The genus Physalis includes a number of commercially important edible and ornamental species. Its high nutritional value and potential medicinal properties leads to the increased commercial interest in the products of this genus worldwide. However, lack of molecular markers prevents the detailed study of genetics and phylogeny in Physalis, which limits the progress of breeding. In the present study, we compared the DNA sequences between Physalis and tomato, and attempted to analyze genetic diversity in Physalis using tomato markers. Blasting 23180 DNA sequences derived from Physalis against the International Tomato Annotation Group (ITAG) Release2.3 Predicted CDS (SL2.40) discovered 3356 single-copy orthologous genes between them. A total of 38 accessions from at least six species of Physalis were subjected to genetic diversity analysis using 97 tomato markers and 25 SSR markers derived from P. peruviana. Majority (73.2%) of tomato markers could amplify DNA fragments from at least one accession of Physalis. Diversity in Physalis at molecular level was also detected. The average Nei’s genetic distance between accessions was 0.3806 with a range of 0.2865 to 0.7091. These results indicated Physalis and tomato had similarity at both molecular marker and DNA sequence levels. Therefore, the molecular markers developed in tomato can be used in genetic study in Physalis.
Citation: Wei J, Hu X, Yang J, Yang W (2012) Identification of Single-Copy Orthologous Genes between Physalis and Solanum lycopersicum and Analysis of Genetic Diversity in Physalis Using Molecular Markers. PLoS ONE 7(11): e50164. https://doi.org/10.1371/journal.pone.0050164
Editor: Randall P. Niedz, United States Department of Agriculture, United States of America
Received: August 1, 2012; Accepted: October 19, 2012; Published: November 16, 2012
Copyright: © 2012 Wei et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: The authors have no support or funding to report.
Competing interests: The authors have declared that no competing interests exist.
Physalis, a member of the plant family Solanaceae, includes more than 90 species . A number of species in the genus are of horticultural and economic importance due to their high nutritional value in vitamins content, minerals and antioxidants as well as potential medicinal properties including anti-bacteria, anti-inflammatory, and anti-cancer –. Some of the species such as P. alkekengi (Chinese lantern) can also be used for decoration. Therefore, the commercial interest in this genus has grown in many regions of the world in recent decade.
High variation of morphological characteristics have been observed and used to identify species in Physalis , , , . However, phenotypic characters are generally influenced by environments and plant developmental stages –. In addition, species with similar morphological characters can not be easily distinguished . Molecular markers are independent of environmental conditions and show higher levels of polymorphisms. They have been widely used in phylogenetic analysis in many organisms –. DNA sequences from few genes and ISSR markers have also been used to investigate the phylogeny of Physalis and their relationship to other genera in the Solanaceae family , , , . Very recently, 5971 SSR markers were discovered through analyzing the assembled P. peruviana leaf transcriptome sequences , . However, only 30 markers have publicly available primer information. The lack of available markers prevents the detailed study of the genetic diversity and phylogeny in Physalis at molecular level.
Tomato (Solanum lycopersicum) is a relative to Physalis in the same family. More than 3000 molecular markers have been developed since 1986 when the restriction fragment length polymorphism (RFLP) markers were used to construct the first tomato linkage map , . The tomato genome has also been sequenced and publicly available . Approximately 4000 genes conserved between tomato and Arabidopsis have been discovered –. These conserved ortholog sets (COS) have been used to analyze genetic diversity and phylogenetics in Solanaceae – and other families –. All these provide a potential to understand the genetics of Physalis at molecular level. Therefore, the objectives of the present study were to determine whether tomato markers can be used to amplify DNA fragments for investigating genetic diversity in Physalis, and to infer the relationships between tomato and Physalis using molecular marker and sequence data.
Materials and Methods
A collection of 38 accessions of Physalis (Table 1) originated from 11 countries were subjected to genetic diversity analysis and estimation of genetic relationship to tomato using bioinformatics and molecular markers. The tomato variety OH88119 and S. lycopersicum var. cerasiforme accession PI435238 were used as out-group controls for DNA amplification using tomato markers and phylogenetic analysis. Seeds of 36 Physalis accessions and PI435238 were kindly provided by Northeast Regional PI Station at Geneva, New York, USA. Seeds of Heirloom Purple and Toma Verde Green were purchased from a supermarket in the United States. Based on the information obtained from the website of Northeast Regional PI Station, 30 of 38 Physalis accessions belong to 6 species including P. acutifolia, P. angulata, P. nicandroides, P. peruviana, P. philadelphica, P. pubescens, while the species information for the remaining are not clear (Table 1). All seeds were sown in 128 Square Plug Tray Deep filled with a mixture of peat and vermiculite (3:1) in the greenhouse. Plants were grown in a protected greenhouse for DNA isolation.
Computational analysis of genes conserved between Physalis and tomato
Nucleotide sequences derived from the genus Physalis were obtained from the National Center for Biotechnology Information (NCBI, http://www.ncbi.nlm.nih.gov/) using the search and retrieval system for nucleotide data and phrase searching of Physalis. The results were then filtered with taxonomic group ‘Physalis’ (i.e. (physalis) AND "Physalis" [porgn:__txid24663]). Repetitions of sequences from the same gene were excluded using self-BLASTn approach with an expect value of less than e−15 and greater than 80% coverage of the query sequence. The unique sequences were then searched against the tomato ITAG Release2.3 Predicted CDS (SL2.40) downloaded from Sol Genomics Network (SGN, http://solgenomics.net/)  using BLASTn with an expect value of less than e−10 and greater than 80% coverage of the query sequence. The best hit of the tomato sequence for each Physalis sequence was then searched back to the Physalis sequence database to identify the reciprocal best match.
Two sets of tomato DNA markers were used to estimate the relationship between Physalis and tomato. The first set of 60 COS markers were used for the initial test of the feasibility of using tomato markers in Physalis. These COS unigenes are conserved among several species including Arabidopsis, tomato, coffee, potato and pepper –, and specifically developed for detecting polymorphisms in tomato . After the initial test, three types of tomato markers (Table S1) including simple sequence repeat (SSR), potential intron polymorphism (PIP) and insertion/deletion (InDel) markers were randomly selected to test their ability to amplify DNA fragments in Physalis. The tomato SSR markers were developed by mining tomato expressed sequence tags (EST) database . PIP was developed by comparing the conserved genes between Arabidopsis and tomato . InDel markers could be divided into two groups. The first group was designated as ‘COS InDel’ because they were developed by comparing intronic region sequences of COS unigenes within cultivated tomatoes , , . The second group was designated as ‘other InDel’ because they were developed by comparing DNA sequences of genes between two Solanum species , . Most of these SSR, PIP, and InDel markers have been used to detect polymorphisms in cultivated tomatoes , –. More recently, Simbaqueba et al.  discovered a set of SSR markers through mining P. peruviana leaf transcriptome shotgun assembly (TSA) database and provided primer sequences of 30 SSR markers. These P. peruviana SSR markers were also used in this study.
DNA isolation and marker analysis
Genomic DNA was isolated from young leaves collected from eight plants of each accession using the modified CTAB isolation method . DNA quantity and quality were examined by running 5 μl of genomic DNA solution mixed with 3 μl loading buffer on a 1% agarose gel. The DNA was then diluted to a concentration of 5 ng.μl−1 for PCR amplification.
To test the feasibility of using tomato markers in Physalis, genomic DNA fragments from six Physalis accessions, Heirloom purple, PI232077, G32541, PI644010, PI203942, and PI512011, as well as the tomato lines PI435238 and OH88119 were amplified using 60 COS markers . Genetic diversity in all Physalis accessions and two tomato lines were analyzed with 122 markers including 97 tomato markers (32 COS InDel, 26 SSR, 15 PIP, and 24 other InDel) and 25 SSR markers derived from P. peruviana (Table S1).
PCRs were conducted in a 10-μl reaction volume. Each reaction consisted of 10 mM Tris-HCl (pH 9.0 at room temperature), 50 mM KCl, 1.5 mM MgCl2, 100 μM each of dNTPs, 0.1 μM each primer, 10 ng of genomic DNA template, and 1 unit of Taq DNA polymerase. Reactions were heated at 94°C for 3 min followed by 36 cycles of 1 min at 94°C, 1 min at specific annealing temperature for each primer pairs (Table S1), and a 1-min extension at 72°C. Final reactions were extended at 72°C for 5 min. Amplification was performed in a programmable thermal controller (PTC-100; MJ Research, Inc., Watertown, MA). Following the amplification reactions, the PCR products were separated on 7% polyacrylamide gel and visualized using silver-staining approach as described in Chen et al. .
Data collection and analysis
The presence or absence of each single fragment was coded by 1 or 0, respectively, and scored for a binary data matrix. Allele frequency of each marker was calculated for each accession. Nei’s genetic distance  were calculated for each pair of accessions using the program in the software package NTSYSpc 2.11a . Since approximately 60% of accessions belong to P. philadelphica, the genetic distance and allele information for this species were also calculated. Unweighted Pair Group Method with Arithmetic Mean (UPGMA) cluster analysis was performed to develop a dendrogram.
Although the 38 accessions were from at least six species of Physalis, model without prior population information was used to assign individuals to population using a free software package of STRUCTURE2.2 –. Number of populations (K) was determined following the instruction in Pritchard et al.  with a burn-in period of 100,000 iterations and Markov Chain Monte Carlo of 100,000. Twenty independent runs were done for K varying from 1 to 10. The K optimum was defined according to the method proposed by Evanno et al. .
Genes conserved between Physalis and Solanum lycopersicum
The total number of nucleotide sequences for Physalis obtained from NCBI were 34616, of which 33916 (98.0%) were from the leaf TSA of P. peruviana, while the remaining sequences were from at least 30 species including P. philadelphica (78 sequences). Self-BLASTn indicated that 11436 sequences were redundant. Of the remaining 23180 sequences, 19044 could be aligned to the tomato CDS with an expect value of less than e−10. However, only 4509 sequences matched to the tomato CDS by adding the parameter of greater than 80% coverage of the query sequence. Among the 4509 sequences, 372 matched to multiple (2–8) tomato CDS, while 811 matched to the same tomato CDS as others. Reciprocal blasting the Physalis sequence database using the best hits of tomato sequences resulted in 3356 genes with single copy in the TSA. These genes were considered as single-copy orthologous genes between Physalis and tomato. According to the GO terms assigned to the Physalis genes , 2200 genes belonged to Molecular Function class, 414 genes belonged to Biological Process class, 248 genes belonged to Cellular Component class, and 494 genes had no GO terms yet. In addition, 4867 sequences in Physalis did not match to any tomato CDS.
Success of PCR amplification of DNA fragments in Physalis
The COS markers developed by comparing Arabidopsis and tomato sequences had a high rate of PCR success in amplification of DNA fragments in Physalis. Of the 60 COS markers (marker information not shown) used for initial test and the 15 PIP markers for genetic diversity analysis, 59 and 15 could amplify DNA fragments from at least one accession of Physalis, respectively. However, the COS markers developed by comparing sequences between tomato and other crops (COS InDel) or specifically for tomato (other InDel and SSR markers) had a low rate of PCR success between 65.4–75.0% (Table 2). Meanwhile, of the 30 SSR markers developed from P. peruviana, five could not amplify any DNA fragments from all accessions. The remaining 25 could amplify DNA fragments from at least one accession of Physalis and 40% had amplicons from tomato lines (Table 2). The average numbers of alleles in Physalis amplified by tomato and P. peruviana markers were 3.2 and 4.4, respectively. In total, 96 markers could amplify DNA fragments from at least one accession in Physalis. These results suggested that markers could be transferred between tomato and Physalis.
Marker polymorphisms in Physalis
A high ratio of polymorphisms at marker level was observed in Physalis. Of the 96 markers having amplicons, 89 (92.7%) showed polymorphisms in Physalis and 71 (74.0%) showed polymorphisms in P. philadelphica (Table 3). All PIP markers could detect polymorphisms in Physalis and 66.7% of PIP markers detected polymorphisms in P. philadelphica. The COS InDel markers showed a relatively low ratio of polymorphisms in both Physalis (50%) and P. philadelphica (34.4%). All the 25 SSR markers derived from P. peruviana could detect polymorphisms in Physalis, while 21 showed polymorphisms in P. philadelphica (Table 3).
A total of 336 alleles with an average of 3.5 alleles per marker were amplified by the 96 markers in Physalis. The number of alleles amplified by each marker varied from 1 to 5 (Table S1). However, the average number of alleles for each marker was 1.4 for each accession (data not shown). The total polymorphic alleles were 319 and 196 with averages of 3.6 and 2.8 in Physalis and P. philadelphica, respectively (Table 3). The average numbers of alleles in Physalis (4.4) and P. philadelphica (3.3) generated by P. peruviana SSR markers were higher than those generated by tomato markers (3.3 in Physalis and 2.5 in P. philadelphica).
Genetic diversity in Physalis
The average Nei's genetic distance was 0.3806 with a range from 0.2865 (G32475) to 0.7091 (PI343934) for each accession in Physalis. The largest genetic distance (0.9006) was between accessions PI343934 and PI644010, while accessions PI232077 and PI291561 had the least genetic distance of 0.0768. The distribution of genetic distance between any two accessions had two peaks at 0.2001–0.3000 and 0.5001–0.6000, respectively (Fig. 1). The genetic distance between species pairs in Physalis ranged from 0.3967 to 0.8534 (Table 4). The lowest was between P. angulata and P. acutifolia, while the highest was for PI343934 (P. sp.) and P. pubescens. The Physalis sp. accession PI343934 had the largest genetic distance to all six species with a range of 0.7080 to 0.8534, while the Physalis sp. accession PI203942 had large genetic distance to four species but a relatively small genetic distance to PI343934 (0.4876) and P. philadelphica (0.4969). Since approximately 60% of the accessions belong to P. philadelphica, the genetic distance within this species was also calculated. The genetic distance between any two accessions in P. philadelphica varied from 0.1102 (G32475 vs G32538) to 0.3437 (PI512006 vs G30318), while the average genetic distance for each accession ranged from 0.1854 (PI512010) to 0.2814 (G32513) with an overall average of 0.2241.
The genetic distance was obtained from pair wise comparisons of 38 accessions of Physalis and 23 accessions of P. philadelphica with molecular marker data using the software NTSYSpc 2.11a.
To test whether there was a difference between dendrograms generated by markers from tomato and P. peruviana, three dendrograms were separately created using only tomato marker data, only P. peruviana marker data and both marker data. UPGMA cluster analysis showed that dendrograms obtained using only tomato marker data (data not shown) and all marker data (Fig. 2A) were very similar. Two tomato lines OH88119 and PI435238 formed a cluster (I) that was distinct from Physalis accessions. Physalis sp. accession PI343934 was also far away from other accessions and thus could be considered as a separate cluster (II). At the genetic distance of 0.5000, the remaining 37 Physalis accessions could be grouped into three clusters (III, IV, and V). Cluster III was the largest one included almost all P. philadelphica accessions, five Physalis sp. accessions, and two P. peruviana accessions. Cluster IV included three accessions PI285705 (P. peruviana), PI644008 (P. pubescens), and PI279231 (P. nicandroides). Cluster V contained four accessions PI360740 (P. philadelphica), PI305457 (P. angulata), PI644010 (P. pubescens), and PI468103 (P. acutifolia). The dendrogram created using the 25 P. peruviana SSR marker data showed the same pattern (Fig. 2B). However, only three main clusters were observed. The Physalis sp. accession PI343934 was grouped into the same cluster (a) as tomato lines. The accessions in cluster b were almost the same as those in cluster III described above except the accession PI290968 was grouped into the cluster c. Cluster c included seven accessions in the clusters IV and V described above as well as PI290968.
Population structure in Physalis
Without specifying prior information concerning species and allowing for admixed individuals, we tested population structure for K = 1 to 10. From the summary plot of membership coefficients (Q), it was clear that the model with K = 1 was completely insufficient to model the data. It was not unexpectedly that the two tomato lines were always grouped into one cluster and were separated from Physalis When K>1. Of course, two tomato lines formed one cluster, while the Physalis accession formed another cluster when K = 2. When K increased from 3 to 10, 25 Physalis accessions always clustered together (cluster 1, Fig. 3), while the remaining 13 Physalis accessions could form one cluster when K = 3 (cluster 2, Fig. 3) and up to five clusters when K = 10. However, the best number of clusters was 3 (Fig. S1), which was supported by the plateau observed for parameter P(X|K) at K = 3 (Fig. S2), which was consistent with the UPGMA clusters (Fig. 2). The 25 accessions in cluster 1 were from the cluster III (Fig. 2A) or cluster b (Fig. 2B). Cluster 2 included all eight accessions in clusters II, IV, and V (Fig. 2A). Five accessions, PI512007, PI291561, PI232077, PI203942, and PI290968 from cluster III (Fig. 2A) or cluster b (Fig. 2B) had less alleles from cluster 1, and thus were grouped into cluster 2 (Fig. 3). Indeed, they were close to cluster IV (Fig. 2A) or cluster c (Fig. 2B) in UPGMA analysis.
Here we compared the DNA sequences between Physalis and tomato, and presented the use of tomato markers in genetic analysis in the genus Physalis. A total of 3356 unigenes accounting for 14.5% sequences analyzed in Physalis were identified as single-copy orthologous genes between tomato and Physalis, which was lower than the numbers of COS genes identified between tomato and other genera including potato, pepper, coffee in the same family . This could be due to the following two reasons. First, previous studies ,  used only one parameter, the E-value, to identify orthologous genes. Here we used two parameters, a moderate E-value of e−10 and 80% sequence coverage. This strategy excluded a number of genes having low E-value but not enough sequence coverage. The number of total hits increased from 4509 to 7498 when the percentage of sequence coverage was decreased from 80% to 70%, which resulted in the increase of the number of orthologous genes subsequently. Second, the TSA data of Physalis only includes transcriptome sequences from leaf sample . Lack of sequences for a number of genes specifically expressed in other organs or tissues prevents genome-wide identification of orthologous genes between tomato and Physalis, which could also result in the low number of orthologous genes identified in this study. This could be supported by molecular marker data. Of the 97 tomato markers evenly distributed on 12 tomato chromosomes used in this study, 54 could amplify DNA fragments from the three P. peruviana accessions, while 10 of 25 SSR markers derived from P. peruviana could amplify DNA fragment from the two tomato lines. These results indicated that molecular markers developed for tomato could be used for genetic study in Physalis.
High diversity at morphological level is observed in wildly distributed species or those that are managed and cultivated in the genus Physalis. However, due to the lack of molecular markers, little is known about the genetics of Physalis at molecular level. A recent study analyzed 12 populations of eight species from Mexico with six ISSR primers and discovered high polymorphisms among species at molecular level . All bands amplified by the six ISSR primers were polymorphic among the eight species. On the contrary, only 22% SSR markers derived from P. peruviana showed polymorphisms between P. peruviana and P. floridana . In this study, the SSR markers derived from P. peruviana revealed high level of polymorphisms among species. All 25 markers showed polymorphisms in Physalis and 84% markers showed polymorphisms with P. philadelphica (Table 3), which was inconsistent with the finding from Simbaqueba et al. . The sample size and divergence might be the cause of this difference. Only seven accessions in P. peruviana and one accession in P. floridana were used in Simbaqueba et al. , while 38 accessions from at least six species were investigated here. The high level of polymorphisms was also supported by using the markers derived from tomato, with 92.7% and 74.0% markers that were polymorphic in Physalis and P. philadelphica, respectively. All these suggested that a broad genetic diversity at DNA level also existed in wild and cultivated species of Physalis, which could be due to their self-incompatibility .
However, the existence of high morphological variation makes taxonomic identification more challenging in Physalis. Similar morphology as well as lack of detailed field notes of collections and experiments result in the estimation of the number of species in the genus varying from 75 to 120 . Although DNA sequences of few genes have been used in phylogenetic analysis , , the relationships of species in Physalis remain unclear. Based on morphological characters, 30 accessions used in the present study belong to 6 species, while the species information for the remaining eight accessions is unknown (Table 1). With the marker data obtained in this study, six of them (PI197691, PI197692, PI194590, PI195810, Heirloom purple, and Toma Verde Green) could be assigned to the species P. philadelphica. The accession PI203942 was also close to the P. philadelphica cluster. The accession PI343934 stood alone. Meanwhile two accessions previously identified as P. philadelphica could not be assigned to the P. philadelphica cluster. Two P. peruviana accessions, PI291561 and PI232077, were close to the P. philadelphica cluster but not in the same cluster as the other P. peruviana accession PI285705. Two P. pubescens accessions PI644008 and PI644010 were not in the same cluster either (Fig. 2). Structure analysis suggested that some of these accessions might have alleles outside its own species (Fig. 3). The results obtained here suggested that it was necessary to take molecular approach to characterize species in the genus Physalis.
Previous study using six ISSR markers to analyze genetic variation in eight species of Physalis found that genetic interspecific similarity values ranged from 0.20 to 0.57, and intraspecific similarity values ranged from 0.55 to 0.71. The genetic distance in P. philadelphica was 0.37 . Same trend was also observed in the present study. The range of Nei’s genetic distance between individual accessions was larger in Physalis (0.0768–0.9006) than in P. philadelphica (0.1102–0.3437), P. peruviana (0.0768–0.4549) and P. pubescens (0.4177). These results indicated that the genetic variation within a species was less than that in the genus Physalis. Majority (71.4%) of the genetic distances between species were greater than 0.5000. The genetic distances between the Physalis sp. accession PI343934 and all six species were among the largest, while that between P. angulata and P. acutifolia was the smallest (Table 4). However, it might not reflect the true genetic relationships between these species because only one accession from each of three species P. nicandroides, P. angulata and P. acutifolia. More accessions are needed to understand the genetic relationships among species at molecular level.
It has been suggested that domestication and inbreeding dramatically reduced the genetic variation in tomato , –. In Physalis, although the fruits of more than 10 wild species grown in natural areas and traditional agroecosystems are collected for consumption, only the species P. philadelphica (tomatillo) has been domesticated and cultivated . In this study, of the 319 polymorphic alleles amplified in Physalis, 196 (61.4%) showed polymorphisms in P. philadelphica. This result suggested that domestication reduced the genetic variation in cultivated species though the reduction was not as severe as in cultivated tomato, which might also be due to their self-incompatibility. Cultivated tomato is self-compatible though several wild species are self-incompatible . Due to domestication, selection and no exchange of genetic information with the wild germplasm for a long time , the genomes of tomato cultivars contain less than 5% of the genetic variation of their wild relatives . By contrast, most genotypes of tomatillo posses gametophytic self-incompatibility and thus are obligate outcrossers . Although P. philadelphica has been domesticated for centuries, the wild forms are frequently found growing in cultivated fields in traditional agricultural systems . Domestication has had little effect on overall levels of tomatillo diversity nonetheless fruits in domesticated varieties are up to 15 times larger than wild fruits . However, wild forms does harbor diversity not found in cultivated types . Therefore, it is essential to pay attention to the reduction of genetic variation in Physalis.
Estimation of optimum number of clusters (K) using the method described in Evanno et al. . ΔK is calculated as the mean of the absolute values of L’’(K) averaged over 20 runs divided by the standard deviation of L(K). ΔK = m(|L’’(K)|)/s[L(K)], which expands to ΔK = m(|L(K+1)-2L(K)+L(K-1)|)/s[L(K)]. L(K) is the Pr(X|K) referred as ‘Ln P(D)’ in the output of STRUCTURE software.
The graph for the parameter L(K) and number of clusters (K). The plateau is achieved at K = 3. Although the log likelihood L(K) is still increasing, an increase of the variance of L(K) between runs is also observed when K is greater than 3. Thus, the optimum number of clusters for 38 Physalis accession and two tomato lines is 3.
Marker information used for PCR amplification and genetic diversity analysis in 38 accessions from the genus Physalis .
The authors thank Northeast Regional PI Station at Geneva, New York, USA for providing the seeds of Physalis accessions and Dr. David Francis for providing the tomato seeds of OH88119.
Conceived and designed the experiments: JW XH WY. Performed the experiments: JW JY. Analyzed the data: JW JY. Contributed reagents/materials/analysis tools: XH. Wrote the paper: JW WY.
- 1. Martínez M (1998) Revision of Physalis section Epeteiorhiza (Solanaceae). Ann Ins Biol Bot 69: 71–117.
- 2. Caceres A, Alvarez AV, Ovando AE, Samayoa BE (1991) Plants used in Guatemala for the treatment of respiratory diseases. 1. Screening of 68 plants against gram-positive bacteria. J Ethnopharmacol 31: 193–208.
- 3. Chiang HC, Jaw SM, Chen CF, Kan WS (1992) Antitumor agent, physalin F from Physalis angulata L. Anticancer Res. 12: 837–843.
- 4. Kennelly EJ, Gerhaeuser C, Song LL, Graham JG, Beecher CWW, et al. (1997) Induction of quinone reductase by withanolides isolated from Physalis philadelphica (tomatillos). J Agr Food Chem 45: 3771–3777.
- 5. Dimayuga RE, Virgen M, Ochoa N (1998) Antimicrobial activity of medicinal plants from Baja California Sur (México). Pharm Biol 36: 33–43.
- 6. Pietro RC, Kashima S, Sato DN, Januario AH, Franca SC (2000) In vitro antimycobacterial activities of Physalis angulata L. Phytomedicine. 7: 335–338.
- 7. Choi JK, Murillo G, Su B, Pezzuto JM, Kinghorn AD, et al. (2006) Ixocarpalactone A isolated from the Mexican tomatillo shows potent antiproliferative and apoptotic activity in colon cancer cells. FEBS J 273: 5714–5723.
- 8. Ji L, Yuan YL, Luo LP, Chen Z, Ma XQ, et al. (2012) Physalins with anti-inflammatory activity are present in Physalis alkekengi var. franchetii and can function as Michael reaction acceptors. Steroids 77: 441–447.
- 9. Jin Z, Mashuta MS, Stolowich NJ, Vaisberg AJ, Stivers NS, et al. (2012) Physangulidines A, B, and C: Three new antiproliferative withanolides from Physalis angulata L. Org Lett. 14: 1230–1233.
- 10. Menzel MY (1951) The cytotaxonomy and genetics of Physalis. Proc Amer Phil Soc 95: 132–183.
- 11. Axelius B (1996) The phylogenetic relationships of the physaloid genera (Solanaceae) based on morphological data. Amer J Bot 83: 118–124.
- 12. Vargas-Ponce O, Pérez-Álvarez LF, Zamora-Tavares P, Rodríguez A (2011) Assessing genetic diversity in Mexican husk tomato species. Plant Mol Biol Rep 29: 733–738.
- 13. Tatineni V, Cantrell RG, Davis DD (1996) Genetic diversity in elite cotton germplasm determined by morphological characteristics and RAPDs. Crop Sci 36: 186–192.
- 14. Van Beuningen LT, Busch RH (1997) Genetic diversity among North American spring wheat cultivars: III. cluster analysis based on quantitative morphological traits. Crop Sci 37: 981–988.
- 15. Garcia E, Jamilena M, Alvarez JI, Arnedo T, Oliver JL, et al. (1998) Genetic relationships among melon breeding lines revealed by RAPD markers and agronomic traits. Theor Appl Genet 96: 878–885.
- 16. Grechko VV (2002) Molecular DNA markers in phylogeny and systematics. Russ J Genet 38: 851–868.
- 17. Halanych KM, Janosik AM (2006) A review of molecular markers used for Annelid phylogenetics. Integr. Comp. Biol. 46: 533–543.
- 18. Agarwal M, _Shrivastava N, Padh H (2008) Advances in molecular marker techniques and their applications in plant sciences. Plant Cell Rep 27: 617–631.
- 19. Arif IA, Khan HA (2009) Molecular markers for biodiversity analysis of wildlife animals: a brief review. Anim Biodivers Conserv 32: 9–17.
- 20. Gao BL, Gupta RS (2012) Phylogenetic framework and molecular signatures for the main clades of the phylum Actinobacteria. Microbiol Mol Biol Rev 76: 66–112.
- 21. Whitson M, Manos PS (2005) Untangling Physalis (Solanaceae) from the physaloids: a two-genes phylogeny of the Physalinaeae. Syst Bot 30: 216–230.
- 22. Olmstead EG, Bohs L, Migid HA, Santiago-Valentin E, García VF, et al. (2008) A molecular phylogeny of the Solanaceae. Taxon 57: 1159–1181.
- 23. Simbaqueba J, Sánchez P, Sanchez E, Núñez Zarantes VM, Chacon MI, et al. (2011) Development and characterization of microsatellite markers for the cape gooseberry Physalis peruviana. PLoS ONE 6(10): e26719.
- 24. Garzón-Martínez GA, Zhu I, Landsman D, Barrero LS, Mariño-Ramírez L (2012) The Physalis peruviana leaf transcriptome: assembly, annotation and gene model prediction. BMC Genomics 13: 151.
- 25. Chen J, Shen HL, Yang WC (2007) Development of tomato molecular markers. Mol Plant Breed 5 (6S): 130–138.
- 26. Foolad MR, Panthee DR (2012) Marker-assisted selection in tomato breeding. Crit Rev Plant Sci 31: 93–123.
- 27. Sato S, Tabata S, Hirakawa H, Asamizu E, Shirasawa K, et al. (2012) The tomato genome sequence provides insights into fleshy fruit evolution. Nature 485: 635–641.
- 28. Fulton TM, Van der Hoeven R, Eannetta NT, Tanksley SD (2002) Identification, analysis, and utilization of conserved ortholog set markers for comparative genomics in higher plants. Plant Cell 14: 1457–1467.
- 29. Wu F, Mueller LA, Crouzillat D, Petiard V, Tanksley SD (2006) Combining bioinformatics and phylogenetics to identify large sets of single copy, orthologous genes (COSII) for comparative, evolutionary and systematics studies: a test case in the Euasterid plant clade. Genetics 174: 1407–1420.
- 30. Yang L, Jin GL, Zhao XQ, Zheng Y, Xu ZH, et al. (2007) PIP: a database of potential intron polymorphism markers. Bioinformatics 23: 2174–2177.
- 31. Mueller LA, Mills AA, Skwarecki B, Buels RM, Menda N, et al. (2008) The SGN comparative map viewer. Bioinformatics 24: 422–423.
- 32. Doganlar S, Frary A, Daunay MC, Lester RN, Tanksley SD (2002) A comparative genetic linkage map of eggplant (Solanum melongena) and its implications for genome evolution in the Solanaceae. Genetics 161: 1697–1711.
- 33. Zarate LA, Cristancho MA, Moncada P (2010) Strategies to develop polymorphic markers for Coffea arabica L. Euphytica. 173: 243–253.
- 34. Enciso-Rodríguez F, Martínez R, Lobo M, Barrero LS (2010) Genetic variation in the Solanaceae fruit bearing species lulo and tree tomato revealed by Conserved Ortholog (COSII) markers. Genet Mol Biol 33: 271–278.
- 35. Tepe EJ, Bohs L (2010) A molecular phylogeny of Solanum sect. Pteroidea (Solanaceae) and the utility of COSII markers in resolving relationships among closely related species. Taxon 59: 733–743.
- 36. Levin RA, Whelan A, Miller JS (2009) The utility of nuclear conserved ortholog set II (COSII) genomic regions for species-level phylogenetic inference in Lycium (Solanaceae). Mol Phylogenet Evol 53: 881–890.
- 37. Bombarely A, Menda N, Tecle IY, Buels RM, Strickler S, et al. (2011) The Sol Genomics Network (solgenomics.net): growing tomatoes using Perl. Nucleic Acids Res 39: D1149–D1155.
- 38. Wang YY, Chen J, Francis DM, Shen HL, Wu TT, et al. (2010) Discovery of intron polymorphisms in cultivated tomato using both tomato and Arabidopsis genomic information. Theor Appl Genet 121: 1199–1207.
- 39. Tam SM, Mhiri C, Vogelaar A, Kerkveld M, Pearce SR, et al. (2005) Comparative analyses of genetic diversities within tomato and pepper collections detected by retrotransposon-based SSAP, AFLP and SSR. Theor Appl Genet 110: 819–831.
- 40. Van Deynze A, Stoffel K, Buell CR, Kozik A, Liu J, et al. (2007) Diversity in conserved genes in tomato. BMC Genomics 8: 465.
- 41. Robbins MD, Sim SC, Yang WC, Van Deynze A, van der Knaap E, et al. (2011) Mapping and linkage disequilibrium analysis with a genome-wide collection of SNPs that detect polymorphism in cultivated tomato. J Exp Bot 62: 1831–1845.
- 42. Sim SC, Robbins MD, Chilcott C, Zhu T, Francis DM (2009) Oligonucleotide array discovery of polymorphisms in cultivated tomato (Solanum lycopersicum L.) reveals patterns of SNP variation associated with breeding. BMC Genomics 10: 466.
- 43. Pei CC, Wang H, Zhang JY, Wang YY, Francis DM, et al. (2012) Fine mapping and analysis of a candidate gene in tomato accession PI128216 conferring hypersensitive resistance to bacterial spot race T3. Theor Appl Genet 124: 533–542.
- 44. Chen J, Wang H, Shen HL, Chai M, Li JS, et al. (2009) Genetic variation in tomato populations from four breeding programs revealed by single nucleotide polymorphism and simple sequence repeat markers. Sci Hortic 122: 6–16.
- 45. Labate JA, Robertson LD, Baldo AM (2009) Multilocus sequence data reveal extensive departures from equilibrium in domesticated tomato (Solanum lycopersicum L.). Heredity 103: 257–267.
- 46. Hu XR, Wang H, Chen J, Yang WC (2012) Genetic diversity of Argentina tomato varieties revealed by morphological traits, simple sequence repeat, and single nucleotide polymorphism markers. Pak J Bot 44: 485–492.
- 47. Kabelka E, Franchino B, Francis DM (2002) Two loci from Lycopersicon hirsutum LA407 confer resistance to strains of Clavibacter michiganensis subsp. michiganensis. Phytopathology 92: 504–510.
- 48. Nei M (1972) Genetic distance between populations. Am Nat 106: 283–292.
- 49. Rohlf FJ (2000) Statistical power comparisons among alternative morphometric methods. Am J Phys Anthropol 111: 463–478.
- 50. Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155: 945–959.
- 51. Falush D, Stephens M, Pritchard JK (2003) Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics 164: 1567–1587.
- 52. Falush D, Stephens M, Pritchard JK (2007) Inference of population structure using multilocus genotype data: dominant markers and null alleles. Molecular Ecology Notes.
- 53. Pandey KK (1957) Genetics of self-incompatibility in Physalis ixocarpa Brot.-A new system. Am J Bot 44: 879–887.
- 54. Evanno G, Regnaut S, Goudet J (2005) Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol 14: 2611–2620.
- 55. Archak S, Karihaloo JL, Jain A (2002) RAPD markers reveal narrowing genetic base of Indian tomato cultivars. Curr Sci 82: 1139–1143.
- 56. Bai YL, Lindhout P (2007) Domestication and breeding of tomatoes: what have we gained and what can we gain in the future? Ann Bot 100: 1085–1094.
- 57. Yi SS, Jatoi SA, Fujimura T, Yamanaka S, Watanabe KN (2008) Potential loss of unique genetic diversity in tomato landraces by genetic colonization of modern cultivars at a non-center of origin. Plant Breed 127: 189–196.
- 58. Peralta IE, Spooner DM (2007) History, origin and early cultivation of tomato (Solanaceae). In: Razdan MK, Mattoo AK, editors. Genetic improvement of solanaceous crops. Vol. 2. Tomato. Enfield, NH: Science Publishers.1–27.
- 59. Miller JC, Tanksley SD (1990) RFLP analysis of phylogenetic relationships and genetic variation in the genus Lycopersicon. Theor Appl Genet 80: 437–448.
- 60. Hudson WD Jr. (1986) Relationships of domesticated and wild Physalis philadelphica. In D'Arcy WG, editor. Solanaceae: biology and systematics. New York, Columbia University Press. 416–432.
- 61. Ross-Ibarra J (2006) Recombination, Genetic Diversity and Plant Domestication. Ph.D. Dissertation. University of Georgia, Athens, Georgia, USA.