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. Author manuscript; available in PMC: 2015 Dec 1.
Published in final edited form as: Plant Gene. 2015 Dec 1;4:29–37. doi: 10.1016/j.plgene.2015.09.003

Genetic diversity and population structure in Physalis peruviana and related taxa based on InDels and SNPs derived from COSII and IRG markers

Gina A Garzón-Martínez a, Jaime A Osorio-Guarín a, Paola Delgadillo-Durán a, Franklin Mayorga a, Felix E Enciso-Rodríguez a, David Landsman c, Leonardo Mariño-Ramírez c,✉, Luz Stella Barrero a,b,✉
PMCID: PMC4630809  NIHMSID: NIHMS731440  PMID: 26550601

Abstract

The genus Physalis is common in the Americas and includes several economically important species, among them Physalis peruviana that produces appetizing edible fruits. We studied the genetic diversity and population structure of P. peruviana and characterized 47 accessions of this species along with 13 accessions of related taxa consisting of 222 individuals from the Colombian Corporation of Agricultural Research (CORPOICA) germplasm collection, using Conserved Orthologous Sequences (COSII) and Immunity Related Genes (IRGs). In addition, 642 Single Nucleotide Polymorphism (SNPs) markers were identified and used for the genetic diversity analysis. A total of 121 alleles were detected in 24 InDels loci ranging from 2 to 9 alleles per locus, with an average of 5.04 alleles per locus. The average number of alleles in the SNP markers was two. The observed heterozygosity for P. peruviana with InDel and SNP markers was higher (0.48 and 0.59) than the expected heterozygosity (0.30 and 0.41). Interestingly, the observed heterozygosity in related taxa (0.4 and 0.12) was lower than the expected heterozygosity (0.59 and 0.25). The coefficient of population differentiation FST was 0.143 (InDels) and 0.038 (SNPs), showing a relatively low level of genetic differentiation among P. peruviana and related taxa. Higher levels of genetic variation were instead observed within populations based on the AMOVA analysis. Population structure analysis supported the presence of two main groups and PCA analysis based on SNP markers revealed two distinct clusters in the P. peruviana accessions corresponding to their state of cultivation. In this study, we identified molecular markers useful to detect genetic variation in Physalis germplasm for assisting conservation and crossbreeding strategies.

Keywords: Cape gooseberry, germplasm, COSII, IRGs, SNPs, Genetic variation

1. Introduction

The genus Physalis consists in more than 90 species, native of the Americas, being Mexico the center of diversity of the husk tomato (Vargas-Ponce et al., 2010). The genus includes different species with nutritional, nutraceutical and commercial interests. Among them, P. peruviana and other related taxa as P. philadelphica, P. pruinosa and P. longifolia has been characterized for different health related compounds with anti-inflammatory and antioxidant properties (Martínez et al., 2010; Yen et al., 2010; Maldonado et al., 2011; Ramadan, 2011; Jin et al., 2012; Kindscher et al., 2014; Takimoto et al., 2014), as well as others with valuable nutritional properties including vitamins A, B and C, polyunsaturated fatty acids, proteins and minerals (Puente et al., 2011; Ramadan, 2011). Therefore the commercial interest of P. peruviana, also known as Cape gooseberry, has increased to the point of being currently the main exported fruit after banana in Colombia (Bonilla et al., 2009).

Accurate knowledge of genetic diversity and relationships among preserved germplasm collections of any crop is essential and important for establishing, managing and ensuring long-term success of appropriate crop improvement programs through breeding (Gwag et al., 2010). Thus, the study on genetic diversity and population structure of germplasm collections has been useful in supporting conservation and genetic improvement strategies (Rao and Hodgkin, 2002; Grandillo, 2014). Furthermore, natural biodiversity found in non-cultivated relatives of crop species are reservoirs representing an important source of genetic variation essential for any crop-breeding program (Grandillo, 2014). Although some Physalis species have been widely recognized by their nutraceutic and economic importance, little is known about their genetic diversity at the molecular level, mainly because of the lack of available markers in accordance with their current status as orphan species. Dominant markers RAMs (Random Amplified Microsatellites) were the first type used to study the genetic diversity of a Colombian P. peruviana collection where high expected heterozigosity (He=0.2559) was found (Bonilla et al., 2008; Morillo Paz et al., 2011). Later, next-generation sequencing technologies for the rapid identification of SSR loci derived from ESTs were used (Simbaqueba et al., 2011; Garzón-Martínez et al., 2012). Nevertheless, since all SSRs loci were located in the UTR regions of the transcriptome, a low polymorphic rate (22%) was found in a panel of 8 accessions from P. peruviana and the related species P. floridana, therefore we decided to develop alternative markers. More recently, 97 tomato markers (COS, SSRs and InDel markers) and 25 P. peruviana SSRs markers were used for genetic diversity analysis in 38 accessions of Physalis (Wei et al., 2012). This study suggested the efficient use of tomato markers in genetic studies of Physalis as species from the family Solanaceae, and a high level of polymorphism (92.7% of markers were polymorphic) in accordance with a broad genetic at DNA level in wild and cultivated species. Furthermore, Berdugo et al. (2015), used 328 COSII and 154 Immunity Related Genes (IRGs) to evaluate an F1 population generated between contrasting pathogen response parents. This population showed a total of 127 alleles with an average of 3.18 per locus, a PIC of 0.358 and high values of heterozygosity (Ho: 0.737 and He: 0.449).

Recent advances in sequencing technologies allowed the identification of large sets of Single Nucleotide Polymorphisms (SNPs) (Patel et al., 2015; Yang et al., 2015). SNP genotyping has become the most useful technique in model and non-model species for genetic diversity and population structure analysis, marker-assisted selection and association studies (Frascaroli et al., 2013). Recently, Enciso et al. (2013) identified 74 IRGs in P. peruviana, from which 17 markers were selected and sequenced in a small subset of P. peruviana and related taxa allowing the identification of one candidate SNP associated to the resistance response against the fungal pathogen Fusarium oxysporum, one of the main constraints to P. peruviana production in Colombia.

Since the development and discovery of new markers for non-model species as well as the use of Conserved Ortholog Set of markers (COSII) (Fulton et al., 2002; Wu et al., 2006) are valuable resources for genetic studies of Physalis, the aim of the present study was to investigate the genetic diversity and population structure of 60 accessions with a large representation of P. peruviana, by the use and comparison of InDels and SNPs derived from COSII and IRG markers, to contribute knowledge on the germplasm genetic base for conservation and use for breeding strategies.

2. Material and methods

2.1. Plant material and DNA isolation

Young leaves of 222 plants belonging to 47 Physalis peruviana accessions (an average of 3 plants per accession, each derived from a single seed) and 13 related taxa (an average of 4 plants per accession) were collected from an in vitro germplasm collection maintained at the Colombian Corporation for Agricultural Research (CORPOICA) (Table 1, Supplementary Table 1). The leaves were stored at −70°C and utilized for genomic DNA isolation using the modified Dellaporta et al. (1983) method, as described by Enciso et al. (2013).

Table 1.

Summary of plant material used in this study

Species Number of
accessions		 Number of
plants used for
InDels* 		Number of
plants
sequenced
Physalis peruviana 47 171 51
Physalis philadelphica 4 18 4
Physalis floridana 2 9 2
Physalis angulate 2 5 2
Physalis viscosa 1 3 1
Physalis pruinosa 1 3 1
Physalis ixocarpa 1 2 1
Nicandra physalodes 1 3 1
Solanum auriculatum 1 8 1
Total 60 222 64
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*

InDel markers = Polymorphic COSII and IRGs on agarose gel

2.2. Molecular marker selection

A total of 454 molecular markers were tested for genetic diversity analyses on P. peruviana and related taxa. We selected 327 COSII markers based on their distribution across the 12 linkage groups of the tomato genome, considering their orthologous nature in a broad range of Solanaceae family species (Fulton et al., 2002; Wu et al., 2006; Bedoya-Reina and Barrero, 2010). Furthermore, 33 COSII genes were selected, based on resistance to biotic factors related ontologies (i.e. against phytopathogenic microorganisms), as a strategy to look for polymorphisms within homologous defense/resistance genes. Each primer pair was obtained from the Solanaceae network database (http://www.solgenomics.net). The remaining 94 markers belonged to IRGs, previously developed from the P. peruviana leaf transcriptome (Enciso-Rodríguez et al., 2013).

The 360 COSII markers were screened on five accessions with contrasting responses to Fusarium oxysporum which includes P. peruviana and related species (Enciso-Rodríguez et al., 2013). In addition, 94 IRGs were tested on six contrasting accessions as described by Enciso et al. (2013). Polymorphic COSII and IRGs markers were selected and used to genotype 222 samples from 60 accessions. From the original set of markers, we selected monomorphic COSII and IRGs molecular markers for SNP identification on a 64-plant panel (Table 1). Complete marker information is shown in Supplementary Table 2.

2.3. PCR amplification and marker visualization

Polymerase chain reactions (PCR) were performed each in 15 µl final reaction volume using 1X PCR Buffer, 2.0 mM MgCl2, 0.2 µM dNTPs, 0.2 µM of each primer, 0.05 U/µl Taq DNA polymerase and 5 ng of genomic DNA. Thermal cycler conditions included a denaturation step at 94°C for 5 min followed by 35 cycles at 94°C for 30 s, 56°C to 60°C for 1 min (depending on the marker), 72°C for 2 min, ending with and extension at 72°C for 10 min. Amplifications were performed in a i-Cycler thermal Cycler (Bio-Rad, Hercules, CA, USA). PCR products were separated on 2% agarose gels stained with ethidium bromide (0.5 µg/mL). Molecular size of the COSII and IRGs bands was estimated using a 1 kb plus ladder as reference.

2.4. Marker sequencing

Each forward and reverse COSII and IRG primer selected for sequencing was modified with a 10-base Multiplex Identifier (MID) adaptor sequence and a 454-sequencing primer (A-primer and B-primer). The MID adapters were used for post-sequencing sample identification. Following the PCR amplification as described above, each of four-sample reactions was pooled. The pooled products were sequenced in two sequencing plates divided in eight regions, using the 454 GS-FLX Titanium platform (Roche) (Engencore Sequencing Facility, Columbia, SC, USA). The SFF files were submitted to the NCBI Sequence Read Archive (SRA, accession number SRX216233).

2.5. Data processing and analyses

COSII and IRG InDels were scored as co-dominants markers using the GeneTools software (Syngene, Frederick, MD). Sequence data were processed using the Mothur software package (Schloss et al., 2009), which involves trimming of adapter sequences and sample identification. Reference sequences for the set of markers were generated from Sanger and 454 reads (Enciso-Rodríguez et al., 2013) by aligning the 454 reads of each sample using the Burrows-Wheeler Alignment tool (BWA) (Li and Homer, 2010). SNP calling was performed with BWA read mapping files (BAM formats) using the SAMtools package (Li et al., 2009), retaining SNPs with a minimum read depth of 20 and a minimum base quality of 30. The final consensus sequence of each genotype was aligned using Muscle (v3.7) (Edgar, 2004), and then manually edited using Bioedit (http://www.mbio.ncsu.edu/bioedit/page2.html). Finally, SNPs with a minor allele frequency (MAF) ≤0.05 were selected by Tassel software v4.1 (Bradbury et al., 2007).

Standard measures of diversity including the average number of alleles per locus (a), expected heterozygosity (He), observed heterozygosity (Ho), polymorphic index content (PIC) were calculated by PowerMarker (version 3.25) (Liu and Muse, 2005) using InDels (COSII and IRG) and SNP markers independently. GenAlex (Peakall and Smouse, 2012) was used to perform Wright’s F statistics and an analysis of molecular variance (AMOVA).

Population structure analysis was carried out by Structure 2.3.4 (Pritchard et al., 2000). Number of populations (K) was set from 1 to 10, repeated 10 times, with a burn-in period of 50,000 iterations and 100,000 MCMC (Markov Chain Monte Carlo) repeats and a model allowing for admixture and correlated frequencies. The K optimum was evaluated by Evanno et al. (2005), using Structure Harvester (Earl and VonHoldt, 2011). The Neighbor-Joining algorithm was used for cluster analyses based on the Nei’s genetic distance using Phylip 3.695 (Felsenstein, 1989) with 1,000 bootstraping replicates. Principal component analysis (PCA) was performed using the R Stats package (R Development Core Team, 2009).

3. Results

3.1. DNA amplification and marker polymorphism

From the 360 COSII and the 94 IRGs molecular markers selected (Table 2), 24 polymorphic InDels (18 COSII and 6 IRGs) were identified and used to screen the entire population (222 plants) (Table 3). The amplicon average size of the 24 InDels markers ranged from 210 to 1700 bp (Supplementary Table 3). Molecular markers with low level of polymorphism (<10% of amplification in the whole population) were discarded.

Table 2.

Summary of markers used for InDels studied and SNPs discovery

Type of study Marker type No. of markers No. of
polymporphic
markers		 No. of SNPs
InDels analysis COSII 360 18 --
IRGs 94 6 --
Sub-Total 454 24 --
SNPs discovery COSII* 15 -- 642
IRGs* 18 --
Sub-Total 33 -- 642
Total 487 24 642
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*

Monomorphic markers selected for SNPs discovery

Table 3.

Summary of *Polymorphic markers used for InDels study and ξ Monomorphic markers used for SNPs discovery

Marker
Type		 Marker ID Tomato
Chromosome		 Forward Reverse Amplicon
Size (pb)		 Annealing
temperature
(°C)
COSII C2_At4g38240* 1 aAGATTAGCcaGCAgCAAGGAAG ATTGtCgGCAcTCCTGGTCCTG 280–530 56
COSII C2_At1g02140* 3 TcCGtTATGCTAAcAATTCCAAC TGTGtTCaTTtCCCATcACAATCTC 280–1100 56
COSII C2_At5g49970* 3 AaTTGGCaGGcTTGAGTGTTGC tcCCACCATTGTTACCAGGACCAC 500–1350 56
COSII C2_At5g51040* 3 tcgccgatgAATATcGATCTTTCC tCCaAGAAcaAggTCtAGCTCCAGG 210–550 50
COSII C2_At5g10920* 4 tatcatgggtagtaaaGCTCATGC TTGCCTCTcTAtCTCtTCAAGACC 280–1450 60
COSII C2_At1g46480* 4 tAAGAGtTTCaTtaggCCTGAAAG TATcTGtTCTTGtGTTGGATTCC 280–700 60
COSII C2_At4g29490* 7 AaGAGCaAACtcGAcAtTGCACC AcAAGTAgGCgAAATAGCTCTCCTG 500–600 60
COSII C2_At2g42750* 7 tCCagTGCAaaGgAgagTTTATGATG aCTCTaGCTCTtCCAaAGTCTTCCTC 300–750 60
COSII C2_At3g09920* 9 AAgCAATcATaAAGGGtCACAGGAG AgACCATTGGgCAATAATCTTTCC 800–1650 56
COSII C2_At1g53000* 10 AgATtCTCGGCAAGCCtATGATCC AAGcTTtGCCCTTTCCCATGTTC 750–1700 56
COSII C2_At5g03905* 10 tgttGAAGCCGGtGGtTGTTCTGG TCgAatGATAcaTTATCcACaACCAATTT 410–1400 56
COSII C2_At2g27450* 11 tCAAGATGAtGGaCTTGATTCTCG TCTcAATTATCTCcTTTCCAATGC 370–750 60
COSII C2_At1g14790* 5 TGG AGA GAA CAC TAA TAT TCT CAA GG ATG TCT TGA TCC CAG CAA ACA AAG 630–650 60
COSII C2_At1g71260* NA TGC TTT ATC AGC AAC AGA GGT TGG ACG AAA TAG CCA CTC CCA TCA GC 280–330 58
COSII C2_At3g10920* 6 TGG CTT GGT GTG GAC AAA GAG C TGC AAG TAG TAT GCG TGT TCC C 650–1500 58
COSII C2_At3g18165* NA ATG GCG ACA AAA GAC GGC GAC TGA TGC TGC AGT AAT CGC TGA GC 250–450 58
COSII C2_At4g23100* NA TGC AAG TCC TCC AAC AGA GGA TGC AGG CCA ATA ATC TTG TCA CCC TCC 840–900 60
COSII C2_At5g51700* 11 AGA TGC CAC CAG GGA TTC TTT TG AGC AGT GTC GTG ATT TTC CTT TTC 450–500 56
COSII C2_At5g58220ξ 1 AATCAACAGCTcTGTCCACTGCTACTG TTcTcCctaTAgCGAGCATTCCAC 290 56
COSII C2_At3g62010ξ 1 TAcCCcaTtTATaGCATGGCAAC ATgTCaTCtTTTAACCGAGCCTCC 325 56
COSII C2_At2g35920ξ 1 TGCTtGCAACcAACATAGCTGAG aagctcttGtAgtGGTGTTCGAAG 400 56
COSII C2_At4g01880ξ 1 ATGCCCaTtttTCAAACAAGCTC TcCCtAGaATtGAGGCCTGCTGC 425 56
COSII C2_At5g27620ξ 1 ATcTaCAATGGTCcGTGATGGAAC tTCcTCTGCCTTGCAAGCTGC 350 56
COSII C2_At3g06580ξ 1 TGCTCAACTcACaTGTGAgTGTGAAAG aGCaAAcCCAgaTTttGCCATAAC 310 56
COSII C2_At3g07100ξ 2 AcGAACGaTGtGCtGCTGGATATAC AgaCCCtggGgaTCTAaGCTCTCTG 275 56
COSII C2_At4g34090ξ 2 TGtTaCGATGGCCaACTGCCCC TCTTCcaCCAAaAccCTGTGAATG 425 56
COSII C2_At5g45950ξ 2 TGAGATTCAAGTGTAGATCCTGGAAA TGTGGGTCTAGAAACgCTCTGGTCAT 490 56
COSII C2_At2g40760ξ 3 AgGAAGAgGCTcTTCATCATGGAC TCTcTCAATTGGTGAAACAGAAGG 290 56
COSII C2_At5g41480ξ 6 TATTCGTgCTGGTCTGGAGAGTGC ATGATCCTTGTCATTCGCCATAGC 490 56
COSII C2_At4g24690ξ 6 TtgCCtTCaGGggAGAAGTTTGG TCAACtAATTCCATGGCAGGGTTTG 320 56
COSII C2_At4g30580ξ 7 TCaGCcgGTGCTgCtTATCCAC TgatgAacTtgAagTTTctCCCAAGAG 425 56
COSII C2_At3g63190ξ 9 TTGGTGcAgCCgTATGACAAATCC TCCATCATTaTTTGGcGTCATACC 490 56
COSII C2_At1g67740ξ 10 ATGTGActCcGCaTTTGCAGCTC ATCTCATCtTaTtaATCTGATTCAAAGC 350 56
IRG PpIRG-7* NA AGTCCAAGGATCGAAGAGCA ACGACCTCCAACTTCCATTG 240–500 59.95
IRG PpIRG-24* NA TTCTTTCTGCTCCTGGTGGT GCATCCTTCTCGCTCAAAAG 800–850 59.84
IRG PpIRG-42* NA GCTCCCTGCATTGTTTTCA TGCCTGGAAAGAGTCAACAA 510–540 59.79
IRG PpIRG-64* NA GATTCCATCATCGAGGCAAT TGATGTTGTTGGAAGGAGGA 470–650 59.86
IRG PpIRG-84* NA GCCAAATACAAGCGCAATTT TCCTAGCATTGGCAATTTGA 450–1000 60.10
IRG PpIRG-86* NA GTTTTCCTCCTCCCACTTCC GCTTCAACATGGCTTTGTCC 400–450 59.91
IRG PpIRG-4ξ NA TCGCTTGGGTCTAGCCTTTA CCATAGAGGAGCTGCGTTTC 480 59.97
IRG PpIRG-5ξ NA AGCAGGACCAGAGAGCATGT CACCGGAAATTTCAATGCTT 500 60.02
IRG PpIRG-30ξ NA AATCTGGAGGCTGCATTGTAA CATTTGAAGGTGATGCATGG 310 59.72
IRG PpIRG-31ξ NA CAAGCTGCGCCATCTACATA AGGCATGGAAAAGCATCATC 510 60
IRG PpIRG-32ξ NA CTGATCCGGAGAGTGGTTTC ACGCTCCAAATCAAAGGTTG 490 59.65
IRG PpIRG-33ξ NA TGGATTCAAGCCTGCAAAAT CCTCCAACATTAAGCCCTCA 410 60.59
IRG PpIRG-43ξ NA AACATGGGATGAAACACTTGC TGAAAAGCGTGCCAACTAAA 320 59.85
IRG PpIRG-44ξ NA TGATGAGTGGGATGACCTGA GCTCAACATCAACGCTGGTA 390 60.05
IRG PpIRG-46ξ NA TTCTGCTGCCACTGATGTTC GAGACCCCAAGCACATTTCA 520 59.99
IRG PpIRG-51ξ NA GCGTTTGGGCTAGATAGGAA GGCCAGGTCATGAACAAGAT 480 59.32
IRG PpIRG-53ξ NA TCCATCAGGCAAAGCAGATA GCAAGTGCCCCAGTCTAATC 520 59.38
IRG PpIRG-56ξ NA CATGCATAGGTTGGGACAAA AGCCTCCAACTCGTGAAAGA 490 59.40
IRG PpIRG-60ξ NA TGAACAACTCCCAAGCATCA GAGGAGATGGCAAAACGAAA 490 60.24
IRG PpIRG-61ξ NA GAAGTTCGCATGGTTGGTCT TGTGGCGGAGAAAGACTGTT 490 60.12
IRG PpIRG-63ξ NA CCTTGTTACCTGGTGGCATT GCCAAATCCTTTTCACCAGA 500 59.85
IRG PpIRG-79ξ NA GGTTGCATGGAATAGGAGGA TTCAGAAAAGTTGGGGATGG 500 59.89
IRG PpIRG-90ξ NA GGCCATCCACAACCGTATTA AGGAGGATGTCGCTGAAAGA 430 60.59
IRG PpIRG-93ξ NA ATTGGCTGCATCCTGAAAAC GTTTTCGTCTTCCTCGTCCA 600 60.23
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Monomorphic markers on agarose gels (15 COSII and 18 IRGs) based on their size (less than 400 pb) and random distribution across the tomato chromosomes were selected for sequencing in order to uncover SNPs on a 64-plant panel (Table 2, 3). A multi-fasta alignment showed a total length of 13,000 nt per plant and 832,000 nt over all sequences. From these, we identify a total of 642 SNPs for the whole collection, 463 SNPs for P. peruviana population and 1,600 SNPs for related taxa germplasm.

3.2. Genetic diversity and population structure

The 24 InDel markers detected a total of 121 alleles in the whole collection, with an average number of alleles per locus of 5.04, ranging from 2 to 9. For SNP markers, the allele number was 2. Based on InDel markers the average observed heterozygosity was 0.47 on average for the whole collection, ranging from 0 (C2_At5g51040 marker) to 1.0 (C2_At1g18165 marker). Null values in observed heterozygosity were found in individuals for some markers suggesting homozigosity. Furthermore, the mean expected heterozygosity value (0.42) was lower than the observed heterozygocity, which ranged from 0.08 for PpIRG64 to 0.718 for C2_At2g53000. Similarly, SNPs markers showed a lower expected heterozygosity value (0.33) than the observed heterozygocity (0.38) for all taxa. Polymorphism information content values (PIC) ranged from 0.094 to 0.663, with a mean value of 0.37 for InDels markers, while SNP markers showed a mean PIC value of 0.26. These parameters are summarized in Table 4 and Supplementary Tables 4 and 5.

Table 4.

Summary statistics of genetic diversity calculated with 24 InDel markers and 642 SNP markers for all taxa, 463 SNPs for P. peruviana population and 1,600 SNPs for related taxa germplasm

Sample N A He Ho PIC
InDels All Taxa 222 5.04 0.42 0.47 0.37
P. peruviana 171 2.75 0.30 0.48 0.24
Related Taxa 51 4.75 0.59 0.4 0.54
SNPs All Taxa 64 2 0.33 0.38 0.26
P. peruviana 51 2 0.41 0.59 0.32
Related Taxa 13 2 0.25 0.12 0.21
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N = sample size; a = mean number of alleles per locus; He = Unbiased expected heterozygosity; Ho = observed heterozygosity; PIC = Polymorphic information content

When P. peruviana and related taxa were analyzed as two different populations, the average number of alleles per locus for InDels were higher for related taxa (4.75) than P. peruviana (2.75) while for SNPs markers the average number of alleles was equal in both populations (2). The observed heterozygosity for P. peruviana with both markers was higher (InDels = 0.48 and SNPs = 0.59) than the expected heterozygosity (0.30 and 0.41). Nonetheless, Ho values in related taxa (InDels = 0.4 and SNPs = 0.12) were lower than gene diversity values (He) (InDels = 0.59 and SNPs = 0.25) for both kinds of markers (Table 4).

Genetic structure of the whole population was analyzed using neighbor joining (NJ), PCA and Bayesian cluster approaches. The cluster analysis based on Nei´s genetic distance derived from InDel and SNP markers showed two major groups (Figure 1) comprised of related taxa and P. peruviana accessions, with high bootstrap values (>50%), as expected. Two exceptions were observed; the related species P. philadelphica 09U056 was grouped with the P. peruviana population while the P. peruviana 09U289 accession was clustered with related taxa with bootstrap values above 70%, using either type of markers. Moreover, NJ tree for InDels (Figure 1a) showed some plants from P. peruviana accessions (09U132-1, 09U138-6, 09U118-1, 09U134-4, 09U108-3, 09U116-1, 2, 3, 4) clustered with related taxa.

Figure 1.

Figure 1

Figure 1

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Neighbor-joining tree based on Nei’s genetic distance from the dataset of (a) 24 molecular markers across 222 plants and (b) 642 SNP markers across 64 plants. Bootstrap values (in percentage) are indicated at the main branches. Related taxa: red, P. peruviana: blue

PCA analyses from InDel and SNP marker data showed a similar result to the NJ clustering approach, supporting the presence of two populations (Related taxa and P. peruviana), and highlighting the misclassification of 09U289 and 09U056 accessions (Figure 2a–b). Besides, PCA analysis based on SNP markers revealed two clusters separated by state of cultivation on P. peruviana population: wild and cultivated accessions (Figure 2b). InDel based analyses showed that the first three components accounted for 61% of the total variation, where each component explained 46%, 8% and 7% of the overall variation, respectively; while SNP based analyses showed that the first to the third component explained 22%, 10% and 7%, respectively, for a total variation of 39%. Non-geographical distribution pattern was found with any type of markers by PCA analysis (data not shown).

Figure 2.

Figure 2

Figure 2

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Principal component analysis of the Cape gooseberry germplasm collection on (a) 24 molecular markers and (b) 642 SNP markers

The identification of genetically homogeneous groups of individuals was performed using the method based on ΔK suggested by Evanno et al. (2005), which showed a peak with a maximum value around K=2 (Figure 3), indicating the presence of two main clusters in the whole collection (P. peruviana and related taxa), consistent with the results displayed by NJ and PCA analysis. Population differentiation analysis for the whole collection using InDel markers showed a higher value (0.143±0.022) among populations than the SNPs FST value (0.038±0.002), while FIS values for both kinds of markers were 0.044±0.103 for InDels and −0.015±0.029 for SNPs markers.

Figure 3.

Figure 3

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Inferred population structure of 60 accessions based on 24 InDel markers. SNPs markers showed a similar pattern. RT=Related taxa, Pp= Physalis peruviana

The analysis of molecular variance (AMOVA) for InDel markers showed that genetic variance within accessions contributed with 73% to genetic diversity (Table 5) while the variance among populations (P. peruviana and related taxa) and among accessions accounted for 23% and 4% of the total variance, respectively. Similarly, for SNPs markers, most of the variation was found within accessions with 95%, while 5% of the variance was observed among populations.

Table 5.

AMOVA analysis for the 222 and 64 plant population based on InDel and SNP markers for the whole collection

Source of variation d.f. SS MS Variance
component		 Total variance
percentage		 P-value
Indel markers
  Among
Populations		 1 258.2 258.2 1.607 23% 0.01
  Among
Accessions		 220 1,255.9 5.7 0.314 4%
  Within Accessions 222 1,128.0 5.0 5.081 73%
  Total 437 2,642.0 7.002 100%
SNPs markers
  Among
Populations		 1 555.3 555.3 13.7 5% 0.01
  Within Accessions 62 16,719.8 269.6 269.6 95%
  Total 63 17,275.1 283.4 100%
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4. Discussion

4.1. Identification of marker polymorphisms by InDels and SNPs

The reduced availability of molecular markers in non-model commercially important crop species such as P. peruviana has been a bottleneck for the assessment of genetic variability that could support breeding strategies. Nonetheless, dominant RAM markers as well as codominant markers such as SSRs and COSII, conserved in related species as tomato have been used in genetic studies in Physalis (Bonilla et al., 2008; Morillo Paz et al., 2011; Wei et al., 2012). COSII markers represent a strong strategy to integrate diversity estimations over extended genetic distances and to map highly variable gene spots (Bedoya-Reina and Barrero, 2010). Besides, due to the fact they are derived from universal primers transferred among species, these are an economic alternative to other types of co-dominant markers such as SSRs (Enciso-Rodríguez et al., 2010). Although, identification of SSRs markers in P. peruviana has been reported (Simbaqueba et al., 2011), one of the main drawbacks of these markers are the presence of null alleles related to mutations in the primer annealing sites which may lead to errors in genotyping scoring (Kumar et al., 2009), and may also be the case for other type of markers. SSRs markers from P. peruviana were designed from expressed sequence tags and UTR regions (Simbaqueba et al., 2011), and usually, markers found in untranslated regions are less polymorphic than genomic SSRs (Ellis and Burke, 2007) and less transferable to other species from the same genera as shown by Lara-Cabrera and Spooner (2005) in potato and its wild relatives. Thus, in the present study we increased the number of co-dominant markers available for the genus Physalis and evaluated these new markers to contribute knowledge of the genetic variation within the genus especially on P. peruviana species. The study showed a low polymorphism level of COSII and IRG markers with 5% of the 360 COSII markers and 8% of the 94 IRGs behaving as polymorphic on agarose gels.

To increase the number of markers for further analyses, this study took advantage of next generation sequencing technologies to uncover SNPs on selected COSII and IRG monomorphic markers, which resulted in the identification of 642 SNPs in 15 COSII and 18 IRGs for the whole collection. According to several reports a larger number of SNPs (7 to 11 or more times as compared to SSRs) can have the same level of information as SSRs (Yu et al., 2009; Van Inghelandt et al., 2010a; Filippi et al., 2015), suggesting that the number used in the present study is within a suitable range. The average number of alleles per locus in these SNP markers was two, suggesting that despite the variable nature in ploidy level of Physalis spp. (Liberato et al, 2014), the species in the genus might behave as allotetraploids, hypothesis that will need further investigation through segregation analyses. The biallelic and multiallelic markers found in this study were used for diversity and population structure analyses as well as for establishing comparisons between InDels and SNPs derived from previously reported markers.

4.2. Genetic diversity and population structure

A broad genetic base is important for genetic resources and plant breeding applications which include but are not limited to generation of core collections, parental lines selection, quantitative trait loci identification, association mapping or genomic selection, all of which require previous information of genetic diversity (Rao and Hodgkin, 2002; Zhu et al., 2008; Rauf et al., 2010). In this study, the application of InDels and SNP markers on the whole collection (all taxa) and the two subpopulations identified (P. peruviana and related taxa) revealed a moderately to high genetic diversity.

InDel markers showed an average PIC value of 0.37 for the entire population, which placed them in the range of loci with intermediate polymorphism (0.25 to 0.5) according to Ge et al. (2013). Highly polymorphic markers such as C2_At1g53000 and C2_At2g42750 derived from COSII (0.66 and 0.60 PIC values, respectively) could be used in future studies to discriminate species in the genus Physalis. Likewise, markers C2_At1g53000 and C2_At3g18165 were identified as the most informative to evaluate the P. peruviana collection with PIC values of 0.592 and 0.545, respectively. PIC values of SNP markers reached until 0.5 due to SNPs biallelic nature and were in general lower than InDels PIC values.

InDel markers have been used to characterize diversity of Physalis and other related species from the same family. A recent study by Wei et al. (2012) with 96 InDel markers, revealed an average number of 3.5 alleles per marker, lower in comparison with the average number for the whole population in this study (5.04 alleles). Nevertheless, this value is difficult to compare, because the sample size used in Wei et al. (2012) study was smaller (38 accessions) and the present study included 60 accessions along with the differences in the populations and marker loci used. Compared to SNPs markers, diversity values are lower that those estimated by InDel markers, probably explained by the biallelic nature of SNP markers. Usually a maximum gene diversity with biallelic markers is 0.5, whereas for multi-allelic markers such as SSRs or InDels can approach 1 (Van Inghelandt et al., 2010b). Additionally, the importance of the number and selection of SNPs, as well as the use of a large panel of accessions to prevent ascertainment bias and enhance the accuracy of diversity studies (Moragues et al., 2010; Emanuelli et al., 2013) should be considered as well.

Some differences are found in the level of diversity when compared to a previous study in a P. peruviana collection (Bonilla et al., 2008). Values in the present study (He = 0.30 and 0.41 for InDels and SNPs, respectively) were slightly higher than those observed by Bonilla et al. (2008) (He = 0.2559). Nevertheless, it should be considered that Bonilla et al. (2008) used a different population and a set of dominant (RAM markers), which may underestimate allelic diversity as compared to the co-dominant nature of markers used in the present investigation. On the other hand, Berdugo et al. (2015) showed slightly higher diversity values (He = 0,44) for InDels markers than this study. However, this difference is due to the fact that, Berdugo et al. (2015) used an F1 population which is expected to have high heterozygosity values.

Diversity estimations from COSII and IRG as well SNP derived markers used in this study should be interpreted with caution due to the fact that these markers were developed and discovered from conserved domains in the Solanaceae and thus probably had been under selective pressures (Wu et al., 2006). Nonetheless, relatively high diversity values were found related to the fact that Cape gooseberry is 53% outcrossing (Lagos et al., 2008) and it became cultivated from the wild without being subjected to a long domestication process as compared to other fruit bearing species of the same family as tomato (Labate et al., 2009; Sim et al., 2012). It is probable then, that its domestication is still in process and natural selection is still playing an important role retaining heterogeneous populations with a broad genetic variability and adaptability (Rauf et al., 2010). These results could have important implications in plant breeding purposes, since the presence of a high genetic diversity is a prerequisite for the efficiency of selection processes (Rao and Hodgkin, 2002; Rauf et al., 2010).

The clustering analysis based on Nei´s genetic distance did not show any geographical distribution pattern. This result might be associated to gene flow via seed exchanges among different regions occurred frequently or possibly by human transfer trying to mitigate the effects of some diseases like F. oxysporum. In other analysis, NJ trees from the P. peruviana population were run separately for each marker type and no clustering was observed in relation to geographical distribution or state of cultivation (Data not shown). Additionally, the position in the dendrogram of accession 09U289 suggests a misclassification; supported by the flow cytometry studies where this accession presented a different ploidy level (24/2n=4x, of DNA= 2.33 pg) from other P. peruviana accessions (ranging from 24/2n=4x;. of DNA = 2.33 pg to 48/2n=4x; of DNA= 8.12 pg) (Liberato et al., 2014). On the other hand, the unexpected clustering of some plants from P. peruviana accessions with the related taxa group using InDels markers, was possibly due to the fact that InDels had a considerable number of missing data (9.3%) in the population as compared to SNP data (4.3% missing).

All three-population analyses supported that the whole population represented by all taxa has two different genetic populations, related taxa and P. peruviana, as expected. Based on the PCA approach, it was possible to identify two different populations within the P. peruviana group, differentiated by state of cultivation, commercial and wild accessions with some mixtures between the two subgroups. Wild samples found in a cluster with commercial materials probably came from commercial fields usually characterized by non-homogeneous materials due to the lack of breeding cultivars grown for this species in Colombia. Most of the accessions used in the present study were cultivars that followed a primary artificial selection for certain desirable characteristics without a breeding program. Besides, the low percentage of variation explained by the first three components in the PCA analysis and the different nature of markers indicates that differentiation of some individuals may not be well captured. Nonetheless, it is important to consider the outcrossing nature of the species, where a greater number of SNPs markers across all genome may be required to detect population structure (Zhu et al., 2008).

Based on the FST values (InDels = 0.143 and SNPs = 0.038) for the whole collection we did not find a high level of differentiation between the two subpopulations (P. peruviana and related taxa). The FST values were closer to zero and the AMOVA values among populations represented 5% and 23% for SNPs and InDels markers respectively, while within populations was much higher for both markers. This indicates more variation within than between populations, aspect that should be considered for conservation and breeding strategies where more variation wants to be captured. According with Hamrick and Godt (1989) the reproductive biology is an important factor in determining the genetic structure, this is because the inbreeding species maintain more of their genetic diversity among populations rather than within populations than do outcrossers. In outcrossers a higher proportion of their variation occurs within individual populations. Furthermore, Silvertown (2009) affirms that the outcrossing populations such as Physalis show lower FST values than self-fertilizing populations; consistent with the high genetic variation that we found in the entire population and the lower FIS values typical of populations with heterozygote excess. The relatively high diversity values of the whole collection support that the P. peruviana population and the related taxa germplasm, is a valuable source to assist crop improvement programs for more efficient and strategic use of genetic resources. This information should be used to identify duplicates or wrong classifications in germplasm collections towards establishment of core collections, conservation and breeding purposes.

Understanding genetic diversity and population structure is essential for conservation efforts and the basis for selection is the first step to establish a crossing program of different genotypes or populations of Physalis peruviana or its related taxa. This is a strategy for capturing novel desirable alleles for valuable traits such as fruit quality, yield or reduced susceptibility to pests.

5. Conclusions

This study found a moderate to high genetic diversity on a 64 plant-panel of P. peruviana and related taxa species. A total of 121 alleles were detected on 24 InDels loci and 642 SNPs were identified in a 222 individuals sampled. Diversity and population structure analysis showed two clusters related to Physalis peruviana species and related taxa. The evaluation and characterization of germplasm through INDels and SNPs markers was useful to infer diversity and population structure analyses that may have important implications for managing conserved germplasm, for avoiding duplicates and wrong classification of the collection, for developing core collections and for selecting material for breeding strategies in this orphan crop.

Supplementary Material

NIHMS731440-supplement.xlsx (824.6KB, xlsx)

Acknowledgments

We thank Edgar Jimenez for supplying in vitro plant material for analyses. This study was partially funded by the Colombian Ministry of Agriculture Contract No. 054/08072-2008 to LSB. We thank Aureliano Bombarely for data analysis advice. This research was supported in part by the Intramural Research Program of the National Institutes of Health, National Library of Medicine, and National Center for Biotechnology Information. We thank Roxana Yockteng for her critical review of this manuscript.

Footnotes

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Authors' contributions

Performed the experiments: GAG-M JAO-G FEE-R PD-D FM. Analyzed the data: GAG-M JAO-G FEE-R. Jointly prepared the manuscript: GAG-M JAO-G FEE-R LMR LSB. Conceived and oversaw the project and its components: FEE-R LSB. Contributed bioinformatics expertise for data analysis LM-R DL. All authors read and approved the final manuscript.

Contributor Information

Gina A. Garzón-Martínez, Email: ggarzon@corpoica.org.co.

Jaime A. Osorio-Guarín, Email: josoriog@corpoica.org.co.

Paola Delgadillo-Durán, Email: ldelgadillo@corpoica.org.co.

Franklin Mayorga, Email: fmayorgac@corpoica.org.co.

Felix E. Enciso-Rodríguez, Email: fenciso@corpoica.org.co.

David Landsman, Email: landsman@ncbi.nlm.nih.gov.

Leonardo Mariño-Ramírez, Email: marino@ncbi.nlm.nih.gov.

Luz Stella Barrero, Email: lbarrero@corpoica.org.co.

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