02 0.02 0.08 2 1.10 0.09 0.00 0.17 2 1.13779 15 6.34 0.84 0.60 0.15 14 6.80 0.85 0.50 0.20 18 9.99782 4 1.14 0.12 0.13 0.06 4 1.16 0.14 0.15 0.06 2 1.03790 12 3.73 0.73 0.72 0.03 13 5.67 0.82 0.70 0.09 11 7.24837 4 2.91 0.66 0.51 0.12 7 3.53 0.72 0.44 0.16 8 3.56Mean 7.17 2.15 0.54 0.43 0.11 6.58 1.91 0.48 0.30 0.15 7.83 2.45

Note: Na, number of alleles detected; ne, number of effective alleles; He, expected heterozygosity; Ho, observed heterozygosity; NullIIM, frequency of null

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Erect Nganda

He Ho NullIIM Na ne He Ho NullIIM Na ne He Ho NullIIM

0.61 0.60 0.05 6 3.15 0.68 0.58 0.08 5 2.23 0.55 0.59 0.060.41 0.48 0.05 4 1.72 0.42 0.11 0.27 5 1.55 0.36 0.16 0.200.73 0.43 0.18 8 5.57 0.82 0.50 0.20 9 5.23 0.81 0.59 0.120.92 0.55 0.18 14 7.74 0.87 0.71 0.09 12 8.62 0.88 0.71 0.100.35 0.00 0.29 1 na na na na 1 na na na na0.74 0.60 0.07 7 4.98 0.80 0.32 0.28 7 3.75 0.73 0.21 0.310.85 0.76 0.06 10 3.02 0.67 0.68 0.07 14 7.69 0.87 0.65 0.130.34 0.03 0.26 2 1.21 0.17 0.19 0.10 3 1.14 0.12 0.13 0.080.63 0.56 0.09 3 1.47 0.32 0.19 0.18 3 1.36 0.26 0.17 0.140.61 0.36 0.17 5 3.21 0.69 0.43 0.20 6 3.17 0.68 0.60 0.120.33 0.30 0.06 3 1.33 0.25 0.24 0.09 7 1.80 0.44 0.33 0.110.11 0.06 0.10 3 1.23 0.18 0.20 0.10 3 1.07 0.07 0.07 0.100.67 0.50 0.10 4 1.91 0.48 0.52 0.08 10 3.85 0.74 0.66 0.080.84 0.63 0.11 8 3.45 0.71 0.67 0.07 12 5.31 0.81 0.42 0.230.72 0.60 0.11 6 2.36 0.58 0.44 0.11 7 3.52 0.72 0.75 0.060.72 0.54 0.11 11 6.30 0.84 0.55 0.17 7 3.67 0.73 0.55 0.140.59 0.49 0.06 5 2.26 0.56 0.58 0.09 4 1.73 0.42 0.46 0.070.53 0.51 0.08 3 2.51 0.60 0.40 0.16 6 2.66 0.62 0.54 0.120.85 0.53 0.17 12 7.77 0.87 0.45 0.24 16 9.20 0.89 0.84 0.050.11 0.00 0.18 2 1.25 0.20 0.00 0.25 1 na na na na0.90 0.70 0.09 8 4.86 0.79 0.59 0.14 10 4.99 0.80 0.57 0.140.03 0.03 0.10 5 3.47 0.71 0.42 0.20 6 2.34 0.57 0.26 0.220.86 0.56 0.16 15 10.53 0.91 0.95 0.04 13 7.76 0.87 0.82 0.050.72 0.63 0.05 9 6.30 0.84 0.80 0.06 9 3.36 0.70 0.70 0.070.59 0.44 0.12 6.42 2.39 0.58 0.44 0.14 7.33 2.32 0.57 0.45 0.12

alleles. Bold numbers represent significant deviation from Hardy–Weinberg equilibrium (P < 0.05). na, not available.

C. canephora populations within Uganda are presented inTable 4. FST values for relationships between cultivated orferal and wild populations were the highest, ranging from0.16 to 0.24. The values observed between the two culti-vated Ugandan populations (Nganda and Erect) and the feralpopulation were low, ranging from 0.04 to 0.11, confirmingtheir common genetic background.

The structure analysis indicated 4 populations (Fig. 5).The Kibale and Itwara wild populations were clearly identi-fied as different groups, with some degree of admixture. Wealso observed some gene flow from the Kibale region to theNganda–Erect group. The Nganda and Erect genotypesmerged in a single group. Finally, the Kalangala islandswere identified as a specific group, with some admixturewith the other Ugandan populations sampled in this study,mainly the Nganda–Erect genotypes.

Genetic diversity analysisTable 5 presents AMOVA results. When 7 groups were

considered — wild from Uganda, cultivated and feral fromUganda, Guinean, and Congolese B, C, SG1, and SG2 —the results indicated that a high percentage of variation wasexplained by the groups defined (20.3%). A small percent-age of variation was explained by the 5 regions within theUgandan group (7.0%) and more than 70% of variation wasexplained among individuals within regions and within indi-viduals. This result indicated a high genetic differentiationfor the 7 groups defined, and a lower differentiation withinUganda compared with that observed for the 7 diversitygroups.

Table 4 presents FST coefficients between all the C. cane-phora regions in Africa. FST values for diversity regionsexcluding Uganda were over 0.20, indicating high differen-tiation between regions and confirming previous results(Montagnon 2000). FST values obtained between wild Ugan-dan regions and the other Congolese and Guinean diversitygroups (0.28 to 0.59) were much higher than those obtainedbetween regions within Uganda. An FST value of 0.10 (sig-nificant) was observed between the Kibale and Itwara re-gions. Pairwise FST values were always lower for Ugandanregions when relating to SG2 than when relating to other re-gions. This implies that Ugandan C. canephora are geneti-cally more related to the SG2 region. Cultivated and feralUgandan regions presented nonsignificant FST values withthe SG2 group.

Discussion

The analyses carried out on the wild Coffea canephoragenotypes from Uganda revealed their originality comparedwith the previously known genetic groups and provided in-sights into the organization of the diversity in this region.The diversity observed within the species at the Ugandanlevel is high. A mean number of 8 alleles per locus wasobserved, which is close to values already observed withincoffee (Cubry et al. 2005) and within species such as maize(9 alleles per locus, Vigouroux et al. 2005) and rice (11.8alleles per locus, Garris et al. 2005). The values of expectedheterozygosity within the species (0.48 to 0.59) are close tothose estimated by Nybom (2004) for diversity of an out-

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crossing perennial plant using microsatellite markers (0.47to 0.68) and comparable to values for maize (0.67) and rice(0.70) as well as those for other perennial tropical species(Domınguez et al. 2005). The FST values between cultivatedand wild compartments within Uganda are also close tothose determined by Nybom (2004). Four groups were iden-tified within Uganda, discriminating between feral, culti-vated, and two wild origins.

We compared Ugandan populations with known geneticdiversity groups from western and central Africa using ge-netic differentiation parameters. Coffea canephora trees ofwild origin in Uganda were found to be different fromknown diversity groups, while cultivated and feral genotypeswere identified as being close to the cultivated Congolesegroup SG2. These results provide significant insight into thediversity structure in C. canephora and some complemen-tary results regarding the refugia in Africa. Our results leadto a comprehensive strategy for future coffee conservationprogrammes and an optimized use of genetic diversity incoffee breeding.

Diversity within Uganda

Differentiation and structure of populationsAmong the Ugandan populations, wild and cultivated

C. canephora were clearly discriminated (Figs. 3 and 4). Al-lelic richness and observed heterozygosity were higher forcultivated compartments than for wild ones, and the valueswere almost identical between the cultivated populations.This high diversity of cultivated genotypes could be due tomultiple origins of these plants, resulting from successivehybridization of wild material with introduced genotypesfrom the cultivated Congolese group SG2. High FIS valueswere observed for all populations (Fig. 4). This may be a re-sult of founder events, since most wild populations are nor-mally in patches comprising a small number of mother trees(Berthaud 1986). Since C. canephora is a strictly alloga-mous species, this may also be related to a Wahlund effect,i.e., some structure within sites or regions. This structurecould be related to limited pollen flow and/or seed dispersaland thus to relatedness between individuals. High FIS couldalso be related to the high proportion of expected null alleles(Table 3). Although the index used for the evaluation of nullalleles takes inbreeding into account and then gives an un-biased estimation of null alleles, the proportion of expectednull alleles was still rather high. Some high numbers of nullalleles have already been found in C. canephora (Poncet etal. 2007). For cultivated genotypes such as Nganda andErect, the high number of null alleles may be a biased esti-

Fig. 3. Principal coordinate analysis based on dissimilarities for 194 Coffea canephora individuals from Uganda (simple matching distancebased on 24 microsatellite loci). I, Itwara forest; Ki, Kibale forest; N, Nganda; E, Erect; Ka, Kalangala islands.

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mate due to the nature of the groups, which include geno-types from different origins.

The two wild populations exhibited contrasting patterns.Although Kibale was sampled over a larger area than Itwara,and the same number of individuals was sampled in both re-gions, observed and expected heterozygosity and allelic con-tent were lower in Kibale. This could be a consequence of

sampling that was not equally balanced between sites in Ki-bale: a large proportion of the individuals were sampled atone site for this forest (site 2), while the other sites wereunder-sampled. In addition, genetic differentiation betweensites was higher in Kibale than in Itwara, which may be re-lated to the pattern of effective dispersion of pollen andseeds if sites were chosen at distances greater than the effec-

Fig. 4. F-statistics within Ugandan regions and sites. FIS values and significance for regions and sites are indicated in the correspondingboxes. FIT, FST, and FIS are indicated for Ugandan regions including different sites, wild Ugandan genotypes, and the country of Uganda. Nis sample size. P values for significance were obtained after 1000 permutations.

Table 4. Pairwise FST coefficients between Coffea canephora regions in Africa, including Uganda; five Ugandan regionsand five regions outside Uganda were considered.

Nganda Kibale Itwara Kalangala B SG2 SG1 C GuineanErect 0.04ns 0.24* 0.23* 0.11* 0.38* 0.19ns 0.48* 0.37* 0.33nsNganda 0 0.16* 0.16* 0.07* 0.35* 0.22ns 0.52* 0.39* 0.34nsKibale 0 0.10* 0.21* 0.42* 0.33* 0.59* 0.49* 0.43*Itwara 0 0.18* 0.37* 0.28* 0.52* 0.43* 0.39*Kalangala 0 0.33* 0.17ns 0.46* 0.35* 0.31*B 0 0.29* 0.63* 0.46* 0.42*SG2 0 0.33* 0.21ns 0.24nsSG1 0 0.37* 0.44*C 0 0.31*

Note: Significance of P value for 1000 permutations is indicated: ns, not significant; *, significant (P < 0.05). Fstat software was used.

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tive neighbourhood size. We observed a high level of intro-gression of Itwara alleles into Kibale (about 40% of thetrees present Itwara alleles, Fig. 5) and Kibale alleles intoNganda and Erect (about 30%, Fig. 5). This suggests thatthere was gene flow between Itwara and Kibale before theforests were separated. The introgression of Kibale allelesinto cultivated populations suggests that Kibale genotypescould have been used as a source for cultivated populations.The definitive split happened in 1920 (Thomas 1944), about100 years ago, meaning that 3 to 5 generations have oc-curred since the populations were completely disconnected.The significant FST coefficient between Kibale and Itwara(0.10) could be explained mainly by the distance of 30 kmbetween the populations and the absence of ‘‘relay’’ popula-tions between them, and to a lesser extent by these 3 ormore generations of separation.

The Kalangala feral compartmentWe observed substantial genetic diversity within the Ka-

langala islands, with a large number of alleles and privatealleles. The originality of the Kalangala region is particu-larly interesting in this study. Kalangala is one of the firstcentres of C. canephora cultivation, with genotypes intro-duced from the defunct Kasai forest (Thomas 1935). Sincethat forest does not exist anymore, it was not sampled. Atthe beginning of the 20th century, farmers were providedwith seeds from parents of Nganda phenotypes. Coffee plan-tations were then abandoned for 15 years and when cultiva-tion resumed, farmers were supplied with seeds derivedfrom Erect genotypes (Thomas 1947). Most of the Erectgenotypes introduced into Kalangala from the mainland inthe 1920s were not adapted to island conditions; most ofthe plants suffered from a low soil pH and died (Thomas1947). This history suggests that the Kalangala C. cane-phora population is mainly a mixture of populations fromforest and cultivated types. Our structure analysis places Ka-langala as a unique group with some admixture with theNganda–Erect compartment, but this analysis is based on re-latedness between individuals with a common ancestral pa-rent. That is why Kalangala appears separated from theothers. Compared with other wild and cultivated compart-ments, Kalangala is a specific source of diversity for coffeein Uganda.

The Nganda–Erect group and the domestication processNganda and Erect displayed the same genetic background

(Figs. 2 and 5), probably because of their cultivation history.Both origins have been cultivated closely in mixtures in cof-fee collections for over 3 generations, and open-pollinatedseedlings have been used for planting new generations. TheErect genotypes were directly imported from the Congo Ba-sin, and then considered as SG2 genotypes (Thomas 1947).The Nganda genotypes are documented as a mixture of wildand cultivated materials (Thomas 1935, 1944).

Relationships between the Kibale forest and Nganda–Erect populations highlight the complexity of coffee geneticdiversity in Uganda. As in most other African countries, cul-tivated coffee in Uganda appeared as a result of naturalcrosses between wild materials and introduced genotypesfrom other regions or countries, leading to a complete mix-ture of all genotypes (Montagnon et al. 1998). Genetic rela-tionships observed between Kibale and Nganda–Erectpopulations and the observed admixture of alleles supportthat hypothesis and indicate Kibale as a likely source ofearly Nganda establishments in Uganda, as suggested byThomas (1935). Compared with Itwara forest, the level ofintrogressed alleles in cultivated genotypes is much higherfrom Kibale (Fig. 5). The presence of cultivated zones closeto Kibale forest could explain these differences. From ourstudy it can be deduced that cultivated Ugandan coffees ori-ginated from wild material sampled partially from the Ki-bale and Itwara regions. Genotypes from these sources havebeen cultivated in admixtures and perhaps with introducedgenotypes from the SG2 region, leading to the Nganda andErect cultivated types.

Ugandan genotypes in the Coffea canephora globaldiversity

At the species level, AMOVA and FST values confirmedhigh differentiation between regions. We confirmed a struc-ture with 6 diversity groups, including the 5 already identi-fied and an additional wild Ugandan group. Compared withmost diversity groups, Ugandan C. canephora is highly di-verse and rich in alleles. The Ugandan genotypes of wildorigin (Itwara and Kibale) present highly significant FST val-ues with other regions, suggesting that wild origins from

Fig. 5. Result of population structure analysis of Coffea canephora within the country of Uganda. Four populations were identified: popula-tion A contains the Nganda (1) and Erect (2) regions. Population B is the Kibale forest (3), population C is the Itwara forest (4), and popu-lation D is Kalangala (5). Admixtures are presented by heterogeneous shading within populations.

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Uganda likely correspond to an additional genetic group notyet sampled.

Coffee conservation and consequences for diversitymanagement

The conservation of coffee resources is acutely threatenedby (i) rapid agricultural development combined with urban-ization, leading to rapid extinction of relict forests, (ii) cli-matic changes, and (iii) the extension of new diseases thathave destroyed about 60% of coffee plantations in Uganda(Oduor et al. 2005). The identification of an original geneticgroup of C. canephora originating from Uganda leads to anew definition of the conservation strategy for this species.A phenotypic evaluation has to be performed for a betteruse of these genotypes in breeding programmes. First, archi-tecture and vigour need to be assessed, as Nganda and Erectgenotypes have specificities regarding their architecture,while wild and feral genotypes are not known for thesetraits. Second, susceptibility to biotic and abiotic stresses,including coffee wilt disease, should be determined.

In Uganda, some mountain forests remained during thelast glaciations (Jolly et al. 1997) and may correspond tothe ‘‘east Central Africa’’ refugium (Anhuf et al. 2006). Ourresults indicate high genetic differentiation of Ugandan wildgenotypes, which tends to confirm the existence of a refu-gium for C. canephora in Uganda. Complementary samplingof this zone should be of interest for diversity conservationand management.

A breeding strategy for C. canephora is already based onhybrid vigour and complementary characteristics obtainedby crossing genotypes from distinct genetic and geographi-cal groups in a recurrent breeding strategy (Leroy et al.1993). Ugandan genotypes could be integrated into this re-current selection scheme.

AcknowledgmentsThis study was carried out in connection with the Euro-

pean INCO project ICA4-CT-2001-10006 on genetic resist-ance to coffee wilt in Uganda, and with financial supportfrom the United States Department of Agriculture withinthe framework of a project of the USDA–InternationalCentre for Research in Agro Forestry (project 58-4001-3-F157) on coffee quality and markers in East Africa. Samplesfrom diversity regions outside Uganda were kindly providedby Centre National de Recherche Agronomique in IvoryCoast. We are grateful to the Uganda Wildlife Authority forallowing us to take samples in the Kibale forest.

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Source of variance df smVariancecomponents

Percentage ofvariance explained

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