Acta agriculturae Slovenica, 118/4, 1–20, Ljubljana 2022 doi:10.14720/aas.2022.118.4.2511 Original research article / izvirni znanstveni članek Genetic diversity in - chilli (Capsicum annuum L.) based on microsatel- lite markers: An evaluation of Bangladeshi germplasm Md. Rezwan MOLLA 1, 2, Iftekhar AHMED 1, 2, Md. Motiar ROHMAN 3, Mohammad Amdadul HAQUE 4, 5, 6, Shah Md. Monir HOSSAIN 7, Lutful HASSAN 2 Received January 16, 2022; accepted September 23, 2022. Delo je prispelo 16. januarja 2022, sprejeto 23. septembra 2022 1 Bangladesh Agricultural Research Institute, Plant Genetic Resources Centre, Gazipur, Bangladesh 2 Bangladesh Agricultural University, Faculty of Agriculture, Department of Genetics and Plant Breeding, Mymensingh, Bangladesh 3 Bangladesh Agricultural Research Institute, Plant Breeding Division, Gazipur, Bangladesh 4 Universiti Putra Malaysia, Faculty of Agriculture, Department of Crop Science, Serdang, Selangor, Malaysia 5 Bangladesh Agricultural Research Institute, Horticulture Research Centre, Gazipur, Bangladesh 6 Corresponding author, e-mail: amdad80@gmail.com 7 Bangladesh Agricultural Research Council, Crops Division, Dhaka, Bangladesh Genetic diversity in - chilli (Capsicum annuum L.) based on microsatellite markers: An evaluation of Bangladeshi germ- plasm Abstract: Genetic diversity analysis is a pre-requisite to develop improve variety of any crop. Hence, 39 SSR markers were used to analyze the genetic diversity of local chilli culti- vars. PCR-amplified microsatellite loci were shown to be poly- morphic in all investigated cultivars. The locus, CAMS-647 produced the highest number of alleles (8) ranging in size from 188 to 279 bp. PIC values for 39 primers ranged from 0.099 for the locus Hpms 1-165 to 0.806 for the locus CAMS-679. All of the SSRs examined were informative in characterizing the ge- notypic variance of the samples while 12 were more informative with higher PIC values (> 0.6). There was a wide range of ge- netic diversity varied from 0.117 (HpmsE075) to 0.806 (CAMS- 647), whereas the highest (1.713) and the lowest (0.205) value of Shannon’s Information Index was registered in the locus CAMS-679 and Hpms 1-165, respectively. There was a higher degree of genetic differentiation (0.927) and a lower amount of gene flow (0.010). Nei’s genetic distance (GD) varied from 0.100 to 0.990. Among 96 cultivars, 55 had distinct status in the dendrogram with higher GD values (> 0.6), while 41 cultivars showed a close relationship and yielded lower GD values. Key words: chilli; genetic diversity; microsatellite (SSR) markers; polymorphism information content Določanje genetske raznolikosti čilija (Capsicum annuum L.) z mikrosateliti: Ovrednotenje genetskega materiala v Bangla- dešu Izvleček: Analiza genetske raznolikosti je predpogoj za vzgojo izboljšanih sort katerekoli kulturne rastline. Zatradi tega je bilo uporabljenih 39 SSR lokusov za analizo genetske razno- likosti genotipov čilija. S PCR pomnoženi mikrosatelitski loku- si so bili polimorfni pri vseh preučenih genotipih. Pri lokusu CAMS-647 smo zaznali največje število alelov (8), ki so obse- gali dolžine od 188 do 279 bp. PIC vrednosti so za 39 začetnih oligonukleotidov (primerjev) znašale od 0,099 za lokus Hpms 1-165 do 0,806 za lokus CAMS-679. Vsi analizirani mikrosa- teliti (SSR) so bili za vrednotenje genenotipske variabilnosti vzorcev informativni, med njimi jih je bilo 12 z večjimi PIC vre- dnostmi (> 0,6) najprimernejših. Genetska raznolikost je bila velika in je variirala od 0,117 (HpmsE075) do 0,806 (CAMS- 647), največja (1,713) in najmanjša (0,205) vrednost Shanno- novega informacijskega indeksa sta bili ugotovljeni za lokusa CAMS-679 in Hpms 1-165. Ugotovljena je bila visoka stopnja genetske diferenciacije (0,927) in majhen pretok genov (0,010). Neijeva genetska distanca je variirala med 0,100 in 0,990. Med 96 genotipi jih je imelo 55 jasen položaj v dendrogramu z večji- mi vrednostmi genske distance (> 0,6) medtem, ko je 41 geno- tipov pokazalo ožjo sorodnost z manjšimi v rednostmi genske distance. Ključne besede: čili; genetska raznolikost; microsatelistki markerji (SSR); informacijska vrednost polimorfizma Acta agriculturae Slovenica, 118/4 – 20222 R. MOLLA et al. 1 INTRODUCTION Chilli (Capsicum spp.) belongs to the Solanace- ae family, having chromosome number 2n = 2x = 24 (Sharmin et al., 2018)with a mean of 2.00 alleles per primer. Gene diversity ranged from 0.333 to 1.00 with an average of 0.567. Polymorphic Information Content (PIC. The genus is native to Central and South Amer- ica (Pickersgill, 1991) which includes five species viz., Capsicum chinense Jacq., Capsicum baccatum L., Capsi- cum frutescens L., Capsicum pubescens Ruiz & Pav. and Capsicum annuum. Of these, C. annuum is the most im- portant one because of its versatile use and is cultivated in both tropical and temperate areas in the world. It is used as vegetables, spice, colorant, and for some medi- cal applications (Hernández-Pérez et al., 2020). Chilli is a valuable spice and one of the most important cash crops grown in Bangladesh. It is available and used in human food preparation in the forms of green, dried and pow- dered. It has become an essential ingredient in Bangla- deshi dietary patterns. A number of cultivars are grown in Bangladesh showing differences in growth habit, size, shape, color, pungency, and yield indicating the pres- ence of wider genetic variation (Farhad et al., 2010). The area and production of Kharif (April to September) chilli was 19,000 ha and 36,000 mt, respectively; while 83,000 ha and 105,000 mt were recorded in Rabi (October to March) chilli, respectively. The average yield was around 1.68 mt ha-1 (BBS, 2021). Bangladesh has a high diversity of cultivars belonging to various cultivated chilli varie- ties. Due to the long history of cultivation, selection and popularity of crops, sufficient genetic variability has been generated. The analysis of genetic diversity and related- ness between or within different species, populations and individuals is a prerequisite towards effective utilization and protection of plant genetic resources (van Zonneveld et al., 2012). As the country has vast chilli resources, and the demand is using those for further development of new materials that can provide a high economic re- turn. Hence, it is necessary to study some basic traits of similarity and/or parent progeny relationship that can indicate their adoption in the country. Selection of useful diversity from the available genetic resources will be an enormous challenge. Collection and maintenance of the genetic diversity in chilli are important to avoid genetic erosion. Besides the identification of species, the charac- terization and evaluation of cultivars maintained in gene banks are of fundamental importance (van Zonneveld et al., 2012). Traditionally, morphological markers known as descriptors have been utilized in plants for varietal identification and genetic diversity study, which is ex- pensive, time-consuming, requiring huge areas of land, and specialized staff, and is subject to variation owing to environmental factors (Molla et al., 2017). However, in elite germplasm, the level of polymorphism for mor- phological traits is sometimes too low and insufficient to allow for variety/genotype discrimination (Dhaliwal et al., 2014). The DNA marker provides a one-stop solu- tion in this case. Different molecular markers for pepper have been established in the previous decade or so. Mi- crosatellite SSR (simple sequence repeat) is a DNA-based marker that based on PCR, multi-allelic, highly poly- morphic, commonly co-dominant, highly repeatable, randomly and extensively distributed across the genome (Jain et al., 2014). Furthermore, SSRs are the most exten- sively used marker system for identifying plant varieties and analyzing diversity, particularly in cultivated species with low levels of polymorphism (Anumalla et al., 2015). Although some research has been conducted regarding chilli diversity in Bangladesh, inadequate information was generated because of the limited number of culti- vars assessed with a limited number of primers. For in- stance, 20 local chilli cultivars were evaluated using 11 SSR makers by Sharmin et al. (2018)with a mean of 2.00 alleles per primer. Gene diversity ranged from 0.333 to 1.00 with an average of 0.567. Polymorphic Information Content (PIC and 22 cultivars using four microsatellite markers by Hossain et al. (2014). The present study was, therefore, undertaken to estimate genetic diversity of 96 winter chilli cultivars collected from diverse locations of Bangladesh by means of 39 microsatellite markers to guide genetic improvement and to promote increased utilization. 2 MATERIALS AND METHODS 2.1 COLLECTION AND EXTRACTION OF GENOMIC DNA FROM A PLANT SAMPLE A total of 96 local cultivars (Table 1) of winter growing chilli (Capsicum annuum L. representing differ- ent geographical distributions were nominated and col- lected at Plant Genetic Resources Centre (PGRC), Bang- ladesh Agricultural Research Institute (BARI) for the current study to investigate molecular diversity by using SSR marker. Seeds of collected cultivars were sown on small plastic pots to grow seedlings. For DNA extraction, we used young, fresh, disease- and insect-free leaves. SDS (Sodium dodecyl sulfate), phenol: chloroform: IAA followed by alcoholic precipitation were used to isolate genomic DNA from the leaf tissue of three-week-old seedlings described by Saghai-Maroof et al. (1984) with some modifications. Excluding the usage of liquid nitro- gen, the modified protocol included digestion with ho- mogenization buffer [Solution: Tris-50 mM, ethylene di- Acta agriculturae Slovenica, 118/4 – 2022 3 Genetic diversity in - chilli (Capsicum annuum L.) based on microsatellite markers ... Sl. No. Cultivars Location of collecting site (Upazila and District) Latitude (N) Longitude (E) 01 BD-10878 Kazipur, Sirajganj 24° 41.516′ 89° 42.83′ 02 BD-10879 Galachipa, Patuakhali 22° 9.8′ 90° 25.8′ 03 BD-10880 Kazipur, Sirajganj 24° 41.711′ 89° 43.059′ 04 BD-10881 Kazipur, Sirajganj 24° 41.711′ 89° 43.059′ 05 BD-10882 Kazipur, Sirajganj 24° 41.711′ 89° 43.059′ 06 BD-10883 Kazipur, Sirajganj 24° 41.925′ 89° 42.978′ 07 BD-10884 Sadar, Sirajganj 24° 31.511′ 89° 40.982′ 08 BD-10885 Sadar, Sirajganj 24° 32.671′ 89° 40.560′ 09 BD-10886 Sadar, Sirajganj 24° 32.671′ 89° 40.560′ 10 BD-10887 Kalapara, Patuakhali 21° 58.918′ 90° 13.60′ 11 BD-10888 Kalapara, Patuakhali 21° 58.918′ 90° 13.60′ 12 BD-10892 Galachipa, Patuakhali 22° 9.48′ 90° 25.48′ 13 BD-10894 Galachipa, Patuakhali 22° 9.48′ 90° 25.48′ 14 BD-10895 Galachipa, Patuakhali 22° 9.48′ 90° 25.48′ 15 BD-10896 Galachipa, Patuakhali 22° 9.48′ 90° 25.48′ 16 BD-10897 Galachipa, Patuakhali 22° 9.48′ 90° 25.48′ 17 BD-10898 Galachipa, Patuakhali 22° 9.48′ 90° 25.48′ 18 BD-10938 Muksudpur, Gopalganj 23° 19.0′ 89° 52.0′ 19 BD-10934 Dohazari, Chittagong 22° 9.46′ 92° 4.22′ 20 BD-10935 Dohazari, Chittagong 22° 9.46′ 92° 4.22′ 21 BD-10936 Dohazari, Chittagong 22° 9.46′ 92° 4.22′ 22 BD-10913 Kotalipara, Gopalganj 22° 59.0′ 89° 59.30′ 23 KASI-49 Kotalipara, Gopalganj 22° 59.0′ 89° 59.30′ 24 BD-10916 Kashiani, Gopalganj 23° 17.618′ 89° 47.259′ 25 BD-10917 Daulatkhan, Bhola 22° 36.24′ 90° 44.60′ 26 BD-10918 Daulatkhan, Bhola 22° 36.24′ 90° 44.60′ 27 BD-10919 Sadar, Bhola 22°37.517′ 90° 38.062′ 28 BD-10920 Sadar, Bhola 22° 37.517′ 90° 38.062′ 29 RISA-23 Sadar, Bhola 22° 37.517′ 90° 38.062′ 30 BD-10921 Sadar, Bhola 22° 37.517′ 90° 38.062′ 31 BD-10922 Sadar, Bhola 22° 37.517′ 90° 38.062′ 32 BD-10923 Charfashion, Bhola 22° 11.60′ 90° 45.48′ 33 BD-10924 Charfashion, Bhola 22° 11.60′ 90° 45.48′ 34 BD-10925 Charfashion, Bhola 22° 11.60′ 90° 45.48′ 35 BD-10927 Charfashion, Bhola 22° 11.60′ 90° 45.48′ 36 BD-10928 Charfashion, Bhola 22° 11.60′ 90° 45.48′ 37 BD-10929 Charfashion, Bhola 22° 11.60′ 90° 45.48′ 38 BD-10930 Daulatkhan, Bhola 22° 36.24′ 90° 44.60′ 39 BD-10931 Daulatkhan, Bhola 22° 36.24′ 90° 44.60′ Continued on the next page Table 1: List of winter growing local chilli cultivars used in molecular characterization with their collection sites in Bangladesh Acta agriculturae Slovenica, 118/4 – 20224 R. MOLLA et al. 40 BD-10932 Borhanuddin, Bhola 22° 30′ 90° 43.3′ 41 BD-10933 Borhanuddin, Bhola 22° 30′ 90° 43.3′ 42 BD-10900 Madarganj, Jamalpur 24° 54.490′ 89° 43.075′ 43 BD-10903 Melando, Jamalpur 24° 56.962′ 89° 52.622′ 44 BD-10904 Melando, Jamalpur 24° 56.962′ 89° 52.622′ 45 BD-10905 Melando, Jamalpur 24° 56.962′ 89° 52.622′ 46 BD-10906 Melando, Jamalpur 24° 56.962′ 89° 52.622′ 47 RT-09 Melando, Jamalpur 24° 56.962′ 89° 52.622′ 48 RT-14 Sadar, Jamalpur 24° 56.170′ 89° 55.721′ 49 BD-10908 Sharishabari, Jamalpur 24° 45.103′ 89° 49.012′ 50 BD-10909 Sharishabari, Jamalpur 24° 45.918′ 89° 49.108′ 51 BD-10910 Sharishabari, Jamalpur 24° 45.440′ 89° 49.828′ 52 BD-10911 Sharishabari, Jamalpur 24° 45.192′ 89° 49.415′ 53 BD-10912 Sharishabari, Jamalpur 24° 45.142′ 89° 49.914′ 54 AM-29 Kazipur, Sirajganj 24° 41.925′ 89° 42.978′ 55 BD-10899 Galachipa, Patuakhali 22° 14.62′ 90° 23.39′ 56 BD-10914 Kotalipara, Gopalganj 22° 59.0′ 89° 59.30′ 57 BD-10926 Charfashion, Bhola 22° 11.283′ 90° 47.124′ 58 BD-10901 Madarganj, Jamalpur 24° 53.026′ 89° 42.296′ 59 BD-10902 Madarganj, Jamalpur 24° 53.026′ 89° 42.296′ 60 BD-10907 Sharishabari, Jamalpur 24° 45.662′ 89° 49.828′ 61 BD-10939 Khetlal, Joypurhat 25° 1.5′ 89° 8′ 62 KASI-115 Muksudpur, Gopalganj 23° 19′ 89° 52′ 63 BD-10940 Sadar, Gazipur 24° 0′ 90° 25.30′ 64 RI-02 Ramgarh, Khagrachori 22°.59.97′ 91° 42.79′ 65 RI-12 Ramgarh, Khagrachori 22°.59.58′ 90° 41.83′ 66 BD-10889 Kalapara, Patuakhali 21°.58.918′ 90° 13.60′ 67 BD-10890 Amtali, Barguna 22°.05.115′ 90° 14.178′ 68 AMS-08 Amtali, Barguna 22°.05.115′ 90° 14.178′ 69 AMS-10 Kalapara, Patuakhali 22°.02.056′ 90° 17.005′ 70 AMS-21 Galachipa, Patuakhali 22°.10.413′ 90° 23.885′ 71 AMS-26 Sadar, Patuakhali 22°.16.437′ 90° 19.355′ 72 AMS-39 Nalsity, Jhalokati 22°.38.203′ 90° 20.966′ 73 AMS-42 Babuganj, Barisal 22°.46.966′ 90° 18.834′ 74 AMS-45 Babuganj, Barisal 22°.47.167′ 90° 19.888′ 75 AHM-46 Babuganj, Barisal 22°.39.03′ 90° 00.51′ 76 AHM-46(1) Wajirpur, Barisal 22° 48.42′ 90° 14.42′ 77 BD-10941 Sadar, Barisal 22°.44.170′ 90° 11.124′ 78 AHM-142 Jajira, Shariatpur 23°.19.259′ 90° 08.421′ 79 AHM-143 Jajira, Shariatpur 23°.15.838′ 90° 12.381′ 80 IA-52 Tongibari, Munshiganj 23°.30.762′ 90° 29.715′ Continued on the next page Acta agriculturae Slovenica, 118/4 – 2022 5 Genetic diversity in - chilli (Capsicum annuum L.) based on microsatellite markers ... 81 BD-10891 Aamtali, Barguna 22° 12.889′ 90° 17.853′ 82 BD-10893 Galachipa, Patuakhali 22° 10.413′ 90° 23.855′ 83 AMS-30 Sadar, Patuakhali 22° 21.90′ 90° 23.90′ 84 AMS-31 Sadar, Patuakhali 22° 21.90′ 90° 23.90′ 85 AMS-12 Aamtali, Barguna 22° 08.008′ 90° 23.831′ 86 AMS-32 Dumki, Patuakhali 22° 27.495′ 90° 21.298′ 87 AMS-33 Bakerganj, Barisal 22° 33.512′ 90° 19.893′ 88 RT-12 Sadar, Jamalpur 24° 56.167′ 89° 55.892′ 89 RT-20 Sadar, Jamalpur 24° 50.909′ 89° 53.465′ 90 RT-22 Sharishabari, Jamalpur 24° 45.312′ 89° 49.112′ 91 RT-11 Sadar, Jamalpur 24° 56.167′ 89° 55.892′ 92 RT-13 Sadar, Jamalpur 24° 56.167′ 89° 55.892′ 93 RT-18 Sadar, Jamalpur 24° 56.909′ 89° 53.465′ 94 RISA-33 Sadar, Bhola 22° 47.582′ 90° 37.837′ 95 RM-01 Akkelpur, Joypurhat 25° 01.958′ 89° 01.610′ 96 KASI-20(1) Kotalipara, Gopalganj 22° 59.0′ 89° 59.30′ amine tetra acetic acid (EDTA) 25 mM, NaCl 300 mM, SDS 1 % and deionized water] at a temperature of 65 ºC for about 30 min, extraction by phenol (25): chloroform (24): IAA (1), precipitation with ice-cold and extra pure isopropyl alcohol and purification with absolute ethanol, sodium acetate (3M) and 70 % ethanol chronologically was used. Finally, DNA sample was added in 50 μl of Tris-EDTA (TE) buffer to a 1.5 ml micro centrifuge tube to dissolve. After completely dissolve the DNA pellet, 4 μl RNase @ 10 mg ml-1 was added to isolate DNA and incubated for 1.5 hours at 37  °C. Finally, DNA sample was kept in freezer at -20 ⁰C. 2.2 DNA CONCENTRATION MEASUREMENT AND OPTIMIZATION The occurrence of quality genomic DNA was con- firmed on a 1 % agarose gel which was photographed uti- lizing a photo documentation technique after being visu- alized under UV light in UV Transilluminator (Uvitec, UK). In this investigation, DNA samples of all cultivars were confirmed to be of good quality. The amount of genomic DNA was quantified through UV spectropho- tometer (Spectronic® GENESYS™ 10 Bio) at 260 nm wave- length. Using the spectrophotometer absorbance; the original DNA concentrations were determined accord- ing to the following equation: Before PCR amplification of DNA, the DNA con- centrations were adjusted to 25 ng µl-1 using the follow- ing formula: S1 × V1 = S2 × V2 Where, S1: Initial strength (ng µl-1), V1: Initial volume (µl), S2: Final strength (ng µl - 1) and V2: Final volume (µl) 2.3 IDENTIFICATION AND SELECTION OF MI- CROSATELLITE OR SSR PRIMERS Preliminarily, 50 microsatellite primer pairs were tested to identify discriminating alleles those are located in 12 chromosomes of chilli from different publications. Among them, 39 were selected for their better respon- siveness with clear and desired amplified product size, and they were used in the present investigation for mi- crosatellite analysis (Table 2). 2.4 STANDARDIZATION OF PCR AND ITS AM- PLIFICATION The PCR was started with 10 μl volumes compris- ing 50 ng template DNA, 5X Green GoTaq® Reaction Buffer included 7.5 mM MgSO4, 1.25 U μl -1 Taq DNA polymerase, 0.4 mM of the deoxyribonucleotide triphos- phate (dNTPs), 10 μM of primer, 0.5 % DMSO (dimethyl sulfoxide) and required amount of deionized water. This reaction was carried out in an oil-free Eppendorf Mas- tercycler® nexus Gradient thermal cycler. The following Acta agriculturae Slovenica, 118/4 – 20226 R. MOLLA et al. Sl . Lo cu s Pr im er se qu en ce (5 ’-3 ’) Re pe at m ot if A nn . T . C hr . n o. Ex pe ct ed S iz e (b p) Re fe re nc e 1 C A M S- 33 6 F: g gt gg aa ac ttg ct tg ga ga R: cc ca ga ac ca tc ca cc ta ct (tc ) 16 53 o C 3 15 7 (M in am iy am a et a l., 2 00 6) 2 C A M S- 35 1 F: cg ca tg aa gc aa at gt ac ca R: a cc tg ca gt ttg ttg ttg ga (t g) 3… (a g) 26 51 o C 4 24 0 3 C A M S- 40 5 F: tt ct tg gg tc cc ac ac ttt c R: a gg ttg aa ag ga gg gc aa ta (tc ) 18 53 o C 8, 1 1 24 1 4 C A M S- 46 0 F: cc ttt ca ct tc ag cc ca ca t R: a cc at cc gc ta ag ac ga ga a (tc ) 20 54 o C 7 21 5 5 C A M S- 67 9 F: tt tg ca tg ttt ta cc ca ttc c R: at gt ga aa ca ca ta gg ta gc ac tg a (t at ) 16 53 o C 1 20 0 6 C A M S- 86 4 F: ct gt tg tg ga ag aa ga gg ac a R: g ct tc ttt ttc aa cc tc ct cc t (a ga ) 32 54 o C 7 22 2 7 C A M S- 07 2 F: cc cg cg aa at ca ag gt aa t R: a aa gc ta ttg ct ac tg gg ttc g (a c) 13 53 o C 5 15 3 8 C A M S- 11 7 F: tt gt gg ag ga aa ca ag ca aa R: cc tc ag cc ca gg ag ac at aa (t g) 21 (t a) 3 52 o C 11 22 3 9 C A M S- 80 6 F: tg tc ac aa gt gt ca ag gt ag ga g R: cc cc aa aa at ttt cc ct ca t (a ga ) 19 54 o C 10 22 7 10 C A M S- 84 4 F: g ca aa ga aa aa ga aa ag cc tg a R: c tg ca ac tg ct gc ttc at tc (g aa ) 6 53 o C 1 22 3 11 C A M S- 01 5 F: tc at gt tg at ta tg ct ttt gt tc a R: cc at gt at tg ta tg at ac ct ga ga aa (a c) 7 at (a c) 8a (t a) 7 53 o C 2 11 2 12 C A M S- 06 5 F: cc ag tc tc at cc ag ca ga ca R: c at at gc tg ct cc tg ca ttc (a c) 12 52 o C 21 3 13 C A M S- 07 5 F: a ct aa tta ca ca ttc tg ca ttt tc tc R: a gg ct cg ag ta cc ac ga ag a (t g) 10 54 o C 5 19 0 14 C A M S- 47 8 F: g ag tg cc at gc tg at ta ag ga R: c ac ga ct gt ct tg cc tg aa c (a g) 12 52 o C 3 24 8 15 C A M S- 83 8 F: cc ag ga tg gt gt ta ag gg ttt R: g tc gc at ca at ga gc at ag g (a ga )1 9 59 o C 6 22 9 16 C A M S- 86 1 F: g ca tg ca ag ct ta gc ca ac R: tg ag at tg aa gc ta ga at ttt gg a (a ga )1 1 52 o C 2 18 3 Co nt in ue d on th e n ex t p ag e Ta bl e 2: L ist o f m ic ro sa te lli te p rim er s u se d in th is st ud y. A nn . T .: A nn ea lin g Te m pe ra tu re , C hr . n o. : C hr om os om e nu m be r Acta agriculturae Slovenica, 118/4 – 2022 7 Genetic diversity in - chilli (Capsicum annuum L.) based on microsatellite markers ... 17 C A M S- 88 0 F: g ag cc aa ga aa aa gg tg ga a R: c aa ct ca tc gt tc aa ca ac ac a (g aa )1 2 53 o C 6 23 7 (M in am iy am a et a l., 2 00 6) 18 C A M S- 23 6 F: tt gt ag ttt gc gt ac ca ttt ga R: at ga at cc ag gg ttc ca ca a (a c) 14 a (t a) 10 54 o C 2 19 1 19 C A M S- 88 5 F: a ac ga aa aa ca aa cc ca at ca R: tt ga aa ttg ct ga aa ct ct ga a (g aa )2 8 53 o C 2 24 8 20 C A M S- 64 7 F: cg ga ttc gg ttg ag tc ga ta R: g tg ct ttg gt tc gg tc ttt c (t at )6 tg (tt a) 3… (t at )2 1 54 o C 3 22 1 21 C A M S- 17 3 F: c aa cc gc ca gt ag ac ag gt t R: g tg cg tg tg cg tg tg tg ta t (c at a) 7… (a c) 4 52 o C 4 16 9 22 C A M S- 16 3 F: tc ca ta ta gc cc gt gt gt ga R: gc gt gg ga at ac aa tg ct ag a (a t)7 (g t)1 4 53 o C 5 25 0 23 C A M S- 82 6 F: c ttg at ct ca ag aa cc ag ct ac aa R: tg ta ca ttg aa ga ca cg ga ag aa (g aa )6 ga (g ga )9 .. (g aa )3 ga (g aa )3 53 o C 8 24 4 24 C A M S- 85 5 F: a ag tg tc aa gg aa gg gg ac a R: cc ta ac ca cc cc ca aa ag tt (a gt )1 4a (g aa )9 54 o C 8 24 3 25 C A M S- 49 3 F: tc ga tg ac ga aa aa gt gt ga a R: a gg gc aa aa ga cc ca ttc tt (a g) 6 53 o C 8 22 5 (M im ur a et a l., 2 01 2) 26 C A M S- 45 4 F: g ag cc tc tta at gt at ct ga aa ac a R: a at ttt gg tg aa tc gc ac ct (c t)3 .. (tc )4 c ( ct )3 .. (tc )5 .. (tc )5 cc (tc )4 54 o C 9 24 3 27 C A M S- 34 0 F: tt ta tg cc ca ttc ac aa aa ta a R: g ga cg aa ttc ac cg ag tg c (t a) 3… (a g) 13 53 o C 10 25 0 28 C A M S- 15 6 F: cc ct at gc ttt ca ca ac tc ct R: a cg tg gt ta tg ac ga ta gg c (a c) 14 a (t a) 6 54 o C 10 18 1 29 H pm s 1 -1 F: tc aa cc ca at at ta ag gt ca ct tc c R: cc ag gc gg gg at tg ta ga tg (c a) 12 (t a) 4 55 o C 1 28 3 (L ee e t a l., 2 00 5) 30 A F2 44 12 1 F: ta cc tc ct cg cc aa tc ct tc tg R: tt ga aa gt tc ttt cc at ga ca ac c (t ag )4 IP (g tt) 3 52 o C 1, 3 23 4 Co nt in ue d on th e n ex t p ag e Acta agriculturae Slovenica, 118/4 – 20228 R. MOLLA et al. 31 H pm s 1- 16 5 F: g gc ta ttt cc ga ca aa cc ct cg R: cc at tg gt gt ttt ca ct gt tg tg (g a) 13 53 o C 4 21 3 (L ee e t a l., 2 00 5) 32 H pm s 2 -2 3 F: cc ct cg gc tc ag ga ta aa ta cc R: cc cc ag ac tc cc ac ttt gt g (tt g) 7 (g t)9 54 o C 5 12 7 33 H pm s 1 -5 F: cc aa ac ga ac cg at ga ac ac tc R: g ac aa tg ttg aa aa ag gt gg aa ga c (a t)1 1 (g t)1 7 53 o C 6 31 1 34 H pm s AT 2- 20 F: tg ca ct gt ct tg tg tta aa at ga cg R: a aa at tg ca ca aa ta tg gc tg ct g (a at )1 8 52 o C 6 14 8 35 H pm s C aS IG 19 F: c at ga at ttc gt ct tg aa gg tc cc R: a ag gg tg ta tc gt ac gc ag cc tta (c t)6 (a t) (g ta t)5 8 54 o C 7 21 8 36 H pm s 2 -2 1 F: tt ttt ca at tg at gc at ga cc ga ta R: c at gt ca ttt tg tc at tg at ttg g (a t) 1 1 (a c) 9 (a ta c) 10 55 o C 10 29 5 37 H pm s 1- 17 2 F: g gg ttt gc at ga tc ta ag ca ttt t R: cg ct gg aa tg ca ttg tc aa ag a (g a) 15 58 o C 11 34 4 38 H pm s 2 -2 F: g ca ag ga tg ct ta gt tg gg tg tc R: tc cc aa aa tta cc ttg ca gc ac (g t) 9 55 o C 11 14 6 39 H pm s E- 07 5 F: g cg gc tc ag ca ga aa ga ga ga g R: tg cc ac ag ct gg ag aa cg ta aa (a cc ) 6 52 o C 12 20 5 (Y i e t a l., 2 00 6) Acta agriculturae Slovenica, 118/4 – 2022 9 Genetic diversity in - chilli (Capsicum annuum L.) based on microsatellite markers ... “touchdown” PCR settings were used to amplify SSRs: 94  oC 3 min-1 denaturation, 11 cycles of 94  oC 0.5 min- 1, 58-60 oC for 1 min, decreasing by 1 oC per cycle, and 72 oC for 1 min; 30 cycles of 94 oC for 0.5 min-, 52-55 oC for 1 min and 72 oC for 1 min; finally, extension for 5 min. The PCR products were resolved electrophoretically on 2 % agarose gel in 1X TBE to check amplification. The PCR procedure was regarded correct when the primer showed decent band, decreased smearing, and amplified the template DNA at target genomic region. 2.5 PCR PRODUCTS SEPARATION AND VISUALI- ZATION USING ELECTROPHORESIS The products of PCR were separated on 5 % dena- tured polyacrylamide gel using acrylamide: bis-acryla- mide (19:1), 10 % APS, 10X TBE buffer, and ultrapure Temed. Triple Wide Mini-Vertical Electrophoresis Sys- tem (Model: MGV-202-33, CBS Scientific, USA) was used to perform the electrophoresis. Upon loading of PCR products, run the gel maintaining 20 0C temperature Figure 1: Microsatellite profiles of 96 winter local chilli cultivars at locus CAMS-679 (A1, A2), CAMS-117 (B1, B2) and CAMS-647 (C1, C2); M: Molecular wt. marker (100 bp DNA ladder). Lane 01: BD-10878; Lane 02: BD-10879; Lane 03: BD- 10880; Lane 04: BD-10881; Lane 05: BD-10882; Lane 06: BD-10883; Lane 07: BD-10884; Lane 08: BD-10885 Lane 09: BD-10886; Lane 10: BD-10887; Lane 11: BD-10888; Lane 12: BD-10892; Lane 13: BD-10894; Lane 14: BD-10895; Lane 15: BD-10896; Lane 16: BD-10897; Lane 17: BD-10898; Lane 18: BD-10938; Lane 19: BD-10934; Lane 20: BD- 10935; Lane 21: BD-10936; Lane 22: BD-10913; Lane 23: KASI-49; Lane 24: BD-10916; Lane 25: BD-10917; Lane 26: BD-10918; Lane 27: BD-10919; Lane 28: BD-10920; Lane 29: RISA-23; Lane 30: BD-10921; Lane 31: BD-10922; Lane 32: BD-10923; Lane 33: BD-10924; Lane 34: BD-10925; Lane 35: BD-10927; Lane 36: BD-10928; Lane 37: BD- 10929; Lane 38: BD-10930; Lane 39: BD-10931; Lane 40: BD-10932; Lane 41: BD-10933; Lane 42: BD-10900; Lane 43: BD-10903; Lane44: BD-10903; Lane 45: BD-10905; Lane 46: BD-10906; Lane 47: RT-09; Lane 48: RT-14; Lane 49: BD-10908; Lane 50: BD-10909 Lane 51: BD-10910; Lane 52: BD-10911; Lane 53: BD-10912, Lane 54: AM-29; Lane 55: BD-10899; Lane 56: BD-10914; Lane 57: BD-10926; Lane 58: BD-10901; Lane 59: BD-10902; Lane 60: BD-10907; Lane 61: BD-10939; Lane 62: KASI-115; Lane 63: KASI-115; Lane 64: RI-02; Lane 65: RI-12; Lane 66: BD-10889; Lane 67: BD-10890; Lane 68: AMS-08; Lane 69: AMS-10; Lane 70: AMS-21; Lane 71: AMS-26; Lane 72: AMS-39; Lane 73: AMS-42; Lane 74: AMS-45; Lane 75: AHM-46; Lane 76: AHM-46(1); Lane 77: BD-10941; Lane 78: AHM- 142; Lane 79: AHM-143; Lane 80: IA-52; Lane 81: BD-10891; Lane 82: BD-10893; Lane 83: AMS-30; Lane 84: AMS- 31; Lane 85: AMS-12; Lane 86: AMS-32; Lane 87: AMS-33; Lane 88: RT-12; Lane 89: RT-20; Lane 90: RT-22; Lane 91: RT-11; Lane 92: RT-13; Lane 93: RT-18; Lane 94: RISA-33; Lane 95: RM-01; Lane 96: KASI-20(1) Acta agriculturae Slovenica, 118/4 – 202210 R. MOLLA et al. at 80-90 V for a set period of time (usually 1 hour for 100 bp) depending on the size of the amplified DNA frag- ment. Once electrophoresis was completed, the gel was stained with ethidium bromide. For analysis, the indi- vidual bands on the glass plate were colored and scored. 2.6 MICROSATELLITE DATA SCORING AND ANALYSIS Three expert scientists separately assessed the bands representing specific alleles at the microsatellite loci and labelled from the top to the bottom of the gel as A, B & C. Cultivars were hypothetically scored as homozygous (AA, BB, CC) or heterozygous (AB, AC, BC). All loci were combined into a single genotypic data matrix. Al- lelic frequency estimations were generated to produce statistics of genetic variation (number of observed and effective alleles, Nei’s gene diversity, Shannon’s informa- tion index, heterozygosity, and polymorphism) from genotypic frequency of SSR loci using POPGENE (Ver- sion 1.31) (Abouzied et al., 2013). The microsatellite data matrix was deployed to calculate Nei’s distance (Nei, 1972), and to produce the corresponding matrix of ge- netic distance among accessions, while cluster analyses were carried out on the genetic distance matrix by using the UPGMA to determine the relations among accessions (dendrograms) using POPGENE (Version 1.31) (Abouz- ied et al., 2013). The PIC (polymorphism information content) or gene diversity value of the SSR utilized was computed as PIC= 1- 1- ΣXi2; Where, Xi is the frequency of the i-th allele of a particular locus. The software DNA FRAG version 3.03 was used to estimate allelic length (Is- lam et al., 2012). 3 RESULTS 3.1 MICROSATELLITE POLYMORPHISM All 39 microsatellite primers employed in this study were confirmed to be polymorphic based on DNA am- plification patterns. Figure 1 illustrates three typical SSR profiles. Table 3 shows the results of the variability pa- rameters analysis for the 39 SSRs in the 96 chilli cultivars. With the 39 SSR loci investigated herein, a total of 123 alleles were found among all chilli cultivars, averaging 3.154 alleles per locus. Variation of allele number ranged from 2 to 8. The locus CAMS-647 yielded the most alleles (8) with sizes ranging from 188 to 279 base pairs. Like- wise, 6 alleles (142 to 176 bp and 184 to 240 bp) and 5 al- leles (223 to 291 bp) were detected at the loci CAMS-679, CAMS-117 and CAMS-855, respectively, in descending order (Table 3). When all cultivars were considered, the expected heterozygosity (HE, average 0.484) values for each SSR locus were always higher than the observed heterozygosity (HO), indicating that the population was homozygous. PIC values for the 39 primers tested in this work ranged from 0.099 for Hpms 1-165 to 0.806 for CAMS- 679, with an average value of 0.484 (Table 3). Among the studied markers CAMS-679, CAMS-855, CAMS- 117, CAMS-647, CAMS-236, CAMS-351, CAMS-885, CAMS-340, CAMS-864, CAMS-460, CAMS-844, and CAMS-880 showed higher PIC values (> 0.6) followed by HpmsAT2-20 (0.278), CAMS-015 (0.256), CAMS-156 (0.249), Hpms 1-1 (0.249), CAMS-838 (0.170), Hpms 1-172 (0.170), HpmsE075 (0.117), and Hpms 1-165 (0.099) in descending order. Among the studied mark- ers, allele frequency ranged from 0.281 to 0.948 (Table 3). Effective allele number was also the highest (5.166) for CAMS-679 following 4.046, 3.912, 3.436, 3.364 and 3.322 for CAMS-855, CAMS-647, CAMS-885, CAMS- 236 and CAMS-251, respectively (Table 4). Nei’s expect- ed heterozygosity (genetic diversity) ranged from 0.117 (HpmsE075) to 0.811 (CAMS-679) with an average value of 0.484. The mean Shannon’s information index (I) was 0.842, and ranged from 0.205 to 1.713 (Table 4). The high- est Shannon’s information index (1.713) was recorded in the locus CAMS-647 followed by CAMS-679 (1.617), CAMS-117 (1.589), CAMS-855 (1.444) as against the lowest (0.205) in Hpms 1-165. Ranges of genetic differ- entiation (Fst) values were 0.834 to 1.000 with an average of 0.927 and gene flow (Nm) values ranged from 0.000 to 0.050 with an average of 0.010 (Table 4). 3.2 NEI’S GENETIC DISTANCE BETWEEN THE CULTIVARS The genetic distance value (GD) of 4560 (1+2+3+...+95) pairs resulting from a permutation com- bination of 96 winter chilli cultivars ranged from 0.103 to 0.990 on average. While analyzing 96 cultivars, com- paratively higher genetic distance values were observed between the pairs of 55 cultivars, while the pairs of 41 cultivars showed lower GD values (Table 5 and Table 6). The pair BD-10879 vs RT-22 and BD-10926 vs BD-10920 showed the highest (0.990) genetic distance followed by 0.921 and 0.911 in BD-10887 vs RT-12, BD-10931 vs IA-52, BD-10927 vs BD-10879, RT-11 vs BD-10887, RT-11 vs BD-10926, RT-22 vs BD10931 and IA-52 vs BD-10934, respectively (Table 5). The pair be- tween AMS-30 and BD-10893 showed the lowest (0.103) genetic distance followed by BD-10883 with BD-10880 Acta agriculturae Slovenica, 118/4 – 2022 11 Genetic diversity in - chilli (Capsicum annuum L.) based on microsatellite markers ... Locus No. of allele Allele sizes (bp) Major allele frequency Obs Het (Ho) Exp Het (HE) PIC CAMS-015 3 100, 106, 110 0.854 0.000 0.258 0.256 CAMS-065 4 197, 209, 215, 239 0.479 0.000 0.578 0.575 CAMS-072 3 153, 166, 173 0.677 0.000 0.476 0.474 CAMS-075 4 178, 194, 207, 218 0.615 0.000 0.554 0.551 CAMS-117 6 184, 193, 208, 222, 227, 240 0.500 0.000 0.633 0.750 CAMS-156 2 176, 185 0.854 0.000 0.250 0.249 CAMS-163 2 136, 148 0.802 0.000 0.319 0.317 CAMS-173 3 146, 159, 170 0.688 0.000 0.459 0.457 CAMS-236 4 182, 198, 199, 202 0.385 0.000 0.706 0.703 CAMS-336 3 152, 173, 183 0.750 0.000 0.401 0.398 CAMS-340 4 245, 260, 272, 287 0.432 0.000 0.662 0.658 CAMS-351 4 179, 189, 200, 220 0.427 0.000 0.703 0.699 CAMS-405 3 207, 226, 244 0.552 0.000 0.574 0.571 CAMS-454 2 221, 240 0.583 0.000 0.489 0.486 CAMS-460 3 195, 209, 218 0.490 0.000 0.633 0.630 CAMS-478 2 215, 230 0.646 0.000 0.460 0.457 CAMS-493 3 201, 213, 225 0.510 0.000 0.571 0.568 CAMS-647 8 188, 198, 206, 220, 235, 239, 256, 279 0.406 0.000 0.748 0.744 CAMS-679 6 142, 147, 154, 160, 168, 176 0.281 0.000 0.811 0.806 CAMS-806 3 209, 222, 233 0.667 0.000 0.476 0.474 CAMS-826 3 215, 229, 258 0.823 0.000 0.307 0.306 CAMS-838 2 160, 164 0.906 0.000 0.171 0.170 CAMS-844 3 198, 210, 219 0.490 0.000 0.633 0.630 CAMS-855 5 223, 239, 252, 270, 291 0.292 0.000 0.757 0.753 CAMS-861 3 209, 230, 240 0.563 0.000 0.569 0.566 CAMS-864 4 205, 231, 264, 291 0.427 0.000 0.649 0.645 CAMS-880 3 205, 219, 231 0.521 0.000 0.615 0.612 CAMS-885 4 200, 209, 216, 224 0.354 0.000 0.713 0.683 Hpms 1-1 2 247, 262 0.854 0.000 0.250 0.249 Hpms 1-5 3 266, 285, 312 0.531 0.000 0.559 0.556 Hpms 1-165 2 191, 202 0.948 0.000 0.099 0.099 Hpms 1-172 2 280, 300 0.906 0.000 0.171 0.170 Hpms 2-2 2 156, 167 0.406 0.000 0.485 0.482 Hpms 2-21 2 273, 294 0.625 0.000 0.471 0.469 Hpms 2-23 2 205, 218 0.740 0.000 0.387 0.385 HpmsAT2-20 2 143, 152 0.833 0.000 0.279 0.278 HpmsCaSIG19 2 209, 220 0.760 0.000 0.366 0.364 HpmsE075 2 208, 220 0.938 0.000 0.118 0.117 AF244121 3 93, 111, 120 0.542 0.000 0.526 0.523 Mean 3.154 0.617 0.000 0.484 0.484 Table 3: Variability of simple sequence repeat marker used for genetic analysis of chilli cultivars Acta agriculturae Slovenica, 118/4 – 202212 R. MOLLA et al. Locus Observed number of alleles (na) Effective number of alleles (ne) Genetic diversity Shannon’s Infor- mation Index (I) Genetic differentiation (Fst) Gene flow (Nm*) CAMS-336 3 1.662 0.398 0.703 1.000 0.000 CAMS-351 4 3.322 0.699 1.288 1.000 0.000 CAMS-405 3 2.331 0.571 0.942 1.000 0.000 CAMS-460 3 2.700 0.630 1.046 1.000 0.000 CAMS-679 6 5.166 0.753 1.617 1.000 0.000 CAMS-864 4 2.818 0.645 1.159 1.000 0.000 CAMS-072 3 1.900 0.474 0.802 1.000 0.000 CAMS-117 6 2.703 0.744 1.589 1.000 0.000 CAMS-806 3 1.900 0.474 0.781 0.989 0.003 CAMS-844 3 2.700 0.630 1.046 1.000 0.000 CAMS-015 3 1.345 0.256 0.491 0.838 0.093 CAMS-065 4 2.351 0.675 0.968 0.955 0.005 CAMS-075 4 2.227 0.651 0.972 0.978 0.003 CAMS-478 2 1.843 0.458 0.650 1.000 0.000 CAMS-838 2 1.205 0.170 0.311 0.968 0.004 CAMS-861 3 2.305 0.566 0.937 1.000 0.000 CAMS-880 3 2.577 0.612 1.020 1.000 0.000 CAMS-236 4 3.364 0.703 1.287 1.000 0.000 CAMS-885 4 3.436 0.709 1.292 0.863 0.040 CAMS-647 8 3.912 0.806 1.713 1.000 0.000 CAMS-173 3 1.841 0.457 0.762 0.951 0.004 CAMS-163 2 1.465 0.318 0.498 0.901 0.005 CAMS-826 3 1.441 0.306 0.582 0.891 0.031 CAMS-855 5 4.046 0.734 1.444 0.972 0.007 CAMS-493 3 2.312 0.568 0.916 1.000 0.000 CAMS-454 2 1.946 0.486 0.679 1.000 0.000 CAMS-340 4 2.922 0.658 1.145 0.834 0.050 CAMS-156 2 1.332 0.249 0.415 0.962 0.004 Hpms 1-1 2 1.332 0.249 0.415 0.932 0.007 AF244121 3 2.097 0.523 0.804 0.911 0.008 Hpms 1-165 2 1.110 0.099 0.205 0.874 0.034 Hpms 2-23 2 1.627 0.385 0.574 0.901 0.009 Hpms 1-5 3 2.251 0.556 0.894 0.911 0.008 HpmsAT2-20 2 1.385 0.278 0.451 0.904 0.009 HpmsCaSIG19 2 1.573 0.364 0.551 0.952 0.004 Hpms 2-21 2 1.882 0.469 0.662 0.899 0.023 Hpms 1-172 2 1.205 0.170 0.311 0.879 0.032 Hpms 2-2 2 1.932 0.482 0.676 0.918 0.008 HpmsE075 2 1.133 0.117 0.234 0.895 0.022 Mean 3.154 2.220 0.490 0.842 0.927 0.010 Table 4: Summary of genetic variation statistics for all loci used for 96 winter chilli cultivars analysis Nm* = Gene flow estimated from Fst = 0.25 (1 - Fst)/Fst, Fst = Genetic differentiation Acta agriculturae Slovenica, 118/4 – 2022 13 Genetic diversity in - chilli (Capsicum annuum L.) based on microsatellite markers ... Genotype pair Genetic Distance Genotype pair Genetic Distance 1 BD-10879 vs RT-22 0.990 29 RT-20 vs BD-10896 0.799 2 BD-10926 vs BD-10920 0.990 30 IA-52 vs BD-10900 0.799 3 BD-10887 vs RT-12 0.921 31 BD-10940 vs BD-10909 0.799 4 BD-10931 vs IA-52 0.921 32 BD-10931 vs BD-10913 0.799 5 BD-10927 vs BD-10879 0.921 33 RT-11 vs BD-10914 0.799 6 RT-11 vs BD-10887 0.921 34 RT-22 vs BD-10916 0.799 7 RT-11 vs BD-10926 0.921 35 RT-22 vs BD-10930 0.799 8 RT-22 vs BD-10931 0.921 36 AHM-46 vs BD-10906 0.790 9 IA-52 vs BD-10934 0.911 37 RT-20 vs BD-10938 0.790 10 BD-10878 vs BD-10918 0.857 38 BD-10917 vs BD-10922 0.745 11 BD-10879 vs RT-20 0.857 39 BD-10918 vs AM-29 0.745 12 BD-10917 vs BD-10902 0.857 40 BD-10920 vs BD-10911 0.745 13 BD-10926 vs BD-10941 0.857 41 BD-10903 vs BD-10891 0.745 14 BD-10926 vs RT-18 0.857 42 IA-52 vs BD-10925 0.745 15 BD-10931 vs RT-13 0.857 43 BD-10887 vs BD-10939 0.745 16 BD-10917 vs BD-10884 0.857 44 BD-10934 vs BD-10929 0.736 17 BD-10902 vs BD-10917 0.857 45 BD-10920 vs BD-10901 0.695 18 RT-13 vs BD-10935 0.857 46 BD-10920 vs BD-10912 0.695 19 BD-10926 vs AHM-46 0.848 47 BD-10926 vs RM-01 0.695 20 BD-10878 vs RI-02 0.799 48 RT-22 vs BD-10886 0.695 21 BD-10884 vs RT-11 0.799 49 IA-52 vs BD-10888 0.695 22 BD-10909 vs BD-10940 0.799 50 IA-52 vs BD-10933 0.695 23 BD-10917 vs BD-10908 0.799 51 BD-10884 vs BD-10893 0.686 24 BD-10927 vs IA-52 0.799 52 RT-20 vs BD-10885 0.648 25 BD-10935 vs BD-10903 0.799 53 RT-22 vs BD-10890 0.648 26 BD-10916 vs BD-10878 0.799 54 BD-10908 vs BD-10921 0.648 27 BD-10926 vs BD-10881 0.799 55 BD-10939 vs BD-10936 0.648 28 BD-10879 vs BD-10889 0.799 Table 5: List of genotype pairs of winter chilli showed higher values of Nei’s (1972) genetic distance and BD-10882 (0.122), BD-10923 vs BD-10932 (0.144), RISA-33 vs KASI-20(1) (0.167), BD-10897 vs BD-10898 (0.181) and so on (Table 6). 3.3 PHYLOGENETIC DENDROGRAM The UPGMA cluster analysis generated a dendro- gram that divided 96 chilli cultivars into two main group “A” and “B” where only one cultivar i.e. BD-10917 con- gregated in a separate group “B” and others (95 cultivars) belong to group “A” (Figure 2). However, Group “A” di- vided in two sub-group “A1” and “A2”. Sub-group “A1” was split into two more sub-group (“A1.a” and “A1.b”), where sub-clusters “A1.a1” gathered eight cultivars (BD- 10878, BD-10879, BD-10880, BD-10883, BD-10885, BD- 10881, BD-10882 and BD-10884). Sub-cluster “A1.a2” grouped 17 cultivars forming, A1.a2.a3.a5, A1.a2.a3.a6 and A1.a2.a4 sub-clusters contained five (BD-10900, BD-10903, BD-10904, BD-10905, BD-10906), nine (BD- 10908, BD-10909, BD-10911, BD-10910, BD-10901, BD- 10902, BD-10912, BD-10907 and BD-10939) and three (RT-09, RT-14 and AHM-142) cultivars, respectively (Figure 2 and Table 7). A total of 29 cultivars were clustered into sub-clus- ter A1.b, which was further divided into four sub-clus- ters viz., A1.b1, A1.b2.b3.b5, A1.b2.b3.b6 and A1.b2.b4. Similarly, sub-cluster A2 divided into another two sub- Acta agriculturae Slovenica, 118/4 – 202214 R. MOLLA et al. clusters (“A2.a”, “A2.b”), where sub-clusters “A2.a1” as- sembled seven cultivars (BD-10886, KASI-49, BD-10916, KAI-115, RI-12, BD-10934 and BD-10913) whereas only four cultivars viz., BD-10935, BD-10918, BD-10919 and BD-10920 accumulated in sub-cluster “A2.a2” (Figure 4 and Table 7). However, 30 cultivars separated into seven sub-clusters of A2.b in where maximum five cultivars accumulated in four sub-clusters (A2.b1.b4, A2.b2.b5, A2.b2.b6.b8.b11 and A2.b2.b6.b8.b12), four cultivars gathered in 2 sub-clusters (A2.b2.b6.b7.b9 and A2.b2. b6.b7.b10) and sub-cluster A2.b1.b3 accumulated only two cultivars viz., BD-10929 and AMS-39 (Figure 2 and Table 7). 4 DISCUSSION Among 50 primers screened, only 39 produced pol- ymorphism and were used for final analysis of 96 winter chilli cultivars on the basis of easily scorable amplified bands (Table 3). All markers were observed to be poly- morphic, expressing a total of 123 alleles with an average value of 3.15 alleles per locus in the analysis of 96 winter chilli cultivars. The majority of the primers (25) amplified 3-8 alleles per locus (Table 3), where the highest num- ber of alleles (8) were amplified by the locus CAMS-647. However, Di Dato et al. (2015) observed 10 alleles in Cap- sicum annuum while analyzing with CAMS-647 marker. In another experiment carried out by Dhaliwal et al. (2014) identified the most divergent genotype among the six elite lines of chilli pepper by employing 58 SSR mark- ers. Thirty produced polymorphic bands, revealing a total of 83 alleles with an average of 2.67 alleles per locus. Hos- sain et al. (2014) evaluated the genetic diversity within 22 chilli germplasm by using four microsatellite markers. All the microsatellite markers were found polymorphic in all studied germplasm. A total of 27 alleles were de- tected and the number of alleles per marker ranged from 4 to 13 (size range was 153-315 bp). The average numbers of allele (3.15) showed substantial variations compared with those of previous studies might be due to the high number of diverse chilli cultivars used in present study. The observed differences in allelic length for each locus indicated the presence of broad genetic base amongst the chilli cultivars. The wide genetic base might due to the high yield of polymorphic markers as reported by Molla et al. (2015)but there is possible uncertainty of linkage with the important genes. In contrast, there are better possibilities of linkage detection with important genes if SSRs are developed from candidate genes. To the best of our knowledge, there is no such report on SSR markers development from candidate gene sequences in rice. So the present study was aimed to identify and analyse SSRs from salt responsive candidate genes of rice. Results: In the present study, based on the comprehensive literature survey, we selected 220 different salt responsive genes of rice. Out of them, 106 genes were found to contain 180 microsatellite loci with, tri-nucleotide motifs (56%. The PIC values, the reflection of allele diversity, of- fer an estimate of the discriminating power of a marker by taking into account not only the number of alleles at a locus, but also relative frequencies of these alleles. The genetic diversity of the cultivars chosen determines the PIC values, and this study featured a large number of tra- ditional varieties, which would increase the PIC values. It is important to point out that the selection by breed- ers have increased the frequency of the alleles or allelic combination with favorable effects at the expense of the others, eventually eliminating many of them (Cao et al., 1998). All of the SSRs were found to be polymorphic and useful for defining genotypic variation (i.e., PIC values different from zero) (Table 3). Twelve of these SSRs were very informative with higher PIC values (> 0.6) which was in accordance with the previous findings reported by Lee et al. (2005), Mimura et al. (2012) and Minamiyama et al. (2006). Lower PIC values indicate the presence of closely related cultivars; while higher PIC values indicate the presence of diverse cultivars. The observed high PIC values could be related to the utilization of di-nucleotide repeats as well as genotypic variations as reported by Is- lam et al. (2018). The present investigation had a high proportion of traditional varieties which would have the effect of increasing the PIC values. It is important to in- dicate that the selection by the breeders had increased the frequency of alleles or allelic combination with fa- vorable effects at the expense of the others, eventually eliminating many of them (Roychowdhury et al., 2014). The number of alleles amplified by a primer and its PIC values also depends upon the repeat number and the re- peat sequence of the microsatellite (Rahman et al., 2010). The results of present investigation are in agreement with those of Minamiyama et al. (2006) who showed that (tat), (tg), (ta) and (gaa) repeats yield higher number of alleles and higher PIC values. CAMS-647, CAMS-679, CAMS-117, CAMS-855, CAMS-885 and CAMS-236 having (tat)n, (tg)n, (gaa)n and (ac)n repeat were the most informative microsatellite markers for this set of cultivars, as they yielded five to eight alleles. For CAMS- 647 [(tat)6tg(tta)3…(tat)21], CAMS-117 [(tg)21(ta)3] and CAMS-679 [(tat)16], showed eight, six and six alleles and average PIC values 0.744, 0.808, 0.806 and 0.750, respec- tively in analysis of 96 winter chilli cultivars that were not uncommon in terms of the number of repeats and the repeat motif (Table 2 and Table 3). Indeed, the incred- ibly beneficial markers are extremely valuable for genetic Acta agriculturae Slovenica, 118/4 – 2022 15 Genetic diversity in - chilli (Capsicum annuum L.) based on microsatellite markers ... Genotype pair Genetic Distance Genotype pair Genetic Distance 1 AMS-30 vs BD-10893 0.103 22 RT-09 vs BD-10910 0.267 2 BD-10883 vs BD-10880 0.122 23 AMS-21 vs AMS-08 0.267 3 BD-10883 vs BD-10882 0.122 24 BD-10895 vs AMS-10 0.267 4 BD-10932 vs BD-10923 0.144 25 AMS-21 vs AMS-39 0.267 5 KASI-20(1) vs RISA-33 0.167 26 AHM-143 vs AHM-142 0.267 6 BD-10898 vs BD-10897 0.181 27 AMS-32 vs AMS-31 0.267 7 BD-10894 vs BD-10892 0.191 28 AHM-46(1) vs AMS-45 0.276 8 BD-10892 vs BD-10895 0.191 29 AHM-143 vs AHM-46(1) 0.286 9 RT-09 vs BD-10904 0.191 30 AHM-46(1) vs KASI-20(1) 0.286 10 RT-14 vs RT-09 0.191 31 BD-10924 vs BD-10932 0.295 11 BD-10892 vs AMS-26 0.191 32 BD-10910 vs BD-10907 0.295 12 BD-10895 vs BD-10894 0.216 33 AMS-39 vs AMS-42 0.295 13 BD-10892 vs BD-10928 0.216 34 RISA-33 vs AHM-143 0.295 14 AMS-26 vs AMS-21 0.216 35 AMS-33 vs RT-12 0.295 15 AMS-33 vs AMS-12 0.216 36 BD-10894 vs BD-10898 0.314 16 RISA-33 vs AMS-33 0.216 37 BD-10894 vs RISA-23 0.323 17 BD-10895 vs BD-10924 0.241 38 AMS-08 vs BD-10919 0.353 18 RT-12 vs KAI-115 0.241 39 AMS-10 vs BD-10899 0.353 19 BD-10928 vs AMS-32 0.241 40 AMS-08 vs RI-12 0.353 20 AMS-31 vs AMS-30 0.258 41 BD-10880 vs RT-14 0.416 21 AHM-143 vs KASI-49 0.267 - - - - - Table 6: List of cultivars pairs of winter chilli showed lower values of Nei’s (1972) genetic distance investigations and determining the level of variation on a certain marker locus (Minamiyama et al., 2006; Sunda- ram et al., 2008). According to Nei (1972), higher level of gene diver- sity values were observed in loci CAMS-679, CAMS-855, CAMS-647 and CAMS-117 and the lower level of gene diversity value was observed in loci HpmsE075, Hpms 1-172, Hpms 1-165 and Hpms 1-1 in analysis of 96 win- ter chilli cultivars (Table 4). It was observed that marker which detected the highest/higher number of alleles showed higher gene diversity than those detected lower number of alleles which revealed lowest/lower gene di- versity. The maximum number of repeats within the SSRs was also positively correlated with the genetic diversity. This result is consistent with previous work done by Chen et al. (2012) and Hossain et al. (2014), who observed that the gene diversity at each SSR locus was significantly cor- related with the number of alleles detected, number of repeat motif and with the allele size range. The higher genetic diversity as observed in the current study has also been reported in rice (Rahman et al., 2010), mung bean (Molla et al., 2016) and musk melon (Molla et al., 2017). The current study’s findings are similar to those of previ- ous could be owing to higher diversity of cultivars used in this analysis. Study results also demonstrated higher level of ge- netic differentiation and low level of gene flow values in 96 chilli cultivars were indicative of diversity among the cultivars due to local origin/cultivars (Table 4). Higher genetic distance between genotype pair indicates that genetically they are diverse compare to lower genetic distance value. Basically, this value is an indication of their genetic dissimilarity. Genotype pair with higher value is more dissimilar than a pair with a lower value. The analysis of molecular data revealed different levels of gene diversity among 96 winter chilli cultivars as de- termined based on the Nei (1972) genetic distance. Ac- cording to the results of genetic distance, higher values generated in participation of 55 out of 96 cultivars while rest 41 cultivars yielded lower values (Table 5 and Table 6). Hence, 55 chilli cultivars having higher GD values were selected for further evaluation. From the difference between the highest and the lowest genetic distance val- ues, it was revealed that there was wide variability among Acta agriculturae Slovenica, 118/4 – 202216 R. MOLLA et al. Figure 2: Dendrogram based on Nei’s genetic distance, which summarizes the data on the variation between 96 winter local chilli cultivars according to microsatellite analysis Acta agriculturae Slovenica, 118/4 – 2022 17 Genetic diversity in - chilli (Capsicum annuum L.) based on microsatellite markers ... Sl. no. Genotype Cluster position Sl. no. Selected cultivars Sl. no. Genotype Cluster position Sl. no. Selected cultivars 1 BD-10878 A1a1 1 BD-10878 50 BD-10931 A1.b2.b4 28 BD-10931 2 BD-10879 2 BD-10879 51 AM-29 29 AM-29 3 BD-10880 3 BD-10885 52 BD-10899 30 BD-10914 4 BD-10883 4 BD-10881 53 BD-10914 31 BD-10926 5 BD-10885 5 BD-10884 54 BD-10926 6 BD-10881 55 BD-10886 A2.a1 32 BD-10886 7 BD-10882 56 KASI-49 33 BD-10916 8 BD-10884 57 BD-10916 34 BD-10934 9 BD-10900 A1.a2.a3.a5 6 BD-10900 58 KAI-115 35 BD-10913 10 BD-10903 7 BD-10903 59 RI-12 11 BD-10904 8 BD-10906 60 BD-10934 12 BD-10905 61 BD-10913 62 BD-10935 A2.a2 36 BD-10935 13 BD-10906 63 BD-10918 37 BD-10918 14 BD-10908 A1.a2.a3.a6 9 BD-10908 64 BD-10919 38 BD-10920 15 BD-10909 10 BD-10909 65 BD-10920 16 BD-10911 11 BD-10911 66 BD-10929 A2.b1.b3 39 BD-10929 67 AMS-39 68 BD-10891 A2.b1.b4 40 BD-10891 17 BD-10910 12 BD-10901 69 BD-10893 41 BD-10893 18 BD-10901 13 BD-10902 70 AMS-30 19 BD-10902 14 BD-10912 71 AMS-31 20 BD-10912 15 BD-10939 72 AMS-32 21 BD-10907 73 BD-10940 A2.b2.b5 42 BD-10940 22 BD-10939 74 RI-02 43 RI-02 23 RT-09 A1.a2.a4 75 BD-10889 44 BD-10889 24 RT-14 76 AMS-08 25 AHM-142 - - 77 AMS-21 26 BD-10887 A1.b1 16 BD-10887 78 AMS-42 A2.b2.b6.b7.b9 45 AHM-46 27 BD-10938 17 BD-10938 79 AHM-46 28 BD-10888 A1.b2.b3.b5 18 BD-10888 80 AMS-45 29 BD-10892 19 BD-10922 81 AHM-46(1) 30 AMS-26 20 BD-10925 82 BD-10941 A 2 . b 2 . b 6 . b 7 . b10 46 BD-10941 31 BD-10928 21 BD-10933 83 AMS-12 47 IA-52 32 BD-10924 22 BD-10927 84 AHM-143 33 BD-10922 23 BD-10930 85 IA-52 Continued on the next page Table 7: Distribution of 96 cultivars according to cluster analysis and selection of diverse cultivars from this cluster Acta agriculturae Slovenica, 118/4 – 202218 R. MOLLA et al. studied chilli cultivars. However, closeness may be pos- sible in the genetic makeup of the locus for which the primers were responsible to distinguish along with low variation also in the morphological traits and geographi- cal sources. The highest genetic distance may be eluci- dated by the fact that local cultivars or land races col- lected from different location have been included in the study. The existing distance can further be used to add gene sources from the traditional varieties to HYVs, us- ing genetic fingerprinting and correlating the values with that of the morpho-physiological features to find out the best performing varieties through appropriate breeding programs. Information on variability expression rate through genetic distance based on morphological traits and geographical origin was also reported in previous in- vestigations conducted by Rahman et al. (2010), Hossain et al. (2014), Molla et al. (2016) and Molla et al. (2017). Dendrogram portrayed winter chilli cultivars based on Nei (1972) genetic distance UPGMA cluster analysis broadly placed 96 chilli cultivars into two major groups “A” and “B” in which only one genotype namely BD- 10917 congregated in a distinct group “B”, and other 95 cultivars clustered in group “A” (Figure 2). The genotype BD-10917 had a distinct status in the dendrogram, be- cause there might have effect of higher genetic distance (Table 5) which might be designated through geographi- cal sources and morphological traits. This genotype was collected from Daulatkhan upazila of Bhola district which is island district of Bangladesh. Moreover, distinct mor- phological features like hypocotyl color (Purple), stem color before transplanting [Mixture (Green+Purple)], leaf pubescence density (Intermediate), fruit shape (Triangular), blossom end (Sunken and pointed) was observed in this genotype (Molla et al., 2021). Locus CAMS-117 generated 227 bp fragments which was dis- tinguishing band pattern for the cultivars BD-10917, BD-10889 and AHM-46. Among the representation of 96 cultivars, BD-10879 and RT-22 scattered in different sub-cluster (A1a1 and A2.b2.b6.b8.b11) exhibiting the highest genetic distance (0.990) (Table 5 and Figure 2). These two cultivars varied in respect of 14 morphologi- cal descriptors in which notable were stem color before transplanting, pedicel position at anthesis, calyx margin shape, anther color, fruit shape at peduncle attachment, fruit shape at blossom end (Molla et al., 2021). Moreover, two cultivars, BD-10879 collected from Galachipa, Patu- akhali, and RT-22 collected from Sharishabari, Jamalpur (Table 1) are two widely distanced locations of Bangla- desh. However, BD-10880 and BD-10883 were grouped together in same sub-cluster (A1a1) and those cultivars showed similar states in respect of 19 morphological traits such as stem color before and after transplanting, leaf shape, leaf color, pigmentation at node, calyx margin shape, filament color, fruit shape at peduncle attachment, fruit shape, fruit shape at blossom end were remarkable (Molla et al., 2021). In addition, similar geographical sources viz. Sirajganj was observed in case of both cul- tivars (Table 1). Results of the present study and those reported by Rahman et al. (2010), Hossain et al. (2014), Molla et al. (2016) and Molla et al. (2017) suggested that genetic distance value separated the cultivars in different sub-clusters where such values depend on their morpho- logical characters as got selected in different geographical locations. 34 BD-10925 24 BD-10896 86 AMS-33 A 2 . b 2 . b 6 . b 8 . b11 48 RT-22 35 BD-10923 87 RT-22 49 RT-12 36 BD-10932 88 RT-12 50 RT-20 37 BD-10933 89 RT-20 51 RT-11 38 BD-10927 90 RT-11 39 BD-10930 91 RT-13 A 2 . b 2 . b 6 . b 8 . b12 52 RT-13 40 BD-10896 92 RISA-33 41 BD-10897 93 KASI-20(1) 42 BD-10898 94 RT-18 53 RT-18 43 BD-10894 A1.b2.b3.b6 25 BD-10936 95 RM-01 54 RM-01 44 BD-10895 26 BD-10921 96 BD-10917 B 55 BD-10917 45 BD-10936 27 BD-10890 46 BD-10921 47 BD-10890 48 AMS-10 49 RISA-23 Acta agriculturae Slovenica, 118/4 – 2022 19 Genetic diversity in - chilli (Capsicum annuum L.) based on microsatellite markers ... 5 CONCLUSIONS From this study, it can be concluded that a com- parative assessment of the reproducibility of molecular markers has been made for determination of genetic var- iability among winter growing chilli cultivars in Bangla- desh. Higher genetic variability within populations and significant genetic differentiation between populations indicate rich genetic resources of a species. The study also indicated that 55 cultivars derived from different origin were identified as diversified and could be utilized in breeding program for traits of interest. SSR markers have proved to be powerful tools for molecular genetic analysis of chilli cultivars for plant breeding program to assess genetic diversity available. This would allow for the development of new varieties aiming at the improve- ment of crop productivity withstanding biotic and abi- otic stresses. 6 ACKNOWLEDGEMENTS For the financial support provided in this research, the Secretariat of AFACI, RDA, Korean Republic, has greatly been recognized by the AFACI PAN-ASIAN Pro- ject for “Rice, Chili, Cucumber and Melon Collection, Characterization and Promotion in Bangladesh.” 7 REFERENCES Abouzied, H. M., Eldemery, S. M. M., & Abdellatif, K. F. (2013). 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