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. 2019 Feb 4;17(7):1418–1430. doi: 10.1111/pbi.13066

Development and evaluation of a genome‐wide Coffee 8.5K SNP array and its application for high‐density genetic mapping and for investigating the origin of Coffea arabica L.

Virginie Merot‐L'anthoene 1,†, Rémi Tournebize 2,†, Olivier Darracq 1, Vimel Rattina 1, Maud Lepelley 1, Laurence Bellanger 1, Christine Tranchant‐Dubreuil 2, Manon Coulée 2, Marie Pégard 2, Sylviane Metairon 3, Coralie Fournier 3, Piet Stoffelen 4, Steven B Janssens 4, Catherine Kiwuka 5,6, Pascal Musoli 5, Ucu Sumirat 7, Hyacinthe Legnaté 8, Jean‐Léon Kambale 9, João Ferreira da Costa Neto 10, Clara Revel 11, Alexandre de Kochko 2, Patrick Descombes 3, Dominique Crouzillat 1, Valérie Poncet 2,✉
PMCID: PMC6576098  PMID: 30582651

Summary

Coffee species such as Coffea canephora P. (Robusta) and C. arabica L. (Arabica) are important cash crops in tropical regions around the world. C. arabica is an allotetraploid (2n = 4x = 44) originating from a hybridization event of the two diploid species C. canephora and C. eugenioides (2n = 2x = 22). Interestingly, these progenitor species harbour a greater level of genetic variability and are an important source of genes to broaden the narrow Arabica genetic base. Here, we describe the development, evaluation and use of a single‐nucleotide polymorphism (SNP) array for coffee trees. A total of 8580 unique and informative SNPs were selected from C. canephora and C. arabica sequencing data, with 40% of the SNP located in annotated genes. In particular, this array contains 227 markers associated to 149 genes and traits of agronomic importance. Among these, 7065 SNPs (~82.3%) were scorable and evenly distributed over the genome with a mean distance of 54.4 Kb between markers. With this array, we improved the Robusta high‐density genetic map by adding 1307 SNP markers, whereas 945 SNPs were found segregating in the Arabica mapping progeny. A panel of C. canephora accessions was successfully discriminated and over 70% of the SNP markers were transferable across the three species. Furthermore, the canephora‐derived subgenome of C. arabica was shown to be more closely related to C. canephora accessions from northern Uganda than to other current populations. These validated SNP markers and high‐density genetic maps will be useful to molecular genetics and for innovative approaches in coffee breeding.

Keywords: SNP array, single‐nucleotide polymorphism, Coffea arabica origin, C. canephora, C. eugenioides, genetic map

Introduction

The Coffea genus (Rubiaceae family) is a large genus that currently comprises 125 accepted species (http://www.theplantlist.org/1.1/browse/A/Rubiaceae/Coffea/). Despite the large number of species in the genus, only two are economically important and cultivated on a large scale: Coffea canephora Pierre ex Froehner (commonly known as Robusta) and Coffea arabica L. (Arabica). Both species occur in the inter‐tropical region of Africa with C. arabica being mainly restricted to Ethiopia and C. canephora native to West and Equatorial Africa (Davis et al., 2006).

Coffea arabica is an allotetraploid (amphidiploid; CaCaEaEa, 2n = 4x = 44 chromosomes) resulting from a natural hybridization event estimated to have taken place 0.665 million years ago at the most, between the ancestors of present‐day C. canephora (Ca subgenome donor) and Coffea eugenioides S. Moore (Ea subgenome donor) (each with 2n = 2x = 22) (Yu et al., 2011). The relatively recent origin and the self‐fertilization of C. arabica certainly contributed to its relatively low genetic diversity compared to diploid Coffea species (Lashermes et al., 2000). Coffea arabica was probably introduced to Arabia (now Yemen) from its Ethiopian origin during the 14th century (Chevalier, 1929) and has been cultivated there for at least five centuries. The human dispersal of Arabica coffee from Yemen to the rest of the world started in the early 18th century and occurred mainly via two genetic lineages: Typica and Bourbon. These lineages gave rise to most of the current commercial Arabica cultivars grown worldwide (Anthony et al., 2002) and as such this explains the low genetic diversity among the cultivated forms. The lack of genetic diversity in Arabica breeding lines has been recognized as a significant limitation of the varietal tolerance towards biotic and abiotic stresses (Anthony et al., 2002).

The closely related wild species (Crop Wild Relatives: CWR) of C. arabica harbour a much greater level of genetic variability. They belong to the secondary gene pool of C. arabica such as its progenitor species C. canephora (Gomez et al., 2009). They also represent an important source of gene variants to broaden the cultivated C. arabica genetic base. These wild genetic resources might help in developing novel Arabica cultivars with higher resilience to the ongoing changes in climatic conditions.

The development of new genomic tools can help us explore, more deeply and more precisely, the genomic diversity at intra‐ and inter‐specific levels. Two examples of high‐throughput platforms include next‐generation sequencing (NGS) (Davey et al., 2011) and the development of DNA microarrays (Gupta et al., 2008). Compared to a whole‐genome sequencing methodology, an SNP array approach provides time‐effective, low‐cost and more straightforward genotyping technology for germplasm screening (You et al., 2018; Yu et al., 2014).

With its relatively small, diploid, sequenced and annotated genome, Coffea canephora is highly compliant with genomic‐based breeding approaches using Genome Wide Association Studies (GWAS). Thanks to the rapid development of genomic resources and the publication of the reference genome (Denoeud et al., 2014), third‐generation markers based on single‐nucleotide polymorphisms (SNPs) have gradually been identified and assayed in Coffea, particularly in C. arabica (Sant'Ana et al., 2018; Tran et al., 2018).

However, high‐throughput genotyping assays are still needed in order to rapidly characterize the coffee genetic diversity and to evaluate the introgression of different CWRs in a cost‐effective way. These assays would ultimately ensure more efficient and time‐effective breeding programmes. To conduct such programmes, measures must be taken to construct high‐density genetic maps. Such maps have already been initiated for C. canephora (Denoeud et al., 2014; Lefebvre‐Pautigny et al., 2010; Leroy et al., 2011), and C. arabica (Moncada et al., 2016; Pearl et al., 2004). Some of these maps were also used to identify QTLs for agronomic and quality‐related traits in C. canephora (Leroy et al., 2011; Mérot‐L'Anthoëne et al., 2014) and C. arabica (Moncada et al., 2016). However, the use of SNP markers to generate denser maps has been poorly exploited so far.

Here, we are reporting on the development of the Coffee8.5K SNPs array that contains 8580 unique and informative SNPs, covering the whole Coffea canephora genome. The genome‐wide distribution and accurate identification (coding vs. non‐coding regions) of the SNPs allowed to generate a high‐quality array. This high‐density SNP array has greatly improved the high‐density genetic maps of C. canephora. This array also provides a valuable resource for genetic diversity analyses and for the investigation of genetic relatedness between Coffea arabica CWRs.

Results

The whole‐genome coffee SNP array, Coffee8.5K, was primarily designed to provide an efficient screening of the wild genetic germplasm resources in Coffea, with two considerations: SNPs had to (i) provide an adequate representation of the genome diversity based on genotyped mapping populations and diverse germplasm collections and (ii) additionally harbour sufficient allelic variations at the level of the functional genes that control important breeding traits. We define the ‘Discovery Panel’ as the set of samples used to design the Coffee8.5K array; the ‘Diversity Panel’ is the set of samples used to validate the array and perform subsequent genetic analyses; the ‘Mapping Populations’ are the Robusta and Arabica mapping progenies used to construct linkage maps.

Filtering and SNP statistics

SNP selection and array design

Analysing resequencing data generated from both C. arabica (five genotypes) and C. canephora (twelve genotypes) mapped against the C. canephora reference genome (Denoeud et al., 2014) led to the selection of 9827 high‐quality SNPs.

The selection of SNPs was based on three main criteria: (i) SNPs from the C. arabica Discovery Panel were chosen to be associated with the intra‐subgenome polymorphism and had to be polymorphic between the two parent species of the Arabica mapping population, (ii) SNPs selected from the C. canephora Discovery Panel had to be found in at least two genotypes in order to avoid rare and individual‐specific alleles; (iii) both genic and intergenic SNPs, in equivalent numbers, were selected with a minimum distance of 40 kb between SNPs, to provide an accurate representation of the genome without redundancy. The final SNP set also included informative markers associated with 149 genes related to traits of agronomic or organoleptic importance (Table S1).

In the end, our Coffee8.5K array comprised 8580 SNPs that met Illumina‐quality criteria during the manufacturing process and were finally successfully synthesized (Table S3), representing an 87.3% effective conversion rate. For initial data extraction and filtering, SNPs with low GenTrain (<0.6) and GenCall (<0.2) scores were considered as missing data prior to genotype calling. Then, a series of quality control steps was performed to ensure the accuracy of the genotype‐calling process. For a given genotype, the CallRate (i.e. the percentage of scorable SNPs) ranged from 81.7% to 97.7% in the different Mapping and Diversity Panels screened, with an average call of 92.3% in the total 262 investigated samples (Table 1). Genotyping was consistent over the two replicates of the four parents (C. canephora BP409, Q121, and C. arabica Ar8 and Ar36B) and also between the two SNPs duplicated on the SNP array: the signal and genotyping qualities were efficient and reproducible. Moreover, 42 of the 50 SNPs previously targeted by KASPar assays were successfully synthetized on the Coffee8.5K and used for validation purposes. These 42 SNP‐generated genotypes were 100% concordant with those obtained from the KASPar assay, thereby validating the presence of these SNPs in the coffee genome as well as the SNP detection process itself.

Table 1.

(a) Utilization and efficiency of the Coffee8.5K array. Evaluation of the Coffee8.5K array for application in the three Coffea species (C. canephora, C. arabica and C. eugenioides), genetic diversity assessment in the C. canephora panel and (b) genetic mapping in the segregating populations of C. canephora and C. arabica

(a)
SNP source Genomic region Synthesized loci C. canephora (N = 27)* C. arabica (N = 16)* C. eugenioides (N = 6)
Scorable (%†) Polymorphic (%‡) Scorable (%) Polymorphic in Dihaploid Et39 (Ca/Ea) Polymorphic (%) Scorable (%) Polymorphic (%)
C. arabica 3050 1978 (64.9%) 465 (23.5%) 1790 (58.7%) 383 (21.4%) 705 (39.4%) 1441 (47.2%) 110 (7.6%)
Coding 208 168 48 154 66 46 138 16
Non‐coding 2842 1810 417 1636 317 659 1303 94
C. canephora 5530 5087 (92.0%) 4977 (97.8%) 5034 (91.0%) 1270 (25.2%) 19 (0.4%) 4742 (85.8%) 145 (3.1%)
Coding 3220 3046 3000 3028 801 14 2912 95
Non‐coding 2310 2041 1977 2006 469 5 1830 50
Total 8580 7065 (82.3%) 5442 (77%) 6824 (79.5%) 1653 (24.2%) 724 (10.6%) 6183 (72.1%) 255 (4.1%)
Coding 3428 3214 3048 3182 867 60 3050 111
Non‐coding 5152 3851 2394 3642 786 664 3133 144
(b)
SNP source Genomic region Synthesized loci Segregating in
C. canephora Mapping progeny* (N = 93) C. arabica Mapping progeny* (N = 138)
C. arabica 3050 158 (5.2%) 900 (29.5%)
Coding 208 14 69
Non‐coding 2842 144 831
C. canephora 5530 1149 (20.8%) 45 (0.8%)
Coding 3220 651 25
Non‐coding 2310 498 20
Total 8580 1307 (15.2%) 945 (11%)
Coding 3428 665 94
Non‐coding 5152 642 851
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(a) *See Table S2; †The percentage of scorable/used loci to successfully synthesized loci; ‡The percentage of polymorphic loci to scorable loci in the species.

(b) *See Table S2.

Bold values represent cumulative values of coding + non‐coding statistics.

SNP genomic position and distance between SNPs

Linkage disequilibrium (LD) occurs when alleles at two or more loci appear together in the same individual more often than would be expected by chance. Two SNPs that are in strong LD provide redundant genotyping information. The requisite knowledge of the LD in the C. canephora genome was evaluated over the whole‐genome resequencing dataset of the C. canephora Discovery Panel (12 genomes). The linkage disequilibrium declined rapidly across all accessions to half its maximum value at a distance of 8.1 kb (Figure S1). Since this C. canephora discovery panel included genotypes from different genetic groups, thereby maximizing the sequence diversity, the LD decayed rapidly. It was particularly higher than when the LD decay was calculated within a single‐genetic group: 17.5 ± 2.7 kb on average over the major genetic groups (min–max: 14.7–21.9 kb) (data not shown). When filtering SNPs with a distance of over 40 kb (Figure 1), we captured the largest proportion of genetic variation. This is due to the fact that the lower the LD between the SNPs is, the more independent information they will represent.

Figure 1.

Figure 1

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Design and development workflow of the Coffee8.5K array. SNPs markers were identified, filtered and validated from (a) the C. arabica or (b) the C. canephora Discovery panels. (c) Summary of variant source origin of the Coffee8.5k SNPs. Filters criteria are mentioned together with related tools and programming languages (in blue).

In addition, we assessed the quality of coverage by studying the physical distribution of selected Coffee8.5K SNPs along the pseudo‐chromosomes of the C. canephora reference genome (Denoeud et al., 2014). As shown in Figure 2, the SNPs were widely and evenly distributed along the 11 pseudo‐chromosomes of the genome, no matter which Discovery Panel was used to design them, namely C. canephora or C. arabica. The average density was 18.8 SNPs per megabase with a mean distance of 54.4 kb between the SNPs (excluding the pseudo‐chromosome 0 made of unanchored sequences).

Figure 2.

Figure 2

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Genetic and genomic distribution of the Coffee8.5K array SNPs. (a) Genome distribution of the 8580 single‐nucleotide polymorphisms (SNPs) synthesized for the array along the 11 pseudo‐chromosomes and the virtual pseudo‐chromosome 0 of unanchored sequences. For each pseudo‐chromosome, we determined: the number of SNPs markers according to their source (C. arabica, Ara or C. canephora, Can), the mean distance between markers and their density related to the estimated size of the pseudo‐chromosome. (b) C. canephora high‐density genetic map of BP409xQ121 progeny, with 11 linkage groups. SNP markers (1307) obtained from the Coffee8.5K array are indicated in red.

Lastly, the published annotation of the C. canephora reference genome (Denoeud et al., 2014; Dereeper et al., 2015) was also used to classify the SNPs of the Coffee8.5K array with respect to their position in the genome. A total of 5152 (60%) and 3428 (40%) SNPs were located in non‐coding and coding regions respectively (Table 1). A total of 227 SNPs were located in 149 selected genes of interest (Table S1).

Coffee8.5K evaluation and usefulness

Construction of genetic maps

High‐density Robusta genetic map

The Robusta mapping population was derived from an F1 cross between BP409 (Congolese hybrid) and Q121 (Conilon‐type derived accession) comprising 93 individuals. After the removal of low‐quality SNPs from the 8580 scorable SNPs, 1307 (15.2%) SNPs were polymorphic between the two parents (BP409 and Q121) and their segregation scored in the Robusta mapping population (Table 2). The majority of these SNPs (80%, 1149) were derived from the C. canephora Discovery Panel (Table S2) and 50.9% (665 SNPs) were located in coding regions including 46 SNPs in candidate genes (Table S1). All these polymorphic SNPs were successfully added to the existing markers of the framework linkage map (Denoeud et al., 2014), leading to almost double (+43%) the total number of markers on the Robusta map (Table 2). These SNPs were distributed on the 11 linkage groups coded from A to K (Figure 2) and separated by a mean distance of 0.99 cM. However, 76.2% of these markers were clustered (i.e. mapped in groups of markers displaying no recombination) and so finally the polymorphic SNPs were mapped on 620 unique positions separated by a mean distance of 2.1 cM. The clustering of the SNPs could be explained by the small size of the mapping population (93 individuals). Significant segregation distortions (P value < 0.01) were observed on 11.4% of the SNPs mapped. The majority (69%) of the strong segregation distortions (P value < 0.001) were grouped in two regions located on the LG C (22 SNPs) and LG H (27 SNPs). The final high‐density Robusta genetic map consisted of 3039 loci distributed over 11 linkage groups covering 1370 cM, with a mean distance between markers of 2.2 cM (Table 2). Ninety‐eight percent of the gaps between two adjacent loci were smaller than 2 cM with the largest being 8.9 cM at the distal end of LG B (Figure 2).

Table 2.

Number of SNP markers added to the Robusta genetic map and coverage in cM of each linkage group in the Robusta linkage maps. The Robusta genetic map based on a F1 cross between BP409 (Congolese hybrid) and Q121 (Conilon‐type‐derived accession) comprising 93 individuals. The previous high‐density genetic map has been published by Denoeud et al. (2014) with various types of markers (e.g. SSR, RADseq, RFLP)

LG Total Number of markers Number of SNPs %a Coverage (cM) Mean distance between markers (cM)
A 287 104 36 112 2.6
B 528 220 42 238 2.2
C 194 76 39 129 1.5
D 266 125 47 109 2.4
E 257 125 49 105 2.4
F 341 149 44 155 2.2
G 284 119 42 105 2.7
H 223 108 48 128 1.7
I 158 61 39 90 1.8
J 263 111 42 104 2.5
K 238 109 46 95 2.5
TOTAL 3039 1307 43 1370 2.2
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a

The ratio of SNPs from Coffee8.5K array to total number of loci mapped.

Segregation in Arabica mapping progeny

The Arabica F2 segregation population (138 individuals) was derived from a cross between two wild Ethiopian Arabicas: Ar8 and Ar36B. We successfully identified 945 polymorphic SNPs between the two parents (Ar8 and Ar36B) (Table 1). As expected, the very large majority (95.2%) of these SNPs were derived from the C. arabica Discovery Panel, since they had been selected to discriminate the two wild Ethiopian parents. Based on the C. canephora reference genome, these SNPs were evenly distributed along the 11 pseudo‐chromosomes and 41% of them were located on the pseudo‐chromosome 0 (of unanchored sequences). Only 1.3% of the 945 SNPs selected for genetic mapping presented significant segregation distortion (P value < 0.01) in the progeny. Consequently, this pool of 945 new SNPs markers provides a promising tool for constructing our Arabica genetic map.

Among the 1307 and 945 SNPs segregating in the Robusta and Arabica mapping populations, respectively, only 46 SNPs (2%) were common and would thus be used for co‐linearity analyses. The mapped SNP markers of the Coffee8.5K SNP array are available in the newly implemented MoccaDB v 2.0 database (Figure S2). This database allows access and visualization of the SNP information, including flanking sequences and possible alleles as well as locations on both the C. canephora genome and on the Robusta linkage map.

Array polymorphism and its application across Coffea species

The 8580 SNP markers on the Coffee8.5K were evaluated for their transferability and genotyping capacity in three Coffea species—C. canephora, C. arabica and C. eugenioides Diversity Panels—(Table 1a). Across these three species, 7065 (82.3%) markers successfully genotyped C. canephora, 6824 (79.5%) C. arabica and 6183 (72.1%) C. eugenioides. As expected, we observed a slow decline in the call rate as a function of divergence from the species of the Discovery Panel together with a decrease in polymorphic SNPs. However, the cross‐species application of our array, and particularly the transferability of C. canephora‐discovered SNPs to C. eugenioides, was shown to be efficient, with a success rate of 85.8% (4742 of the SNPs derived from the C. canephora Discovery Panel, Table 1).

The percentage of polymorphic SNPs was much higher for C. canephora compared to the C. arabica (77% vs. 10.6%). A lower diversity within the allotetraploid, self‐compatible, and recent C. arabica species was expected. Because of its alloploidy, homeologous SNPs, that is polymorphism between the two subgenomes Ca/Ea, were estimated based on the heterozygous SNPs scored in the dihaploid Et39 (Figure 4b) and represented 24.2% of the scored markers. After filtering for these homeologous markers, we estimated the allelic SNPs (polymorphic positions occurring within a single‐subgenome among individuals) on the 16 tretraploid individuals of the Diversity Panel, which accounted for 10.6% of polymorphic SNPs.

The C. arabica neighbour‐joining tree (Figure S3) confirmed the high genetic relatedness of both wild and cultivated individuals, even though the SNP markers were informative enough to discriminate the individuals and this, with high bootstrap levels.

While both C. canephora and C. eugenioides are outcrossing species, their polymorphism discrepancy (77% vs. 4.1%) is probably due to differences in global genetic diversities of these two species, which present a very different geographical extension (very large for C. canephora vs. reduced for C. eugenioides). Even if differences in the sampling size (n = 24 vs. n = 6) may be evoked to explain such a difference, Diversity Panels were chosen on purpose to represent a maximum of the known species diversity.

As a large proportion of the SNPs on the array are Robusta‐derived, filtered to maximize the within C. canephora diversity, this may induce an ascertainment bias when other species, such as C. eugenioides, are concerned.

Characterization of C. canephora genetic groups

The capacity of Coffee8.5K to generate genotyping data and to estimate the genetic relatedness among the genotypes was evaluated on the C. canephora Diversity Panel (Table S2). Some accessions from this panel had been previously genotyped using 19 microsatellite markers either by (Gomez et al., 2009); (Pégard et al., 2014) or Nestlé R&D Tours (unpublished). Thus, from these studies, a priori classification into the diversity groups was determined (Tables S2 and S4). Additional individuals from Angola and the Democratic Republic of the Congo, with unknown genetic identity, have also been included in this study (Table S2).

Genotyping of this Diversity Panel with the Coffee8.5K SNP array resulted in the generation of 4095 polymorphic SNPs without missing data. The population structure and relatedness among accessions was examined using population clustering with sNMF. The genetic structure analysis identified the most likely number of genetic clusters as K = 3 (Cluster‐AG, Cluster‐CD and Cluster‐BEOR) (Figure 3b). At higher levels of K, new groups emerged revealing a finer subdivision, and at K = 8 the accessions were partitioned into eight well‐differentiated groups (A, G, B, C, D, E, O and R), corresponding to different geographic origins (Figure 3a). Among these eight groups, six were confirming the a priori classification into the groups A, B, C, D, E and O, whereas two new groups, never previously described, were identified: G (Angola) and R (southern DRC). Three individuals showed mixed ancestry: AG‐Q121, BE‐110, OE‐KL.1.2, and ER‐BP409.

Figure 3.

Figure 3

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The population structure of the Coffea canephora diversity panel (27 accessions). Note that the same colour code is used in all graphs (a) Global distribution of the genetic groups across the C. canephora distribution range. Map data from US Dept of State Geographer ©2018 Google Image Landsat/Copernicus Data SIO, NOAA, U.S. Navy, NGA, GEBCO. (b) Population structure analysis using sNMF with three or eight numbers of clusters (K). Each colour represents a single cluster, each vertical column represents one accession. (c) A neighbour‐joining tree based on Euclidean distances between the C. canephora accessions, together with C. arabica and C. eugenioides accessions. Individuals from the same group are represented by the same colour, whereas admixed individuals (AG* and EB *, the two parents of the Robusta progeny, and BE, OE) do not have colour.

These results were further supported by the neighbour‐joining tree, which distinguished the six known and the two new groups with high bootstrap supports of 100% (Figure 3c). The admixed individuals presented intermediate positions between their ancestral genetic groups. In particular, AG‐Q121 (a hybrid between Groups A and G) and ER‐BP409 (a hybrid between Groups R and E), the two parents of the Robusta mapping progeny, are separated by a maximum genetic distance (at the opposite sides of the tree).

Arabica's closest current relatives

To identify the putative ancestral Coffea source populations of each C. arabica subgenome, we assigned the C. arabica haplotypes to current sampled individuals of the two progenitor species C. canephora and C. eugenioides. However, because of standard deviation overlaps, the assignation of the Arabica Ea subgenome to sampled C. eugenioides individuals could not discriminate a specific individual or an origin (Kenya or Uganda) (Figure 4d). On the contrary, since C. canephora populations and individuals were well discriminated by our Coffee8.5K array, the C. arabica genotypes were differentially assigned to the different genetic groups. In fact, the Ca subgenome was more closely related to individuals belonging to Group O (Uganda) or B (Northern DRC) and in particular to an individual of the Zoka forest (Diversity Group O, Northern Uganda) (Figure 4c). Group O encompasses individuals from the Eastern edge of the C. canephora distribution; the population from the Zoka Forest (Northern Uganda) is geographically the closest to the current distribution of C. arabica (Figure 4a).

Figure 4.

Figure 4

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Coffea arabica and its progenitor species. (a) Range distribution of the three related species. Dotted lines represent their schematic distribution limit, whereas names with colour labels correspond to sampled sites (C. canephora in blue, C. eugenioides in gold, and C. arabica in red), Map data ©2018 Google, ORION‐ME; (b) Origin of the genomes of the allotetraploid species Coffea arabica and of the Dihaploid Et39; (c, d) Haploid Identity‐by‐state distances (IBS) distances between C. arabica and accessions of the C. canephora with colour code as in Figure 3; and (c) C. eugenioides species. The average IBS distances and their standard deviations were calculated over the 17 C. arabica individuals.

Discussion

Coffee8.5K was designed and developed to assist in mapping C. canephora and C. arabica and also in assessing the diversity of C. arabica, C. canephora and other C. arabica‐related species (CWRs). The genotypes of the Discovery Panels were chosen to represent a range of common C. arabica and C. canephora types.

Fair representation of the genome

The genome size of C. canephora is reported to be about 710 Mbp (Noirot et al., 2003), with pseudo‐chromosome size ranging from 22.3 Mb (chr9) to 54.5 Mb (chr2) (Denoeud et al., 2014). The Coffee8.5K array developed in this study provides complete genome coverage and a fair pseudo‐chromosome representation, with an average density of 18.8 SNPs/Mb and a mean distance between SNPs of 54.4 kb. This density is comparable to other developed SNP arrays in hexaploid wheat (Rimbert et al., 2018) (for which the SNP density was one marker every 52 kb on the A‐genome, one every 53 kb on the B‐genome and one every 92 kb on the D‐genome), oil palm (Kwong et al., 2016) (one SNP per 11 kb), groundnut (Pandey et al., 2017) (1 SNP per 36 kb in A subgenome and 1 SNP per 48 kb in B subgenome) and rice (Yu et al., 2014) (12 SNPs per 1 Mb).

Because SNPs located within genic regions have a greater potential to affect gene function and are usually more effective in targeting genes, 40% (3428) of the effective SNPs were selected to be located in genic regions in the Coffee8.5K array.

Towards high‐density linkage maps

The mapping of 1307 (15.2%) of the Coffee8.5K SNPs on the C. canephora map (BP409 X Q121) demonstrates the usefulness of the Coffee8.5K for developing high‐density genetic maps for this species. Combined with previously developed markers (Denoeud et al., 2014; Mérot‐L'Anthoëne et al., 2014), we obtained a reliable and accurate final map of 3039 markers, including 665 SNPs in coding regions, with only a low fraction of distorted markers. The total map length was 1370 cM with an average distance of 2.2 cM between markers. The length of each linkage group ranged from 238 cM for LG B to 90 cM for LG I. The map obtained in this study is to date the densest map of C. canephora and provides access to hundreds of gene‐based markers.

On the contrary, the generation of a high‐density Arabica map is still severely hampered by its allotetraploid nature and the narrow genetic diversity among C. arabica accessions. Despite this situation, some mapping efforts have been previously undertaken with AFLP markers (Pearl et al., 2004) or more recently with both SSR and SNP markers (Moncada et al., 2016). For instance, Moncada et al. (2016), after screening 5785 SSR markers, could only find 338 (5.8%) polymorphic markers in the F2 offspring from a cross between C. arabica var. Caturra and a wild C. arabica accession from Ethiopia. The main difficulty in designing a SNP array useful for C. arabica mapping has been detecting co‐dominant SNP markers which were polymorphic within each Arabica subgenome. The parental accessions we selected for our mapping population were two distant wild C. arabica accessions, Ar8 and Ar36B, collected in Ethiopia (Guillaumet and Hallé, 1978). By comparing these genotypes, one Bourbon cultivar and a Mundo Novo cultivar (a Bourbon X Typica hybrid) to the Et39 (a spontaneous C. arabica dihaploid (Berthaud, 1976; Guillaumet and Hallé, 1978)), our filtering strategy aimed to select within subgenomes polymorphic SNP markers. This approach was especially efficient since it yielded more than 29% (900) of Arabica‐derived SNPs segregating in the mapping progeny and finished with a total of 945 genome‐wide co‐dominant SNP markers.

The high‐density Robusta genetic map generated from this study will not only be useful in ordering future genetic maps such as the one for Arabica, but also in providing a higher marker density than the previous one (Mérot‐L'Anthoëne et al., 2014). It will aid in selecting appropriate markers for various molecular breeding applications and in tracking introgression within each linkage group. Markers with even spacing along the genome would be beneficial for generalized use or for identifying marker associations with previously unstudied traits.

Moreover, because our SNP markers are both well‐distributed on the genome and genetically mapped, an additional application of the Coffee8.5K array would be to assist or to validate the genome sequence assembly of both C. canephora and C. arabica (de Kochko, 2018) by further comparing the genetic position with the physical position.

Better access to crop wild relative diversity

The Coffee8.5K SNP array was evaluated for its efficiency in studying genetic diversity and genetic relatedness in the Diversity Panel. This set includes representative individuals of the three related species C. canephora, C. eugenioides and C. arabica.

Genotyping data was generated successfully for all three species of the panel with 7065 (82.3%), 6824 (79.5%) and 6183 (72.1%) high‐quality SNP markers in C. canephora, C. arabica and C. eugenioides respectively. This high level of transferability across these species is similar to that previously observed for microsatellite markers (Poncet et al., 2004, 2006, 2007). C. eugenioides and C. canephora belong to different Coffea clades (Davis et al., 2011; Hamon et al., 2017): the East–Central Africa (EC‐Afr) clade and the West and Central Africa (W/C‐Afr) clade respectively (Davis et al., 2011). The good transferability of SNP markers discovered from C. canephora to the more distantly related species C. eugenioides (85.8%), suggests that a high percentage of loci represented on our Coffee8.5K array would also be transferable to other species of the Coffea genus, at least from EC‐Afr and W/C‐Afr clades, and represent a substantial improvement in the identification potential of the genetic diversity available within the genetic resources of the Coffea wild species.

A high proportion of the tested SNPs (77%) were polymorphic in the C. canephora Diversity set. They were also shown to be very efficient for diversity analyses by their application: they allowed to assign each C. canephora individual to its a priori genetic group—as previously defined by the SSR markers—as well as to discriminate between closely related accessions and to estimate admixture levels. Thanks to these markers, two additional, well‐differentiated genetic groups have been identified and characterized, one from Angola (group G) and the other from DRC (group R). This yielded a whole updated genetic structure of C. canephora within eight well‐differentiated and geographically localized groups (see Figure 3 and Table S4).

For C. arabica, from the 6824 scored SNPs, 24.2% (1653) turned out to differentiate Ca and Ea subgenomes in the dihaploid Et39, corresponding to fixed heterozygosity, whereas an estimated 10.6% (724) SNPs represented the within subgenomes allelic polymorphism varying across the C. arabica species. Fixed heterozygosity has been suggested to act as a buffer against the reduced population‐level diversity that resulted from the genetic bottleneck associated with polyploid speciation as well as to facilitate the transition to inbreeding (Soltis and Soltis, 2009). The genetic bottleneck undergone by C. arabica has been probably severe as suggested by its low within species differentiation, and might have arisen from a single‐polyploidization event (see below).

For the C. eugenioides Diversity set, although sharing more than 90% of the called SNP markers with the C. canephora set, the polymorphism rate was 19 times lower than for C. canephora, certainly due to the lower global genetic diversity of that species. A reduced number of tested C. eugenioides individuals can also be evoked, but our sampling covers the available accessions of the species in worldwide collections (Berthaud et al., 1980). Furthermore, additional analyses would be challenging since field sampling in the species distribution range has been proven to be difficult.

The Coffee8.5K array has been shown to be especially efficient in fingerprinting individuals within C. canephora, C. eugenioides and C. arabica species and its use by researchers or breeders on larger datasets should provide an opportunity to gain deeper insights into the genetic relatedness among the genotypes and also into the genetic architecture of these important crop wild relative germplasm resources.

The closest current relatives of Arabica

Coffea arabica allotetraploidy resulted from recent natural hybridization between the ancestors of present‐day C. canephora (Ca genome donor) and C. eugenioides (Ea genome donor) (Lashermes et al., 1999) and is probably the result of a single event (Lashermes et al., 2014). Coffea arabica displays disomic inheritance with bivalent pairing of homologous chromosomes (Krug and Mendes, 1940), which is in accordance with our present observations in mapping experiments of usual disomic patterns of marker inheritance.

Yu et al. (2011) provided evidence of recent C. arabica speciation no more than 0.665 million years ago, but also low divergence between the two constitutive subgenomes of C. arabica (Ca and Ea) and those of its progenitor species, demonstrating that the nuclear genomes have remained essentially unaltered since the formation of the hybrid.

Using the 17 C. arabica accessions, we took advantage of the fact that they exhibited close genetic relationships in order to apply a haploid‐genotype‐based assignation procedure to discover its closest current relatives in our sampling. Using our Coffee8.5K genotyping, we could not preferentially associate the Ea subgenome to any of the sampled individuals (Figure 4d).

On the contrary, we were able to preferentially assign the Ca subgenome to C. canephora of the O (Ugandan) Diversity group (Figure 3) and the shortest distance was observed with a North Ugandan C. canephora individual from the Zoka Forest. Geographically, this forest contains the closest C. canephora population to the current C. arabica distribution (Figure 4a–c).

At the present time, C. arabica is mainly found in the southwestern highlands of Ethiopia (Figure 4a), with some occurrence on the Boma plateau in southeastern South Sudan (Thomas, 1942), and on Mount Imantong in Sudan and Mount Marsabit in northern Kenya (Berthaud and Charrier, 1988). C. arabica is the main Coffea species that occurs in those regions and is geographically isolated from all diploid coffee species in the genus, which includes its two progenitor species C. canephora and C. eugenioides. Moreover, C. arabica also differs from C. canephora in terms of current environmental requirement and predicted niche distribution (Gomez et al., 2016). Thus, precise localization in Africa of the cradle of C. arabica, based on the present distribution of its two progenitor species appears difficult.

Two non‐exclusive scenarios could be suggested for the origin and geographical isolation of C. arabica. (i) Upon hybridization, C. arabica could have followed one of the typical patterns of the polyploid distribution with peripheral expansion outside the range of the distribution of its diploid parental species. Indeed, it has been suggested that plants with double genomes—auto‐ or allo‐polyploids—have the potential to develop phenotypic novelties, increase their adaptability and obtain higher fitness features that would render them more tolerant towards changing conditions than their diploid counterparts (Amborella Genome et al., 2013). This was particularly well illustrated with the survival and proliferation of polyploid plant lineages during the Cretaceous–Tertiary mass extinction event (Fawcett et al., 2009). Following the hypothesis that hybridization may favour establishment in novel habitats (Pillon et al., 2009; Rieseberg et al., 2007), C. arabica could have migrated northwards—away from the overlapping distribution range of C. canephora and C. eugenioides—towards its current distribution range with specific environmental requirement (Gomez et al., 2016). Its original population is genetically related to the current Zoka population, which thus represents the southernmost remnant of the original distribution. (ii) Meanwhile, considering that the constitution and the extent of tropical forests have varied considerably during the late Quaternary period (Mumbi et al., 2008), past C. canephora and C. eugenioides distributions may have been more widespread in higher latitudes and could have overlapped with the current C. arabica distribution in habitats that were suitable for all three species. In fact, C. canephora was probably able to find suitable habitats in Ethiopia in the recent past (mid‐Holocene, ~6000 years before present, R. Tournebize, data not shown). The climatic changes could have reduced the diploid distributions to their current locations. As a consequence, the birthplace of C. arabica could possibly be not only in Ethiopia but also in the entire region (South Sudan, Uganda, North Kenya) followed either by migration to present‐day Ethiopia or by survival in that region alone.

Due either to past habitat shift in Eastern Africa or to the colonization of new peripheral geographical areas, the presence of C. arabica in different environments and a reduction in diploid competitors would have increased the divergence of initially con‐specific populations and eventually would have given rise to the C. arabica speciation. C. arabica's self‐fertility would have further contributed to its genetic isolation.

Conclusion

The availability of our Coffee8.5K to the Coffee community provides an opportunity to generate high‐throughput genotyping data on different types of genetic and breeding populations for accelerating genetic diversity, high‐resolution trait mapping and breeding applications. It will help in identifying the allelic diversity present in the wild relatives of the two cultivated species, offering a clue to the transferability of beneficial alleles to C. canephora and/or to C. arabica.

Thanks to our stringent selection of genome‐wide distributed and informative SNPs, our array is an efficient tool for fingerprinting. As an application, detecting admixtures up to individual levels has allowed us to assign the Arabica Ca subgenome to its closest present‐day C. canephora relatives. Our array is definitely a powerful tool for quality control and traceability of all merchant coffees.

Materials and methods

Plant material

Accessions used to design the Coffee8.5K array (12 C. canephora and 5 C. arabica) were chosen to obtain an array for optimized use in mapping and genetic diversity analyses in coffee trees (Table S2).

SNP calling was generated from two Discovery Panels. The C. arabica Discovery Panel included five genotypes whose genome sequences were kindly provided by the Arabica Coffee Genome Consortium (ACGC): the parents of the Arabica mapping population (Ar8 and Ar36B), two cultivated varieties (Mundo Novo and Bourbon) and Et39, which is a spontaneous C. arabica dihaploid, that is with only one set of chromosomes from each subgenome (see Figure 4b). The C. canephora Discovery Panel included 12 genotypes of C. canephora belonging to the six genetic groups (Pégard et al., 2014).

Coffee8.5K array SNP segregation was monitored in the Robusta BP409 X Q121 and the Arabica Ar8 X Ar36B mapping populations (Mapping Panels, Table S2).

To evaluate the transferability and genotyping performance of the Coffee8.5K array, we genotyped a Diversity Panel of six C. eugenioides, the C. arabica dihaploid Et39, 16 wild and cultivated C. arabica and 27 C. canephora accessions representative of the C. canephora diversity groups as previously defined with SSR markers (Gomez et al., 2009; Pégard et al., 2014) (Table S2), as well as uncharacterized individuals from Angola and DRC.

SNP selection and array design

SNP selection and array characterization

We performed SNP calling and SNP selection thanks to the genome resequencing of the five accessions of C. arabica kindly provided by the Arabica Coffee Genome Consortium, ACGC (de Kochko, 2018) and of 12 C. canephora accessions (this study). SNP calling was performed by mapping the resequencing reads to the reference genome (Denoeud et al., 2014) using the NGS workflow manager TOGGLe (http://toggle.southgreen.fr, (Monat et al., 2015; Tranchant‐Dubreuil et al., 2018)).

All identified SNPs were filtered using custom Perl scripts to select bi‐allelic SNPs of high‐quality (QUAL > 40) and SNP positions with suitable flanking sequences (60 bp on both sides with no variation). Accessions used for SNP discovery and SNP selection criteria were chosen to design an array for optimized use in mapping and genetic diversity analyses in coffee trees.

SNPs from C. arabica

Screening for C. arabica SNPs was conducted on the Arabica Discovery Panel. Set 1, represented by the two wild Ethiopian parents of the Arabica mapping population, Ar8 and Ar36B, together with the dihaploid Et39. We identified 8 208 891 raw SNPs (Set 1, Figure 1), whereas Set 2, represented by Mundo Novo, Bourbon and Et39 sequences generated 10 063 130 raw SNPs (Set 2, Figure 1). Since any SNP scored as heterozygous in Et39 represents polymorphism between the two subgenomes (Figure 4b), to minimize these polymorphisms and to maximize the proportion of true‐allelic SNPs within the subgenome, the following filters were applied to both sets: (i) we removed the polymorphic SNPs in Et39; (ii) we selected SNPs that were homozygous for different alleles in at least two different C. arabica accessions; (iii) we considered SNPs spaced with a minimum distance of 40 kb.

SNPs from C. canephora

The Discovery Panel of 12 C. canephora genotypes was used for SNP discovery. To build a reliable and balanced genotyping array, accessions belonging to the six genetic groups (Pégard et al., 2014) were selected.

The analysis of the sequencing data yielded a total of 23 590 929 raw SNPs. The following sets were defined when choosing SNPs for microarray design (Figure 1): (i) a set of SNPs were selected on 149 candidate genes with functions related to cup quality (Privat et al., 2008), secondary metabolism (Denoeud et al., 2014; Lepelley et al., 2007, 2012) and drought tolerance as characterized and defined by their gene models on the reference genome (Denoeud et al., 2014) (Table S1); (ii) two sets of SNPs of equivalent size selected either on coding or non‐coding regions.

To minimize the impact of SNP ascertainment bias on downstream analyses, only SNPs found in at least two genotypes within the surveyed genotypes were chosen (allelic representation).

Genotyping assay

DNA extraction

Total DNA was extracted from adult leaves using DNeasy Plant Maxi Kit (Qiagen, Hilden, Germany) following the manufacturer's instructions. Final DNA concentration was determined with the Quant‐iT™ PicoGreen® dsDNA Assay Kit (Molecular Probes, Eugene, OR, USA) using the LightCycler® 480 (Roche Diagnostics, Rotkreuz, Switzerland). The DNA quality control was performed according to the Agilent Genomic DNA ScreenTape assay with the Agilent 2200 TapeStation system. Templates were diluted or concentrated to obtain a concentration of 50 ng/μL as required by Illumina for the amplification step.

KASPar™ genotyping

The KASPar™ Genotyping System from LGC Genomics® is a competitive allele‐specific dual FRET‐based assay for SNP genotyping (Cuppen, 2007). Among the 9824 SNPs selected for the SNP array design, a subset of 50 single‐nucleotide polymorphism sequences (Figure 1c) distributed across the genome were submitted to LGC Genomics for a KASPar assay design based on the SNP locus‐flanking sequence. The genotyping was conducted at LGC Genomics following their own protocol and the genotype calling was conducted with SNPviewer (LGC Genomics, Hoddesdon, UK).

SNP synthesis and calling

The 9827 selected SNPs were submitted to the Illumina Assay Design Tool for design score calculation (www.illumina.com) and 8580 of them were successfully synthesized by Illumina manufacturing processes. The SNP genotyping assay was performed on an Illumina® Infinium HD iSelect Custom Genotyping Array according to the standard Illumina's protocol, using 200 ng of genomic DNA per sample. The extension and staining steps were operated on a Tecan Evo‐150 liquid‐handling robot. Fluorescence intensities were read with Illumina® iScan Control software and allele calling was carried out using the Genome Studio v2.0 software (Illumina Inc., San Diego, CA, USA). All data were visually inspected and manually re‐scored if any errors were evident in the calling of the SNP clusters by the default algorithm. Reproducibility error rates were calculated between the control sample and SNP replicates.

SNP array applications

To evaluate the performance of the Coffee8.5K array, we genotyped the C. canephora and C. arabica mapping populations, a set of six C. eugenioides, 17 C. arabica individuals including the dihaploid Et39 and a Diversity Panel of 27 C. canephora individuals representative of the species diversity groups, previously genotyped with microsatellite markers with known genetic group assignation (Gomez et al., 2009; Pégard et al., 2014), as well as uncharacterized individuals from Angola and DRC.

Mapping population and linkage analysis

Two mapping populations were used, one Robusta progeny based on a F1 cross between BP409 (Congolese hybrid) and Q121 (Conilon‐type derived accession) comprising 93 individuals and an Arabica F2 segregation population (138 individuals) originating from a cross between two wild Ethiopian Arabica: Ar8 and Ar36B (see Mapping Panels, Table S2).

The Robusta (BP409xQ121) genetic linkage map was built using JoinMap®4.1 software (Van Ooijen, 2011) with the regression mapping algorithm and the grouping and recombination settings as described in Mérot‐L'Anthoëne et al. (2014). The Kosambi mapping function was used to convert recombination frequencies into map distance and the linkage maps were drawn using MapChart 2.3 (Plant Research International, Wageningen, Netherlands).

To facilitate the access and use of genome‐wide SNPs for geneticists and breeders, we implemented the MoccaDB v 2.0 database initially published in Plechakova et al. (2009) with new modules to include information and search tools related to SNPs. The query results can be visualized as text format in tables displayed on interfaces, the maps can be viewed and compared as graphics using the C‐Map (http://gmod.org/wiki/CMap) and in a genome browser (Figure S2).

Linkage disequilibrium

Linkage disequilibrium analysis was performed using the whole‐genome resequencing set of C. canephora Discovery Panel (n = 12). The linkage disequilibrium decay curve was calculated as r 2, the squared value of the correlation coefficient of the allelic states of two given polymorphic loci, against biallelic SNPs with MAF > 0.10 and with <10% missing genotypes (excluding the virtual pseudo‐chromosome 0 of unanchored sequences) using the PopLDdecay software (Xu et al., 2012).

Transferability to related species (C. arabica, C. eugenioides and C. canephora) and diversity analyses

Diversity analyses were performed to confirm the resolution capacity of this array system between accessions on each of the Diversity Panels (Table S2). The number of usable SNP markers for each of the three species was scored in relation to the number of total scorable loci, whereas the level of polymorphic markers was calculated for each species in relation to their number of usable markers. Since Et39 is a dihaploid with only one set of chromosomes from each subgenome, any SNP scored as heterozygous in Et39 represents polymorphism between the two subgenomes (Figure 4b). To optimize the estimation of within subgenomes polymorphism, the proportion of polymorphic SNPs within the set of 16 C. arabica was thus calculated after removing the heterozygous SNPs or SNPs with missing genotypes in Et39 (Table 1).

Population structure within the C. canephora subset was analysed using an unsupervised clustering algorithm, sNMF (Frichot et al., 2014) using the 27 C. canephora accessions representative of the species diversity including the parents of the Robusta mapping population. The most likely number of genetic clusters (K) was determined as the one minimizing the cross‐entropy criterion, for K ranging between 1 and 10 (with 20 estimation runs per K).

Two neighbour‐joining dendrograms were constructed based on the Euclidean distances between genotypes, one for the three species together (4095 polymorphic markers without missing data) and the other one for the C. arabica individuals (595 intra‐specific polymorphic markers without missing data). The stability of each node was evaluated using 100 bootstraps by blocks of 100 SNPs (R package ape) (Popescu et al., 2012).

Assignation of the arabica subgenomes

Coffea arabica contains two diploid genomes Ca and Ea that originated from two different wild diploid ancestor species, C. canephora and C. eugenioides (Lashermes et al., 1999). The similarity of the canephora‐derived subgenome Ca to current C. canephora genotypes and eugenioides‐derived subgenome Ea to current C. eugenioides accessions was evaluated based on the representative Diversity Panels of both species. Based on 4258 shared SNPs without missing data, Identity‐by‐state (IBS) distances were computed between each of the 17 C. arabica accessions with each individual without admixture of the two related species. We used haploid genotype downscaling to mitigate the impact of the polymorphic SNPs in C. arabica that cannot be precisely assigned to one or the other related species. Haploid genotypes were randomly drawn for each SNP and each accession over 200 runs. The IBS distances were averaged over all runs for each C. arabica accession. The average IBS distances and their standard deviations were then calculated over all 17 C. arabica accessions.

Conflicts of interest

The authors declare no conflicts of interest.

Supporting information

Figure S1 Linkage disequilibrium

PBI-17-1418-s006.png (47.6KB, png)

Figure S2 MoccaDB v2 database screenshots

PBI-17-1418-s005.png (527.5KB, png)

Figure S3 C. arabica neighbour‐joining tree

PBI-17-1418-s004.png (60.6KB, png)

Table S1 List of the Candidate Genes with their corresponding SNP markers (Locus ID and location of each original SNP is provided according to Denoeud et al. (2014) (2014) annotation)

PBI-17-1418-s003.XLSX (20.4KB, XLSX)

Table S2 List of accessions‐ List of accessions and their use: Discovery/Diversity/Mapping panel‐ Geographical location of the C. arabica samples used in this study overlaid on the Guillaumet (1978) map‐ Location of analysed C. eugenioides individuals on the microsatellite neighbour‐joining tree

PBI-17-1418-s007.xlsx (8.2MB, xlsx)

Table S3 SNP information (origin, genome location, sequence, polymorphism)

PBI-17-1418-s002.xlsx (882.7KB, xlsx)

Table S4 Summary table of the historical definition of C. canephora genetic groups with the references and the marker types in use.

PBI-17-1418-s001.docx (13.8KB, docx)

Acknowledgements

This project was partially supported by the Agropolis Foundation under References ID 1002‐009 and ID 1402‐003 through the « Investissements d'avenir » program (Labex Agro:ANR‐10‐LABX‐0001‐01), in the framework of I‐SITE MUSE (ANR‐16‐IDEX‐0006). The authors are grateful to the Arabica Coffee Genome Consortium (ACGC) for providing C. arabica sequencing data.

References

  1. Amborella Genome, P. , Albert, V.A. , Barbazuk, W.B. , dePamphilis, C.W. , Der, J.P. , Leebens‐Mack, J. , Ma, H. et al. (2013) The Amborella genome and the evolution of flowering plants. Science, 342, 1241089. [DOI] [PubMed] [Google Scholar]
  2. Anthony, F. , Combes, M.C. , Astorga, C. , Bertrand, B. , Graziosi, G. and Lashermes, P. (2002) The origin of cultivated Coffea arabica L. varieties revealed by AFLP and SSR markers. Theor. Appl. Genet. 104, 894–900. [DOI] [PubMed] [Google Scholar]
  3. Berthaud, J. (1976) Etude cytogénétique d'un haploïde de Coffea arabica L. Café, Cacao, Thé, XX, 91–96. [Google Scholar]
  4. Berthaud, J. and Charrier, A. (1988) Genetic resources of Coffea . In Coffee. Vol 4 : Agronomy ( Clarke, R.J. and Macrae, R. eds), pp. 1–42. Londres: Elsevier Applied Science. [Google Scholar]
  5. Berthaud, J. , Guillaumet, J.‐L. , Le Pierres, D. and Lourd, M. (1980) Rapport de prospection des caféiers du Kenya. Café, Cacao, Thé, XXIV, 101–112. [Google Scholar]
  6. Chevalier, A. (1929) Les caféiers du globe. I. Généralités sur les caféiers. In Encyclopédie Biologique, p. 196, Paris, France: Lechevalier P. [Google Scholar]
  7. Cuppen, E. (2007) Genotyping by allele‐specific amplification (KASPar). CSH Protoc. 2007, pdb.prot4841. [DOI] [PubMed] [Google Scholar]
  8. Davey, J.W. , Hohenlohe, P.A. , Etter, P.D. , Boone, J.Q. , Catchen, J.M. and Blaxter, M.L. (2011) Genome‐wide genetic marker discovery and genotyping using next‐generation sequencing. Nat. Rev. Genet. 12, 499–510. [DOI] [PubMed] [Google Scholar]
  9. Davis, A.P. , Govaerts, R. , Bridson, D.M. and Stoffelen, P. (2006) An annotated taxonomic conspectus of the genus Coffea (Rubiaceae). Bot. J. Linn. Soc. 152, 465–512. [Google Scholar]
  10. Davis, A.P. , Tosh, J. , Ruch, N. and Fay, M.F. (2011) Growing coffee: Psilanthus (Rubiaceae) subsumed on the basis of molecular and morphological data; implications for the size, morphology, distribution and evolutionary history of Coffea . Bot. J. Linn. Soc. 167, 357–377. [Google Scholar]
  11. Denoeud, F. , Carretero‐Paulet, L. , Dereeper, A. , Droc, G. , Guyot, R. , Pietrella, M. , Zheng, C. et al. (2014) The coffee genome provides insight into the convergent evolution of Caffeine biosynthesis. Science, 345, 1181–1184. [DOI] [PubMed] [Google Scholar]
  12. Dereeper, A. , Bocs, S. , Rouard, M. , Guignon, V. , Ravel, S. , Tranchant‐Dubreuil, C. , Poncet, V. et al. (2015) The coffee genome hub: a resource for coffee genomes. Nucleic Acids Res. 43, 1028–1035. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Fawcett, J.A. , Maere, S. and Van de Peer, Y. (2009) Plants with double genomes might have had a better chance to survive the Cretaceous‐Tertiary extinction event. Proc. Natl Acad. Sci. USA, 106, 5737–5742. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Frichot, E. , Mathieu, F. , Trouillon, T. , Bouchard, G. and François, O. (2014) Fast and efficient estimation of individual ancestry coefficients. Genetics, 196, 973–983. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Gomez, C. , Dussert, S. , Hamon, P. , Hamon, S. , de Kochko, A. and Poncet, V. (2009) Current genetic differentiation of Coffea canephora Pierre ex A. Froehn in the Guineo‐Congolian African zone: cumulative impact of ancient climatic changes and recent human activities. BMC Evol. Biol. 9, 167. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Gomez, C. , Despinoy, M. , Hamon, S. , Hamon, P. , Salmon, D. , Akaffou, D.S. , Legnate, H. et al. (2016) Shift in precipitation regime promotes interspecific hybridization of introduced Coffea species. Ecol. Evol. 6, 3240–3255. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Guillaumet, J.‐L. and Hallé, F. (1978) Echantillonnage du matériel Coffea arabica récolté en Ethiopie. Bulletin IFCC, 14, 13–18. [Google Scholar]
  18. Gupta, P.K. , Rustgi, S. and Mir, R.R. (2008) Array‐based high‐throughput DNA markers for crop improvement. Heredity, 101, 5–18. [DOI] [PubMed] [Google Scholar]
  19. Hamon, P. , Grover, C.E. , Davis, A.P. , Rakotomalala, J.J. , Raharimalala, N.E. , Albert, V.A. , Sreenath, H.L. et al. (2017) Genotyping‐by‐sequencing provides the first well‐resolved phylogeny for coffee (Coffea) and insights into the evolution of caffeine content in its species: GBS coffee phylogeny and the evolution of caffeine content. Mol. Phylogenet. Evol. 109, 351–361. [DOI] [PubMed] [Google Scholar]
  20. de Kochko, A. and ACGC (2018) Deciphering the Allotetraploid Genome of Coffea arabica L. In Plant and Animal Genome Conference XXVI (Januray 13‐17, 2018). San Diego, CA, USA.
  21. Krug, C.A. and Mendes, A.J.T. (1940) Cytological observations in Coffea. IV. J. Genet 39, 189–203. [Google Scholar]
  22. Kwong, Q.B. , Teh, C.K. , Ong, A.L. , Heng, H.Y. , Lee, H.L. , Mohamed, M. , Low, J.Z. et al. (2016) Development and validation of a high‐density SNP genotyping array for African oil palm. Mol. Plant, 9, 1132–1141. [DOI] [PubMed] [Google Scholar]
  23. Lashermes, P. , Combes, M.C. , Robert, J. , Trouslot, P. , D'Hont, A. , Anthony, F. and Charrier, A. (1999) Molecular characterisation and origin of the Coffea arabica L. genome. Mol. Gen. Genet. 261, 259–266. [DOI] [PubMed] [Google Scholar]
  24. Lashermes, P. , Andrzejewski, S. , Bertrand, B. , Combes, M.C. , Dussert, S. , Graziosi, G. , Trouslot, P. et al. (2000) Molecular analysis of introgressive breeding in coffee (Coffea arabica L.). Theor. Appl. Genet. 100, 139–146. [Google Scholar]
  25. Lashermes, P. , Combes, M.C. , Hueber, Y. , Severac, D. and Dereeper, A. (2014) Genome rearrangements derived from homoeologous recombination following allopolyploidy speciation in coffee. Plant J. 78, 674–685. [DOI] [PubMed] [Google Scholar]
  26. Lefebvre‐Pautigny, F. , Wu, F.N. , Philippot, M. , Rigoreau, M. , Priyono, P. , Zouine, M. , Frasse, P. et al. (2010) High resolution synteny maps allowing direct comparisons between the coffee and tomato genomes. Tree Genet. Genomes, 6, 565–577. [Google Scholar]
  27. Lepelley, M. , Cheminade, G. , Tremillon, N. , Simkin, A. , Caillet, V. and McCarthy, J. (2007) Chlorogenic acid synthesis in coffee: an analysis of CGA content and real‐time RT‐PCR expression of HCT, HQT, C3H1, and CCoAOMT1 genes during grain development in C. canephora . Plant Sci. 172, 978–996. [Google Scholar]
  28. Lepelley, M. , Mahesh, V. , McCarthy, J. , Rigoreau, M. , Crouzillat, D. , Chabrillange, N. , de Kochko, A. et al. (2012) Characterization, high‐resolution mapping and differential expression of three homologous PAL genes in Coffea canephora Pierre (Rubiaceae). Planta, 236, 313–326. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Leroy, T. , De Bellis, F. , Legnate, H. , Kananura, E. , Gonzales, G. , Pereira, L.F. , Andrade, A.C. et al. (2011) Improving the quality of African robustas: QTLs for yield‐ and quality‐related traits in Coffea canephora . Tree Genet. Genomes, 7, 781–798. [Google Scholar]
  30. Mérot‐L'Anthoëne, V. , Mangin, B. , Lefebvre‐Pautigny, F. , Jasson, S. , Rigoreau, M. , Husson, J. , Lambot, C. et al. (2014) Comparison of three QTL detection models on biochemical, sensory and yield characters in Coffea canephora . Tree Genet. Genomes, 10, 1541–1553. [Google Scholar]
  31. Monat, C. , Tranchant‐Dubreuil, C. , Kougbeadjo, A. , Farcy, C. , Ortega‐Abboud, E. , Amanzougarene, S. , Ravel, S. et al. (2015) TOGGLE: toolbox for generic NGS analyses. BMC Bioinformatics, 16, 374. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Moncada, M.D. , Tovar, E. , Montoya, J.C. , Gonzalez, A. , Spindel, J. and McCouch, S. (2016) A genetic linkage map of coffee (Coffea arabica L.) and QTL for yield, plant height, and bean size. Tree Genet. Genomes, 12, 5. [Google Scholar]
  33. Mumbi, C.T. , Marchant, R. , Hooghiemstra, H. and Wooller, M.J. (2008) Late Quaternary vegetation reconstruction from the Eastern Arc Mountains, Tanzania. Quatern. Res. 69, 326–341. [Google Scholar]
  34. Noirot, M. , Poncet, V. , Barre, P. , Hamon, P. , Hamon, S. and de Kochko, A. (2003) Genome size variations in diploid African Coffea species. Ann. Bot. 92, 709–714. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Pandey, M.K. , Agarwal, G. , Kale, S.M. , Clevenger, J. , Nayak, S.N. , Sriswathi, M. , Chitikineni, A. et al. (2017) Development and evaluation of a high density genotyping ‘Axiom_Arachis’ array with 58 K SNPs for accelerating genetics and breeding in groundnut. Sci. Rep. 7, 40577. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Pearl, H.M. , Nagai, C. , Moore, P.H. , Steiger, D.L. , Osgood, R.V. and Ming, R. (2004) Construction of a genetic map for Arabica coffee. Theor. Appl. Genet. 108, 829–835. [DOI] [PubMed] [Google Scholar]
  37. Pégard, M. , deKochko, A. , Merot‐L'anthoene, V. , Tranchant, C. , Rigoreau, M. , Bellanger, L. , Legnate, H. et al. (2014) Genetic diversity of Coffea canephora and selection of genotypes potentially resilient to climatic changes. In 25th International Conference on Coffee Science Sept. 8‐13. Armenia (Colombia)
  38. Pillon, Y. , Munzinger, J. , Amir, H. , Hopkins, H.C. and Chase, M.W. (2009) Reticulate evolution on a mosaic of soils: diversification of the New Caledonian endemic genus Codia (Cunoniaceae). Mol. Ecol. 18, 2263–2275. [DOI] [PubMed] [Google Scholar]
  39. Plechakova, O. , Tranchant‐Dubreuil, C. , Benedet, F. , Couderc, M. , Tinaut, A. , Viader, V. , De Block, P. et al. (2009) MoccaDB ‐ an integrative database for functional, comparative and diversity studies in the Rubiaceae family. BMC Plant Biol. 9, 123. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Poncet, V. , Hamon, P. , Minier, J. , Carasco‐Lacombe, C. , Hamon, S. and Noirot, M. (2004) SSR cross‐amplification and variation within coffee trees (Coffea spp.). Genome, 47, 1071–1081. [DOI] [PubMed] [Google Scholar]
  41. Poncet, V. , Rondeau, M. , Tranchant, C. , Cayrel, A. , Hamon, S. , de Kochko, A. and Hamon, P. (2006) SSR mining in coffee tree EST databases: potential use of EST‐SSRs as markers for the Coffea genus. Mol. Genet. Genomics, 276, 436–449. [DOI] [PubMed] [Google Scholar]
  42. Poncet, V. , Dufour, M. , Hamon, P. , Hamon, S. , de Kochko, A. and Leroy, T. (2007) Development of genomic microsatellite markers in Coffea canephora and their transferability to other coffee species. Genome, 50, 1156–1161. [DOI] [PubMed] [Google Scholar]
  43. Popescu, A.A. , Huber, K.T. and Paradis, E. (2012) ape 3.0: New tools for distance‐based phylogenetics and evolutionary analysis in R. Bioinformatics, 28, 1536–1537. [DOI] [PubMed] [Google Scholar]
  44. Privat, I. , Foucrier, S. , Prins, A. , Epalle, T. , Eychenne, M. , Kandalaft, L. , Caillet, V. et al. (2008) Differential regulation of grain sucrose accumulation and metabolism in Coffea arabica (Arabica) and Coffea canephora (Robusta) revealed through gene expression and enzyme activity analysis. New Phytol. 178, 781–797. [DOI] [PubMed] [Google Scholar]
  45. Rieseberg, L.H. , Kim, S.‐C. , Randell, R.A. , Whitney, K.D. , Gross, B.L. , Lexer, C. and Clay, K. (2007) Hybridization and the colonization of novel habitats by annual sunflowers. Genetica, 129, 149–165. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Rimbert, H. , Darrier, B. , Navarro, J. , Kitt, J. , Choulet, F. , Leveugle, M. , Duarte, J. et al., International Wheat Genome Sequencing, C. , Le Gouis, J. , on behalf The BreedWheat, C. , Davassi, A. , Balfourier, F. , Le Paslier, M.C. , Berard, A. , Brunel, D. , Feuillet, C. , Poncet, C. , Sourdille, P. , Paux, E. (2018) High throughput SNP discovery and genotyping in hexaploid wheat. PLoS ONE, 13, e0186329. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Sant'Ana, G.C. , Pereira, L.F.P. , Pot, D. , Ivamoto, S.T. , Domingues, D.S. , Ferreira, R.V. , Pagiatto, N.F. et al. (2018) Genome‐wide association study reveals candidate genes influencing lipids and diterpenes contents in Coffea arabica L. Sci. Rep., 8, 465. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Soltis, P.S. and Soltis, D.E. (2009) The role of hybridization in plant speciation. Annu. Rev. Plant Biol. 60, 561–588. [DOI] [PubMed] [Google Scholar]
  49. Thomas, A.S. (1942) The wild arabica coffee of the Boma Plateau, Anglo‐Egyptian Sudan. Empir. J. Exp. Agric. 10, 207–212. [Google Scholar]
  50. Tran, H.T.M. , Ramaraj, T. , Furtado, A. , Lee, L.S. and Henry, R.J. (2018) Use of a draft genome of coffee (Coffea arabica) to identify SNPs associated with caffeine content. Plant Biotechnol. J. 16, 1756–1766. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Tranchant‐Dubreuil, C. , Ravel, S. , Monat, C. , Sarah, G. , Diallo, A. , Helou, L. , Dereeper, A. et al. (2018) TOGGLe, a flexible framework for easily building complex workflows and performing robust large‐scale NGS analyses. bioRxiv 245480.
  52. Van Ooijen, J.W. (2011) Multipoint maximum likelihood mapping in a full‐sib family of an outbreeding species. Genet. Res. (Camb) 93, 343–349. [DOI] [PubMed] [Google Scholar]
  53. Xu, X. , Liu, X. , Ge, S. , Jensen, J.D. , Hu, F.Y. , Li, X. , Dong, Y. et al. (2012) Resequencing 50 accessions of cultivated and wild rice yields markers for identifying agronomically important genes. Nat. Biotechnol. 30, 105–U157. [DOI] [PubMed] [Google Scholar]
  54. You, Q. , Yang, X. , Peng, Z. , Xu, L. and Wang, J. (2018) Development and applications of a high throughput genotyping tool for polyploid crops: single nucleotide polymorphism (SNP) array. Front. Plant Sci. 9, 104. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Yu, Q.Y. , Guyot, R. , de Kochko, A. , Byers, A. , Navajas‐Perez, R. , Langston, B.J. , Dubreuil‐Tranchant, C. et al. (2011) Micro‐collinearity and genome evolution in the vicinity of an ethylene receptor gene of cultivated diploid and allotetraploid coffee species (Coffea). Plant J. 67, 305–317. [DOI] [PubMed] [Google Scholar]
  56. Yu, H. , Xie, W. , Li, J. , Zhou, F. and Zhang, Q. (2014) A whole‐genome SNP array (RICE6K) for genomic breeding in rice. Plant Biotechnol. J. 12, 28–37. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Figure S1 Linkage disequilibrium

PBI-17-1418-s006.png (47.6KB, png)

Figure S2 MoccaDB v2 database screenshots

PBI-17-1418-s005.png (527.5KB, png)

Figure S3 C. arabica neighbour‐joining tree

PBI-17-1418-s004.png (60.6KB, png)

Table S1 List of the Candidate Genes with their corresponding SNP markers (Locus ID and location of each original SNP is provided according to Denoeud et al. (2014) (2014) annotation)

PBI-17-1418-s003.XLSX (20.4KB, XLSX)

Table S2 List of accessions‐ List of accessions and their use: Discovery/Diversity/Mapping panel‐ Geographical location of the C. arabica samples used in this study overlaid on the Guillaumet (1978) map‐ Location of analysed C. eugenioides individuals on the microsatellite neighbour‐joining tree

PBI-17-1418-s007.xlsx (8.2MB, xlsx)

Table S3 SNP information (origin, genome location, sequence, polymorphism)

PBI-17-1418-s002.xlsx (882.7KB, xlsx)

Table S4 Summary table of the historical definition of C. canephora genetic groups with the references and the marker types in use.

PBI-17-1418-s001.docx (13.8KB, docx)

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