Characterization of Perilla frutescens (Linn.) Britt based on morphological, biochemical and STMS markers

Industrial Crops & Products 109 (2017) 773–785

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Research paper

Characterization of Perilla frutescens (Linn.) Britt based on morphological, biochemical and STMS markers

MARK

S.K. Singha, P.C. Koleb, A.K. Misrac, Somnath Royd, Lalit Aryaa, Manjusha Vermaa, R. Bhardwaje, ⁎ P. Sunejae, Med Ram Vermaf, K.V. Bhata, Rakesh Singha, a

Division of Genomic Resources, ICAR-National Bureau of Plant Genetic Resources, Pusa Campus, New Delhi, 110 112, India Deaprtment of CIHAB, Palli Siksha Bhavana,Visva Bharti, Sriniketan, West Bengal, 721 235, India c ICAR-National Bureau of Plant Genetic Resources Regional Station, Umiam, Meghalaya, 793 103, India d ICAR– Central Rainfed Upland Rice Research Station, Hazaribag, Jharkhand, 825 301, India e Germplasm Evaluation Division, ICAR-National Bureau of Plant Genetic Resources, Pusa Campus, New Delhi, 110 012, India f Division of Livestock, Economics and Statistics, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, UP, 243122, India b

A R T I C L E I N F O

A B S T R A C T

Keywords: Perilla frutescens Morphological markers Biochemical markers STMS North- eastern hill region

In the present study, morphological, biochemical and molecular diversities were assessed for 62 accessions and four checks of Perilla frutescens collected from Meghalaya, Nagaland, Arunachal Pradesh, Manipur, Mizoram and Sikkim states of the North-eastern Hill (NEH) region of India. In the field, 8 qualitative and 13 quantitative traits were recorded to assess the diversity at the morphological level. Oil, protein, total phenol and fatty acids profiling was done to estimate diversity present at biochemical level and accessions namely IC524554, IC524504, IC52445, IC422885, IC275959, IC016443, IC006444, IC006442 and IC599246 were found with more than 50% oil content. Fourteen STMS markers which showed reproducible and clear amplification were selected for diversity study. Total 92 bands were amplified with 14 STMS primers out of which 92.85 (%) bands were polymorphic. Polymorphic information content (PIC) for STMS primers varied from 0.32 for primer GBFM75 to 0.92 for primer KWPE57. The average PIC was with an average of 0.62, and the gene diversity varied from 0.41 to 0.92. Cluster analysis based on biochemical and molecular characterization divided Perilla accessions into four groups whereas; morphological (qualitative and quantitative) markers divided accessions into three groups. Based on population structure analysis three clear populations were obtained. The principal coordinate analysis (PCoA) in Perilla accessions with STMS markers showed that the variation explained by the first three axes was 31.16%. Analysis of molecular variance (AMOVA), based on the hierarchical cluster showed 6% diversity among population, 87% among individual and 6% within the individual, whereas the model based approach showed 21% diversity among population, 73% among individual and 6% within the individual. The study based on genetic diversity and population structure showed that great extent of diversity exists in Perilla accessions collected from NEH region of India and accessions; more than 50% oil content can be utilized for oil improvement programme and industrial applications.

1. Introduction Perilla [Perilla frutescens (Linn.) Britt. from family Lamiaceae is considered to be a commercial oilseed crop in Asia. This crop is native to India and China (Godin and Spensley, 1971) and China, India, Japan and Korea are major producer of this crop. History of cultivation of perilla crop is very old in China (Li, 1969), although, the wild ancestral species of the cultivated Perilla species is unknown. Later, the Perilla has been introduced as oil seed crop in Europe, Russia and USA (Nitta et al., 2003). The genus perilla contains one tetraploid species, Perilla

⁎

frutescens, and three diploid species, P. citriodora (Makino, 1961) Nakai, P. hirtella Nakai and P. setoyensis G. Honda (Honda et al., 1994).1961 In Asia, Perilla frutescens has three principal variants; these are crispa, acuta, and japonica in addition to var. proper. P. frutescens var. frutescens is generally taller, larger in seed size and has soft seeds, green leaves and stems, and non-wrinkled leaves, which are usually grown in the north-eastern hill region (NEH) of India (Nitta et al., 2003; Palmer, 1989). The Western and the Eastern Himalayan region of India exhibit rich variability in Perilla (Pandey and Bhat, 2008). In NEH region it is generally grown in the courtyards and has wider occurrence in humid

Corresponding author. E-mail address: [email protected] (R. Singh).

http://dx.doi.org/10.1016/j.indcrop.2017.09.045 Received 17 February 2017; Received in revised form 20 September 2017; Accepted 20 September 2017 0926-6690/ © 2017 Elsevier B.V. All rights reserved.

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Fig. 1. All the accessions of Perilla used for study are conserved in the National Gene bank of ICAR- National Bureau of Plant Genetic Resources, New Delhi.

sub tropical, sub temperate, and temperate parts of the country (Arora and Pandey, 1996). The indigenous names of Perilla are ‘Bhanjira’ (Hindi), ‘Unei’ (Meghalaya), ‘Kenie’ (Nagaland), ‘Ngamum’ (Arunachal Pradesh), ‘Thoiding’ (Manipur), ‘Silam’ (Sikkim) and ‘Chhawhchi’ (Mizoram). Worldwide the seed oil is used as an edible oil with some industrial uses as in manufacturing of paints, varnishes, linoleum, printing ink, lacquers and protective water proof coatings on cloth, artificial leather, enamels, etc. (Brenner, 1993). Medicinally it is used in cough, bronchitis, uterine ailments and rheumatic arthritis (Pandey and Bhat, 2008). Green and red Perilla frutescens (L.) Britt. (perilla) essential oils were investigated for their deterrent and larvicidal activity on A. aegypti, a dengu fever spreading vector. Green perilla oil showed more promising deterrent and larvicidal activity than red perilla oil (Tabanca et al., 2015). Perilla seeds contain 31–51% of drying oil similar to linseed oil. The seed has saturated acids 6.7-7.6%, oleic acid 14–23%, linoleic acid 11–16%, linolenic acid 50–70% (Food and Agricultural Organization, 1992). According to Mandal et al., 2009, Perilla oil is very similar to flax oil with respect to fatty acid composition. They reported omega-6 fatty to omega-3 fatty acid ratio of 0.33:1 and omega-3 rich PUFA composition of about 79.6%, which is similar to flax oil. Although, Perilla is an important food and oil seed crop of NEH region of India, however, no systematic cultivation practices being followed, as it is grown in kitchen garden and in jhoom (shifting) land. Thus are produced in very limited quantity for personal uses. The genotypes, which are available, are mostly semi-wild, semi-domesticated types. In order to develop high-yielding cultivars with exploitation of the gene pool is of paramount importance. Therefore, attention needs to be paid on germplasm collection, characterization, conservation and utilization of Perilla in India. Germplasm as a source of adaptive genes, accessions have been used as the raw materials from which modern and often high yielding crop varieties have been developed (Harlan, 1975; Frankel and Soule, 1981). The genetic diversity of extent accessions of the Perilla crop is thus a key component for its improvement. However, the lack of characterization and evaluations limits the utility of this collected and conserved germplasm particularly from NEH region of India in breeding programme. The genetic diversity can be assessed based on morphological markers, but it is difficult to classify these accessions solely based upon morphological characters. Therefore, the use of molecular markers, such as random amplified polymorphic DNA (RAPD), amplified fragment length polymorphism (AFLP), inter simple sequence repeat (ISSR) and sequence tagged microsatellite (STMS) has gained popularity as a genetic diversity assessment method in crops. Recently, the variability of some of Perilla accessions has been described in terms of their morphology, cultivation, use and diversification (Hussain et al., 2014; Lee and Ohnishi, 2001a, 2001b; Nitta et al., 2005; Sa et al., 2012; Verma et al., 2008), as well as their genetic diversity using molecular markers like random amplified length polymorphism (Nitta and Ohnishi, 1999; Verma et al., 2009), amplified fragment length polymorphism (Lee et al., 2002; Lee and Ohnishi, 2003) and microsatellite markers (Kwon et al., 2005; Lee and Kim. 2007, Park et al., 2008; Sa et al., 2013; Verma et al., 2009; Woo et al., 2016). These workers have studied genetic diversity of Perilla either using only molecular markers or with few agronomical characters. In this study an efforts have been made to assess the genetic diversity present in 62 accessions collected from all NEH states of India using morphological, biochemical and SSR markers.

2.2. Morphological characterization The morphological characterizations of the collected accessions were carried out at the experimental field of the ICAR-NBPGR Regional Station, Umiam, Meghalaya (25.6 N latitude, 91.9 E longitude and 990 m altitude). Agronomic practices as recommended were used for proper growth and development of crop. 21 morphological descriptors (8 qualitative and 13 quantitative) as per the standard descriptors for the Perilla crop (Mahajan et al., 2000) were recorded on five competitive plants randomly selected from three middle rows of each plot for two consecutive years (2012-13 and 2013-14). 2.3. Biochemical characterization For determination of biochemical parameters first, the moisture contents of grains were reduced to 15–17% by sun drying. The grains of all the accessions were stored in the same room and temperature to ensure equal moisture content. For recording observations on oil content and other biochemical parameters, seed grains of each accession from all the three replications of the second year (2013-14) crop were mixed together to prepare a composite sample. Subsequent samples were drawn randomly from each composite sample of each corresponding accession for estimation of oil content, fatty acid, protein and total phenol. 2.4. Seed oil percentage The oil contents of the seed samples were determined by a nondestructive method using a Newport NMR analyser (model- 4000) from Oxford Analytical Instruments Ltd. U.K. after calibrating with pure perilla oil. 2.5. Fatty acid profiling For fatty acid analysis by gas liquid chromatography (GLC), a Hewlett Packard gas chromatograph, model 6890 equipped with the flame ionization detector (FID) was used. The injector and detector temperatures were 260 °C and 275 °C, respectively. Oven temperature was programmed from 150 °C holding at 1 min to 210 °C at the rate of 15 °C/min, followed by 210 °C to 250 °C at the rate of 5 °C/min for 12 min. Peaks of fatty acid methyl esters were identified by comparing their retention time with that of the known standards, run under similar separation conditions. Peak integration was performed by applying HP3398A software. 2.6. Protein percentage Total protein was estimated as per AOAC (2005) official method 976.05 with some modifications in the digestion of samples. 100 mg of dried and homogenized sample was digested with sulfuric acid–selenium–anhydrous sodium sulfate–hydrogen peroxide digestion mixture in glass digestion tubes at 350 °C for 45 min as per the method of Forster (1995). Nitrogen percentage in digest was estimated by the Kjeltech (FOSS Tecator) nitrogen auto-analyser. To ascertain recovery, food reference material AS-FRM 14 (provided by Institute of Nutrition, Mahidol University, Thailand) was used as a control. The recovery percentage of 98.9 ± 1.9 for AS-FRM 14 was obtained.

2. Materials and methods 2.1. Collection of plant materials The experimental material was comprised of total 62 accessions (Table 1) which were collected in various exploration trips from diverse agro-ecological areas of NEH region of India from different habitat’s

2.7. Total phenol Phenols content with ethanolic extract was determined by the

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Table 1 List of Perilla frutescens accessions used in present study. SN

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62

IC Number

IC599231 IC599232 IC599233 IC599234 IC599235 IC599236 IC599237 IC599238 IC599239 IC599240 IC599241 IC599242 IC599243 IC599244 IC599245 IC599246 IC599247 IC599248 IC599249 IC599250 IC599251 IC599252 IC599253 IC599254 IC599255 IC599256 IC599257 IC599258 IC003908 IC003913 IC003942 IC003955 IC006443 IC006440 IC006441 IC006442 IC006444 IC006446 IC006447 IC010240 IC016443 IC284512 IC211611 IC275959 IC422850 IC422885 IC422905 IC326132 IC326277 IC276084 IC524455 IC524504 IC524551 IC524554 IC524583 IC524600 IC419710 IC521284 IC524619 IC521281 IC521292 IC526701

Place of collection Village

District

State

Latitude

Longitude

Mambrang Along Basar Ziro Pyllun II Pyllun II Chawngtlai Bhorimbong New Chalrang Baktung Venglai Tlangnuam Phanzwal Khanpui Zotlang Peducha Sajirok Laimakhong Thawai Manmoutissue Wakro Namgao Pankhao Balek Balek Remgeng Sikatode Renging Seling Tuikhuralu Kawnpui Kolasib Chizami Kiruphema Khonoma Phek Tuensang Phek Wokha Serchhip Mokokchung Changlang Chowkham Yingkiong Tuidam Zamuang Dipengueng Saiha Saiha East Siang Dimapur Dimapur Kohima Kohima Kohima Kohima Kiphire Phek Kohima Anutangree Chipoketami Saiha

South Sikkim West Siang West Siang Lower Subansiri Ribhoi Ribhoi Champhai Ribhoi Champhai Serchhip Kolasib Aizwal Lunglei Aizwal Champhai Kohima Imphal West Senapati Ukhrul Lohit Lohit Lohit Lohit Lower Dibang Valley Lower Dibang Valley Lower Dibang Valley East Siang East Siang Aizawl Aizawl Kolasib Kolasib Phek Kohima Kohima Phek Tuensang Phek Wokha Serchhip Mokokchung Changlang Lohit Upper Siang Mamit Mamit Mamit Saiha Saiha East Siang Dimapur Dimapur Kohima Kohima Kohima Kohima Kiphire Phek Kohima Phek Phek Saiha

Sikkim Arunachal Pradesh Arunachal Pradesh Arunachal Pradesh Meghalaya Meghalaya Mizoram Meghalaya Mizoram Mizoram Mizoram Mizoram Mizoram Mizoram Mizoram Nagaland Manipur Manipur Manipur Arunachal Pradesh Arunachal Pradesh Arunachal Pradesh Arunachal Pradesh Arunachal Pradesh Arunachal Pradesh Arunachal Pradesh Arunachal Pradesh Arunachal Pradesh Mizoram Mizoram Mizoram Mizoram Nagaland Nagaland Nagaland Nagaland Nagaland Nagaland Nagaland Mizoram Nagaland Arunachal Pradesh Arunachal Pradesh Arunachal Pradesh Mizoram Mizoram Mizoram Mizoram Mizoram Arunachal Pradesh Nagaland Nagaland Nagaland Nagaland Nagaland Nagaland Nagaland Nagaland Nagaland Nagaland Nagaland Mizoram

28.02 28.01 27.53 27.78 27.77 27.75 27.80 28.08 28.08 28.12 27.85 28.13 27.13 27.81 28.64 28.09 24.76 24.95 24.89 25.70 25.70 25.72 25.72 23.43 23.37 23.33 24.23 23.70 23.07 23.86 23.47 23.73 23.73 24.05 24.23 23.31 23.93 24.10 22.32 22.55 22.70 22.47 25.67 25.59 25.71 25.65 25.71 26.23 25.69 26.09 26.32 25.82 25.83 25.68 25.68 25.67 25.92 25.71 25.67 25.71 25.71 27.27

94.80 94.67 93.80 96.15 96.32 96.02 96.15 95.85 95.85 95.08 95.03 95.27 95.73 96.05 95.03 95.31 93.79 93.84 94.17 91.93 91.93 91.99 94.05 93.20 93.13 92.83 92.68 92.72 92.90 92.90 93.03 92.86 92.81 92.67 92.68 92.86 92.37 92.40 92.79 93.04 93.00 92.97 94.10 94.39 94.02 94.02 94.46 94.81 94.45 94.26 94.51 93.77 93.70 94.11 94.11 94.12 94.79 94.47 94.12 94.46 94.48 88.28

2.8. Molecular characterization

method of Folin-ciocalteu reagent (FCR) method (Slinkard and Singleton, 1977) using gallic acid (0–100 μg/ml) as standard for total phenolics. To ascertain recovery samples was spiked with known amounts of standard before extraction. Recovery of 98.7 ± 1.2 was obtained.

2.8.1. DNA extraction and SSR genotyping Genomic DNA was isolated from Perilla seeds using CTAB method of Saghai-Maroof et al., 1984 with minor modifications. The relative purity and concentration of extracted DNA were estimated by the help 775

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Fig. 1. Collection sites of Perilla frutescens accessions in NEH region of India\.

Morphological and biochemical data were subjected to interval analysis using NTSYS software version 2.02 (Rohlf, 2002). Distance coefficient was used to develop the cluster using UPGMA method. For STMS markers, the molecular sizes (bp) of amplified alleles were determined based on DNA ladder using the PyElph-1.4 gel image analysis software (Pavel and Vasile, 2012). Power Marker V3.25 was used to calculate the average number of alleles per locus, major allele frequency, gene diversity, heterozygosity and polymorphism information content (PIC) for each locus (Liu and Muse, 2005). In addition, genetic distances (Nei et al., 1983) across the genotype and neighbour-joining (NJ) tree were calculated using Power Marker V3.25 (Liu and Muse, 2005). The dissimilarity matrix generated by Power Marker was used to construct un-weighed neighbour joining tree using DARwin software 5.0.158 (Perrier and Jacquemoud-Collet, 2006). GenAlEx V6.5 software was used for Principal coordinate analysis (PCoA) and analysis of molecular variance (AMOVA) (Peakall and Smouse, 2012). Software STRUCTURE V2.3.1 was applied to infer historical lineages that show clusters of similar genotype (Pritchard et al., 2000). The membership of each genotype was run for range of genetic clusters from value of K = 1-20 with the admixture model and correlated allele frequency. For each K it was replicated 3 times. Each run was implemented with a burn in the period of 100,000 steps followed by 100,000 Monte Carlo Markov Chain replicates (Pritchard et al., 2000). Ln(PD) derived for each K was plotted to find the exact plateau of the ΔK values (Evano et al., 2005). Online available programme “structure harvester” was used (http://taylor0.biology.ucla.edu) to calculate final population structure. The admixture was also estimated from the portion of genome of an individual to that each belongs.

of Nano Drop ND-1000. DNA quality was checked by gel electrophoresis on 0.8% agarose gel. A working DNA concentration of each DNA sample 20 ng/μl was prepared and stored at 4 °C for further use. 2.8.2. Genotyping of perilla accessions using STMS markers Twenty two primers were selected for polymorphism study (Kwon et al., 2005; Park et al., 2008) and these primers were amplified on 10 perilla accessions selected randomly. Total 14 polymorphic primer pairs were selected for final amplification. Polymerase chain reaction (PCR) was performed in a total volume of 25 μl containing 100 ng of template DNA, 200 μM of dNTPs, 2.5 mM MgCl2, 1 x PCR buffer, 0.4 μM of each primer, 1U Taq DNA polymerase and double distilled water to make the final volume up to 25 μl. Amplification were carried out using thermocycler Bioer Gene Pro and PCR amplification conditions were 94 °C for 3 min, 10 cycles of denaturation at 94 °C for 30 s, touchdown annealing starting at 62 °C for 30 s, and decreasing 0.7 °C per cycle and extension at 72 °C for 1 min. This was further followed by 35 cycles of denaturation at 94 °C for 30 s, primer annealing at temperature 55 °C for 30 s and primer extension at 72 °C for 1 min, final extension step at 72 °C for 4 min. The amplified products were separated on 3% metaphor agarose gels and visualized by ethidium bromide stain. Gel image was recorded in SYNGENE G-Box gel documentation unit. In case of non-amplification, the PCR amplification was repeated to exclude technical failure and in case of failure in both amplifications, null alleles were recorded. 2.8.3. Statistical analyses Quantitative data were analysed using SPSS package 22 (IBM Corp, 2013); mean, standard deviation and correlation between the traits were studied with respect to biochemical and morphological traits. The STMS profiles were scored based upon the size (bp) of fragments amplified across all the 62 accessions and four checks. The weak bands of negligible intensity and smeared bands were excluded from the final data analysis.

3. Results and discussion 3.1. Morphological marker base diversity study The present study was conducted on 62 accessions of Perilla 776

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quantitative characters into three major clusters, cluster-A, cluster-B and cluster-C (Fig. 2). Cluster-A consists of 2 accessions both were collected from Arunachal Pradesh and the accession IC599254 of this cluster was recorded for maximum main inflorescence length (22.40 cm) and 1000 seed weight (3.43 g). In Cluster-B 3 accessions were grouped out of which accession IC599235 and IC599238 were collected from Meghalaya and IC599246 was collected from Nagaland. In cluster-B, accession IC599238 showed the highest yield per plant (32.68 g) and number of inflorescence per plant (329.72) and IC599235 (30.94 g) were recorded second highest yielder. The Cluster-C was the largest cluster with 56 accessions and 4 checks and out of 13 quantitative characters studied, cluster-C had the highest value for 10 characters. Accession IC599251 is separate from all the cluster was collected from Arunachal Pradesh and at par of highest 1000 seed weight (26.92 g). Accession IC599231 collected from Sikkim was bold seeded in cluster-C. Hierarchical cluster analysis indicated that the accessions collected from different states were grouping together. An examination of the clustering pattern of the 62 accessions and 4 checks of Perilla accessions in three major clusters revealed that the genotypes of heterogeneous origin were frequently present in the same cluster. There were some accessions, which originated in same place or geographic locations were grouped together in cluster-C. This indicated lack of any definite relationship or correlation between genetic diversity and geographic origin of the Perilla accessions on the basis of qualitative and quantitative traits. Therefore, the selection of parental material based on qualitative and quantitative traits simply on geographic diversity may not be good for hybridization programme. Selection of parents based on their genetic diversity rather than geographical distances would be more fruitful in the hybridization programme. This finding is in conformity with the previous reports advocating the lack of parallelism between genetic and geographic diversity in Perilla (Hussain et al., 2014; Nitta et al., 2003; Pandey and Bhat, 2008; Sa et al., 2012; Verma et al., 2008).

Table 2 Frequency distribution of qualitative traits in 62 accessions and four checks of Perilla frutescens.

1.

2.

3.

4.

5.

6.

7.

8.

Plant trait

Frequency

Percent

Early plant vigour 1 (Poor) 2 (Good) 3 (Very Good)

24 18 24

36.4 27.3 36.4

Plant shape 0 (Spreading) 1 (Eract)

24 42

36.4 63.6

Leaf colour 3 (Greenish White) 4 (Greenish) 5 (Dark Green)

21 35 10

31.8 53.0 15.2

66

100.0

26 40

39.4 60.6

53 13

80.3 19.7

66

100.0

Anthocyanin colouration 0 (Absent) Inflorescence structure 0 (Compact) 1 (Lax) Secondary branching 0 (Absent) 1 (Present) Flower colour 1 (White) Leaf trichomes 1 (Present)

66

100.0

Seed colour 1 (White) 2 (Grey) 3 (Light Brown) 4 (Brown) 5 (Dark Brown)

12 26 9 7 12

18.2 39.4 13.6 10.6 18.2

Seed size 1 (Small) 2 (Medium) 3 (Large)

23 38 5

34.8 57.6 7.6

3.2. Biochemical marker based diversity study All fatty acids, oil, protein and phenol contents were estimated in 62 accessions and 4 checks of Perilla. In Table 4 mean, range, standard deviation and coefficient of deviation of each accession are given. The perilla accessions exhibited large coefficient of variation for stearic acid (27.54), total phenol (19.41) and oleic acid (16.29) while low variation was observed for oil, protein, linoleic acid, palmitic acid and linolenic acid (Table 4). Shin and Kim (1994) estimated 38.6–47.8 percent of oil value; and Song et al. (2012) estimated 37.3–43.5 percent in accessions collected from Japan and Korea, respectively. Similar results were reported for Perilla frutescens by Verma et al. (2012), Mandal et al. (2009). In our study the accessions namely, IC524554, IC524504, IC52445, IC 422885, IC275959, IC016443, IC006444, IC006442 and IC599246 were recorded for more than 50 percent oil hence can be considered as high oil yielding accessions. The average individual fatty acid values as established for the present accessions were palmitic acid 7.75, stearic acid 3.66, oleic acid 14.85, linoleic acid 18.32 and linolenic acid 51.74. Linolenic acid (omega-3 fatty acid) was major component in the seed oil among all the fatty acids and contributed 35.3–65.2%. Average protein content was 19.25% with a range of 16.75–22.74% while, total phenol averages 14.72 with a range of 7.00–21.00. The oil percent of accessions available in NEH region of India was more than reported by Shin and Kim (1994) (38.6–47.8 percent) and Song et al. (2012) (37.3–43.5 percent) on accessions collected from Japan and Korea, respectively. The results of fatty acid, protein and total phenol content were similar to the findings of Mandal et al. (2009) and Song et al. (2012). Hierarchical cluster analysis based on biochemical characters grouped Perilla accessions into four major clusters, which were designated as clusters A, B, C and D (Fig. 3). The cluster-A includes a total of two accessions, the accession IC524504 was collected from Nagaland and recorded highest linoleic acid (23.00%) and palmitic acid (10.05%)

collected from six states (Meghalaya, Mizoram, Arunachal Pradesh, Nagaland, Manipur and Arunachal Pradesh) of NEH region of India. Frequency distribution for qualitative traits was observed in discrete classes presented in Table 2. The accessions under present study showed, poor (24) to very good (24) early plant vigour, whereas plant shape was divided in to spreading (24) and erect (42) categories, leaf colour was greenish white (21) to dark green (10), seed colour had 5 categories, and seed size varied from small (23), medium (38) to large (5) (Table 2), small variation was observed for inflorescence structure (compact 26; lax 40) and secondary branches (absent 53; present 13). While anthocynin coloration of leaves (absent), flower colour (white) and leaf trichomes (present) showed no variation within the population (Table 2). Thirteen quantitative characters were recorded and mean values, range and%CV was calculated based on randomly selected five plants (Table 3). High variance was observed for yield and related traits like number of inflorescence per plant (38.56), yield per plant (35.06), oil yield per plant (32.53), 1000 seed weight (30.24), main inflorescence length (29.23) and plant height (Table 3). While characters like leaf length (21.96), petiole length (19.99), primary branches per plant (15.68), seed size (14.50), leaf breadth (12.10), days to 50% flowering (6.26) and days to 80% maturity (4.60), showed medium to low variance. The earlier workers (Bahuguna and Prasad, 2014; Hussain et al., 2013; Pandey and Bhat, 2008; Sharma and Hore, 1994; Verma et al., 2008) has also reported wide range of variation for various qualitative and quantitative characters and have identified superior Perilla accessions for further breeding programmes. The Hierarchical clustering using wards method grouped total 62 accessions and 4 checks of Perilla based on 21 qualitative and 777

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Table 3 Descriptive statistics for quantitative traits 62 accessions and 4 checks of Perilla frutescens. Traits

Mean + SD

Range

%CV

Check Mean ± SD

Check Range

Leaf length (cm) Leaf breadth (cm) Primary branches/plant Petiole length (cm) Days to 50% flowering Main Inflorescence length (cm) No. of inflorescence/plant Plant height (cm) Days to 80% maturity Seed wt (g) Yield plant (g) Oil yield/plant (g) Seed size (mm)

14.71 +3.23 10.76 + 1.30 15.53 + 2.43 5.90 + 1.18 135.28 + 8.47 11.16 + 3.26 145.37 + 56.05 119.83 + 28.79 172.42 + 7.93 1.69 + 0.51 15.25 + 5.34 7.11 + 2.31 1.68 + 0.24

9.82 − 24.00 7.39 − 14.22 8.96 − 20.86 3.88 − 11.39 120.33 − 154.33 5.88 − 22.4 71.65 − 329.71 64.92 − 198.27 155.67 − 192.33 0.70 − 3.43 6.73 − 32.68 2.98 − 14.63 1.24 −2.32

21.96 12.10 15.68 19.99 6.26 29.23 38.56 24.03 4.60 30.24 35.06 32.53 14.50

11.71 ± 0.62 9.72 ± 0.89 11.79 ± 2.73 5.13 ± 0.58 136.17 ± 4.95 12.98 ± 1.66 103.21 ± 22.61 109.13 ± 16.60 172.38 ± 4.53 1.89 ± 0.21 10.51 ± 1.95 4.96 + 1.00 1.62 ± 0.17

11.15−12.48 8.73−10.79 8.15−14.13 4.32−5.59 130.00−141.83 11.34−14.52 82.44–127.04 84.49–120.51 168.83–178.67 1.67−2.11 9.25−13.42 4.29−6.44 1.47−1.86

Fig. 2. Cluster analysis of 62 accessions and 04 checks of Perilla frutescens on the basis of quantitative and qualitative traits.

while the accession IC275959 was collected from Arunachal Pradesh with high values for stearic acid (8.15%). In the cluster-B two accessions were grouped, the accession IC006444 was collected from

Nagaland with maximum seed oil percentage (55.44%). Three accessions clustered in the cluster-C, all were collected from Nagaland. Accession IC524551 was recorded highest for oleic acid (18.85%), 778

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Table 4 Range of variability for eight biochemical traits in 62 accessions and 04 checks of Perilla frutescens. Traits

Oil% Palmitic acid Stearic acid Oleic acid Linoleic acid Linolenic acid Protein% Total Phenol (GAE)

Accessions

Checks

Mean ± SD

Range

%CV

Mean + SD

Range

%CV

46.86 ± 3.04 7.75 ± 0.66 3.66 ± 1.01 14.85 ± 2.42 18.32 ± 1.49 51.74 ± 4.86 19.25 ± 1.49 14.72 ± 2.86

41.0−55.4 6.67−10.05 2.20−8.15 11.45−28.85 13.78−23.0 35.31–65.23 16.73−22.74 7.00−21.00

6.50 8.52 27.54 16.29 8.13 9.40 7.77 19.41

47.07 ± 0.81 7.41 ± 0.61 3.24 ± 0.44 15.09 ± 1.82 17.84 ± 0.73 50.19 ± 2.54 19.26 ± 0.89 14.35 ± 2.18

56.33–48.00 6.80−7.98 2.98−3.89 13.89−17.80 17.23−18.90 47.19–53.24 18.57−20.56 12.60−17.50

1.73 8.21 13.47 12.08 4.09 5.06 4.61 15.17

value observed in the present study was 6.57, similar results were reported by Kwon et al., 2005 (6.5 alleles) and Lee et al., 2007 (6.5 allele). The Na value is lower when compared to a value 8.7 per allele by Park et al. (2008), 9.2 allele by Lee and Kim (2007), 7.6 allele reported by Song et al. (2012) and 9.2 allele by Sa et al. (2013) on accessions collected from Korea and Japan. The genetic distance based neighbour-joining divided the 62 accessions and four local checks of Perilla germplasm into four clusters named as the cluster-A (three accessions), cluster-B (15 accessions and four checks), cluster-C (41 accessions) and cluster-D (three accessions), (Fig. 4). In Cluster-A accession collected from Arunachal Pradesh (1), Mizoram (1), Sikkim (1) were clustering together while in the cluster-B total 15 accessions and four checks representing Arunachal Pradesh (1), Mizoram (3), and Nagaland (11) grouped. In cluster-C, 41 accessions from Manipur (3), Meghalaya (3), Nagaland (9), Mizoram (13) and Arunachal Pradesh (13) were grouped, which reveals that the accession of this group is genetically similar or seeds of accessions were exchange by the farmers at the local level, which is very common in NEH India. In cluster-D, three accessions collected from Mizoram (2) and Arunachal Pradesh (1) was grouped. Though, at the major cluster level, there was no association between geographical origin and genetic diversity but at the sub-cluster level, there was some association observed such as in the cluster-B accessions collected from Nagaland (11) were grouping together. Similarly in the cluster-C Mizoram, Meghalaya and Manipur accessions were grouping together to indicate some geographical isolation based on their genetic diversity (Fig. 4)

separate from all the clusters. Remaining 51 accessions and four checks were grouped in the cluster-D and recorded highest value for rest of three biochemical traits. Correlation study between morphological (quantitative traits) and biochemical traits showed that leaf length is significant positively correlated with days to maturity, seed size, yield per plant while negatively correlated with oil percentage and linoleic acid (Table 5). Main inflorescence length was significant positively correlated with petiole length, plant height, 1000 seed weight, seed size while negatively with traits days to flowering and day’s maturity. Number of inflorescence per plant was significant positively correlated with leaf length, petiole length, days to flowering, yield per plant, and oil yield per plant while significant negatively correlated with oil percent, linoleic acid and protein percentage. Plant height was only significant positively correlated trait with primary branches and oil percent. Days to maturity were significant positively correlated with leaf length and days to flowering while negatively significant correlated with main inflorescence length, plant height and protein percent. Yields per plant were significant positively correlated with primary branches, number of inflorescence per plant and oil yield per plant while negatively significant correlated with oil percentage and linoleic acid. Being oil seed crop oil percent is very important trait and it was significantly positively correlated with plant height, palmitic acid, linoleic acid and protein while negatively correlated with leaf length, leaf width, petiole length, days to flowering, number of inflorescence per plant and yield per plant. Liolenic acid which was reported high in Perilla crop did not showed significant positive correlation with any trait while it was negatively correlated with palmitic acid, stearic acid, oleic acid and linoleic acid. The Mandal et al. (2009) and Verma et al. (2013) also observed similar correlations in Perilla.

3.4. Population structure based study Three populations were observed based on STMS data for 62 accessions and 04 checks perilla accessions (Fig. 5). Out of 62 accessions and 04 checks of Perilla accessions, in population1, 26 accessions, population2 and population3 20 accessions each were grouped. Further, accessions under different populations were categorised as pure or admixture, the accessions with more than 0.80 score were considered as pure and less than 0.80 as admixture (Fig. 5). In population1 23 pure and 03 admixture, population2 19 pure and 01 admixture and population3 16 pure and 04 admixture were present and in total 57 pure and 09, admixture were identified (Fig. 5). Based on structure analysis the population2 maximum accessions from the Nagaland (11 out of 20) present along with 4 checks. This group shows that the accessions from Nagaland have not shown any inter mixing with accessions of other states (Meghalaya, Arunachal Pradesh, Manipur, Mizoram and Sikkim).

3.3. STMS marker based genetic diversity study Fourteen STMS markers which were polymorphic and reproducible were used to genotype 62 perilla accessions and four local checks (Table 6). The STMS markers generated a total of 92 alleles. The highest number of alleles were scored at the locus KWPE57 (18 allele) and the lowest was scored at the loci GBPFM75 (two allele) with a mean of 6.57. The PIC value for the STMS loci ranged from 0.32 (GBPFM75) to 0.92 (KWPE57) with an average 0.62. The average PIC value (0.62 per marker) obtained in the present study was similar to the value (0.59) reported in a set of accessions collected from Korea and Japan (Song et al., 2012). Major allele frequency was observed, minimum for primer KWPE57 (0.14) and maximum for primer GBPFM75 (0.72) with a mean of 0.43. The observed heterozygosity (Ho) was 0.048 while, gene diversity or expected heterozygosity (He) was the maximum for several SSR loci such as KWPE57 (0.92), KWPE26 (0.82), KWPE53 (0.82), GBPFM204 (0.81) and KWPE58 (0.80) and minimum for GBPFM75 (0.41) with a mean of 0.67. The major allele frequency calculated for all the markers ranged from 0.136 to 0.717 with an average of 0.433 (Table 6). The Na

3.5. Analysis of molecular variance (AMOVA) based study A hierarchical cluster and model based AMOVA studies were carried out for all the perilla accessions belonging to different states of NEH region based on STMS data. Hierarchical based AMOVA analyses showed 6% diversity exists among the population, 87% among the individual and 6% within the individual. While, the model based AMOVA 779

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Fig. 3. Cluster analysis of 62 accessions and 04 checks of Perilla frutescens on the basis of biochemical traits.

780

−0.131

0.076 −0.159

−0.022

−0.046 −0.060

0.063 .343**

−0.011

0.059 −0.062

−0.098 0.054

−0.371**

0.062 −0.348**

0.044 0.194

LiA

LiLA PR

TP OYP

**

−0.154 0.183

−0.137 −0.310*

−0.121

0.089 0.137 0.202

0.033 0.045 0.231 −0.308*

.422 0.110 −0.076 0.089 1 −0.385** .538** −0.165 .497**

DF

−0.200 0.013

−0.049 0.045

−0.034

0.141 0.105 0.072

.462** .286* 0.006 0.059

−0.028 0.237 0.199 .455** −0.385** 1 −0.217 .243* −0.258*

MIL **

−0.012 .495**

0.146 −0.438** −0.047 0.146

0.014 0.121

0.126

0.239 −0.026 −0.062

−0.002 −0.090 −0.040 −0.421**

0.217 0.191 0.083 .279*

−0.129 0.125 .458** 0.129 −0.165 .243* −0.121 1 −0.047

pH

0.045 0.040 .549** −0.382**

.563 0.150 0.126 .302* .538** −0.217 1 −0.121 0.225

NI *

0.064 −0.038

−0.163 −0.286*

−0.142

0.179 0.051 0.121

−0.005 −0.034 −0.014 −0.119

.306 0.120 0.090 −0.050 .497** −0.258* 0.225 −0.047 1

DM

−0.237 0.153

0.035 −0.030

−0.106

0.088 −0.116 −0.024

1 .727** 0.198 −0.176

0.238 .359** −0.067 .384** 0.033 .462** 0.045 0.217 −0.005

SW **

−0.007 0.067

0.078 −0.132

−0.002

0.059 −0.034 0.064

.727** 1 0.109 −0.209

.340 .377** −0.032 .373** 0.045 .286* 0.040 0.191 −0.034

SS *

0.130 .981**

0.144 −0.174

−0.284*

0.113 0.021 −0.098

0.198 0.109 1 −0.270*

.291 0.118 .308* 0.241 0.231 0.006 .549** 0.083 −0.014

YP **

−0.008 −0.087

−0.093 .299*

.419**

.289* 0.222 −0.228

−0.176 −0.209 −0.270* 1

−0.542 −0.354** 0.048 −0.290* −0.308* 0.059 −0.382** .279* −0.119

OL

−0.071 0.169

−0.349 −0.021

0.235

1 .417** .277*

0.088 0.059 0.113 .289*

0.018 0.041 0.225 0.072 0.089 0.141 −0.002 0.239 0.179

PA

LL (Leaf length), LB (Leaf Breadth), PB (Primary Branches/plant), PL (Petiole Length), DF (Days to 50% flowering), MIL (Main Inflorescence length). NI (Number of Inflorescence/plant), pH (Plant height), DM (Days to 80%maturity), SW (1000 Seed wt), YP (Yield plant), SS (Seed size), OL (Oil%). PA (Palmitic acid), SA (Stearic acid), OA (oleic acid), LiA (Linoleic acid), LiLA (Linolanic acid), PR (Protein%), TP (Total phenol), OYP (Oil yield per plant).

−0.118 0.194

0.072 0.175 0.081

0.225 .262* 0.125

0.041 0.101 0.068

0.018 −0.019 0.116

PA SA OA

.384** .373** 0.241 −0.290*

−0.067 −0.032 .308* 0.048

.359** .377** 0.118 −0.354**

0.238 .340** .291* −0.542**

SW SS YP OL

.575 .601** 0.159 1 0.089 .455** .302* 0.129 −0.050

−0.028 0.086 1 0.159 −0.076 0.199 0.126 .458** 0.090

.584 1 0.086 .601** 0.110 0.237 0.150 0.125 0.120

1 .584** −0.028 .575** .422** −0.028 .563** −0.129 .306*

**

PL

PB

**

LW

LL LW PB PL DF MIL NI pH DM

LL

Table 5 Correlation studies for quantitative and biochemical traits.

**

−0.150 0.072

−0.179 −0.146

−0.619 0.006

−0.430 −0.010

**

0.170

.277* .493** 1

−0.024 0.064 −0.098 −0.228

0.116 0.068 0.125 0.081 0.202 0.072 −0.040 −0.062 0.121

OA

.311*

.417** 1 .493**

−0.116 −0.034 0.021 0.222

−0.019 0.101 .262* 0.175 0.137 0.105 −0.090 −0.026 0.051

SA

**

**

0.001 −0.211

−0.426 0.070

1

0.235 .311* 0.170

**

−0.106 −0.002 −0.284* .419**

−0.371 −0.011 −0.022 −0.131 −0.121 −0.034 −0.421** 0.126 −0.142

LiA

−0.021 −0.010 0.006

−0.349** −0.430** −0.619**

0.103 0.138

1 0.030

0.064 −0.127

0.030 1

0.070

−0.030 −0.132 −0.174 .299*

−0.426**

**

−0.348 −0.062 −0.060 −0.159 −0.310* 0.045 −0.438** 0.121 −0.286*

PR

0.035 0.078 0.144 −0.093

0.062 0.059 −0.046 0.076 −0.137 −0.049 0.146 0.014 −0.163

LiLA

1 0.145

0.103 0.064

0.001

−0.071 −0.150 −0.179

−0.237 −0.007 0.130 −0.008

0.044 −0.098 0.063 −0.118 −0.154 −0.200 −0.012 −0.047 0.064

TP

0.194 0.054 .343** 0.194 0.183 0.013 .495** 0.146 −0.038 0.153 0.067 .981** −0.087 0.169 0.072 −0.146 −0.211 0.138 −0.127 0.145 1

OYP

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S.K. Singh et al. Table 6 Summary statistics of the STMS markers used in this study of 62 accessions and 04 checks of Perilla frutescens. Marker

Motif

Major Allele Frequency

No. of observations

Allele No

(He)

(Ho)

PIC

KWPE1 KWPE26 KWPE48 KWPE51 KWPE53 KWPE57 KWPE58 GBPFM63 GBPFM70 GBPFM75 GBPFM91 GBPFM157 GBPFM203 GBPFM204 Mean

(GAA)18 [(AG)6(AG)7(GA)13] (GA)9 A(N)9 (CT)16 (CT)16 (TG)9-(AG)12 (TC)21-(CA)12 (ATTTG)3, (AC)5 (CT)12 (AG)9 (ACC)5, (ACA)4, (CAA)7 (GA)5TAA(AG)26 (AG)17

0.50 0.33 0.56 0.52 0.27 0.14 0.28 0.66 0.52 0.72 0.43 0.42 0.44 0.31 0.43

62.0 64.0 57.0 62.0 64.0 66.0 65.0 64.0 64.0 60.0 60.0 59.0 66.0 53.0 61.85

7.00 8.00 4.00 3.00 7.00 18.00 9.00 3.00 3.00 2.00 3.00 5.00 8.00 12.00 6.57

0.687 0.815 0.595 0.554 0.807 0.921 0.801 0.461 0.528 0.406 0.607 0.669 0.718 0.810 0.670

0.016 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.660 0.048

0.653 0.794 0.535 0.456 0.780 0.915 0.774 0.368 0.418 0.324 0.523 0.607 0.679 0.787 0.615

Fig. 4. Cluster analysis of 62 accessions and 04 checks of Perilla frutescens on the basis of STMS markers.

Fig. 5. Model based clustering for 62 accessions of Perilla frutescens and 04 checks using STMS markers.

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Fig. 6. Analysis of molecular variance (AMOVA) of 62 accessions of Perilla and 04 checks based on population obtained by (a) hierarchical and (b) model based approach.

3.7. Co-linearity between hierarchical cluster and model based population analysis

analyses showed 21% diversity exists among the population, 73% among individual and 6% within individual (Fig. 6a and b).

Since hierarchical cluster and model based approach has grouped all 62 accessions and 04 checks into three clusters and three populations respectively, therefore, the Co-linearity between accessions grouping in the hierarchical cluster and model based population structure was confirmed by Venn diagram. In the Venn diagram out of 62 accessions and 04 checks, 15 accessions of Perilla accessions (37.5%) were common between pop1 and cluster2. Similarly, 16 perilla accessions (44.4%) were common between pop2 and cluster1 (Fig. 8a and b). In Chinese wild rice collection this method was used by Liu et al. (2015), and established that Venn diagram is a robust method to study overlapping accessions. This study supports that grouping of Perilla accessions collected from NEH region based on hierarchical cluster and model based approaches were more than 37% overlapping.

3.6. Principal coordinate analyses (PCoA) based study Principal coordinate analysis (PCoA) with STMS markers showed that large diversity existed in Perilla accessions collected from NEH region. On the basis of hierarchical and model based approach, the total collected accessions of Perilla were divided into a total of 03 populations (Fig. 7a and b). The percentage of variation explained by the first 3 axes of hierarchical and model based approach was 31.16% (axis1 − 14.63%, axis2 − 9.17% and axis3 − 7.35%). In both the PCoA approaches all accessions were labelled with different colours representing the population or group they belong, and it was observed that in the model based approach the accessions belonging to different populations were clearly separated from each other with few intermixing across the coordinate (Fig. 7b).

Fig. 7. Principal coordinate analysis (PCoA) of 62 accessions of Perilla and 04 checks based on population obtained by (a) hierarchical based approach and (b) model based approach.

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Fig. 8. (a) Venn diagram showing co linearity between population 1 & cluster 2 and (b) Venn diagram showing co linearity between population2 & cluster1.

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4. Conclusion In this study, the morphological, biochemical and STMS markers were used for characterization of 62 accessions of Perilla collected from NEH region of India, and this study showed that large diversity exists in Perilla germplasm of this region. The biochemical studies showed that, the oil percentage in the accessions from NEH region of India were more than reported in earlier studies by Shin and Kim (1994) (38.6–47.8 percent) and Song et al., (2012) (37.3–43.5 percent). Molecular analysis showed that all accessions were grouped into three clusters based on cluster analysis, and in three populations based on model based approach. At a broad level there was no correlation between geographical origin of accessions and genetic grouping observed in the form of clusters but at the sub-group level some correlation was observed. This shows that there are some distinct gene pools at the subgroup level but at the broad level they share genetic information with each other. The accessions (IC524554, IC524504, IC52445, IC 422885, IC275959, IC016443, IC006444, IC006442 and IC599246) identified for this study with more than 50% percent oil, and with large genetic diversity can be utilized in the future oil content improvement programme which will help oil based industry such as paints, varnishes, linoleum, printing ink, lacquers and protective water proof coatings on cloth, etc. Acknowledgements The author’s thanks to Director, ICAR- National Bureau of Plant Genetic Resources, New Delhi, India for providing research facilities. We are also thankful to Dr Sangita Yadav, Principal Scientist, Seed Technology, ICAR-Indian Agricultural Research Institute, New Delhi and Dr. Amrita Banerjee, Scientist, ICAR Research Complex for NEH Region to provide the lab facilities. References Oil in cereal adjuncts, method 945.16. In: William, H. (Ed.), Official Method of Analysis, 18th ed. AOAC Int, Gaithersburg, MD, pp. 56–132. Arora, R.K., Pandey, A., 1996. Wild Edible Plants of India, National Bureau of Plant Genetic Resources. Indian Council of Agricultural research (ICAR), pp. 178–179 (New Delhi-12). Bahuguna, A., Prasad, B., 2014. Plant development and yield as prejudiced by Perilla (Perilla frutescens) germplasm lines in India hill condition. Res. J. Med. Plant 8 (3), 121–125. Brenner, D.M., 1993. Perilla: Botany, uses and genetic resources. In: Janick, J., Simon, J.E. (Eds.), New Crop. Wiley, New York, pp. 322–328. Evano, G., Regnuat, S., Goudet, J., 2005. Detecting the number of clusters of indi-viduals using the software STRUCTURE: a simulation study. Mol. Ecol. 14, 2611–2620. Food and Agricultural Organization, 1992. Minor Oil Crops 94. Agricultural Services Bulletin, pp. 107. Forster, J.C., 1995. Soil nitrogen. In: Alef, K., Nannipieri, P. (Eds.), Methods in Applied Soil Microbiology and Biochemistry. Academic Press, London, pp. 79–87. Frankel, O.H., Soule, M.E., 1981. Conservation and Evolution. Cambridge University Press, Cambridge. Godin, N.J., Spensley, P.C., 1971. Oils and Oilseeds, In: Crop and Product Digests No. 1. Tropical Products Institute, pp. 104–105. Harlan, J.R., 1975. Our vanishing genetic resources. Science 188, 618–621. Honda, G., Yuba, A., Kojima, T., Tabata, M., 1994. Chemotaxonomic and cytogenetic

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