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Article

Assessment of Genetic Diversity and Relatedness in an Andean Potato Collection from Argentina by High-Density Genotyping

by
Sofía Sucar
1,†,
Martín Federico Carboni
2,3,†,
María Florencia Rey Burusco
1,4,
Martín Alfredo Castellote
1,
Gabriela Alejandra Massa
1,3,4,
Marcelo Nicolás Monte
1 and
Sergio Enrique Feingold
1,*
1
Laboratorio de Agrobiotecnología, IPADS, CONICET-INTA, Balcarce B7620, Argentina
2
Museo Argentino de Ciencias Naturales, Buenos Aires C1405, Argentina; [email protected]
3
Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires C1425, Argentina
4
Facultad de Ciencias Agrarias, Universidad Nacional de Mar del Plata, Balcarce B7620, Argentina
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Horticulturae 2022, 8(1), 54; https://doi.org/10.3390/horticulturae8010054
Submission received: 20 October 2021 / Revised: 17 November 2021 / Accepted: 29 November 2021 / Published: 7 January 2022
(This article belongs to the Special Issue Advances in Horticultural Crops Comparative and Functional Genomics)
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Abstract

:
Native potatoes are the most diverse among cultivated potato species and thus constitute a valuable source for identifying genes for potato improvement. Nevertheless, high-density mapping, needed to reveal allelic diversity, has not been performed for native Argentinian potatoes. We present a study of the genetic variability and population structure of 96 Andigena potatoes from Northwestern Argentina performed using a subset of 5035 SNPs with no missing data and full reproducibility. These high-density markers are distributed across the genome and present a good coverage of genomic regions. A Bayesian approach revealed the presence of: (I) a major group comprised of most of the Andean accessions; (II) a smaller group containing the out-group cv. Spunta and the sequenced genotype DM; and (III) a third group containing colored flesh potatoes. This grouping was also consistent when maximum likelihood trees were constructed and further confirmed by a principal coordinate analysis. A group of 19 accessions stored as Andean varieties clustered consistently with group Tuberosum accessions. This was in agreement with previous studies and we hypothesize that they may be reintroductions of European-bred long day-adapted potatoes. The present study constitutes a valuable source for allele mining of genes of interest and thus provides a tool for association mapping studies.

Graphical Abstract

1. Introduction

Potato (Solanum tuberosum L.) is the most important non-cereal food crop and the third most important food crop for direct human consumption [1]. Its haploid genome is composed of 12 chromosomes with an estimated size of 840 Mb. The reference genome DM1-3516-R44 was completely sequenced in 2011 [2] and 96% of the identified genes have been localized in a physical map [3].
Molecular and phytogeographical data indicate the high south-central Andes as the place of potato domestication [4,5]. More precisely, in the basin of Lake Titicaca, starch grains dating to the Late Archaic period (~3400 BC) were discovered [6]. Dodds classified Solanum tuberosum as a unique species composed of five cultivar groups: Stenotonum, Phureja, Chaucha, Tuberosum, and Andigena [7,8]. The Tuberosum group includes lowland tetraploid potatoes introduced in Europe 400 years ago and spread from there to the rest of the world as “modern potatoes” [9]. They possess a narrow genetic base, probably originating from genetic drift on their way to Europe [5,10]. Contrastingly, the Andigena group is composed of native Andean potatoes that were domesticated 7000–10,000 years ago in the Andean uplands of South America. These are by far the most diverse among cultivated potato species, not only morphologically, but also as described by molecular markers [11]. Hence, they constitute a highly valuable source for identifying genes and allelic variants of agronomic, nutritional, and industrial quality interest [12,13]. Previous studies using SSRs have evidenced their great genetic variability [14,15,16,17]. Colman et al. [18] have reported a wide phenotypic variation in reducing sugar content and chip quality traits of Andean potatoes from Northwestern Argentina (NWA) [18].
Several Andigena landraces are still cultivated in NW Argentina, specifically in the provinces of Jujuy, Salta, and Catamarca. For more than 40 years, the INTA-Balcarce Germplasm Bank (BAL) has been collecting and preserving Andean potato materials. The characterization of genetic diversity and population structure is of great value for Germplasm bank management, and is crucial to ensure germplasm protection [19] under international agreements for the fair and equitable sharing of the benefits arising from the use of plant genetic resources [20]. Moreover, this group was traditionally selected by local farmers for several centuries and constitutes a source of allelic variants of relevant genes that can be used for improving commercial varieties [21,22]. In this sense, molecular markers constitute a very useful tool for characterizing genetic variability.
Simple sequence repeats (SSRs), also known as microsatellite markers, are short tandem repeats of di-, tri-, or tetra-nucleotides that are highly polymorphic. They have been used for varietal identification, germplasm characterization, genetic linkage maps, QTL localization, population structure, and phylogeny in potato [23,24,25,26,27,28,29]. They are a reliable method for genetic analysis [4] and require only a small quantity of DNA to be analyzed [30,31,32].
By employing a set of functional SSR markers, we have determined the genetic diversity and population structure in a collection of 88 Andigena potatoes from NWA [17], revealing the presence of two distinctive groups and the existence of group-specific alleles. Functional markers are advantageous over intergenic DNA markers owing to their linkage to characterized genes [33].
Diversity Array Technology by Sequencing (DArTseqTM) [34] is a genotyping-by-sequencing platform based on genome complexity reduction that provides single nucleotide polymorphism (SNP) marker information for high-throughput and cost-effective genotyping without the need for prior sequence data [35,36]. It has been used for diversity studies, population structure determination, and genome-wide association analyses in different species, such as rice [37], soybean [38], lesquerella [39], canola [40], wheat [41], watermelon [42], genus Secale [43], chickpea [44], and safflower [45]. It was also used to build one of the first maps from the genus Solanum [46]. DArT linkage maps have been constructed for S. bulbocastanum Dunal [47], S. commersonii Dunal, and the Mexican Solanum pinnatisectum Dunal [48]. In this last case, it was also useful for the mapping of a novel major late blight resistance locus.
When performing a detailed mapping analysis, such as association mapping, the use of DArTseq marks is more appropriate. This has to do with the fact that the distance between SSR markers constitutes a great portion of DNA that can potentially contain a high number of associated candidate genes [49,50]. On the other hand, the greater abundance of SNP markers and the possibility of some of them being within genetic regions makes a DArTseq screening more relevant and capable of identifying functional alleles of interest.
To our knowledge, the present study is the first genetic diversity characterization performed for Andean potatoes that makes use of DArTseq markers. The aim of this work was to serve as a first attempt to describe the genetic diversity and population structure of 96 Andigena potatoes from NWA performed with DArTseq SNPs, which are high-density markers distributed across the genome and concentrated in genomic regions.

2. Materials and Methods

2.1. Plant Material

In total, 114 accessions were used: a selection of 96 Andean potato accessions (S. tuberosum group Andigena) provided by the Germplasm Bank of the EEA INTA-Balcarce (BAL), 3 by Jujuy National University, 7 accessions of Imilla Negra provided by Cauqueva Cooperative, 3 accessions collected over several trials in Jujuy (guacha potato); 3 commercial varieties (S. tuberosum Group Tuberosum; cv. Spunta, cv. Pampeana INTA, and cv. Bintje) provided by Potato Group of the EEA INTA-Balcarce, and the doubled monoploid line used for the reference genome DM1-3 516 R44 (S. tuberosum group Phureja) (2×) provided by Potato Genome Sequencing Consortium (PGSC) [2], hereafter referred to as DM (Table 1).
Group assignment was made by Atencio (2011) [51] in accordance with tuber morphologic traits (tuber shape, skin color, eyes description and distribution, and flesh color). Accession code was given by the BAL. The Study Code column refers to denominations used in this study and was used for all the figures in the manuscript. The landrace column corresponds to the traditional name given by the local farmer who provided the material. Location of collection is described in the last column, where province, department, and locality are detailed.
The Solanum tuberosum Andigena group in vitro collection used for this study comprises a selection of accessions collected and propagated in vitro by the BAL [51,52].
Selection was based on tuber morphologic traits, such as shape, flesh and skin color, and distribution and depth of eyes, in order to encompass variability [51]. The BAL material was collected between 1976 and 2001 from farmers in the provinces of Salta and Jujuy [53,54,55,56,57] and stored in vitro under a 16/8 photoperiod, at a controlled temperature of 21 °C.
Tubers were obtained from plants grown in greenhouses and chambers in counter-season and planted in fields in Jujuy, Argentina (23°45′ S 65°30′ O, 3600 masl) from November to March/April, and were used to obtain tissue for DNA extraction.

2.2. DNA Extraction and Preparation

DNA was extracted from young leaf tissue according to [58], as described elsewhere [17]. DNA was examined in 1% agarose gels run in 1× TBE (89 mM Tris–borate, 20 mM ethylenediaminetetraacetic acid) and stained with GelRed (GenBiotech, Argentina) for quality and concentration. DNA quantification was performed in a SmartSpecTM 3000 Spectrophotometer (BIORAD, Hercules, CA, USA).
In order to ensure DNAse-free samples, the samples were incubated at 37 °C with 10× Restriction Enzyme buffer (Promega, Madison, WI, USA) for 2 h. Samples were also tested for the presence of DNAses by Diversity Arrays Pty Ltd. Canberra, Australia, before the DArTseq assay was conducted.

2.3. Genotyping

Genotyping was performed by Diversity Arrays Pty Ltd. (Canberra, Australia), using a combined technology called DarTseq™. This technique makes use of the traditional Diversity Arrays Technology (DArT) combined with next-generation sequencing. It involves a two-step procedure that includes genome complexity reduction by selection of fractions of the genome that correspond predominantly to active genes and the removal of large repetitive sequences. This is achieved by digestion with a combination of restriction enzymes. Samples were digested using a rare cutter, Pst I, and subsequently incubated with a frequent-cutter restriction enzyme, such as Taq I. Adaptors were ligated afterwards to the ends of the Pst I fragments and amplified by PCR using specific primers complementary to the adaptor sequences. This step was followed by Illumina short-read sequencing.

2.4. Diversity Analysis

Percentage heterozygosity was calculated for the potato panel using a subset of 5035 SNPs with no missing data and full reproducibility. It was calculated for each accession as:
(number of heterozygous loci/number of total loci) × 100.
A locus was considered homozygous when the same SNP was detected for both copies and heterozygous if one copy contained the reference allele while the other copy contained a SNP.
The amount of variation among clusters was assessed by partitioning genetic diversity using analysis of molecular variance (AMOVA).

2.5. Population Structure Analysis

In order to identify the number of populations (K) capturing the major structure in the data, STRUCTURE v.2.3.4 software was used [59], choosing an admixture model, with independent allele frequencies, a burn-in period of 50,000 MCMC iterations, and 100,000 run length. Ten independent iterations were performed for each simulated value of K ranging from 1 to 10. The most likely number of K was then resolved by the DeltaK method [60] with the Structure Harvester software [61]. STRUCTURE analysis output permutations were performed employing CLUMPP software [62], using independent runs to obtain a consensus matrix.
To further assess the relationship between the accessions, the SNPhylo pipeline [63] was employed to construct a maximum likelihood tree, with the following steps: (1) filtering of the SNP datasets using minor allele frequency of 10% and 10% missing data; (2) remotion of redundant SNPs based on linkage disequilibrium information (cutoff threshold 1) using SNPRelate [64]; (3) construction of multiple sequence alignment of the SNP dataset using MUSCLE [65]; (4) construction of the maximum likelihood tree using DNAML from Phylip [66]; (5) performing 1000 bootstraps using Phangorn [67]. Before running the pipeline, the script file was edited to set the transition/transversion ratio equal to the previously calculated value of 1.5. The phylogenetic tree was drawn and visualized with the online software Interactive Tree Of Life (iTOL) v3 [68]. The bar graph of the population structure results was added in the same way as a multi-value bar chart dataset in iTOL.
A principal coordinate analysis (PCoA) was carried out to explore group conformation within the collection using the GenAlEx software [69] and accessions were plotted based on the first two principal coordinates. To this end, 5035 SNPs were selected under the two main criteria of (i) high reproducibility and (ii) no missing data for the collection studied.

3. Results

3.1. Genetic Diversity Analysis

In total, 56,163 SNPs were generated (Figure 1). The depth of reads ranged from 5× to 1600× with a mean value of 18×. They were mapped altogether with genomic regions (Figure 1) using an in-house developed tool, Agrobiotechtools (Agrobiotechnology Laboratory, IPADS, CONICET-INTA) to visualize their distribution. This tool was particularly useful for plotting density in a 1 Mbp bin and exact positions of DArTseq markers throughout the 12 potato chromosomes along with their colocalization with genomic regions.
The majority of SNPs detected were transitions (A/G or C/T). In total, 33,806 were found, whereas 22,357 transversions were found, giving a transition/transversion ratio of about 1.5.
It was possible to map the 45,159 SNPs found in the PGSC Version 4.03 Pseudomolecules of the reference potato (S. tuberosum group Phureja DM1-3 516 R44) [3]. On average, 3670 SNPs were detected per chromosome, ranging from 5643 SNPs in chromosome 1 to 2699 in chromosome 10. Only 1123 SNPs were mapped to the unanchored chromosome 00. This chromosome includes superscaffolds that remain unanchored in the published reference genome map [3]. The distribution of SNPs was coincident with regions of high density of coding sequences of the PGSC_DM_v4.03_gene database (Figure 1).
Using a subset of 5035 SNPs with no missing data and full reproducibility, we found that the average percentage heterozygosity observed within the potato panel was 32%. The minimum heterozygosity was found, as expected, in the doubled monoploid accession DM, whereas the maximum heterozygosity was 43.8%, as found in accession 31 (Figure 2).

3.2. Population Structure Analysis

To explore the population structure, a Bayesian approach was conducted using the program STRUCTURE [59] version 2.3.4 and the aforementioned subset of 5035 SNPs scattered across the genome. Using the method of Evanno et al. [60], it was determined that the 114 accessions were partitioned into three clusters (K = 3) (Figure 3, bottom left box). The green group includes cv. Spunta, Bintje, and Pampeana-INTA (S. tuberosum Tuberosum group), DM (S. tuberosum Phureja group), and 19 accessions from the BAL. The smaller group, shown in cyan, comprises 15 accessions and includes colored flesh potatoes, such as Santa María and Azul. The major group is shown in red and contains accessions with different tuber shapes belonging to the S. tuberosum Andigena group.

3.3. Clustering Analysis

To visualize detailed information about the genetic diversity among different groups of potato accessions, two maximum likelihood trees were constructed. The grouping of accessions in the first tree was in general agreement with the Bayesian population structure analysis with only three mislocated accessions (Figure 3). When the same analysis was performed without the ambiguous accessions, the clustering obtained was completely coincident with structure results (Figure S1).
A principal coordinate analysis (PCoA) was conducted to complement the clustering analysis and further visualize the pattern of genetic relationships. The first two principal coordinates collectively explained 40.45% of the total genetic variance (Coord.1 = 22.7% and Coord.2 = 17.8%). The PCoA results showed clear separation into distinct groups, which agreed with the STRUCTURE results (Figure 4).
The analysis of molecular variance (AMOVA) was used for hierarchical partitioning of the genetic variation among the three clusters, as revealed by the STRUCTURE analysis. There was a higher proportion of genetic variation within clusters (68%) than among clusters (32%) in the potato collection.

4. Discussion and Conclusions

The assessment of genetic relatedness, performed by three different analyses, revealed that the collection was composed of three main subpopulations. The fact that this grouping was supported by a Bayesian approach, a genetic distance-based method, and a principal coordinate analysis, provides strong evidence for the resulting population structure.
In addition, a very high marker density was used. A high-density map results is particularly useful when performing a detailed mapping analysis, such as association mapping. Since future association mapping studies will be performed for this collection, DArTseq markers were chosen, given the fact that they present high gene coverage when compared to other types of markers used for genotyping studies, such as DNA microarrays for example [70]. This advantage over other methods is related to the usage of a combination of restriction enzymes that separates low-copy sequences from the highly repetitive fraction of the genome. This was clearly visualized when plotting genomic regions and DArTseq markers together by chromosome, using Agrobiotechtools, an in-house developed tool (Agrobiotechnology Laboratory, IPADS, CONICET-INTA) (Figure 1). It is worth noticing that, even after applying filters to choose markers with high reproducibility and no missing data, the percentage of genomic regions covered was still very high. The majority of SNPs detected were transitions (A/G or C/T) and the ratio obtained was consistent with other studies performed in various species, such as maize [71], oil palm [72], rubber tree [73], or Iranian cannabis germplasm [74]. Previous reports on SNP discovery programs have also detected a high frequency of transitions [75,76,77] showing the high frequency of the C/T mutation that occurs after methylation [71]. Population structure analysis revealed the existence of three defined groups. One of them was composed by modern potatoes and accessions that possibly resulted from reintroductions of commercial potatoes, and were mislabeled as Andean.
Local farmers tend to prefer modern potatoes because—unlike native genotypes—they lack photoperiod sensitivity; thus, in lower latitudes they present a shorter crop cycle that represent an advantage for soil use efficiency where multiple crops are grown within a year.
The mislabeling of accessions in germplasm banks constitutes a problem; the curation of entries becomes necessary. In this study, it is possible that these mislabeled Andigena entries have been erroneously named as such during collection. It is usual that Jujuy farmers treat tuberosum landraces that do not present photoperiodic requirements (such as “Holandesa” or “Desireé“, for example) as short-day potatoes. This can lead to the collection of tuberosum landraces as if they were, in fact, Andigena. Another possibility is that mislabeling occurred during in vitro conservation, when morphologic characteristics used for identification are sometimes not evident. All the above indicate the need for using markers when managing accessions in a germplasm bank.
A study performed by Monte and Rey Burusco et al. [17] with the same potato collection using 26 SSR markers, revealed the presence of only two groups (with the colored flesh accessions as a subgroup of the major one). Given the fact that these DArT markers are homogeneously distributed across the genome and have a high coverage of genomic regions, they allow the discovery of relationships among accessions that cannot be found using other types of markers.
It is worth noticing that most of the accessions were assigned to any of the three clusters with probabilities higher than 0.6. Only 18 out of 114 accessions (15.78%) presented probabilities lower than 0.6. One was accession 32, which displays a rare position in the PCoA analysis, distantly located from all of the three groups. Interestingly, this accession showed a maximum of two alleles for each of the 26 SSRs reported by Monte and Rey Burusco et al. [17], which might indicate that it is a diploid genotype. Moreover, it is highly homozygous (see Figure 2).
Seven ‘Imilla Negra’ (IN) accessions provided by the Cauqueva Cooperative were included in this study. These were sub-selected within the same landrace seed pool due to the morphological variability observed in preliminary analyses (data not shown). IN is a largely diverse landrace that is very common in Bolivia and South Peru [78]. Five of the Cauqueva accessions were grouped together in the dendrogram and apart from the remaining two, which appeared in a separate branch (Figure 3). This grouping was consistent with distribution seen in the PCoA analysis (Figure 4). It is worth pointing out that accession 62, the only IN accession provided by BAL, appears in a distant branch, separated from any of the other INs under study. The results from Monte and Rey Burusco et al. [17], using SSRs, are in agreement with this.
Atencio et al. [11] previously noted the occurrence of genetic diversity within landraces. The study showed that 24 individuals belonging to the “Collareja” landrace, collected from the same parcel in a Jujuy local producer field, presented a high molecular diversity, despite having similar tubers and crop cycle. Moreover, these accessions did not show genetic identity when compared with other “Collareja” individuals collected in different geographic sites in Jujuy. This was indicative of the high level of polymorphism existing within this landrace and agreed with the results from the present study. In addition, this IN grouping can be explained given the fact that landraces names do not necessarily reflect genetic relationships, but respond to the tuber morphological characteristics that describe them.
When analyzing homozygosity, DM was the accession that presented the higher homozygosity level (0.6% heterozygosity) due to its doubled monoploid nature. This was followed by 32 (2.5%), a possible diploid genotype [17]. Heterozygosity percentages were in agreement with those previously described [79]. Accessions 6 and 41, which do not show a consistent grouping throughout the studies performed, showed heterozygosity percentages of 8 and 13%, respectively.
As regards to heterozygosity of potato commercial varieties, Spunta presents 24%, Pampeana 26%, and Bintje 27%. Hirsh et al. [79] reported between 53 and 59 percent heterozygosity in market classes of cultivated potato. The only Andean potato analyzed in their study presented 29% heterozygosity, which is mostly in agreement with values obtained for our Andean cluster. It is worth noting that overall heterozygosity levels could have been underestimated due to the simplification of tetraploid genotypes to two state markers derived from DArTseq data. On the other hand, even though a given SNP could present four different allelic states (A, T, C or G), most SNPs often present two [80,81].
It was not possible to find a consistent pattern that relates genetic composition with collection site. This is due to high tuber interchange between local farmers during fairs and potato producers that are then used as a seed potato. As of today, there is no commercial source of propagules, since they have been traditionally interchanged informally for many years.
Our collection is composed of material collected mostly in areas of great interchange. In addition, collection campaigns were performed in multiple years and different departments were covered in each year of collection [53,54,55,56,57]. Thus, population clustering was neither related to year nor location of collection. Colman [82] proved that there was no statistically significant association between population structure and year or site of collection for a sample of 50 Andean accessions that were included in the present study [82].
For accessions with group assignment higher than 0.6 in the Bayesian analysis, clustering was the same when analyzed by a maximum likelihood tree. On the other hand, for accessions with probabilities lower than 0.6, the results were not consistent, giving rise to branches forming new groups in the tree. When these accessions are removed from the study, the clustering obtained matches the Bayesian analysis with only three mislocated accessions (Supplemental Figure S1). In addition, three accessions, DM, 6, and 41 formed a group with Tuberosum group accessions when analyzed with STRUCTURE, but clustered either in a new group (Figure 3) or with the Andean group (Supplemental Figure S1) in the tree. The fact that some accessions group together with Andean accessions or in a different group depending on the type of analysis shows that these studies might not be sufficient to characterize them. Additional studies including other landraces and wild species could be conducted to determine the appropriate grouping of these genotypes.
Surprisingly, a group of 19 accessions provided by the BAL, classified as Andean varieties, consistently clustered with Tuberosum group accessions. Given the fact that previous studies [83] on Andean potatoes have shown similar results, it could be hypothesized that these 19 accessions were, in fact, reintroductions of lowland commercial potatoes. It should be noted that the 26 SSRs used in the study mentioned above were able to group all these accessions, except for 41, 6, and 4, together with the Tuberosum group. Nonetheless, the grouping of accessions 4 and 6 with Andean potatoes was not strong in the PCoA analyses. On the other hand, 41 is the only accession for which DArTseq markers were able to reveal grouping with commercial genotypes, but SSRs were not.
In vitro studies showed that tuberization behavior for accession 2 (one of the aforementioned accessions) was photoperiod independent, a characteristic shown by commercial potatoes [17]. This was also supported by the cdf1 allele characterization for this genotype. This gene is responsible for triggering cellular signaling cascades for tuberization in response to the photoperiod. Commercial accessions, such as Bintje and Spunta, were found to have an allelic variant with a 7 bp insertion in the cdf1 gene, which makes tuberization independent from photoperiod. The same insertion was found for accession 2, as well as for accessions 25, 45, and 92, Andean accessions that were also grouped together in the commercial potato cluster [17].
The detailed characterization of genetic diversity with a high number of molecular markers is of great importance when performing association mapping studies [22]. A genetically diverse collection, such as the one employed for the present study, provides a good starting point for future association analyses. Unique alleles were identified in all three clusters obtained. It is important to point out that the group with the highest number of unique alleles, 417, was the Andean group, when compared to 225 alleles for the commercial potatoes group and only 29 unique alleles for the colored flesh accessions group. This was in agreement with previous studies revealing a great genetic diversity for Andean potato landraces from Northwestern Argentina [14]. Such a characteristic makes them a valuable source of genes of interest for potato breeding programs.
In conclusion, the present study has revealed genetic diversity of native potatoes from NWA using high-density markers.
Diversity studies that include high marker densities will be useful for future association mapping studies to identify candidate genes and alleles of characteristics of interest.

Supplementary Materials

The following data are available online at https://www.mdpi.com/article/10.3390/horticulturae8010054/s1, Figure S1: STRUCTURE plot of a subset of 96 potato accessions with memberships higher than 0.6 to any of the 3 subpopulations based on 5035 SNP markers.

Author Contributions

Conceptualization, S.S., M.F.C., M.F.R.B. and M.N.M.; methodology, S.S., M.F.C., M.F.R.B. and M.N.M.; software, M.A.C.; validation, G.A.M. and S.E.F.; investigation, S.S., M.F.C., M.F.R.B. and M.N.M.; data curation, M.A.C.; writing—original draft preparation, S.S., M.F.C., M.F.R.B., M.A.C. and M.N.M.; writing—review and editing, S.S., M.F.C., M.F.R.B., M.A.C.; supervision, G.A.M. and S.E.F.; project administration, G.A.M. and S.E.F.; funding acquisition, G.A.M. and S.E.F. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by INTA projects PNAIyAV 1130043 “Estrategias para la Diferenciación de Alimentos y el Desarrollo de Nuevos Productos Alimentarios” [Strategies for food differentiation and development of new food products] INTA 2013-2019, PNBIO 1131024 ¨Desarrollo de sistemas alternativos de generación y utilización de variabilidad genética y su aplicación al mejoramiento de los cultivos¨ [Development of Alternative systems of generation and utilization of genetic variation, and its application on cultivars breeding] INTA 2013-2019; PNBIO 1131023 ¨Prospección y caracterización funcional de genes de interés biotecnológico’ [Prospection and functional characterization of genes with biotechnological interest] INTA 2013-2019, PNBIO 1131042 “Genómica aplicada a la caracterización de la diversidad genética” [Genomics applied to genetic diversity characterization] INTA 2013-2019, AETA 282811 INTA 2009-2012 and AERG- 231221 INTA 2009-2012. In addition, this work was possible thanks to funding from FONCYT, MINCyT ARGENTINA: PICT- 2010-2037 and PE/09/02 and CONCyTEC 2011-2012. MM and MFC hold CONICET doctoral fellowships and MFRB by a CONICET post-doctoral fellowship. 1. PICT2017-1070 “Bases genéticas y moleculares de la tuberización en Solanum tuberosum: Mo-dulación del ciclo de cultivo.” 2. “Proyecto INTA PE-I114: Caracterización de la diversidad genética de plantas, animales y mi-croorganismos mediante herramientas de genómica aplicada.”

Acknowledgments

We would like to thank the BAL for providing most of the germplasm material used in the present study. Also, Javier Rodriguez from CAUQUEVA Cooperative and Berta Andrade from Jujuy National University for providing the remaining accessions for the analyses. This material is maintained in vitro by Silvina Divito at INTA EEA Balcarce. Tubers are annually planted in Jujuy, Argentina (23°45′ S 65°30′ O, 3600 masl) in collaboration with Dario Castro, Daniel Martinez, Pablo Garcia, Irma Cusi and Kevin Dante Tolab from the Agroexperimental Station INTA Abrapampa, and Guillermo Aracena and Damián Alcoba from INTA IPAF NOA.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

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Figure 1. Density of genes and SNP markers per chromosome. Each bar indicates density in a 1 Mbp bin. Blue bars show gene density; light blue, the total SNPs mapped; green, SNPs without missing data; and orange, SNPs without missing data and with best quality. Number of genes or SNPs per Mbp are indicated at the bottom of each sub-figure.
Figure 1. Density of genes and SNP markers per chromosome. Each bar indicates density in a 1 Mbp bin. Blue bars show gene density; light blue, the total SNPs mapped; green, SNPs without missing data; and orange, SNPs without missing data and with best quality. Number of genes or SNPs per Mbp are indicated at the bottom of each sub-figure.
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Figure 2. Percentage of heterozygous loci for each of the 114 accessions was determined with 5035 SNPs and a diploid genotyping model.
Figure 2. Percentage of heterozygous loci for each of the 114 accessions was determined with 5035 SNPs and a diploid genotyping model.
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Figure 3. STRUCTURE plot of the 114 potato accessions based on 5035 SNP markers. Each column corresponds to one accession and is partitioned into colored segments that are in proportion to the estimated membership of the three subpopulations. Maximum likelihood trees are shown on the left side. Accessions were labeled by color according to STRUCTURE results. Accessions with a membership proportion lower than 0.6 were labeled with an asterisk.
Figure 3. STRUCTURE plot of the 114 potato accessions based on 5035 SNP markers. Each column corresponds to one accession and is partitioned into colored segments that are in proportion to the estimated membership of the three subpopulations. Maximum likelihood trees are shown on the left side. Accessions were labeled by color according to STRUCTURE results. Accessions with a membership proportion lower than 0.6 were labeled with an asterisk.
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Figure 4. Principal coordinate analysis (PCoA) of the 114 potato accessions in the panel based on 5035 DArTseq markers. Color coding corresponds to colors used for the STRUCTURE grouping assignment. Accessions with a membership proportion lower than 0.6 are labeled with an asterisk. Numbers in parentheses in the axes depict the percentage of genetic variance.
Figure 4. Principal coordinate analysis (PCoA) of the 114 potato accessions in the panel based on 5035 DArTseq markers. Color coding corresponds to colors used for the STRUCTURE grouping assignment. Accessions with a membership proportion lower than 0.6 are labeled with an asterisk. Numbers in parentheses in the axes depict the percentage of genetic variance.
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Table
Table 1. Collection used for the present study.
Table 1. Collection used for the present study.
Group (Atencio, 2011)Accession CodeStudy CodeLandraceLocation (Province, Department, Locality)
1CCS 132728BayistaJujuy, Cochinoca, Rachaite
1CL 62123ChorcoyeñaSalta, Santa Victoria, Nazareno
1CL 51644ChorcoyeñaSalta, Santa Victoria, Chorro
1CCS 120521ChurqueñaJujuy, Humahuaca, Varas
1CCS 137890Churqueña negraJujuy, Tumbaya, Patacal
1CCS 122460CollarejaJujuy, Humahuaca, Coctaca
1CL 63467CollarejaSalta, Santa Victoria, Arpero
1CL 63668CollarejaSalta, Santa Victoria, Abra Colorada
1CS 14323Collareja or cuarentona overaJujuy, General Belgrano, Cuevas
1CCS138129RunaJujuy, Tumbaya, Patacal
1CL 65045RunaSalta, Santa Victoria, Poscaya
1CL 57646RunaSalta, Santa Victoria, Lizoite
1CL 64149RunaSalta, Santa Victoria, Poscaya
1CL 70856RunaSalta, Iruya, Colanzulí
1CL 48963RunaSalta, Santa Victoria, Rodeopampa
1CL 73970RunaJujuy, Humahuaca, Chaupi Rodero
1CL 75071RunaJujuy, Humahuaca, Chaupi Rodero
1CCS 121884RunaJujuy, Humahuaca, Ocumazo
1CCS 1340116YaguanaJujuy, Susques, Sala
1CL 641144RunaSalta, Santa Victoria, Poscaya
2LC 34862Imilla NegraJujuy, Humahuaca, Huachichocana
2CL 63140AlloSalta, Iruya, Campo Carreras
2CCS 1227109Azul or SallamaJujuy, Humahuaca, Coctaca
2CCS 119680Azul overaJujuy, Humahuaca, Palca de Aparzo
2CL 81577BolivianaJujuy, Valle Grande, Santa Ana
2CCS 120114CondorillaJujuy, Humahuaca, Varas
2CCS 138441CorbatillaJujuy, Tumbaya, Patacal
2CCS 13306MoraditaJujuy, Cochinoca, Rachaite
2CCS 13078MoraditaJujuy, Santa Catalina, Cabreria
2CCS 137439MoraditaJujuy, Cochinoca, Agua Castilla
2CCS 11724Moradita redondaJujuy, Tilcara, Casa Colorada
2CL 78398NavecillaJujuy, Valle Grande, Santa Ana
2CL 82057Negra RedondaJujuy, Valle Grande, Santa Ana
2CCS 130547OjosaJujuy, Santa Catalina, Casir
2CCS 125786OjosaJujuy, Rinconada, Rinconada
2CCS 136627OveraJujuy, Tumbaya, El Moreno
2CL 74835OveraJujuy, Humahuaca, Chaupi Rodero
2CL 79052OveraJujuy, Valle Grande, Santa Ana
2CL 83279Abajeña overaJujuy, Valle Grande, Santa Ana
2CL 79374SallamaJujuy, Valle Grande, Santa Ana
2CL 804139SallamaJujuy, Valle Grande, Santa Ana
2CL 76932Sallama GrandeJujuy, Valle Grande, Santa Ana
2CCS 128426SaniJujuy, Yavi
2CCS 130337YurumaJujuy, Santa Catalina, Casira
2CCS 138542MoraditaJujuy, Tumbaya, Patacal
3CL 83551AirampíaJujuy, Valle Grande, Santa Ana
3CL 83654AirampíaJujuy, Valle Grande, Santa Ana
3CL 52864ColoradaSalta, Santa Victoria, Chorro
3CCS1221102ColoradaJujuy, Humahuaca, Coctaca
3CL 508123ColoradaSalta, Santa Victoria, Chorro
3CCS 134912ColoraditaJujuy, Tumbaya, El Angosto
3CCS 1184108Colorana or SeñoritaJujuy, Humahuaca, Aparzo
3CL 82178Cuarentilla ToscraJujuy, Valle Grande, Santa Ana
3CS 143016CuarentonaJujuy, General Belgrano, Cuevas
3CL 72831CuarentonaSalta, Iruya, Colanzulí
3CCS 11669Cuarentona coloradaJujuy, Tilcara, Casa Colorada
3CS 141494Cuarentona moradaJujuy, General Belgrano, Papachacra
3CS 1425122Cuarentona oquechaJujuy, General Belgrano, Papachacra
3CCS 1353117Cuarentona RedondaJujuy, Tumbaya, El Angosto
3CS 1416120CuellaJujuy, General Belgrano, Papachacra
3CCS 128815DesireeJujuy, Santa Catalina, Cieneguillas
3CL 71269HuareñaSalta, Iruya, Colanzulí
3CCS 117053Ojos coloradosJujuy, Tilcara, Casa Colorada
3CS 140291Ojos coloradosJujuy, Tumbaya, Carcel
3CCS 138334Pera or señoritaJujuy, Tumbaya, Patacal
3CCS 132189RosadaJujuy, Cochinoca, Agua Caliente
3CL 65817Santa MaríaJujuy, Yavi, Yavi
3LC 33597ToncaJujuy, Humahuaca, Patacal
3CL 48233RosadaSalta, Santa Victoria, Rodeopampa
3CCS 12557DesireeJujuy, Rinconada, Rinconada
3CCS 132336ColoradaJujuy, Cochinoca, Agua Caliente
4CL 84999BalcachaSalta, Rosario de Lerma, El Gólgota
4CCS 13501BlancaJujuy, Tumbaya, El Angosto
4CS 141910BlancaJujuy, General Belgrano, Papachacra
4CCS 1310115Blanca alargadaJujuy, Santa Catalina, Cabreria
4CCS 130958Blanca redondaJujuy, Santa Catalina, Cabreria
4CCS 12515ChacareraJujuy, Cochinoca, Cochinoca
4CCS 137120ChacareraJujuy, Cochinoca, Quebraleña
4CS 14182ChaqueñaJujuy, General Belgrano, Papachacra
4CS 140892Chaqueña overaJujuy, General Belgrano, Papachacra
4CCS 130088HolandesaJujuy, Santa Catalina, Casira
4CCS 120983LuquiJujuy, Humahuaca, Chorcan
4CCS 1299100MalgachaJujuy, Santa Catalina, Casira
4CCS 120081Papa ocaJujuy, Humahuaca, Varas
4CCS 120682Papa ocaJujuy, Humahuaca, Varas
4CL 54848Papa paltaSalta, Santa Victoria, Trigohuaico
4CS 141393Papa vallistaJujuy, General Belgrano, Papachacra
4CCS 118513TuniJujuy, Humahuaca, Aparzo
4CCS 119911Tuni blancaJujuy, Humahuaca, Palca de Aparzo
4CCS 124722Tuni blancaJujuy, Cochinoca, Ojo de Agua
4CCS 1375101Tuni BlancaJujuy, Tumbaya, Tumbaya
4CCS 1393119Tuni moradaJujuy, Tumbaya, Cieneguillas
4CL 78273Tuni rosillaJujuy, Valle Grande, Santa Ana
4CCS 127125BlancaJujuy, Santa Catalina, Morco Esquina
4Cl 752B72BlancaJujuy, Valle Grande, Santa Ana
4CL 814A75Holandesa coloradaJujuy, Valle Grande, Santa Ana
Berta104AzulProvided by Jujuy National University
Berta105Santa MaríaProvided by Jujuy National University
Berta106Santa María pulpa blancaProvided by Jujuy National University
70B Guacha potato
89A Guacha potato
89B Guacha potato
DM Potato reference genome
IN1Imilla NegraProvided by Cauqueva
IN2Imilla NegraProvided by Cauqueva
IN3Imilla NegraProvided by Cauqueva
IN4Imilla NegraProvided by Cauqueva
IN5Imilla NegraProvided by Cauqueva
IN6Imilla NegraProvided by Cauqueva
IN7Imilla NegraProvided by Cauqueva
BINTJEBintjeCommercial variety
PAMPEANA-INTAPampeana-IntaCommercial variety
SPUNTASpuntaCommercial variety
SPUNTA2SpuntaCommercial variety
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MDPI and ACS Style

Sucar, S.; Carboni, M.F.; Rey Burusco, M.F.; Castellote, M.A.; Massa, G.A.; Monte, M.N.; Feingold, S.E. Assessment of Genetic Diversity and Relatedness in an Andean Potato Collection from Argentina by High-Density Genotyping. Horticulturae 2022, 8, 54. https://doi.org/10.3390/horticulturae8010054

AMA Style

Sucar S, Carboni MF, Rey Burusco MF, Castellote MA, Massa GA, Monte MN, Feingold SE. Assessment of Genetic Diversity and Relatedness in an Andean Potato Collection from Argentina by High-Density Genotyping. Horticulturae. 2022; 8(1):54. https://doi.org/10.3390/horticulturae8010054

Chicago/Turabian Style

Sucar, Sofía, Martín Federico Carboni, María Florencia Rey Burusco, Martín Alfredo Castellote, Gabriela Alejandra Massa, Marcelo Nicolás Monte, and Sergio Enrique Feingold. 2022. "Assessment of Genetic Diversity and Relatedness in an Andean Potato Collection from Argentina by High-Density Genotyping" Horticulturae 8, no. 1: 54. https://doi.org/10.3390/horticulturae8010054

APA Style

Sucar, S., Carboni, M. F., Rey Burusco, M. F., Castellote, M. A., Massa, G. A., Monte, M. N., & Feingold, S. E. (2022). Assessment of Genetic Diversity and Relatedness in an Andean Potato Collection from Argentina by High-Density Genotyping. Horticulturae, 8(1), 54. https://doi.org/10.3390/horticulturae8010054

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