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Ecology and Evolution logoLink to Ecology and Evolution
. 2019 Apr 29;9(10):5617–5636. doi: 10.1002/ece3.5141

Development of a cost‐effective single nucleotide polymorphism genotyping array for management of greater yam germplasm collections

Fabien Cormier 1,2,, Pierre Mournet 2,3, Sandrine Causse 2,3, Gemma Arnau 1,2, Erick Maledon 1,2, Rose‐Marie Gomez 4, Claudie Pavis 4, Hâna Chair 2,3
PMCID: PMC6540704  PMID: 31160986

Abstract

Using genome‐wide single nucleotide polymorphism (SNP) discovery in greater yam (Discorea alata L.), 4,593 good quality SNPs were identified in 40 accessions. One hundred ninety six of these SNPs were selected to represent the overall dataset and used to design a competitive allele specific PCR array (KASPar). This array was validated on 141 accessions from the Tropical Plants Biological Resources Centre (CRB‐PT) and CIRAD collections that encompass worldwide D. alata diversity. Overall, 129 SNPs were successfully converted as cost‐effective genotyping tools. The results showed that the ploidy levels of accessions could be accurately estimated using this array. The rate of redundant accessions within the collections was high in agreement with the low genetic diversity of D. alata and its diversification by somatic clone selection. The overall diversity resulting from these 129 polymorphic SNPs was consistent with the findings of previously published studies. This KASPar array will be useful in collection management, ploidy level inference, while complementing accurate agro‐morphological descriptions.

Keywords: Dioscorea alata L., ex situ collection, genotyping, KASPar, ploidy, yam

1. INTRODUCTION

Greater yam (Discorea alata L.) is one of the major cultivated yam species (Discorea spp.) and the most widely spread among tropical and subtropical regions. The high importance of D. alata for food security has prompted the establishment of several international and national ex situ collections. Due to the limited shelf‐life of stored tuber, yam genetic resources are conserved in vitro or/and in the field. All of these repeated manipulations are time‐consuming and may affect long‐term conservation. Quality control of genotype purity and general collection management is mainly based on morphological descriptors (IPGRI/IITA, 1997; Mahalakshmi et al., 2007). However, these descriptors are not reliable enough to rationalize ex situ D. alata collection. Indeed, several studies have revealed that morphological variations are not necessarily linked to geographic origin or genetic lineage (Arnau et al., 2017; Lebot, Trilles, Noyer, & Modesto, 1998; Vandenbroucke et al., 2016). Complementary characterization tools are thus required for the conservation and dynamic management of ex situ collections related to germplasm exchange, the development of core collection or identification of future parents for breeding programs. D. alata is also a polyploid species with ploidy levels of 2n = 2x, 3x, or 4x and a basic chromosome number of x = 20 (Arnau, Némorin, Maledon, & Abraham, 2009). Ploidy levels detection is consequently a prerequisite for the identification of possible parents as crosses between the different ploidy levels can fail (Nemorin et al., 2013).

Molecular markers have been used to characterize D. alata diversity: random amplified polymorphic DNA (RAPD; Asemota, Ramser, Lopez‐Peralta, Weising, & Kahl, 1996), isoenzymes (Lebot et al., 1998), amplified fragment length polymorphism (AFLP; Malapa, Arnau, Noyer, & Lebot, 2005), simple sequence repeats (SSRs; Siqueira, Marconi, Bonatelli, Zucchi, & Veasey, 2011; Sartie, Asiedu, & Franco, 2012; Otoo, Anokye, Asare, & Telleh, 2015; Chaïr et al., 2016; Arnau et al., 2017), plastid sequences (Chaïr et al., 2016), and Diversity Arrays Technology (DArT; Vandenbroucke et al., 2016). These studies generated essential information on the diversity and representativity of the germplasm collections. However, these tools were not tailored for routine collection management. They were found to be either poorly discriminating within D. alata species or they were complex and not cost‐effective to use. Besides the development of high‐throughput methods for genome‐wide variant detection, such as genotyping‐by‐sequencing (Davey et al., 2011) paired with cost‐effective SNP assay (Broccanello et al., 2018) as KASPar can lead to the development of appropriate markers for collection management. This approach has been successfully implemented in maize (Semagn et al., 2012), chickpea (Hiremath et al., 2012), Citrus (Garcia‐Lor, Ancillo, Navarro, & Ollitrault, 2013), pigeon pea (Saxena et al., 2014), and Brassica rapa (Su et al., 2018). Regarding the recent release of yam (Dioscorea spp.) genomic resources (Saski, Bhattacharjee, Scheffler, & Asiedu, 2015; Tamiru et al., 2017), the design of such markers for D. alata collection management would be worthwhile. Indeed, once developed they do not require any specific bioinformatics or wet chemistry skills. The results contain few erroneous and missing data and can be easily analyzed and interpreted.

The main objectives of this study were (a) to identify genome‐wide polymorphic SNP markers, (b) to develop a cost‐effective SNP genotyping array using KASPar technology and (c) to test its use as a tool in managing yam ex situ collections.

2. MATERIALS AND METHODS

2.1. Materials

Based on a previous microsatellite markers study (Arnau et al., 2017), a set of 48 accessions representing worldwide D. alata diversity was selected and genotyped to identify polymorphic SNPs and design KASPar markers. Then, for the purpose of validating these markers, 141 landraces from the Tropical Plants Biological Resources Centre (CRB‐PT) and CIRAD ex situ collections maintained in the West French Indies (Guadeloupe) were used.

2.2. Genotyping‐by‐sequencing (GBS) and SNP discovery

SNP discovery was based on genotyping‐by‐sequencing (GBS). First, DNA extractions were performed with dried leaves from the 48 accessions as described by Risterucci et al. (2009). The genomic DNA quality was checked using agarose gel electrophoresis, and the quantity was estimated using a Nanodrop ND‐1000 spectrophotometer (Thermo Scientific, Wilmington, USA). For GBS, a genomic library was prepared using the PstI‐MseI restriction enzymes (New England Biolabs, Hitchin, UK) with a DNA normalized quantity of 200 ng per sample. The procedures published by Elshire et al. (2011) were adapted as described in Cormier et al. (2019).

Digestion and ligation reactions were conducted in the same plate. Digestion was conducted at 37°C for 2 hr and then 65°C for 20 min to inactivate the enzymes. The ligation reaction was achieved using T4 DNA ligase enzyme (New England Biolabs, Hitchin, UK) at 22°C for 1 hr, and the ligase was then inactivated, prior to sample pooling, by heating at 65°C for 20 min. Pooled samples were PCR‐amplified in a single tube. Single‐end sequencing was performed on a paired‐end lane of an Illumina HiSeq3000 (at the GeT‐PlaGe platform, Toulouse, France). The Tassel 5.2 pipeline (Glaubitz et al., 2014) was used for SNP and indel calling. Sequence tags were aligned to D. alata contigs (http://www.ebi.ac.uk/ena/data/view/PRJEB10904) using Bowtie2 v2.2.6 (Langmead & Salzberg, 2012). Accessions with more than 70% missing data were removed. Vcf filtering was performed using Vcftools 0.1.14 (Danecek et al., 2011; option: ‐‐minDP 8, ‐‐maf 0.1, ‐‐max‐missing 0.60, ‐‐max‐alleles 2, ‐‐thin64).

2.3. KASPar genotyping and allele calling

Polymorphic SNP flanking sequences (60 bp upstream and 60 bp downstream around the variant position) were selected using SNiPlay3 (Dereeper et al., 2011). In order to assess their putative physical positions, these sequences were then blasted to the D. rotundata reference genome (TDr96_F1 Pseudo_Chromosome: BDMI01000001–BDMI01000021; Tamiru et al., 2017). The physical position of each SNP was defined using their flanking sequences best hit using a BLAST E‐value threshold of 1e−30 (Basic Local Alignment Search Tool). Finally, 192 SNPs were selected by forming 192 k‐means cluster based on their relative physical distance (Euclidean distance) and selecting the SNP nearest to the centroid of each cluster using R 3.4.0 (R core team, 2017).

The 192 SNPs were converted into a KASPar assay at LGC genomics where the primer design and wet chemistry was conducted (Middlesex, UK) on a validation panel of 141 landraces from the CRB‐PT and CIRAD ex situ collections. From raw fluorescence data, allele calling was performed using LGC Kluster Caller software by defining fluorescence clusters. Some accessions with known ploidy level were used as reference to identify fluorescence clusters and assess allelic dosage.

2.4. Diversity analysis

To identify duplicate accessions and compare accessions with different ploidy levels, a matrix of dissimilarity between each accession pair was computed as the percentage of shared alleles based on the allele presence/absence.

Then, to refine the kinship assessment, similarities between accessions with the same ploidy level were computed in the same way but using the allelic dosage. For diploid accessions, genotypes were coded as 0, 1, and 2 where the number represents the number of nonreference allele. Heterozygous genotypes assessed as polyploid during allele calling were converted to 1. Moreover, for triploid accessions, genotypes were coded as 0, 1, 2, and 3 with allelic dosage score as 1:1 during allele call converted to 1.5. For tetraploid accessions, genotypes were thus coded as 0, 1, 2, 3, or 4 and no correction was needed.

Diversity analysis was conducted in two steps. During the first step, groups of duplicate accessions (redundancy groups) were defined by grouping accessions having up to one allele mismatch. Then, in the second step, the diversity analysis focused on the similarity between those groups. Clustering based on allele frequencies within redundancy groups followed by a bootstrap approach (pvclust R package, ward.D2, 10,000 boots, AU threshold = 0.95; Suzuki & Shimodaira, 2006) was used to identify gene pools. A diversity network between redundancy groups was also drawn using significant kinship detected through genotype permutations (1,000), with a significance threshold of 0.05.

3. RESULTS

3.1. KASPar assay development and validation

Genotyping‐by‐sequencing (GBS) produced more than 344 million reads resulting in 521,918 sequence tags out of which 207,810 (39.82%) aligned exactly once on D. alata contigs. The remaining reads aligned at multiple locations (25.18%) or did not align to any contig (35%). From these sequence tags, SNP calling produced a raw vcf file of 158,695 SNPs. This raw vcf file was then filtered resulting in a dataset of 40 accessions (Appendix A), and 4,593 good quality SNPs out of which 3,879 (84%) SNPs were mapped by BLAST on the D. rotundata reference genome. The KASPar assay was then developed by selecting 192 SNPs representative of SNPs mapped along the D. rotundata reference sequence, and they were tested on 141 accessions.

Among the 192 SNPs, 26 (13%) SNPs failed as they did not produce any amplification signal. From the remaining 166 SNPs (87%), 129 SNPs (Appendix C) with less than 20% missing data and a minor allele frequency of over 5% were retained as high‐quality SNPs. This final dataset (129 SNPs × 141 accessions) contained an overall missing data rate of only 0.5% with a maximum of 3% missing data per accession.

The 129 validated KASPar SNPs were distributed on all linkage groups used to construct the D. rotundata reference genome (Figure 1). Their distribution was not homogeneous along chromosomes as their position was planned to be representative of that of the initial set of 3,879 mapped SNPs and not equally spaced.

Figure 1.

Figure 1

Location of KASPar SNPs on the D. rotundata reference genome (Tamiru et al., 2017). The 21 linkage group are aligned from left to right. Black dots, failed or bad quality SNPs; red dots, the 129 validated SNPs

3.2. Assessment of ploidy levels

In our D. alata validation panel, three ploidy levels (2x, 3x and 4x) coexisted (Appendix B). Thus, the KASPar assay could theoretically produce a maximum of seven types of fluorescence signal (Table 1) corresponding to two types of fluorescence signal in homozygous states (2:0 = 3:0 = 4:0; 0:2 = 0:3 = 0:4), the fluorescence signal of mixed and balanced allelic dosages (1:1 for diploids or 2:2 for tetraploids) and the four types of fluorescence signal corresponding to the different possible unbalanced allelic dosages at heterozygotic loci (“polyploid‐like” in Table 1) of triploids and tetraploids (1:3; 1:2; 2:1; 3:1). In our case, due to insufficient fluorescence resolution, it was not possible to distinguish fluorescence signals of the 1:3 tetraploid allelic dosage from the 1:2 triploid allelic dosage, or the 2:1 triploid allelic dosage from the 3:1 tetraploid allelic dosage. Consequently, a maximum of five types of fluorescence signals were identified. Overall, five, four, three, and two allelic dosages were detected for 64 (50%), 41 (32%), 19 (15%), and 5 (4%) SNPs, respectively, because some allelic dosages were not present in the validation panel or they were cofounded.

Table 1.

Summary of genotype, allelic composition and fluorescence signals

Type of genotype Ploidy Allelic Type of fluorescence signal
Dosage Composition Theo. Obs.
Diploid‐like Diploid 0:2 X:X 1 1
1:1 X:Y 4 3
2:0 Y:Y 7 5
Triploid 0:3 X:X:X 1 1
3:0 Y:Y:Y 7 5
Tetraploid 0:4 X:X:X:X 1 1
2:2 X:X:Y:Y 4 3
4:0 Y:Y:Y:Y 7 5
Polyploid‐like Triploid 1:2 X:X:Y 3 2
2:1 X:Y:Y 5 4
Tetraploid 1:3 X:X:X:Y 2 2
3:1 X:Y:Y:Y 6 4

However, the overall allele call and allelic dosage assessment quality were good. Indeed, the ratio of genotypes scored as “polyploid‐like” on overall heterozygous genotypes by accession was low (0.09 ± 0.05) for diploids and high for triploids (0.83 ± 0.05). In addition, the three distributions of this ratio corresponding to the three ploidy levels did almost not overlap (Figure 2).

Figure 2.

Figure 2

Distribution of the percentage of polypoid‐like genotypes (1:3, 1:2, 2:1, and 3:1 allelic dosage) on overall heterozygous genotypes by ploidy level (red, diploid; green, triploid; blue, tetraploid)

We were thus not able to differentiate all allelic dosage from each other when looking at one SNP. However, ploidy level could be deduced when taking all the KASPar array into account and considering the proportion of genotypes scored as “polyploid‐like” per accession. This KASPar assay thus differentiated the accession ploidy level and allowed us to assign it for 12 accessions originally of unknown ploidy. Nine were set as diploid and three as triploid.

3.3. Diversity analysis

Overall, 141 accessions from CRB‐PT and CIRAD ex situ collections in Guadeloupe were used to validate the KASPar assay (96 diploids, 36 triploids, and nine tetraploids including accessions with known and deduced ploidy level).

The allele presence and/or absence was used to assess the similarity between accessions and thus to identify duplicate accessions (Figure 3). Indeed, by defining redundancy groups, we ended up with 43 nonredundant groups each containing one to 24 accessions.

Figure 3.

Figure 3

Dendrogram of dissimilarity between 141 D. alata accessions (red, diploid; green, triploid; blue, tetraploid)

These groups of genetically similar accessions were partially expected based on the accession vernacular names. For example, the second biggest group (redundancy group 6, Appendix B) was composed of 18 accessions, five of which had a name related to “Saint Vincent.” The third biggest group contained 14 accessions, four of which had a name related to “Pacala.”.

The main group of redundant accessions was composed of 24 triploids collected at several distant locations (Caribbean islands, New Caledonia and Madagascar). This group consisted of 67% (24/36) of the triploid accessions present in the CRB‐PT and CIRAD collections.

More generally, redundancy groups only consisted of accessions with the same ploidy level (Figure 4). Moreover, similarities within triploids or within tetraploids were higher than within diploids.

Figure 4.

Figure 4

Distribution of similarity between all accession pairs by ploidy (red, diploid; green, triploid; blue, tetraploid)

The diversity analysis was based on these 43 redundancy groups to avoid bias. After clustering, the bootstrap procedure detected five significant gene pools, named “cluster” here, represented in the kinship network (Figure 5). Only one (cluster C, Figure 5) consisted of accessions from the three ploidy levels. This cluster encompassed accessions from the Caribbean and Pacific regions. Clusters A, B, and D contained triploids from the Caribbean and Madagascar, tetraploids from the Pacific and diploids from the Caribbean, respectively (Figure 5, Appendix B). Cluster E was the biggest one, with 21 nonredundant diploid accessions originating from India, Nigeria, Côte d'Ivoire, the Caribbean and Pacific (Figure 5, Appendix B).

Figure 5.

Figure 5

Network of kinship for the 43 D. alata redundancy groups based on significant similarity (p < 0.05, edge‐weighted spring‐embedded layout). Nodes shape and letter, cluster of diversity identified by a bootstrap procedure; red nodes, diploids; green nodes, triploids; blue nodes, tetraploids; edge colors, similarity from gray (0.64) to black (1)

Genotype permutations and network analysis gave a more detailed view of kinship between redundancy groups and Clusters. This approach revealed a low number of significant links between the diversity clusters D or E and the others (Figure 5) revealing that these clusters could consist of original genepools.

4. DISCUSSION

4.1. Assessment of allelic dosage and detection of ploidy levels

KASPar technology is based on competitive allele‐specific amplification followed by allele‐specific fluorescence assessment (Semagn, Babu, Hearne, & Olsen, 2014). Detection of allelic dosage in polyploid species is thus possible (Cuenca, Aleza, Navarro, & Ollitrault, 2013). However, several parameters may influence the fluorescence, such as the DNA quality or primer specificity, and consequently the ability to discriminate fluorescence signals and the allelic dosage. In our case, we were able to discriminate five types of fluorescence signal. At heterozygous loci, fluorescence signals were a mixture of two types of allelic‐specific fluorescence. Fluorescence signals should also be balanced for diploids which have a balanced allelic dosage (1:1) at heterozygous loci. Diploids should therefore theoretically have no genotypes assessed as “polyploid‐like.” Conversely, triploids should theoretically have only genotypes assessed as “polyploid‐like” at heterozygous loci. A balanced allelic dosage is impossible for triploids. Our results showed that 91 ± 5% and 83 ± 5% of heterozygous genotypes were correctly called for diploids and triploids, respectively. Regarding the recent explosion of genotyping related to next‐generation sequencing, bioinformatics tools have been developed to accurately determine dosages (e.g., GBS2ploidy; Gompert & Mock, 2017). However, this requires deep sequencing and usually an assumption of ploidy levels present in the dataset (Bourke, Voorrips, Visser, & Maliepaard, 2018).

Application in collection management may nevertheless not require allelic dosage assessment at each locus. Our aim was thus to develop a tool for estimating ploidy levels and not variations in copy number. Moreover, the results showed that ploidy levels for each accession can be accurately deduced from the percentage of “polypoid‐like” genotypes on overall heterozygous genotypes. Regarding the overlapping distributions of this ratio (Figure 2), the only risk is to confuse triploids and tetraploids estimated at 3%. Consequently, ploidy level assessment is possible and fairly accurate for D. alata using the KASPar assay developed in this study.

4.2. Identification of duplicate accessions

The dataset included 129 SNPs validated on 141 accessions corresponding to 43 unique redundancy groups. The resuming of the 141 accessions to 43 unique redundancy groups was related to the narrow D. alata genetic diversity, above all in polyploid germplasm (i.e., triploids and tetraploids) already identified in previous studies. For example, using DarT markers, a low varietal richness was revealed by Vandenbroucke et al. (2016), who studied 80 landraces from six different Vanuatu islands and differentiated only seven unique genotypes. Using isozyme markers, Lebot et al. (1998) studied 269 worldwide distributed cultivars and concluded that the genetic diversity of the most widespread cultivars was narrow.

Regarding the accession vernacular names, redundant accessions were expected in our sample. Some of these redundancy groups contained accessions detected in duplicate, while they could be differentiated by morphological characterization. For example, redundancy group five (including Lupias, Malalagi, or Malankon) exhibited diversity in tuber shape and tuber flesh color in agreement with previous genetic diversity studies that already pooled these accessions together and highlighted this intragroup variability in tubers (Arnau et al., 2017; Malapa et al., 2005).

Morphological variability within a redundancy cluster mostly arises via D. alata clonal reproduction and farmers' selection of new morphotypes resulting from somatic mutations (Lebot et al., 1998; Malapa et al., 2005; Vandenbroucke et al., 2016). Small genetic or epigenetic variations are commonly selected to create new diversity in horticultural crops such as yam as reviewed by Krishna et al. (2016).

The ability of KASPar assay developed in this study to differentiate duplicates in collections from genetically close accessions was related: (a) to the low number of studied loci (129), but also (b) to the D. alata diversification process (i.e., selection of somaclonal mutants) and (c) the presence of real duplicates within collections. This tool is thus efficient for attributing accessions to a genetic lineage (e.g., germplasm exchange), but a good complementary agro‐morphological and ecophysiological characterization of collections should also be done to completely differentiate somaclonal mutant clones from duplicates (e.g., identification of promising genitors for breeding programs).

4.3. Diversity and collection management

The CRB‐PT collection has been shown to be representative of worldwide D. alata diversity (Arnau et al., 2017). A subset of this ex situ collection has been genotyped in this study. However, all diversity groups identified by Arnau et al. (2017) were present (except one containing five very similar Indian accessions). Our validation panel was thus representative of the worldwide D. alata diversity. Moreover, a good correlation was obtained between the findings of the previous study of worldwide D. alata diversity of Arnau et al. (2017) and the gene pools identified in this study (Appendix B). We can thus hypothesize that the 129 SNPs KASPar array developed for D. alata allow us to accurately assess genetic diversity and the findings may be transferable to other collections. Moreover, this genotyping tool is a robust method: (a) to assess complementarity/redundancy between the different collections, (b) to identify under represented genetic groups, and (c) to plan future collects to fill gaps in collections.

5. CONCLUSION

This is the first SNP array designed for D. alata and validated on a subset of accessions representative of worldwide D. alata diversity. This tool will allow users to estimate accession ploidy levels and genetic lineages. The results showed a good correlation between the diversity assessed by this KASPar array and the findings of previous studies. This KASPar array is a robust and cost‐effective tool for diversity assessment and collections management. Regarding the importance of vegetative reproduction and somaclonal selection in D. alata, it is a good tool to complement agro‐morphological description in collections.

CONFLICT OF INTEREST

The authors declare that they have no conflict of interest.

AUTHOR CONTRIBUTIONS

C.P., F.C., H.C., and P.M. designed the study. C.P., F.C., E.M., G.A., and R‐M.G. contributed to collecting materials and sample preparation. P.M. and S.C. developed GBS protocol, carried out DNA extraction, and GBS library preparation. H.C. and P.M. performed SNP discovery. F.C. and H.C. designed the KASPar assay and performed its analysis. C.P., F.C., and H.C. wrote the manuscript with the input of all authors.

ACKNOWLEDGMENTS

This study was financially supported by the European Union and Guadeloupe Region (Programme Opérationnel FEDER—Guadeloupe—Conseil Régional 2014–2017). The authors would like to thank Suzia Gélabale, Marie‐Claire Gravillon, Jean‐Luc Irep, David Lange, and Elie Nudol for their involvement in CRB‐PT and CIRAD in vitro and field collections conservation. Finally, we are grateful to Patrick Ollitrault for his valuable discussion and to David Manley for English proofing.

APPENDIX A.

Table A1.

Description of the 40 D. alata accessions used to detect polymorphic SNP

Collection Code Name Origin Ploidy
CRB‐PT PT‐IG‐00002 Pakutrany Nlle Caledonie  
PT‐IG‐00006 Fénakué Puerto Rico 2
PT‐IG‐00010 Divin 1 Guadeloupe 2
PT‐IG‐00020 DA 26 Guyane Fr 3
PT‐IG‐00338 HYB 30 Guadeloupe  
PT‐IG‐00350 Pacala Guadeloupe 2
PT‐IG‐00029 Plimbite Haïti 2
PT‐IG‐00033 Pyramide Puerto Rico 2
PT‐IG‐00046 Sea 190 Puerto Rico 2
PT‐IG‐00053 Kokoéta Nlle Calédonie 2
PT‐IG‐00686 Roujol   4
PT‐IG‐00687 INRA C 143    
PT‐IG‐00688 INRA AL 56    
PT‐IG‐00690 INRA AL 18    
PT‐IG‐00692 INRA X 154 Guadeloupe  
PT‐IG‐00693 INRA X 17 Guadeloupe  
PT‐IG‐00694 Dou   4
PT‐IG‐00695 INRA X 142 Guadeloupe  
PT‐IG‐00696 Ciradienne   4
PT‐IG‐00697 TiViolet   4
PT‐IG‐00698 Malalagi Vanuatu 2
PT‐IG‐00702 Manlankon Vanuatu 2
PT‐IG‐00689 Nureangdan Vanuatu 3
PT‐IG‐00077 Kinabayo Puerto Rico 2
PT‐IG‐00078 Toro Haïti 3
Cirad Vu 024a Tépuva Vanuatu 2
Vu 528a Tacharamivar   2
Vu 564a Mendrovar Vanuatu 2
Vu 567a Homb Vanuatu 2
Vu 754a Intejegan Vanuatu 4
Vu 231a Tagabé Vanuatu 4
Ovy taty   Madagascar  
Vu 247a n.a Vanuatu 2
Vu 401a Basa Vanuatu 2
Kabusa     2
74F     2
42F     2
61F     2
14M     2
H4x200     4

APPENDIX B.

Table B1.

Description of the 141 D. alata used as the KASPar assay validation panel

Collection Code Ploidya Div. Clust.b Redund. Grpc Accession name Origin SSRd
PT‐IG‐00087 3 A 26 65 Martinique XII
PT‐IG‐00070 3 A 26 66 Martinique XII
PT‐IG‐00090 3 A 26 Caillade 1 Haïti XII
PT‐IG‐00020 3 A 26 DA 26 French Guyana XII
PT‐IG‐00037 3 A 26 DA 27 French Guyana XII
PT‐IG‐00022 3 A 26 De agua Puerto Rico XII
PT‐IG‐00061 3 A 26 Igname d eau Martinique XII
PT‐IG‐00550 3 A 26 Montpellier   XII
PT‐IG‐00075 3 A 26 Renta Yam Jamaica XII
PT‐IG‐00072 3 A 26 Sassa 1 Martinique XII
PT‐IG‐00063 3 A 26 Sassa 2 Martinique  
PT‐IG‐00088 3 A 26 St Martin Martinique XII
PT‐IG‐00034 3 A 26 Sweet yam Jamaica XII
PT‐IG‐00557 3 A 26 Tahiti couleuvre Guadeloupe XII
PT‐IG‐00068 3 A 26 Tahiti cultivé Guadeloupe XII
PT‐IG‐00069 3 A 26 Tahiti French Guadeloupe XII
PT‐IG‐00018 3 A 26 Tahiti messien Guadeloupe  
PT‐IG‐00064 3 A 26 Tana New Caledonia XII
PT‐IG‐00021 3 A 26 Telemaque Martinique XII
PT‐IG‐00044 3 A 26 Ti Joseph 1 Haïti XII
PT‐IG‐00078 3 A 26 Toro Haïti XII
CT257_CIV 3 A 26 OvyTaty AmbalaKindresy‐Ambohimasoa Madagascar  
CT258_CIV 3 A 26 OvyTaty Amboasary‐Ambohimasoa Madagascar  
PT‐IG‐00685 3 A 26 Sainte Anne    
PT‐IG‐00030 3 A 33 67 Martinique XII
PT‐IG‐00558 4 B 3 Wabé New Caledonia XVIII
Vu472a 4 B 3 Toufi Tetea Vanuatu XVIII
Vu231a 4 B 3   Vanuatu XVIII
Vu750a 4 B 3 Wanorak Vanuatu  
Vu534a 4 B 3 Bisoro Vanuatu XVIII
Vu754a 4 B 30 Noulelcae Vanuatu XVI
Vu408a 4 B 31 Manioc Vanuatu  
PT‐IG‐00039 2 C 2 Americano Dominican Republic VII
PT‐IG‐00023 2 C 2 Florido Puerto Rico  
PT‐IG‐00553 2 C 2 Pro 1   VII
PT‐IG‐00095 2 C 2 SEA 144 Puerto Rico IV
PT‐IG‐00555 2 C 2 SRT 29   VII
PT‐IG‐00041 2 C 2 St Domingue Dominican Republic VII
Vu401a 2 C 2 Basa Vanuatu VII
CT256 2 C 2      
PT‐IG‐00009 4 C 12 Nouméa New Caledonia XVI
Vu247a 2 C 14   Vanuatu  
Vu528a 2 C 16 Sinoua Vanuatu  
PT‐IG‐00025 3 C 22 Goana New Caledonia XIII
PT‐IG‐00002 3 C 22 Pakutrany New Caledonia XIII
Vu699a 3 C 22 Tumas Vanuatu  
Vu461a 3 C 22 Tumas Vanuatu XIII
Vu755a 4 C 24 Nepelev Vanuatu  
PT‐IG‐00014 2 C 37 Divin 2 Guadeloupe  
PT‐IG‐00006 2 C 37 Fénakué Puerto Rico  
PT‐IG‐00053 2 C 37 Kokoéta New Caledonia  
PT‐IG‐00559 2 C 39 Wassa New Caledonia  
PT‐IG‐00001 2 D 7 64 Martinique  
PT‐IG‐00010 2 D 7 Divin 1 Guadeloupe  
PT‐IG‐00568 2 D 25 77 Martinique IV
PT‐IG‐00092 2 D 34 Caplaou Puerto Rico  
PT‐IG‐00561 2 D 42 H 23    
PT‐IG‐00562 2 D 42 H 50    
74F 2 E 4   India  
PT‐IG‐00049 2 E 5 Cinq Puerto Rico III
PT‐IG‐00027 2 E 5 Lupias New Caledonia III
PT‐IG‐00046 2 E 5 Sea 190 Puerto Rico III
Vu590a 2 E 5   Vanuatu III
Vu423a 2 E 5 Manlankon Vanuatu III
Vu639a 2 E 5 Malalagi Vanuatu III
Vu024a 2 E 5 Ptris Vanuatu III
PT‐IG‐00065 2 E 6 DA 28 French Guyana IV
PT‐IG‐00093 2 E 6 DA 32    
PT‐IG‐00395 2 E 6 Fafadro bis   IV
PT‐IG‐00060 2 E 6 Grand Etang Guadeloupe IV
PT‐IG‐00051 2 E 6 Morado Cuba IV
PT‐IG‐00073 2 E 6 Purple Lisbon Puerto Rico IV
PT‐IG‐00333 2 E 6 Sainte Catherine Guadeloupe IV
PT‐IG‐00052 2 E 6 Smooth Statia Puerto Rico IV
PT‐IG‐00024 2 E 6 St Vincent blanc 1 Martinique IV
PT‐IG‐00036 2 E 6 St Vincent blanc 2 Martinique IV
PT‐IG‐00556 2 E 6 St Vincent mart. Guadeloupe IV
PT‐IG‐00045 2 E 6 St Vincent Violet Martinique IV
PT‐IG‐00016 2 E 6 St Vincent Yam St. Lucia IV
PT‐IG‐00374 2 E 6 Ti Joseph Haïti IV
PT‐IG‐00067 2 E 6 Wénéféla bis New Caledonia IV
Vu487a 2 E 6 Teroosi Vanuatu VI
770 2 E 6      
PT‐IG‐00623 2 E 6      
PT‐IG‐00396 2 E 8 A 24    
PT‐IG‐00071 2 E 10 72 Martinique VIII
PT‐IG‐00055 2 E 10 76 Martinique VIII
PT‐IG‐00089 2 E 10 Asmhore    
PT‐IG‐00058 2 E 10 Bété Bété Côte d'Ivoire VIII
PT‐IG‐00091 2 E 10 Campêche 2    
PT‐IG‐00546 2 E 10 Jardin Haitien   VIII
PT‐IG‐00547 2 E 10 Kourou 1 French Guyana VIII
PT‐IG‐00548 2 E 10 Kourou 2 French Guyana VIII
PT‐IG‐00350 2 E 10 Pacala Guadeloupe VIII
PT‐IG‐00551 2 E 10 Pacala cacao French Guyana VIII
PT‐IG‐00552 2 E 10 Pacala Guyane French Guyana VIII
PT‐IG‐00017 2 E 10 Pacala station Guadeloupe VIII
PT‐IG‐00554 2 E 10 SRT 24   VIII
19 2 E 10      
PT‐IG‐00057 2 E 11 Vino Purple forme Puerto Rico  
61F 2 E 15   India  
PT‐IG‐00019 2 E 19 Gordito New Caledonia IX
PT‐IG‐00047 2 E 20 Buet New Caledonia  
PT‐IG‐00029 2 E 20 Plimbite Haïti  
PT‐IG‐00048 2 E 21 Bacala 1 Haïti  
PT‐IG‐00413 2 E 21 St Vincent St. Vincent  
Cuba6 2 E 23   Cuba  
PT‐IG‐00542 2 E 27 AL 10   I
PT‐IG‐00042 2 E 27 Brazzo Fuerte Puerto Rico I
PT‐IG‐00038 2 E 27 Brésil 1   I
PT‐IG‐00564 2 E 27 KL 10   I
PT‐IG‐00565 2 E 27 KL 21    
PT‐IG‐00566 2 E 27 KL 40   I
PT‐IG‐00054 2 E 27 MP1 16H56   I
PT‐IG‐00033 2 E 27 Pyramide Puerto Rico I
PT‐IG‐00074 2 E 28 Oriental Barbados II
14M 2 E 29   India  
PT‐IG‐00077 2 E 32 Kinabayo Puerto Rico II
PT‐IG‐00085 2 E 35 St Sauveur Guadeloupe  
PT‐IG‐00560 2 E 35 Yam jamaïque    
PT‐IG‐00543 2 E 36 Cross lisbon    
PT‐IG‐00392 2 E 38 A 13    
PT‐IG‐00398 2 E 38 A 2    
PT‐IG‐00563 2 E 40 Sc.c 1.1    
PT‐IG‐00008 2 E 41 AIA 445 Nigeria  
PT‐IG‐00015 2 E 43 Igname rouge Guadeloupe X
Vu703a 3 F 1 Nawanurunkimanga Vanuatu  
PT‐IG‐00544 3 F 9 Cuello largo Puerto Rico XV
PT‐IG‐00026 3 F 9 Féo Puerto Rico XV
Vu696a 3 F 9 Nowateknempian Vanuatu XV
PT‐IG‐00076 3 F 13 Bélep New Caledonia XIV
Vu735a 3 F 13 Noplon Vanuatu XIV
Vu760a 3 F 13 Nureangdan Vanuatu XIV
PT‐IG‐00397 2 F 17 SEA 119, Toki    
Vu613a 2 F 17 Peter Vanuatu VI
Vu589a 2 F 17 Makila Vanuatu XI
VU590a 2 F 18   Vanuatu III
Vu554a 2 F 18 Nourembor Vanuatu VI
Vu567a 2 F 18 Letsletsbolos Vanuatu IV
Vu564a 2 F 18 Makila Vanuatu VI
Vu026a 2 F 18 Dammasis Vanuatu VI
a

In italic, ploidy detected using the percentage of polyploid genotype type on overall heterozygous loci.

b

Group of diversity from diversity analysis

c

Group of similarity used to select nonredundant accessions. Genotypes in the same group have a maximum of one allele mismatch).

d

Cluster of diversity identified by SSR in Arnau et al. (2017).

APPENDIX C.

Table C1.

KASPar assay description for the 129 high‐quality SNPs: Number of fluorescence type detected, chromosome and position on D. rotundata reference genome assessed by BLAST (E‐value)

SNP_ID # Fluo.type Chr. Pos. E‐value Sequence
S1_78464789 4 1 1066677 8E−52 GTTTCCCAATGGTAACACTTTCTGCAAAGCCTGAAAGGCACTTGACTTGACATTGCCAAG[T/G]GCATTAGTTGCCACAGCCCCAATTCTAACTATAGCTGCAGCAGCAGCTAACGGTGAAGCT
S1_166177831 4 1 3002321 2E−40 CATCACAAGCGAAACAATGCAAGATCACTGCAGCGCTAAACAAGACGATGAAAACTGCTA[G/T]AACTGCCCACTCTTCCAAAGATAGACTGCAGCAAAACAAAAGCCGCTTGGATGATCACAC
S1_73817882 5 1 5214076 8E−52 TGATTCTTCTTCCTCTTCATCTGCAGACTTTTTGGATGATGCTACTTCTTCACTAAACAA[A/G]CAACCTCTTTATCAGATGTCTTCTATCAAGGCTGAACTTCCAATCAAGTTGTGTGATTTG
S1_308276216 4 1 22422993 2E−54 GAAGCTGATTGAGCTGCTTGATATCGATCTGCAGTGGAGGATGCATAAAGTTTCTGATGG[G/C]CAGCGTCGTCGTGTGCAAATTTGCATGGGACTTTTACAACCATACAAGGCAATGATTTTT
S3_3283493 4 2 6259005 3E−51 TCTCAAGTAACTATTATGGTAGTAACAGATGATGCAAATGTGAAGGCAAGATAAGAAATA[C/G]CATACCTCCCCATCTGCAGCACAAGTAACGAGGGTCCGATCATCTGTGTAAGGCATGAAT
S1_15620393 5 2 21404053 8E−52 GATCAACTGCAATGCCAATGGTTGGTGCAAGTTTCTTGGGAATACCTGCTGCCTGAAATG[T/G]AAAACCCGTACAATATGATACAAATAAGTGGAGTGCCTGTGCTGCAGCTGAGAATTGAGA
S1_194680124 3 2 25133441 8E−52 TTTTGACTGACAGCCTTTAGTGAACTGCAGGCTTACATGGAAAACCTCTTGACCTCGCTG[C/T]GAGGCTGGATATAGCAATTGATGTTGCTCATGCTATTACATACCTTCACATGTACACAGG
S1_149013038 4 2 29033873 1E−43 CTCTCAGGTATGAATGGATGGTGCCCAAATGATTTTGAAACGCCCACATGGGTTTGTTGA[A/T]TGTGTTATCTACGCAACCAAATTGTAATAAATGACTAAATGTGGTAAATCTTTTCCCTGC
S1_54908604 5 2 31946176 8E−52 AACTGACAAAATGGCAATGCAATGCCCTTTGTCACTGATCACAAAGAAGAGAAGACATAT[A/T]AGGGTATTTTTATGGAAAAACAAAGATGGTCCATCTTATTATTATTTTCTCCTGCAGGGG
S1_220358774 4 3 562364 8E−52 GGGATTGACAAAAGCACAATCATTTACATGCTGCAGATTCGGCAGATTTTGCTGCAGATG[G/T]TGCTCCACCATCATCATTGGCAAAGGGGTAGCCTGATTTCCATGGGACACTTGGAGAAAG
S1_294347489 4 3 3182257 5E−48 AGGAAGAGTATGTTCTCCATCAATTACATTCTCATTACGCAACTTCAATATATCCATCAG[A/G]AAAGGGTTATTCTGGTGAGCAATACAATACACATTTTCTGCAGCAGGAATAGAACATATG
S1_213496700 5 3 6608590 2E−53 TATATAAACATTCCATTTTGATGAGAATGAGAGACCATTGTTGCTAGCATCCCATTGACT[A/G]CCATATCTGCAGGGATCTGTATGGAAAAAGTGCATGCATGAAAGACAAATATAATAATAT
S1_123502462 5 3 12211409 2E−46 ATAGAGAAAAGACCTGCAGAAGCAGAAGCAACACGATCATCCTTGTTGACATCTCGCAAA[C/T]GAAGAGAAAGCTTTTTGGTGAAGTTTGAGTGAGAATTGTAGAAGTCCTCCATGGCCATGG
S2_13394502 4 3 12986600 1E−50 GTCTATTTAGCATGTCTTAGTTTCTTGGTGATGATGACTGCAGTTGAAGTCAAAATTTGA[G/T]GATCTCTCATCTGAACATCATCATGCTTGTGAAGAAATGAATAAATTGCAAGAAAAGCTG
S1_212477984 4 3 18808497 2E−53 TTGTAGTCGTAATCGAAATCGTATTCTTTGTGGAGTATTATTTTAGGTGGAAGATGTTGA[T/G]ATTTCTGAAAGAGTTCAGAGAGGATTAGAATCACCAGCCTACTGCAGTGGAAGATATGTA
S1_40517926 5 4 2442039 3E−50 TGGTTAATCGCAGATGGGGCTTGGAAAGACTCTGCAGGCGATGTCTTTGTTGAGCTATCT[G/A]AAGGTCAATGGCATCTCAACGGGGCCGTTCTGTGAGTCTTGGTTTATCTCCGATGAGCTT
S1_341690721 3 4 4172027 7E−46 TTGTGCATTCCTCCCTGCATCTCTTGGAACTGCAGCCTGCCCACTCCATCCTCCCATGCT[A/G]CTCTTGTGAACCATCTCAACCACTCTTCTTCTTTCTCTCTCTCTTTCTCTCTGTTGTCTC
S1_94822591 5 4 6518043 4E−37 TCTGATGATCGTGCTTCTCTCATCAAATGTTAGATGTTGTTCTAACTCTTCAAGCAATCA[G/A]GAACTTATTTGCTACTATGAGTTGTGACTTATTGTTGCTGGTCACTGGATACTGCAGGTC
S1_223059854 5 4 9590872 4E−49 GTTGCTGCCAGCAATCATGAGACCATTATGAGCTATGTTGATGGATGGTGAGGTTCGGGA[C/T]GTCTGTGGATAAGCTGTAACTGCAGGCAGGCTACCTACCATTGGAGAAAATTGGCCAGCT
S3_1940455 4 4 11602024 4E−49 ATGCCTGAATCTGGAGGACAAGCTACTGCAGTGTGTCAAGAAATAGACATACTTGAAGAG[A/C]ATTATGAATCAGAACAGTTCCAGGCCGGTAATGTTGAGTTCATGCTTTCTGTTCCTTTCT
S2_59089982 4 4 13022373 2E−47 CTCAATAATGGTGGACAAATGTGCCTTCTAATTCAGAAAAAAAATGTCTACTAATTACCT[C/G]TCCAGCTCTGTCATTTACTTGTTATAGGATGACATAATTGATGACTCTGCAGAGGATGAT
S1_29508975 5 4 13956140 8E−52 TAGTGAGCAGTTGAGATCATTAACGAGCATAGAAGAACTCACTGCAGAAGCCACAAAGCC[A/T]GAAGTCAATGGAGTTTCTATGGAACATAATGACGAGGAATTGGGAACACTATATTGCACT
S1_78099239 4 4 19009721 2E−33 CAGGTTTTCTTTTCAATTGCAAGAGACATCAAGCAAAGGCTTGCAGAAACCGATACCAAA[C/G]CTGAGGTAGTATGCTTATCATTTGTGATAAATTCAGTAAACCTGCAGGCAATTAGTAATG
S1_98182899 5 4 25059970 2E−47 TGTGCACAATGCTATGACCACACATATGGTTTCAAACAGACAAGGAACAGAATCAACAGA[T/C]ACACTACTTACAGTGACAAGCTCCGATATCAGTTGCGCAGACAACCCTGTTTTCTGCAGC
S1_13376874 3 4 25692581 2E−40 CTTGATATGTCTGTATTGGGTGTCATTTCTGCAGTATTATTTGGTCCGCAAAGGTAAATC[A/G]ATAGAAATTGGCGTCATTTAGCAATATCTAAACTGTATTTGAATTTGTAGTTCGAGCATT
S1_50863270 5 4 27382503 2E−54 GTGGCTCTCAAAGACTGAGGAAGTGCGTGAAGATAGGGCTCATTGGGGTACCAATATAAC[T/C]GGTGATATTTATGGTCAGGGTTGGATCAGTGAAATGTATGGATATTCATTTGGTGCTGCA
S1_30240813 5 5 4418552 4E−43 TCAGAGAGTGAGTCACATAAAAATAAGATTCATGTTGCATTGTGATGCCCCCTGATTCTT[A/C]TTACTTGCCCCCAAACGATAAGGACATCTTTTCTGAAAACTGCAGAGCCTAAGGAAATTA
S1_284257251 5 5 8809466 2E−41 AATTTTCTGATCTGAGTATTGGTCAACAAGAATCCAACATAAAACTCAAGTAAAATGCAG[T/C]AAATTACAATTGTTACATAATTGTTTCTCCACATAACTTGCTAATAATTATTTCTGCAGA
S1_172013713 2 5 15915339 5E−48 AACGCATGATACTCAATGTGTTGTTACTAATTGAATCTCAATTAATATGACCTGCAGTTG[C/T]TTGAATTTTCATGCTATGTTTGTAAGGCCTCTAGTGTTGCCAAAACCTCAGACATCTTCG
S2_46046547 5 5 18636385 5E−54 CACTGCAGGGGCTGCTTCCTTGATGATGAACCCAAAGAACTCTATTTCTCAAATAAAGCG[C/A]TTCATTGGAAAGAAATTCTCTGACCCGGAGCTTCAGTCTGACTTGCAGTTATTTCCTTTT
S1_161404508 4 5 19741536 5E−42 TGCTAATCAAAACTGATGCCTCTGCAGGGACCAAGTTGATCATTGAAAGAATCAGGGATT[A/C]TCACTTTCTATCACCTGTAGAGTACTGCATGTACATAAGTCACCATGAAAAGCACCGGGC
S1_86749745 5 5 20784276 1E−48 CCCTTCTGATATTTGCTTGGAGTTGAGATGTCTGTTGAACTTTAGCTGGAAATTTTACAG[T/C]CAACTCTATGAATTGTGTTTTCTGATTCATACGCACATTTGTGATTTTGTGCCTGCAGAG
S1_349430697 5 5 22888769 8E−52 TCCATCCTGGGAAGCACTGGCAATGGTTGATTTCGGTAGGCCAAGATTTGGAGCCCAAGC[G/A]ACATCTCTAACCCAATCGGAATGCATCTGCAGGGCAGGAAAGCAGTCCATTTTCCAGCTC
S3_43353096 5 5 24048351 8E−52 TGATGGGGGGAAATAACCCAAACTGGTGGAGTTCTATCAGCAACATGAAGCCAACTGCAG[G/C]AGATGAAACCTCTCTTCTTTATCCTTTTCCATCTTCTTCACCTCTCTTCCATCACTACTC
S1_51060666 3 5 26180154 1E−42 GAAGGAAGGCAGCAGCCTTTCAAACCTGCAGATGAAGTCGCCACGTCTTTTGAAAATTGC[A/T]AACCCAGCCAATTTTGCAGCTGCTAGTTTATAGGTAATGATAGATAGTTATCTAGGCTAT
S1_126396048 5 5 27405531 4E−49 CAACTCTGTCGTAGGAAAAGAAACTGACCCGTCCATGATCAACAAAACAGAATTTTAGAG[G/A]AATGCCAAGGCACTTCTTCTCAAGGTTTTCTAATTCTGTTCTTGAGGGTGGCTTGTCTCT
S1_189523417 3 5 30765836 4E−49 TTTAAAGCTTGTGACAGCGAGTTTGAAGACCTCTGCTGCAGGCATGCATCCAGCTTGCGC[A/G]GCAGGAACAGCTACACCGGCTGTTACTGCCGTGGCGCTTTCACTCAGTGGACTGAAAACA
S1_210690510 5 5 31577924 2E−53 ATCACCTGTCATTCTTAGCATACGCTGACAGCATCCAGCAATAAGCCATGATGCTGGGCA[A/T]GATTCCCAAGGACTGGATCGCTTGAAACTGCTCTTTACTCTCATCTACAAGCCCTGCAGA
S1_199164936 5 6 10295314 1E−37 ATTATTATTATTATTATTTCTTCTTCTGCAGTAACGGGTCACATTGCTTGGAAGAAGTTG[T/C]TGGAGCTTGAAACGCAAATAATGATACGAACACAGCCTCAGCAATGCACGATTACTCGCT
S1_282211588 5 6 17925427 2E−40 CACTTTGCACAATTATCTGCTGTAGAATGTTCTATTTGTTAAACCTGCAGATTAGGAAAT[C/T]CTAAATTCTATCTGCTGTTATGAAGTCCTGGTAGTATGTACAAGCAGGTTGATTATACAT
S1_116006917 4 6 21845952 8E−52 TTGTCAAGGAACGATCCCTTCACCTCCTCGGAGAAGAATCGCCCGAACACACGAGAGATG[G/C]ATATGGCCGGAACCTGCAGAGGAGAAAGCGAAGCCCTAACCCTGAGGTGCTTCAGCACAG
S1_45761963 5 6 27083504 1E−50 TTTTATCATGAACCGATCATCCTGAGACAGGTAGAAGAAGCTCCCACTCTTCCCAGGGGA[G/A]GATAATTCCCTCAAAGCATCACTTCCACAGATCGTCAACATATAATCTGCAGCATCAACC
S1_289563297 3 6 31210760 2E−53 AATGAACCATATCATAATCAACTAGATGTGAAAAAAGAATATTTGCACAACTGCAGGTGG[A/G]CAGGAAACCAAGGGGCTAAATAGACACACCTCATGACCTAGTTTCACACCCATCTCCTGT
S1_210284742 5 6 32022412 1E−50 AAAAGACCCAAGGAAATGACACAGCAGAACCATTGTCCCATTGGACATTTTCAACTACAT[T/G]CAAACTGCAGCATAAAAACCAAGATTTATATCACATATCCACACTAGTTCAATGAAACAA
S1_244041680 5 7 891269 4E−49 ATCAAAACATCGCTCTCTGCAGCCAAATCACAGACGTTAGAGAAATATTTATAGGCGAGT[G/A]ATGGCCTTGTTGTTCTAGAATGGTACAAGATTGTGCAGCCAAAGGCTTCGAGTCGTTTTG
S4_1831247 3 7 3180748 5E−48 GACAAACCAGAAATCTTTCCTTTCCATTAAGGAAGCAAATCCACCAAGGAGAACTGCAGT[T/A]GCCCAAATGAATCCGAGGGCTCCCAAACCACTGGCTGCCTTTTCAAGGATTGCCAAGCGA
S1_156520859 4 7 10367143 8E−52 TCTTTTACTGATATAAAGAGACTACCAGAATCCATTTGTATGTTGGTTAATCTGCAGACA[C/T]TGAAACTCTATTGTTGTTATAAACTTTCCGAGCTTCCCAAGAGCATAACATACATGAACA
S1_109907043 3 7 15658966 2E−53 AGGAGAAAAAATTCATGTGATGTCCTCCATATCTCAGCCTCGTCTCGGGTGGTCAAATGA[A/G]ACTGCAGCAAGTATTGGGACAATTGCAAGAATAGACATGGATGGCACTCTCAATGTGAGT
S1_365833705 3 7 17541018 1E−50 ACACATCTTCACCATTCAATCACTTTCATCCAACTGCAGCAACGTCTCAACAAGATCTCC[C/A]TGAGCTAGGTATCATCAATTTTCTACAAGCAATCTGCATTGGAAAGTGATCATGGACCGA
S1_215375978 5 7 17828247 4E−49 TGCTGTGCTCACGCCGATGGATACTGTGAAGCAGCGGCTGCAGCTTGAGAGTAGTCCGTA[C/T]AGAGGGGTGGGTGATTGTGTGAGGAGAGTGATGAGGGAAGAGGGGGTGCGTGCGTTTTAT
S1_5956960 5 8 1990861 5E−35 AGATAAGCACTTTGTATCTTGCTATTTTTGTTGCTCTTTATTATTGATGTGCAACAATGT[C/T]CCCAACAACCACACACACACACACACACACAATTTTGTATTTTTATGTTAGCTACTTCAT
S1_142832546 5 8 4073006 4E−49 AACTAACATGAATTTTGGCTCAATGATATAAGATTAACAACAAAAACGTTTTTGCTGCAG[G/A]GTTCTTGAACAAGTTTGATGAAATCACAAAATGGATATTGAAAGTTTGTAAGAATGTTAT
S1_102926938 3 8 5771893 3E−50 AATCTTTGATGACAAAGCTGCAGCTTCTTTTCATGCAAAACAATAAAAAGTATACCGGAT[C/T]TGATGTGATATGGGATGATCAGATCACTATACTGAAAATGAAACCTGTGCCAGCTTCTCT
S1_29231327 5 8 6476874 1E−48 TCCAGCAAATAGGTGGGGAACATCATACAACGGGCACGGAATGTTCATCGAAAATGCACC[C/T]GCTAACATCGTGGCCAAAGCTATGGAAAACATTGCAATAGAAAGTATAACCTGCAGCTGA
S3_54678463 5 8 7748403 4E−56 TCAAGAGCTTCAAGAAGAGGAAAGAAGGATAATAAGTGAAATATCAGAGTTAGAGTGTGG[G/A]AGACCTGCAGAAGAACAAAGAGTTTGTGTTACTGAAATGATGGATTGTGTTATTGATCCA
S1_208236889 2 8 9479207 4E−49 AGGAAGGAAAGAGAAAGAAGTTTCTGCTGCAGTCTCAGCCCCTTCTTCGAATTCTTCTTG[T/C]AGTTTATCAACAATCACTCAATCATACGGTGACAATGCCACTCTTCAAATAACTCAGCAT
S1_169356495 5 8 12133164 8E−52 ATACAGAAATTATCAGTGTAATATATTACAGAAGTAGAATGCTTCATCACCAGAATCTGA[T/A]TTTATATGAAAACACACTGACCTCTTGATGAAGAATTAGGCAAAACAGGGAGTCTGCAGA
S3_35309770 5 8 19352521 2E−47 CATTTCCAAATTTCAGAAAATAAATCGGTTTCCATAAGATTTGAGGTACAAATAGTTTCC[A/G]CAAAGGAGGTTTATCTGATACAACACTGCAGCTTGAATATGGTAAATAACTAGTCTCACA
S1_235419648 4 8 23125222 5E−48 GAACTTCAGAAATTGTTATACGCTGCAGATTGCCCAAAATGAAGCATTCATTAGACATAA[C/T]TGATCCCATAAATCAGCCCAGCTTCTTTTATGTTGTACATAAAAGTTCAATTAGCAAGAT
S2_30875426 3 8 27554786 1E−43 TTCATGTTTGGAAGATCTAATGTCAATTTAGATGTCATATGGTTTAGTTTTGTATTAGTA[C/G]TTTATGTTGTATTATTCCAATATAATCCAATATCATATCTGCAATTCTGCAGCAGGTCTT
S1_71285261 4 9 1039396 2E−53 AGAGAATGTCCGGATAAATCCTCAGAGAAGACTCCACCTTCGCAAGGTGCCCAGCTCGCA[C/A]GGCCATGAAGAACTCGAGCTCTGTGGGGTTACGGCTGCAGCTCAGGCCATGCCCCATCTT
S1_352413390 4 9 3168774 2E−53 CTTTCTTGGCAAACAGTTCTGCAGTAGATTTGAAGTCAGCTTCTTCTACAAGTCTACCAA[A/G]AGAGGATTCCAAGTCAGTGAATGGATATGATCAATTTGCATGACTGCTTGAAAAGTCGGG
S1_58454213 5 9 3570970 2E−53 TGTCCTGTGGCCTCATCGAGGAGCCATTTCTCTAGCATTGATAGAGGAGGGTTGTTATGG[C/T]TCTCAACTCTCTCCTTCCCTTCAGAATCACCATTGAAGATTGCTGATTCTGCAGATGATT
S2_9856110 5 9 5066064 1E−50 TGGAAAATTAGGTATCCCAGTTACCATGGAAATCGCTAGTGATCTGCTTGATAGGCAAGG[T/C]CCAATTTACAGAGAGGATACTGCCGTGTTCGTTAGCCAATCTGGAGAAACTGCAGATACC
S1_157448006 5 9 7773168 2E−46 TTTAACTTTTGAAAGGCTGCAGGGTATGAAATCACAGGCCCCGAGCCTGCTAATGTAGAT[C/T]ATGGTGAAGAAGCTGCATCTGAGGATGAAGAGGAATCTTATGATGGACATGATGCAGATG
S3_14699960 5 9 14771343 1E−43 AACTAAAAGCAAACCAAAAATAAATTCCTCTGCCGTTAAATAACCTGCAGAAAAAAATAG[C/A]GAAATGTGACAAGGAGAATATTTACATACCTTCGCCTCCATGACACTTCTTTCAGAGTTC
S2_58843160 5 9 17855377 5E−54 CAATTCATTTCAGGCATAATGTTATCAAGTAATGCATATTCTACCAGAAATGAACTTTAT[G/A]TGGAACATCATTCTTGACATTTGAAGAACTGCAGTTGATTACAAGTGAATTGCTTATAAC
S1_108505610 5 9 23138342 1E−48 GGAAAATATCCAAAAAGCAATAAAAGCTGCAATGGACGCAGCCAATGCCGCTGTTTCACA[A/G]TCGAAGGCATTCTGCCTAAGCCGCGTCGATGTGGGTCTAGACACCACTGCAGTTCGAGAA
S1_96409136 4 10 273696 9E−45 TGAAGTTTGATGATTCAATTTACCAATGATTTTCATGCACAGGAGTATATCTGAGAATAA[C/T]GAGCAAAATGATGCAGAGGTTACTTCAGCAAGAAATGCTGCAGAGAAGGATGTGACCAAG
S1_75907479 5 10 1295979 8E−52 AATGTCTTGACAGAAGCCATGAAAAAGCTCCACAAAATAAGTCCAAATTGAGATTGGAGA[G/A]CATACTCTCCACTGCAGCAAGTCTGTACCCTGTCTATGTGACTGCTGGAGGGGCTCTTGC
S3_17911820 3 10 2220748 8E−39 AGTAGTTTCTGAACTGGTACTTTGATCAATACCTGCAGAGTTAGTAGCAGTAGCAATAAG[C/T]GAGGAGACCTCAATATCTTGCACATGATCACTCACCGAAAATGGAACATTATCAGCATGA
S1_282032037 4 10 5002351 7E−40 GATTTACAGGACAATTACATTTCAGATTTCCATAATGATGGTAACTACAAGAATATTATT[C/A]TGTAAGCACCATGATACTTGTATCCATTACATGCATTGAATCAAAAGAACTGCAGTTTTA
S2_66969333 2 10 5976818 2E−52 TTTAACTGTATTGGCAGTGTCTGCAGACAGAGCTACGTCTAGCAAAGTGAGCAACTCATC[A/G]TCTGAAACAACTCCAATCTGTAAAACAGAGCAAGTCACAGAAAATTTATACCAAGATAAG
S2_53836832 3 10 10477461 5E−48 GAACACTTTCCTGGATTGAAAATTATTTCTGCTGCAGATCATCGTTTCTTTGGCACCACA[C/A]CCTTTTTCTAATAAAATATTCTTGAGCTCTTTCTCATCTTTGAGATGTGAAACATCTAAC
S1_21668183 4 10 16038685 2E−53 AGCTGCAGAAATTACATCAAGGATGGATCTCAGAGCTTCCAACCTGCATAACATCTCATC[G/A]ATTGCAATGTCATCAACTGGAAGGTGCCCTATGATCCCTGCCACGGTTGATCTCTCCTCT
S1_333790152 5 11 2622759 4E−56 TGCTTTGAAAGGCCAGCATGATCTTTTTTCTATTTTGTGTTCTTGCAATGAGTTCTGTCT[A/T]ACATTGCTGATTTTTGTTCTTGTGCAGTCTGCAGTAGATTTTGATGATAAAACCAGTTGG
S1_238131512 2 11 14447426 1E−43 AAATATCGAGATGAATTTCTGGGAACAAGCCTGCAGTTAGTTTGAGGATGTGTTTGGAAT[A/T]AGTGATCAAATGGCATTTGAGAAGCATAGTATGTAATTTTTCCGTAAAAAATGATCAAGA
S1_38918393 3 12 17162245 2E−46 AATCCCATCAAATTTGTCTTTCAAATTTCTGAATTTTTTCCCCTATCCCAAAAAATTCAG[C/G]CCATCAAGAAAATATGAGGCAAAGCATAAAACTGCAGCATAATCTCTATTAGATCTCATC
S1_102041015 4 12 24327364 3E−45 GCAGTATACAACTTCATTACCATGTTAGACCAGCAAATCTCAGTCTGATTTCTTCCTAGC[T/C]AACACACACACACACACACACAAAAGAAAACTTCAATCTCTGTTTGTTTTCTGAGTGCAT
S1_65460849 4 13 1824700 1E−42 GCATCTGCTAGTTCAAAATAAAAGGCAAGCATGGTATCACTAATACTGCAGAAAAATGAT[A/G]GTGCATAAATATCTAATGGGAAATGATGCAGCGAAGATCAATAACTTAGAATGAAATTCA
S2_23695000 5 13 8252985 8E−52 TACATCTGGGGGACTGCAGAGCTTTGAGCATCCACTGAACCTGGTGAAACCAGTGCCCAG[G/A]GTTGACAGCAAAGGGCAAGTATGTGGTGCCAATTTCAAAGTTGATGCCCAAGCTAAGAAG
S1_279910017 5 13 27313051 2E−53 GGCAGCTTCCAGGGCTCGGGAGCTACCAAGATGGAGTTTGCTATCCACGGATTTGTTCCA[C/T]GAATCTGTAGCTCCATCGTATACCCTGAGCCTGCAGCCGTCTCTGCAGTCCAGAGCATAA
S1_297529258 3 14 3933202 7E−46 CTTATCTATGGCCAGGTTCATCAACATATAAGGCTTCAATGGTCTCAAATTTCATCGGTG[A/C]TGCTTCTGCTGTGTGTACTTTTACTTGTCACTTACGCCAAAGTAACTGCAGCCTGCAGGT
S1_252699764 5 14 12391182 4E−49 ACTACTGAATAAATGGAATCAACTTTATTTGCTGCAGTTGGACTAGCCTAAGAGGAACTA[A/T]GTGGCTTTGGAAGAGTGTTGATACTTGGGATTTTATATCAATGTGTGAAAATCAGTGACT
S1_64347285 5 14 12868762 1E−50 GGATCAAAGTTCTCAGAGATTATTGATTTTGAGAAGTCAAGATATGCATCAAATCCGTGG[T/G]TGATCTCTACTGCAGCATTTACCATCCCTTTCATGTCAATTGACCAGAGAGGTGTCAGAT
S1_108652759 4 14 13736821 3E−45 GAGGTAATCTGTGACTTGTCCATATTACTGCAGAACAGCAATTTATTGCTGATCTTGGAC[T/C]ACTGATATCCAGCTTTCCCCAGATTATGTCATATTGCACCCAGCAAACCAGTACAATTTA
S2_68074878 5 15 320619 6E−47 CACCACCATCACATCCGCACCATTGTTGTCCACCTTTGAACCCAGTGTGCCTGAGAAGGA[C/T]ACCTGCAACATATCCGGTGATGACTTCTGGAAAGTTGCCTGCAGGGCAGATTAATAAAAT
S2_15031349 4 15 2754888 3E−50 TAAGAGAAAAATAGCTTACATTATCTGTGTCGACCTCTGATATAATCTCTTTGATTGTAG[T/C]GGCATCGCCCATTCCGTATTTCTCCATGGCTGATTCCAACTCATCTCTTGTGATATAGCT
S1_361168697 5 15 3199245 2E−54 ATTACTAAGATGCAATTTATAAACATATCTGCAGTTCATTGCTCTTGAAATCTCTGCACA[A/G]GCAGAAGAAGTTGAGATTTCCGTAAAAAAGCCTGAAAATGGTGGTAGTGCTTCAGAAGAG
S1_42812024 5 15 3456280 8E−52 GAACATTTTATAGTACCTCAAGGGGAGTGCCTTTACTGTCAAGAGGAAGGGGAACACCAA[C/T]TCCGCATCCTCTCGGAAATCTGAAGCAGCTAGGCCTGTCATCAATGGCTGCTGCAGTAGC
S1_31917523 5 15 3839679 2E−54 TTTACATCTATTGAACTCTCTGCAGGTTGAATCTGAAATATTTTGCTTGCATGGTGGTTT[G/A]TCCCCTTCTATTGAGACCCTTGATAACATACGCAATTTTGATCGTGTTCAAGAGGTTCCT
S1_16064479 5 15 5173749 1E−50 GCTCAGAAGAGCTCCATATGTAAGTTGGTTCTGGACTACCTCCGGGAGACTATTGAAGTA[A/T]CTCTCTGCAGCATCTACCCCCTTGACTTTGGATATAAGATCTATTCGTATTGCATGGGTC
S1_116148629 4 15 6642827 8E−52 GCAGGAATACAAGAGTATTGCCAGAATGAGGCTGGTACATATTAGCCAATCTCCGAATCC[A/G]AGTCATTTTACAGTTCTTGTACGTTCAATTCCAAAATCACCTGAGGAATCATACAGTGAT
S1_359692995 4 15 8357684 4E−49 GTGGCTGTCTAATTTGCAGTACTGCAGAAGTAAATATGAAAAAACATGAAATGATAATCA[C/T]AATTCATTGACTAACCTGTGCAGATCTAGAATAGTAGATGTAGGAGCGATTCACTTCATC
S1_290181714 4 15 9566725 2E−54 TCCTCGTATGGGGCCCAAGAGGGAACTCAAGTTTGCTCTGGAATCCTTTTGGGATGGGAA[G/A]AGCAGTGCTGAAGATCTGCAGAAGGTTGCTGCAGATCTCAGGTCTTCCATTTGGAAGCAA
S1_282323032 4 16 4930222 4E−49 ACTTGGGATTGTGATGATCAGTGTGTCACTATTTTGTGCTTATGTCCTTTGTCAAAGAAA[C/T]CGTAATAGTTCTGATTCAAAAACAAATCAAACTGCAGGTATTGCTTTCATTTGGATTTGA
S1_262914420 4 16 8184285 4E−49 AGCAGCACTGTCAGCAAGAAAACTATAAACCTGCAGACGAGAATATAACTAACATCACCA[T/A]GAGACTTCAAAACAACAATTTTAGTCAACAAGGTTCAAGAAAACAGAACAAGACCTTGCA
S2_63978772 5 16 14079687 2E−53 AAATACTGGTCATAAATATTAGATCACACATATGTGCCAGTGTGGTCAACAGATAATGCG[C/T]TGCTGCAGTAAAAGAAAGAACTTCAGCAGTGTGGCTAACAGACCTATTGTAACACTAGCA
S2_42975314 5 16 19099744 5E−54 CTGGGCCACGAGGGGAGGGCTGGTGAAAACTGACTGGAATAAAGCTCCCTTCACAGCCTC[G/A]TACCGAAACTTCACTGCTGATGCTTGTGTTTGGTCATCTGGCGCCTCAAGCTGCAGATCA
S1_349651036 4 16 22229815 5E−54 GCTTTGGAAGATAAAATTCAATCGAAAGTCAGAAGAAGCAAGCAAATTACACAATTCGTC[T/A]GCTGCAGTTCGTCCTCTAGTTCATCCACAGGCCCTTGGATATGACAACTCCCTTCCAGGA
S1_131821504 4 17 11148474 6E−41 ATCACTAGTACAATGCAACATGACAAAACCTGAAGTGCTATACAAGTGACTGCTTATTTC[A/G]AACAGGACAAATCTGAAGCATAGCTTGTAATACTTCTGCAGAAAAATAATGGCAAAGTTT
S1_305511589 5 17 13065352 5E−48 TGATGTGTAGATATGAGTGGAACTGATTTTTATAACTTTATTACAAAGCATTATTTTTTA[T/G]CCATCTAATGTTCTGCAGATTAGGTGGATGCATTTTTTTTAATAATTTTTTTAGCAATTT
S1_162377692 4 17 14256863 2E−53 ACAGAAGAGAAAGCTTGATGAGATGTATGATCAGTTGAGAAATGAGTATGAGTCAGTGAA[G/A]CGATCAGCTATACAACCTGCAGGCAACTTCTTCCAAAGAGCTGACCCAGACTTGTTTTCA
S1_7095019 5 17 18481610 3E−44 CACGGGTGAGCCATCAGTAGTACCTGATGCCAATGTTGAAACCATCGAATTCCCTTTCAC[C/T]GACTGCAGGTATACGACCTATAGAAAGGTTGCGGAATTAGTGACTACTTTTTTTTTTTGT
S1_164752250 5 17 19197258 1E−50 CGTTCAACAGCAAACTGCAGAGAACATCAATCAGATGATCGGAGCGAGCTCATCAGCAGA[T/G]AAAGCAATTGATGATTGTGTTTCAGGTTTTGATCCAAGCATCAGTGAGGACCTGTTCCAG
S1_104430414 4 18 7027601 6E−47 GGTACACACTGCAGACCTGGAGTCTTTGTTCCCTCTTATAACATGAGAGCTTGTTCTTCT[T/G]GTCATATACTTGCTGAAGCTTTTGCAACTCTTTTTACATCTGAAGCCAATTTCTTTACCC
S2_23048737 4 18 14902976 8E−52 TGATTATGCTCGCCATTTCTTGTCCTGCTGCAGTAGAATGCTTGGCCTGTCTTATGAATC[C/A]AAGAGAGGCTACATTGGTTTGGAGTACTATGGCAGGACAGTGAGCATCAAGATTTTGCCT
S1_259660238 3 18 18609390 8E−52 TTGTAGGCATCAATCTCCAAGAAATCATTATCTATTTGATGGTGTAAGTTCTTGTTGAGG[C/T]TGATAACTGCTGCAGCCTTCTCTTAGTTGAAGCATAACCTTTTTTTCTATTTTCCCTTTT
S1_170367846 5 18 21499070 8E−52 CCATGTCTCGGCCTGCAAACATTGGATACCAAGAATTATAAGAAATGGAAGTGAACGGAT[G/A]CGATGGTGAAAACATTTCAGTACCTGAAACTCTGCAGTAAGCACTCTTCTGAATGAGTTC
S1_31156518 5 19 1787283 2E−53 AGGACGTCCACACTTGATAGTGTTTCCATTTGCATCATGTTCATTCCACATGAATGCCTC[T/C]GGAGACATGTAGTTCAGTGTGCCAACCTGCAGTTTAGTAACATGAAATGACATACTACAA
S4_1766101 5 19 2912318 2E−41 TTCAGATGGATCAGTCGTAGATTGAGCTTTGAATCTCTGAACAAGGAGCATGACGCATAG[G/T]AAGCGGAAGCAGGAACAGGAGAAGGGAAACATGATCTGCAGCTGCAGCACCATCATAAGA
S1_130386685 5 19 3858772 2E−54 AGAGTTGATTATAATTTTATGATCACATAATAATTGAACAAAAACTGCAGAAACAATACC[A/G]GCTAAGTCATTCAGCACCAAATTATTTAGAGGTAAGTTTTGATTACCTTCAACTTTCAAG
S1_66878388 4 19 4662624 6E−47 AATTGTGACATCAGCAGCAATAAAATTTTTGAAAAACCACCGCCCAAAAAATTTATCAAA[A/G]AAAATTATCACTGTCTTGTGTCAAACATCATCACCAACTCCAAACCCATACTGCAGAAAA
S1_48170206 4 19 8029141 3E−50 ATTATACAAACTGACAAACGTATTCTATATCAAAATTGAATTACAGGAGAGATTGGTAGG[A/T]CGTAAAGATCCAAACATAGAAATCAAGATGTTAGAAGGATATGATAGAAACAACTGCAGA
S1_85054393 4 19 9504285 1E−50 CACATCTGTTTTCTTCCTTGAGATTTCTACAATACAACCTGAGAAGTTCTGCCAAAAATT[G/A]TCTCTTTGCGGGGTAAAATATTTTTCTCTCCATAGGAGTGACAACAATATCTGCAGGCTT
S1_16428287 5 20 725184 8E−52 CAACGTACTGCAGACCCTGTCAAAATGCCATGAATAGGTTATTATAGTTATGAACATCTC[G/A]TCAAACAAAGATTTGCAACCAATGGTATGAGCATTCAGATTTCAAGACTGTCCAAAAAAC
S1_43488339 5 20 2904117 1E−50 GACAAAGTCATCAAGTAATTGCCTTCTCATGACAGATAAATCTGACTGCAGGGATGTGCC[G/A]AGCTTATAAGGCCAATTAGACAGCATAAAACAAATAGAAATGCTGGTCATTGAGACAATG
S1_114295977 4 20 6774493 1E−48 GCTTGGCATGTTTATGATGAGTATTCCTTTTGATAGCAGTAAAAATAGTACTTATTGGCT[A/G]ACTTGAAAGATCTAGAAGATTGGTTGGACTGCAGTTATATTTTTCTCTAGCTCAGCTAGG
S1_30976509 3 20 8500327 2E−54 ATTGTTTGGCTCCATCAATATAGGAAGATATCTAAGGAGTTACTGACTTGTAATATGGAA[C/T]CATAACTGACAATATAGCTTCAACATGATGTACTTACTTCGTGAATGCCTGCAGAGGAGG
S5_17384653 5 20 8742611 1E−36 TTAACCATTAAAATTTATTTACAATGTAAAATTTCCACAGAGAGTAGGTTCTGTAATCTC[C/A]GATTTTCTTCGACAAAATCAGCGGATTCATTCTTTATTCTGTTTATTGTGCTCTGCAGCT
S1_160073245 4 20 9514825 1E−42 TTCCGCCTGATGCTTTAACATATAAAAAGCTGCAGATGCAGAAACTAAATATCTGACATG[A/G]TCAATAGTTAGATTATGACTTTCAGCTTGTAGATTACCAATTACCAGACCACCATAAAGC
S2_46633476 3 20 9808073 1E−50 AGATCATGTCGTGAAAATACCTTCAGATCCTTCTCCTCCTCCCCCGTTTCCCTCGCGTCC[T/A]CCAAATTCGCCTTTCCTTCCTCCACCACCTCCGCCTCCGCCTATGATGAGCAGCAGTGGA
S1_231563096 5 20 9944570 4E−49 GTTGAGTTTCAGAGTTTCTGCAGCTCCCTTTGCAGTAGTTAGGCAGTAACCCATCACCTG[A/G]AAAGCATATCGGTTTCGATACTGTATGAACTTCCACAAGAACAGCCCTGCCATTGCTCCA
S1_60794376 5 20 10523563 7E−46 TTCAATACTTGTTGTCATTATAACATCAGAGATTGCAATTCCTTCATGATGCCACTGCAG[G/C]ATCCCCATATGGACAGCTGGTTTCAGTTGTCATTATCCTATTGCACTCAAATCTTGAAAT
S1_199666449 5 21 240474 4E−49 AGTTCCACTCATAAAACCAGTTCTCTACCCAAGCGGTAGCACGAATTCCTCAACAAAATT[A/G]TAGAATTTCCACTTTGTTCTCCGGAAGCATTGCCATTGGTGGTGCTGCAGTTACTCCATA
S1_257000706 2 21 928527 2E−54 TACAAAATGAGATGGTTCACAAGCTGAAGAACAAACCTCATCTGCAGTAAAAAGAAGAAC[A/G]TCACTGTGCCTCAAAAAAAGTTCAAGAGCTCTCTGTTGTTGAGGCAGCCCAAGCAAAAGA
S1_171090323 5 21 2104297 1E−50 CGAGGGAAGGAGAATGTGGAGCTACTGATTGCAGATATACTTATTGGTTCAGCAGACTAC[G/A]TCCATCATGACCCCTATAATACATTGTTCTGCAGGCAATATCAGTGTTCTTCCTAGGACC
S1_25663258 3 21 2772381 8E−33 TGTGAAAATTTGGCTGTTTTGCTGCAGTTTCATCATATTGCAAACTAATATTATAAACTC[A/T]AAAAGTTTTGTCCACTATAGAACTAGTGTCAGCATCAATTTCCAATATTAGCCAGGCTAA
S1_371159620 4 21 3932249 1E−50 GTCTGCAGGGGAGGTCAAACTCGAGCAGGTTTCGCATCAATGAGTGTAGCACCTTTATCA[C/T]CAGCATTTATTCCGGCTGCTGCCGCCACCACTCATGTTGGACTGCATCCAACAATCCTGA

Cormier F, Mournet P, Causse S, et al. Development of a cost‐effective single nucleotide polymorphism genotyping array for management of greater yam germplasm collections. Ecol Evol. 2019;9:5617–5636. 10.1002/ece3.5141

DATA ACCESSIBILITY

Plant materials may be requested at the CRB‐PT of Guadeloupe http://intertrop.antilles.inra.fr/Portail/accessions/find/11. KASPar primers sequence is available in Appendix B.

REFERENCES

  1. Arnau, G. , Bhattacharjee, R. , Mn, S. , Chair, H. , Malapa, R. , Lebot, V. , … Pavis, C. (2017). Understanding the genetic diversity and population structure of yam (Dioscorea alata L.) using microsatellite markers. PLoS One, 12(3), e0174150. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Arnau, G. , Némorin, A. , Maledon, E. , & Abraham, K. (2009). Revision of ploidy status of Dioscorea alata L. (Dioscoreaceae) by cytogenetic and microsatellite segregation analysis. Theoretical and Applied Genetics, 118, 1239–1249. 10.1007/s00122-009-0977-6 [DOI] [PubMed] [Google Scholar]
  3. Asemota, H. N. , Ramser, J. , Lopez‐Peralta, C. , Weising, K. , & Kahl, G. (1996). Genetic variation and cultivar identification of Jamaican yam germplasm by random amplified polymorphic DNA analysis. Euphytica, 92, 341–351. 10.1007/BF00037118 [DOI] [Google Scholar]
  4. Bourke, P. M. , Voorrips, R. E. , Visser, R. G. F. , & Maliepaard, C. (2018). Tools for genetic studies in experimental populations of polyploids. Frontiers in Plant Science, 9, 513 10.3389/fpls.2018.00513 [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Broccanello, C. , Chiodi, C. , Funk, A. , McGrath, J. M. , Panella, L. , & Stevanato, P. (2018). Comparison of three PCR‐based assays for SNP genotyping in plants. Plant Methods, 14, 28 10.1186/s13007-018-0295-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Chaïr, H. , Sardos, J. , Supply, A. , Mournet, P. , Malapa, R. , & Lebot, V. (2016). Plastid phylogenetics of Oceania yams (Dioscorea spp., Dioscoreaceae) reveals natural interspecific hybridization of the greater yam (D. alata). Botanical Journal of the Linnean Society, 180, 319–333. [Google Scholar]
  7. Cormier, F. , Lawac, F. , Maledon, E. , Gravillon, M.‐C. , Nudol, E. , Mournet, P. , … Arnau, G. (2019). A reference high‐density genetic map of greater yam (Dioscorea alata L.). Theoretical and Applied Genetics. 10.1007/s00122-019-03311-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Cuenca, J. , Aleza, P. , Navarro, L. , & Ollitrault, P. (2013). Assignment of SNP allelic configuration in polyploids using competitive allele‐specific PCR: Application to citrus triploid progeny. Annals of Botany, 111, 731–742. 10.1093/aob/mct032 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Danecek, P. , Auton, A. , Abecasis, G. , Albers, C. A. , Banks, E. , DePristo, M. A. , … 1000 Genomes Project Analysis Group (2011). The variant call format and VCFtools. Bioinformatics, 27, 2156–2158. 10.1093/bioinformatics/btr330 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Davey, J. W. , Hohenlohe, P. , Etter, P. , Boone, J. , Catchen, J. , & Blaxter, M. (2011). Genome‐wide genetic marker discovery and genotyping using next‐generation sequencing. Nature Reviews Genetics, 12, 499–510. 10.1038/nrg3012 [DOI] [PubMed] [Google Scholar]
  11. Dereeper, A. , Nicolas, S. , Lecunff, L. , Bacilieri, R. , Doligez, A. , Peros, J. P. , … This, P. (2011). SNiPlay: a web‐based tool for detection, management and analysis of SNPs. Application to grapevine diversity projects. BMC Bioinformatics, 12, 134. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Elshire, R. J. , Glaubitz, J. C. , Sun, Q. , Poland, J. A. , Kawamoto, K. , Buckler, E. S. , & Mitchell, S. E. (2011). A robust, simple genotyping‐by‐sequencing (GBS) approach for high diversity species. PLoS One, 6, e19379 10.1371/journal.pone.0019379 [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Garcia‐Lor, A. , Ancillo, G. , Navarro, L. , & Ollitrault, P. (2013). Citrus (Rutaceae) SNP markers based on Competitive Allele‐Specific PCR; transferability across the Aurantioideae subfamily. Applications in Plant Sciences, 1(4), apps.1200406 10.3732/apps.1200406 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Glaubitz, J. C. , Casstevens, T. M. , Lu, F. , Harriman, J. , Elshire, R. J. , Sun, Q. I. , & Buckler, E. S. (2014). TASSEL‐GBS: A high capacity genotyping by sequencing analysis pipeline. PLoS One, 9(2), e90346 10.1371/journal.pone.0090346 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Gompert, Z. , & Mock, K. E. (2017). Detection of individual ploidy levels with genotyping‐by‐sequencing (GBS) analysis. Molecular Ecology Resources, 17, 1156–1167. 10.1111/1755-0998.12657 [DOI] [PubMed] [Google Scholar]
  16. Hiremath, P. J. , Kumar, A. , Penmetsa, R. V. , Farmer, A. , Schlueter, J. A. , Chamarthi, S. K. , … Varshney, R. K. (2012). Large‐scale development of cost‐effective SNP marker assays for diversity assessment and genetic mapping in chickpea and comparative mapping in legumes. Plant Biotechnology Journal, 10, 716–732. 10.1111/j.1467-7652.2012.00710.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. IPGRI/IITA (1997). Descriptors for Yam (Dioscorea spp.). International Institute of Tropical Agriculture, Ibadan, Nigeria/International Plant Genetic Resources Institute, Rome, Italy.
  18. Krishna, H. , Alizadeh, M. , Singh, D. , Singh, U. , Chauhan, N. , Eftekhari, M. , & Sadh, R. K. (2016). Somaclonal variations and their applications in horticultural crops improvement. 3 Biotech, 6, 54 10.1007/s13205-016-0389-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Langmead, B. , & Salzberg, S. (2012). Fast gapped‐read alignment with Bowtie 2. Nature Methods, 9, 357–359. 10.1038/nmeth.1923 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Lebot, V. , Trilles, B. , Noyer, L. J. , & Modesto, J. (1998). Genetic relationships between Dioscorea alata L. cultivars. Genetic Resources and Crop Evolution, 45, 499–509. [Google Scholar]
  21. Mahalakshmi, V. , Ng, Q. , Atalobor, J. , Ogunsola, D. , Lawson, M. , & Ortiz, R. (2007). Development of a West African yam Dioscorea spp. core collection. Genetic Resources and Crop Evolution, 54, 1817–1825. 10.1007/s10722-006-9203-4 [DOI] [Google Scholar]
  22. Malapa, R. , Arnau, G. , Noyer, J. L. , & Lebot, V. (2005). Genetic diversity of the greater yam (Dioscorea alata L.) and relatedness to D. nummularia Lam. and D. transversa Br. as revealed with AFLP markers. Genetic Resources and Crop Evolution, 52, 919–929. 10.1007/s10722-003-6122-5 [DOI] [Google Scholar]
  23. Nemorin, A. , David, J. , Maledon, E. , Nudol, E. , Dalon, J. , & Arnau, G. (2013). Microsatellite and flow cytometry analysis to help understand the origin of Dioscorea alata polyploids. Annals of Botany, 112, 811–819. 10.1093/aob/mct145 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Otoo, E. , Anokye, M. L. , Asare, P. A. , & Telleh, J. P. (2015). Molecular categorization of some water yam (Dioscorea alata L.) germplasm in Ghana using microsatellites (SSR) markers. Journal of Agricultural Science 7(10), 226–238. [Google Scholar]
  25. R Core Team (2017). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; Retrieved from https://www.R-project.org/ [Google Scholar]
  26. Risterucci, A.‐M. , Hippolyte, I. , Perrier, X. , Xia, L. , Caig, V. , Evers, M. , … Glaszmann, J.‐C. (2009). Development and assessment of diversity arrays technology for highthroughput DNA analyses in Musa. Theoretical and Applied Genetics, 119, 1093–1103. 10.1007/s00122-009-1111-5 [DOI] [PubMed] [Google Scholar]
  27. Sartie, A. , Asiedu, R. , & Franco, J. (2012). Genetic and phenotypic diversity in a germplasm working collection of cultivated tropical yams (Dioscorea spp.). Genetic Resources and Crop Evolution, 59, 1753–1765. 10.1007/s10722-012-9797-7 [DOI] [Google Scholar]
  28. Saski, C. A. , Bhattacharjee, R. , Scheffler, B. E. , & Asiedu, R. (2015). Genomic resources for water yam (Dioscorea alata L.): Analyses of EST‐sequences, de novo sequencing and GBS libraries. PLoS One, 10(7), e0134031. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Saxena, R. K. , von Wettberg, E. , Upadhyaya, H. D. , Sanchez, V. , Songok, S. , Saxena, K. , … Varshney, R. K. (2014). Genetic diversity and demographic history of Cajanus spp. illustrated from genome‐wide SNPs. PLoS One, 9, e88568. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Semagn, K. , Babu, H. , Hearne, S. , & Olsen, M. (2014). Single nucleotide polymorphism genotyping using Kompetitive Allele Specific PCR (KASP): Overview of the technology and its application in crop improvement. Molecular Breeding, 33, 1–14. 10.1007/s11032-013-9917-x [DOI] [Google Scholar]
  31. Semagn, K. , Beyene, Y. , Makumbi, D. , Mugo, S. , Prasanna, B. m. , Magorokosho, C. , & Atlin, G. (2012). Quality control genotyping for assessment of genetic identity and purity in diverse tropical maize inbred lines. Theoretical and Applied Genetics, 125, 1487–1501. 10.1007/s00122-012-1928-1 [DOI] [PubMed] [Google Scholar]
  32. Siqueira, M. V. , Marconi, T. G. , Bonatelli, M. L. , Zucchi, M. I. , & Veasey, E. A. (2011). New microsatellite loci for water yam (Dioscorea alata, Dioscoreaceae) and cross‐amplification for other Dioscorea species. American Journal of Botany, 98, 144–146. [DOI] [PubMed] [Google Scholar]
  33. Su, T. , Li, P. , Yang, J. , Sui, G. , Yu, Y. , Zhang, D. , … Zhang, F. (2018). Development of cost‐effective single nucleotide polymorphism marker assays for genetic diversity analysis in Brassica rapa . Molecular Breeding, 38, 42 10.1007/s11032-018-0795-0 [DOI] [Google Scholar]
  34. Suzuki, R. , & Shimodaira, H. (2006). Pvclust: An R package for assessing the uncertainty in hierarchical clustering. Bioinformatics, 12, 1540–1542. 10.1093/bioinformatics/btl117 [DOI] [PubMed] [Google Scholar]
  35. Tamiru, M. , Natsume, S. , Takagi, H. , White, B. , Yaegashi, H. , Shimizu, M. , … Terauchi, R. (2017). Genome sequencing of the staple food crop white Guinea yam enables the development of a molecular marker for sex determination. BMC Biology, 15, 86 10.1186/s12915-017-0419-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Vandenbroucke, H. , Mournet, P. , Vignes, H. , Chaïr, H. , Malapa, R. , Duval, M. F. , & Lebot, V. (2016). Somaclonal variants of taro (Colocasia esculenta Schott) and yam (Dioscorea alata L.) are incorporated into farmers’ varietal portfolios in Vanuatu. Genetic Resources and Crop Evolution, 63, 495–511. 10.1007/s10722-015-0267-x [DOI] [Google Scholar]

Associated Data

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

Data Availability Statement

Plant materials may be requested at the CRB‐PT of Guadeloupe http://intertrop.antilles.inra.fr/Portail/accessions/find/11. KASPar primers sequence is available in Appendix B.


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