Introduction
India is considered as a secondary centre of origin for custard apple (Annona squamosa L.), which belongs to the family Annonaceae. The chromosome number of A. squamosa is 2n = 2x = 14 (Anuragi et al., 2016). Annonaceae, a family in the class Magnolideae, comprises 129 genera and more than 2000 species. Of these, Annona cherimola Mill. (cherimoya), Annona glabra L. (pond apple), Annona muricata L. (soursop), Annona reticulata L. (custard apple), Annona atemoya (a hybrid of A. squamosa and A. cherimola) and A. squamosa L. (sweetsop or sugar apple) are of major commercial importance (Vinay et al., 2017).
A. squamosa L. is commonly known as ‘custard apple’ or ‘sugar apple’. While most species of Annona are thought to have originated in South America and the Antilles, wild soursop (A. muricata) is believed to have originated in Africa (Pinto et al., 2005). Now, these important species are found in nearly all continents, with soursop and sugar apple being widely distributed, particularly in the tropical regions.
The pulp of Annona fruits is rich in minerals and vitamins (Gyamfi et al., 2011) and is a potential source of dietary fibre (up to 50% w/w dry basis). The seeds, especially those of A. squamosa, contain a significant amount of oil, which can be used for industrial purposes (Mariod et al., 2010). Custard apple is a versatile plant with multiple uses, and it is hardy and deciduous by nature. However, its cultivation is still in the early stages of domestication (Van Zonneveld et al., 2012).
The genetic resources and plant diversity of Annona species are being eroded due to agricultural modernisation, urbanisation and land-use changes. Therefore, the genetic resources of edible custard apples, mostly found in situ and in natural populations, need to be conserved. Characterising genetic diversity is essential for the efficient conservation and utilisation of genetic resources. Despite this, few efforts have been made to identify the diverse germplasm of custard apples and assess the diversity using molecular markers.
There are limited reports on the use of molecular markers to assess genetic diversity in Annona species. Some of these markers include random amplified polymorphic DNA (RAPD) markers (Ronning et al., 1995; Bharad et al., 2009), amplified fragment length polymorphism (AFLP) markers (Rahaman et al., 1998; Zhichang et al., 2011) and simple sequence repeat (SSR) markers derived from related species like A. cherimola (Escribano et al., 2004; Kwapata et al., 2007; Pereira et al., 2008; Escribano et al., 2008; Van Zonneveld et al., 2012; Anuragi et al., 2016; Thanachseyan et al., 2017). However, no species-specific SSR markers have been developed for A. squamosa, which would provide more precise information on the diversity of this economically important species. In light of this, in this study, we have attempted to develop SSR markers and assess the genetic diversity of A. squamosa in the Indian subcontinent and to examine transferability across related species.
MATERIALS AND METHODS
Plant material and DNA isolation
For molecular diversity analysis, a total of 40 A. squamosa cultivars, along with five related species – A. cherimola, A. reticulata, A. glabra, A. muricata and A. atemoya – were selected from the field gene bank of Indian Council of Agricultural Research-Indian Institute of Horticultural Research (ICAR-IIHR), Bengaluru, India (Supplementary Table 1). The 40 cultivars were collected from different regions of India and are maintained in the field germplasm bank. Total genomic DNA was isolated from young, tender leaves using a modified CTAB method (Ravishankar et al., 2000). The quantity and quality of DNA were assessed using a UV spectrophotometer (NABI, Microdigital) at 260 nm and visualised via agarose gel electrophoresis (0.8%) under a UV transilluminator. DNA samples were then diluted to 20 ng ⋅ μL−1 with sterile water and stored at 4°C for further analysis.
Genome sequencing and microsatellite identification
Total genomic DNA from the A. squamosa cultivar Balanagar was used for partial genome sequencing and microsatellite marker identification. Genome sequencing was performed using the Illumina HiSeq 2500 platform at M/Sandor Specialty Diagnostics Pvt. Ltd., Hyderabad, following the manufacturer’s instructions. The reads were assembled into scaffolds using the de novo assembly tool SOAPdenovo2 (version 2.04; Altschul et al., 1990). Microsatellites (SSRs) were identified from the assembled genome fragments using MISA software (Beier et al., 2017), and primers for the identified microsatellites were designed using PRIMER3 software (Rozen and Skaletsky, 2000). Sequence data have been submitted to NCBI (Bioproject PRJNA682654).
PCR amplification and fragment size analysis
A total of 100 SSR primers were randomly selected for PCR standardisation. Pooled DNA was initially used to standardise PCR conditions at various annealing temperatures. Primers were tailed with M13 sequences, and PCR was conducted using M13-tailed primers labelled with fluorophores (Schuelke, 2000). M13 tails were added to both the forward (GTAAAACGACGGCCAGT) and reverse (GTTTCTT) primers at the 5′ end. We tested the 40 A. squamosa genotypes and the 5 related species (A. cherimola, A. reticulata, A. glabra, A. muricata and A. atemoya).
DNA amplification was performed in a 20 μL reaction volume, containing 2.5 μL of template DNA (20 ng · μL−1), 1 μL of each forward and reverse primers (5 pM), 0.4 μL of Taq DNA polymerase (3 U · μL−1), 2.0 μL of Taq buffer A (with 15 mM MgCl2) (10X), 2 μL of dNTPs (1 mM), 1 μL of M13 primers with fluorescent labels (5 pM) (fluorophores such as FAM, ATTO 550, ATTO 565 and YAKAMA YELLOW were synthesised using M/S Eurofins, Bengaluru) and 9.6 μL of sterile water. PCR amplification was carried out using the T100™ Thermal Cycler (BIO-RAD, California, USA). The temperature profile was 94°C for 2 min, followed by 35 cycles of 94°C for 30 s, annealing at 50°C/55°C/60°C (Table 3) for 30 s, 72°C for 1 min and a final extension at 72°C for 10 min. Amplified products were confirmed via 1.5% agarose gel electrophoresis, and the primers with strong, distinct amplification products were selected based on Tm values.
The PCR products from the four different fluorescent dyes were multiplexed and resolved using an automated ABI 3730 DNA analyser (Applied Biosystems, USA) at M/S Eurofins, Bengaluru. The resulting data were analysed using Peak Scanner software (Applied Biosystems) to determine the exact fragment size of the PCR products in base pairs (bp).
Statistical analysis
Using the fragment size data, polymorphic information content (PIC), probability of identity (PI), observed heterozygosity (Ho), expected heterozygosity (He) and the number of alleles per locus were calculated using CERVUS software (version 3.0; Kalinowski et al., 2007). SSR genotypic data were used to construct a dendrogram via unweighted pair group method with arithmetic mean (UPGMA), employing a neighbour-joining (NJ) tree and simple matching (SM) dissimilarity matrix using DARwin software. Bootstrap analysis was done with 10000 iterations (version 6.0.10; Perrier and Jacquemoud-Collet, 2003).
A Bayesian model-based clustering was performed using STRUCTURE software (version 2.3.1; Pritchard et al., 2000). The number of subgroups (K) was set between 2 and 10, with 10 iterations for each K value. The project parameters included a burn-in period of 10000, followed by 100000 Monte Carlo Markov Chain (MCMC) replications. Structure Harvester software was used to generate ΩK (Evanno et al., 2005) to estimate the number of populations. Additionally, analysis of molecular variance (AMOVA) was conducted to assess genetic variability among and within populations using GenAlEx software (version 6.5; Peakall and Smouse 2006).
RESULTS
Genome sequencing and microsatellite identification
Next-generation sequencing (NGS) technology, using the Illumina HiSeq 2500 platform, was employed to sequence the total genomic DNA isolated from the A. squamosa cultivar Balanagar. The sequencing run produced 3.9 million bases from 47476322 reads, after low-quality reads were filtered out. A total of 1388525 contigs were generated, and three assemblies were created based on different K-mer lengths of 40, 62 and 89. These assemblies were compared using QUAST software, and the K-mer length of 89 was selected as the best genome assembly based on the number of contigs, overall size, N50 value (954) and contig length distribution. A total of 58527 scaffolds were assembled and screened for microsatellite identification using MISA software (Supplementary Table 2).
The results revealed that the scaffolds contained 179080 microsatellites in total. Among the microsatellite repeats, mono-nucleotide repeats were the most abundant, accounting for 56.2% of all repeat types, followed by di-nucleotide repeats (25.96%), tri-nucleotide repeats (8.7%), tetra-nucleotide repeats (1.26%), penta-nucleotide repeats (0.27%), hexanucleotide repeats (0.11%) and compound nucleotide repeats (7.4%) (Table 1).
Table 1.
Distribution of microsatellite types in A. squamosa genome.
| Motif types | Number of SSRs | Frequency (%) |
|---|---|---|
| Mono-nucleotide | 100658 | 56.20 |
| Di-nucleotide | 46493 | 25.96 |
| Tri-nucleotide | 15590 | 8.70 |
| Tetra-nucleotide | 2266 | 1.26 |
| Penta-nucleotide | 495 | 0.27 |
| Hexa-nucleotide | 202 | 0.11 |
| Compound nucleotide | 13376 | 7.4 |
| Total | 179080 |
Using Primer 3.0 software, 58527 primers were designed (excluding mono-nucleotide repeats). Of these, 100 microsatellite markers were randomly selected, and primers were synthesised. After initial screening for amplification of reproducible PCR products, 70 SSR primers were selected for future analysis. Seventy of these primers were used to amplify DNA from 40 A. squamosa genotypes and five related Annona species. The product sizes for the 70 microsatellites ranged from 200 bp to 450 bp. The analysis of data from these microsatellite markers showed a total of 1878 alleles, with an average of 26.82 alleles per locus. The number of alleles ranged from 15 (ASIIHR38) to 46 (ASIIHR19, ASIIHR31 and ASIIHR35) per locus. The three markers with the highest number of alleles (ASIIHR19, ASIIHR31 and ASIIHR35) had simple di-nucleotide repeat motifs. The PIC ranged from 0.78 (ASIIHR61) to 0.96 (ASIIHR40 and ASIIHR19), with a mean of 0.90.
Ho ranged from 0.250 to 1.000, with a mean of 0.647, while He ranged from 0.781 to 0.978, with a mean of 0.926. The PI showed a maximum value of 0.0758 for the ASIIHR21 locus (16 alleles) and a minimum value of 0.0022 for the ASIIHR19 locus (46 alleles), with an average value of 0.01283 across all SSR loci. The characteristics of the 70 polymorphic SSR markers are summarised in Table 2.
Table 2.
Genetic analysis of SSR primers data using. A. squamosa genotypes.
| Locus | Forward sequence and reverse sequence 5′–3′ | Repeat motif* | Tm (°C) | No. of alleles per locus (k) | Ho | He | PIC | PI |
|---|---|---|---|---|---|---|---|---|
| ASIIHR01 F ASIIHR01 R | ACATGCCCTCAATCATCTCC AGTTAAAAATTAATGAATGAGCCAGG | (TA)6 | 55 | 23 | 0.50 | 0.93 | 0.91 | 0.0103 |
| ASIIHR02 F ASIIHR02 R | CTGAATTCTAACAGATGTGCTGGG GCTTTGGACGTCTACGCAT | (TA)8 | 60 | 20 | 0.60 | 0.91 | 0.90 | 0.0150 |
| ASIIHR03 F ASIIHR03 R | TCAGACTTCAACTCAAGTCGATCC TAATACCCAAGTCATGAAGACCAA | (AT)8 | 55 | 25 | 0.62 | 0.93 | 0.91 | 0.0114 |
| ASIIHR04 F ASIIHR04 R | ACTCGTACTGCTATAAAAGTGGGT ATGAGAGCTGCCCACGAC | (AT)7 | 55 | 37 | 0.65 | 0.97 | 0.95 | 0.0032 |
| ASIIHR05 F ASIIHR05 R | AAAACTGTCGGCTTCCATGT AGA AC C A AC AC AGC TTC C ATT | (TA)6 | 55 | 20 | 0.80 | 0.84 | 0.82 | 0.0364 |
| ASIIHR06 F ASIIHR06 R | CTTCTGTTGTCATCACTCGCA CACGATAGAAATCGAAGAACAGA | (TA)6 | 55 | 22 | 0.52 | 0.90 | 0.88 | 0.0186 |
| ASIIHR07 F ASIIHR07 R | AAATATCAAGTTTAAAGCAGTATTTGC TACAAGCGGGACATAGAAGC | (TA)6 | 55 | 30 | 0.97 | 0.95 | 0.93 | 0.0064 |
| ASIIHR08 F ASIIHR08 R | GATAGGAAGACAAACAGTTAGTTTAGG CGGCAGCTTTCTTCTTGTTC | (AG)6 | 55 | 28 | 0.92 | 0.91 | 0.90 | 0.0143 |
| ASIIHR09 F ASIIHR09 R | CCAAGAAATCTCACGTTCGC TCTTTCTTTAGCAAGTAGTGGTTTTT | (AG)13 | 60 | 30 | 0.82 | 0.95 | 0.93 | 0.0065 |
| ASIIHR10 F ASIIHR10 R | TGTGGGTTTATATTGACCATCATT GTGGAGCTAGGGTTCTTCGTC | (GA)8 | 60 | 31 | 0.97 | 0.93 | 0.94 | 0.0057 |
| ASIIHR11 F ASIIHR11 R | ACGCTTTTCTTCTCCGGC CTCGCCTGCTCCTCTCAC | (GA)9 | 55 | 26 | 0.52 | 0.95 | 0.91 | 0.0110 |
| ASIIHR12 F ASIIHR12 R | GCTTTGAGAGAAAATGAGAGACAA CTACCTTTCCGGCGAATCC | (AG)11 | 55 | 30 | 0.92 | 0.91 | 0.93 | 0.0065 |
| ASIIHR13 F ASIIHR13 R | GATATTCAAAGAGCACGAGAGGA AAAAGTTAGTCGGCAAATCCC | (GA)6 | 55 | 33 | 0.70 | 0.88 | 0.89 | 0.0147 |
| ASIIHR14 F ASIIHR14 R | GTGAGAGAGAGAGAAGGAAGGC GCGAATCTCTCTTCCGACAG | (GA)10 | 55 | 19 | 0.70 | 0.93 | 0.85 | 0.0284 |
| ASIIHR15 F ASIIHR15 R | TTTTCTCTTTTCTTCGTTCTTGC GAGGGGGCTGGTGACCTAT | (GA)12 | 60 | 25 | 0.82 | 0.92 | 0.91 | 0.0107 |
| ASIIHR16 F ASIIHR16 R | CCTAATCGGAAAGGTGCAAA TGGCTTTATTGGATGTGTTTGA | (AC)7 | 55 | 23 | 0.70 | 0.85 | 0.91 | 0.0126 |
| ASIIHR17 F ASIIHR17 R | GCTAAGACGGGGCCAACC TGCCTTATTTCTTTAAACAGGGTC | (AC)6 | 60 | 16 | 0.75 | 0.87 | 0.83 | 0.0388 |
| ASIIHR18 F ASIIHR18 R | CTCTCTCTTGTGCTTCTCCCA CTTTCTCTCTCTCCCCCTCC | (AC)6 | 55 | 18 | 0.82 | 0.97 | 0.84 | 0.0338 |
| ASIIHR19 F ASIIHR19 R | TGACGAGATCGAATTAAGTACCC TGATGCCTATAAAAGGGGCA | (CA)8 | 60 | 46 | 0.92 | 0.94 | 0.96 | 0.0022 |
| ASIIHR21 F ASIIHR21 R | ACCAGCAAATCCTGGGAAG TGAATCTGCAACTCAAAACTGA | (AC)6 | 55 | 29 | 1.00 | 0.78 | 0.93 | 0.0076 |
| ASIIHR21 F ASIIHR21 R | TTGATGCAATTCTTCAGTTTGA TACAAGGGTTGGGAATTTGG | (AC)8 | 55 | 16 | 0.92 | 0.92 | 0.74 | 0.0758 |
| ASIIHR22 F ASIIHR22 R | CATACATTTTGCCCACGACC CACAGATACACACACACGAACAA | (GT)8 | 60 | 25 | 0.77 | 0.95 | 0.90 | 0.0132 |
| ASIIHR23 F ASIIHR23 R | AAAAAGTCCATTCTTTTTCTCCA TCATTTCTTCATCCCATTGC | (GT)6 | 55 | 28 | 0.92 | 0.94 | 0.93 | 0.0066 |
| ASIIHR24 F ASIIHR24 R | CACATCACCCATATAAAAAGCG GTTGGGACAACTCTTCACCC | (TG)6 | 60 | 35 | 0.82 | 0.93 | 0.93 | 0.0065 |
| ASIIHR25 F ASIIHR25 R | CAGCGATGGTTGCTTAATTTG AGTAGGTGGAGAGACCCACG | (GT)6 | 55 | 25 | 0.77 | 0.93 | 0.92 | 0.0099 |
| ASIIHR26 F ASIIHR26 R | AGCAAAAGTGGTCATCCGAA TTTTAGATCGCTAAGAAGATCACA | (TG)13 | 60 | 30 | 0.80 | 0.96 | 0.91 | 0.0102 |
| ASIIHR27 F ASIIHR27 R | TCGCTATTTCAAAATTAAGTAAAAGAA TGTTTTAGTTGAGCAAGGCTAGG | (TG)8 | 55 | 37 | 0.82 | 0.96 | 0.95 | 0.0044 |
| ASIIHR28 F ASIIHR28 R | TCTTGTTTTTGCCAGTTCCC GCTTGAACTCAAAGCATGTTG | (GT)8 | 60 | 34 | 0.87 | 0.87 | 0.95 | 0.0038 |
| ASIIHR29 F ASIIHR29 R | TGTTACTGTTGGGCATGGAA GGTTGGGTTGAAAATTTAAGCA | (GT)6 | 55 | 23 | 0.67 | 0.95 | 0.85 | 0.0263 |
| ASIIHR30 F ASIIHR30 R | CCTTCCACCCTTGGATCTTA GATCAATGTGGATAAAACTTCGC | (CT)6 | 55 | 33 | 0.82 | 0.97 | 0.94 | 0.0058 |
| ASIIHR31 F ASIIHR31 R | CTTTTCTTCTCCATTTTCCCG CGTTGGAGATTCCAAGAGAAA | (CT)8 | 55 | 44 | 0.95 | 0.95 | 0.95 | 0.0031 |
| ASIIHR32 F ASIIHR32 R | AGGTGGATCGCTTAAGATGAA TCTGAGCAGGTGTGTAGTCCT | (TC)6 | 55 | 29 | 0.80 | 0.92 | 0.93 | 0.0069 |
| ASIIHR33 F ASIIHR33 R | ACTGGCCGAGGAAAGGGT GAGAGGGAGGAAAAAGTTAATCG | (TC)12 | 55 | 28 | 0.97 | 0.97 | 0.91 | 0.0117 |
| ASIIHR34 F ASIIHR34 R | CTCCCCGTTACCCAACTG GGTTCCTTCCGTCCTCCTG | (CT)9 | 55 | 35 | 0.72 | 0.96 | 0.95 | 0.0034 |
| ASIIHR35 F ASIIHR35 R | TTTC ATAGC TTTTATTGC TTTCTTAG AGTCGGGTCGAATCCACA | (GA)8 | 55 | 40 | 0.85 | 0.96 | 0.95 | 0.0036 |
| ASIIHR36 F ASIIHR36 R | CTTCTGTCTTCCTCATTTTCTCG TGGGAGAGAAGTGAGAGAGC TT | (AG)8 | 60 | 35 | 0.77 | 0.94 | 0.94 | 0.0047 |
| ASIIHR37 F ASIIHR37 R | GGCCACACTTGCTCAAAAAT TTCTTATTGATGCGTAGCGG | (GC)7 | 55 | 19 | 0.92 | 0.90 | 0.92 | 0.0090 |
| ASIIHR38 F ASIIHR38 R | GGGAGGAAACTTGATCCCTT AAAATTATGGTGCAGTGGCG | (GC)6 | 60 | 15 | 0.27 | 0.94 | 0.88 | 0.0196 |
| ASIIHR39 F ASIIHR39 R | GGCCACACTTGCTCAAAAAT TTCTTATTGATGCGTAGCGG | (GC)7 | 55 | 28 | 0.27 | 0.97 | 0.92 | 0.0087 |
| ASIIHR40 F ASIIHR40 R | CCAATCCCTTTATCCAAGCA GTGCAATTTGAAAAGCAGCA | (GC)7 | 55 | 35 | 0.27 | 0.93 | 0.96 | 0.0027 |
| ASIIHR41 F ASIIHR41 R | CATCTCCGCAACACCAGATA GCCAGAAGAGGCAGTGATTC | (GC)8 | 55 | 27 | 0.67 | 0.94 | 0.92 | 0.0092 |
| ASIIHR42 F ASIIHR42 R | AGAGGAAAACTTACAAAAACATAGACG GCCCTAAGAGAGCAGTTTACCC | (ATG)6 | 55 | 26 | 0.30 | 0.93 | 0.92 | 0.0089 |
| ASIIHR43 F ASIIHR43 R | GTATGTCATGGAGGATACAGGGA CATCCTCATCATCTCCCACA | (GAA)5 | 55 | 23 | 0.25 | 0.90 | 0.92 | 0.0091 |
| ASIIHR44 F ASIIHR44 R | ACTGCTGCTGAGATGTGCG CTGCTGCTGCTGTTGACG | (GAT)7 | 55 | 23 | 0.30 | 0.92 | 0.91 | 0.0112 |
| ASIIHR45 F ASIIHR45 R | TTATTGTATAAAACACCCCAAAGAA TTCTTATCATTTTGCCCGTCT | (TTC)10 | 60 | 18 | 0.45 | 0.96 | 0.89 | 0.0183 |
| ASIIHR46 F ASIIHR46 R | TTGGCAACCATCAGAATAAGA ACAACCCTGCTTCCATTCAA | (AAT)5 | 60 | 17 | 0.35 | 0.94 | 0.90 | 0.0152 |
| ASIIHR47 F ASIIHR47 R | AAAAACCTTGGGCTTGTGC CCTTGCCCCTATTATTTTCC | (TCT)6 | 55 | 32 | 0.37 | 0.95 | 0.95 | 0.0037 |
| ASIIHR48 F ASIIHR48 R | TTGGTGAAGCATTCAAAAATTC CCTTGCCCCTATTATTTTCC | (ATG)10 | 55 | 32 | 0.52 | 0.93 | 0.93 | 0.0072 |
| ASIIHR49 F ASIIHR49 R | TCAAACGCCCGCATATTTA GCTGGAGAAAGACGGCAAG | (AGC)5 | 55 | 26 | 0.40 | 0.94 | 0.93 | 0.0070 |
| ASIIHR50 F ASIIHR50 R | ACCTCAAAGCTAGGGGGTAAA CCGAAGTAGAGATACGCCTCTT | (GAA)5 | 55 | 26 | 0.35 | 0.93 | 0.92 | 0.0103 |
| ASIIHR51 F ASIIHR51 R | AAAATGAGCATGAAGAAAAGAAAAA GATTGTAAGACAAATTGAGATGTAATG | (ATT)10 | 55 | 22 | 0.30 | 0.94 | 0.93 | 0.0081 |
| ASIIHR52 F ASIIHR52 R | TCCCATTTTCTGATCGAGTTG TAACCCTCGCCGTGAATAG | (TCA)5 | 55 | 23 | 0.42 | 0.93 | 0.92 | 0.0101 |
| ASIIHR53 F ASIIHR53 R | AGACTAATCTAAGTTTAAAGCAAGCAA TGATC TTTGTTGAAGCGTCTCT | (AAG)5 | 55 | 21 | 0.45 | 0.94 | 0.92 | 0.0090 |
| ASIIHR54 F ASIIHR54 R | TTCAAGAATCATCTTTTAAGTCAACC TTTTCATGGAAACAACCAAATG | (GTG)5 | 55 | 30 | 0.45 | 0.94 | 0.92 | 0.0079 |
| ASIIHR55 F ASIIHR55 R | TCACTTGGAATAATGTGGAACG CAGTCCCCAGCTCCAAAC | (GTC)11 | 55 | 19 | 0.40 | 0.91 | 0.89 | 0.0181 |
| ASIIHR56 F ASIIHR56 R | CCCCAATCCCAATCCTTAGT GGAGTTCGTGTGCTTTACCG | (AGC)5 | 55 | 28 | 0.75 | 0.96 | 0.94 | 0.0047 |
| ASIIHR57 F ASIIHR57 R | GACGTGCTGCTG GGATTCTTCACCAGGCAGTT | (CGA)5 | 55 | 24 | 0.35 | 0.92 | 0.90 | 0.0134 |
| ASIIHR58 F ASIIHR58 R | AAAATGCATGCCTTGTTTGT TAGTCCTTGAGCAACACATGC | (ATTC)7 | 60 | 23 | 0.55 | 0.91 | 0.90 | 0.0146 |
| ASIIHR59 F ASIIHR59 R | AAGGGCATGTTGTCTTCTCAA TTTGCAAGTTTATTTCTGCTCAA | (TTGT)6 | 60 | 29 | 0.45 | 0.95 | 0.93 | 0.0070 |
| ASIIHR60 F ASIIHR60 R | TCCCGACCTTTCTTACGGAT TTTATCTCTATCTCTCCACCGGA | (ATCTC)6 | 60 | 22 | 0.67 | 0.87 | 0.85 | 0.0276 |
| ASIIHR61 F ASIIHR61 R | AATATGTTAACCCGAAACTCAACC AATAATTACATAATTGATGAGGGTCAA | (TAGGGT)5 | 55 | 16 | 0.60 | 0.95 | 0.78 | 0.0571 |
| ASIIHR62 F ASIIHR62 R | CTCTGTTTCTATCTCTCTCAAACTCA GGAATGGGAGAGTATTTGAAGG | (TC)9tgtctctcatt(TC)6 | 55 | 34 | 0.50 | 0.87 | 0.94 | 0.0058 |
| ASIIHR63 F ASIIHR63 R | TACCGGATCTCTCATTTTCG GCTGGGAGAGTGAGCTGAAA | (TC)5cgccatttctatctct ctctccgtcattcct ctctctctttctctgttttccttt ggaaaaatcggca aacccaaat(TC)6 | 55 | 24 | 0.62 | 0.81 | 0.90 | 0.0139 |
| ASIIHR64 F ASIIHR64 R | CATGTTGGACATGTGAGCCA ATGCCTATTTTAGGCTGGGT | (A)10g(A)10 | 55 | 28 | 0.85 | 0.95 | 0.91 | 0.0101 |
| ASIIHR65 F ASIIHR65 R | GGCGTCCAAAAATTGAGATT TTTGGGGAGTATCTACTCAGGC | (AT)7gtatttgccccatggg ccccaaaaaaaataaa(AT)7 ttttagactttcaa(AT)7 | 55 | 31 | 0.67 | 0.92 | 0.92 | 0.0082 |
| ASIIHR66 F ASIIHR66 R | TCTACGCTACCCAGCAAATG AATGACAATATGATTTGCCTTGA | (T)11(TA)6* | 55 | 28 | 0.67 | 0.93 | 0.90 | 0.0128 |
| ASIIHR67 F ASIIHR67 R | CCGACTTCAACCTTCTGAGC TCAATGGAATATTCACTTTTCTAGG | (A)11ttaaaacagattta tcaaaaatgttctta cgagaaagggaaaa taggagaaaaaggt agaatgagggttttc tttttgtgcgtgttttgga(AG)8 | 55 | 27 | 0.60 | 0.94 | 0.91 | 0.0075 |
| ASIIHR68 F ASIIHR68 R | GTGGATACTCCCCGACTGG TTCACATACTTTTGCCTGAGTAGA | (AAT)6 | 55 | 26 | 0.55 | 0.95 | 0.92 | 0.0066 |
| ASIIHR69 F ASIIHR69R | TTCACATACTTTTGCCTGAGTAGA GCCATCTTGGGCTTTTTAGA | (AT)6gacctcttcgctcaatccaagcctcaatgtc(A)10 | 55 | 21 | 0.67 | 0.92 | 0.90 | 0.0143 |
| ASIIHR70F ASIIHR70 R | GAAGGGAGATGCAAACGTTAAG AACTGCTTGCTTTATGCACTTT | (A)11(T)10 | 55 | 27 | 0.60 | 0.92 | 0.91 | 0.0118 |
* Indicates the number of times a particular motif was repeated in the microsatellite locus. For example: (TA6 – means TA has been repeated six times in the sequence results analysed during SSR identification. He, expected heterozygosity; Ho, observed heterozygosity; PI, probability of identity; PIC, polymorphic information content; SSR, simple sequence repeat.
Genetic diversity and population structure analysis of A. squamosa genotypes
A dendrogram (Figure 1) was generated using the UPGMA method, based on a shared allele matrix, revealing two major clusters. Of the 40 cultivars, 23 (57.5%) were grouped in Cluster I, while the remaining 17 (42.5%) were grouped in Cluster II, based on their genetic relatedness. Cultivars native to Andhra Pradesh were distributed across both clusters. A bar plot of ΔK, following the method described by Evanno et al. (2005), indicated that the optimal ΔK was at K = 2 (Figure 2).
Figure 1.
Dendrogram analysis of A. squamosa genotypes using SSR markers data. SSR, simple sequence repeat.
Figure 2.
Structure analysis of Annona genotypes using SSR data.
An AMOVA revealed limited variation among populations, with 30% of the variation occurring among individuals and 70% within individuals of the populations. The Fst value from AMOVA was 0.065, indicating low genetic differentiation (Table 4).
DISCUSSION
In this study, we utilised the Illumina HiSeq 2500, an NGS platform, to sequence the genome and isolate microsatellites for A. squamosa (L.). NGS technologies have become essential tools in plant biology for wholegenome sequencing, marker development and gene identification. Our sequencing efforts yielded 3.9 million bp sequences, and the assembly of 47476322 raw reads resulted in 58527 scaffolds. Microsatellite analysis revealed that mono-nucleotide repeats were the most abundant (56.20%), followed by di-nucleotides (25.96%), tri-nucleotides (8.7%) and tetra-nucleotides (1.26%). Mono-, di-, tri- and tetra-nucleotide repeats accounted for the majority (92.12%) of microsatellites in custard apple. This pattern of mono-repeat predominance and followed by di-repeats is consistent with findings in other plant species (Ravishankar et al., 2015, 2017a, 2017b).
Among the 179080 identified SSRs, 46493 were dinucleotide repeats, 15590 were tri-nucleotide repeats, 2266 were tetra-nucleotide repeats, and the remaining were penta- and hexa-nucleotide repeats (Table 2). From the 58527 designed primers, 70 SSRs were randomly selected and standardised for PCR conditions. These SSRs were employed for DNA amplification of 40 Annona cultivars and 5 related species (A. cherimola, A. reticulata, A. glabra, A. muricata and A. atemoya) (Table 3).
Table 3.
Cross species amplification of 70 SSR loci derived from A. squamosa.
| No. | Locus | A. cherimola | A. reticulata | A. glabra | A. muricata | A. atemoya |
|---|---|---|---|---|---|---|
| 1 | ASIIHR01 | A | A | A | A | A |
| 2 | ASIIHR02 | A | A | A | A | A |
| 3 | ASIIHR03 | A | A | A | A | A |
| 4 | ASIIHR04 | A | A | A | A | A |
| 5 | ASIIHR05 | A | A | A | A | A |
| 6 | ASIIHR06 | A | A | A | A | A |
| 7 | ASIIHR07 | NA | A | NA | A | NA |
| 8 | ASIIHR08 | A | A | A | A | A |
| 9 | ASIIHR09 | A | A | A | A | A |
| 10 | ASIIHR10 | A | A | A | A | A |
| 11 | ASIIHR11 | A | A | A | A | A |
| 12 | ASIIHR12 | A | A | A | A | A |
| 13 | ASIIHR13 | A | A | A | A | A |
| 14 | ASIIHR14 | A | A | A | A | A |
| 15 | ASIIHR15 | A | A | A | A | A |
| 16 | ASIIHR16 | A | A | A | A | A |
| 17 | ASIIHR17 | A | A | A | A | A |
| 18 | ASIIHR18 | A | A | A | A | A |
| 19 | ASIIHR19 | A | A | A | A | A |
| 20 | ASIIHR20 | A | A | A | A | A |
| 21 | ASIIHR21 | A | A | A | A | A |
| 22 | ASIIHR22 | A | A | A | NA | NA |
| 23 | ASIIHR23 | A | A | A | A | A |
| 24 | ASIIHR24 | A | A | A | A | A |
| 25 | ASIIHR25 | A | A | A | A | A |
| 26 | ASIIHR26 | A | A | A | A | A |
| 27 | ASIIHR27 | NA | A | A | A | NA |
| 28 | ASIIHR28 | A | A | A | A | A |
| 29 | ASIIHR29 | A | A | A | A | A |
| 30 | ASIIHR30 | A | A | A | A | A |
| 31 | ASIIHR31 | A | A | A | A | A |
| 32 | ASIIHR32 | A | A | A | A | A |
| 33 | ASIIHR33 | A | A | A | A | A |
| 34 | ASIIHR34 | A | A | A | A | A |
| 35 | ASIIHR35 | A | A | A | A | NA |
| 36 | ASIIHR36 | A | A | A | A | A |
| 37 | ASIIHR37 | A | A | A | A | A |
| 38 | ASIIHR38 | A | A | A | A | A |
| 39 | ASIIHR39 | A | A | A | A | A |
| 40 | ASIIHR40 | A | A | A | A | A |
| 41 | ASIIHR41 | A | A | NA | A | NA |
| 42 | ASIIHR42 | A | A | A | A | A |
| 43 | ASIIHR43 | A | A | A | A | A |
| 44 | ASIIHR44 | A | A | A | A | A |
| 45 | ASIIHR45 | A | A | A | A | A |
| 46 | ASIIHR46 | A | A | A | A | A |
| 47 | ASIIHR47 | A | A | A | A | A |
| 48 | ASIIHR48 | A | A | A | A | NA |
| 49 | ASIIHR49 | A | A | A | A | A |
| 50 | ASIIHR50 | A | A | A | A | A |
| 51 | ASIIHR51 | NA | A | A | A | A |
| 52 | ASIIHR52 | A | A | A | NA | NA |
| 53 | ASIIHR53 | NA | A | A | A | NA |
| 54 | ASIIHR54 | A | A | A | A | A |
| 55 | ASIIHR55 | A | A | A | A | A |
| 56 | ASIIHR56 | A | A | A | A | A |
| 57 | ASIIHR57 | A | A | A | A | A |
| 58 | ASIIHR58 | A | NA | A | NA | NA |
| 59 | ASIIHR59 | A | A | A | A | A |
| 60 | ASIIHR60 | A | A | A | NA | NA |
| 61 | ASIIHR61 | A | A | A | A | A |
| 62 | ASIIHR62 | A | NA | A | NA | NA |
| 63 | ASIIHR63 | A | A | A | A | A |
| 64 | ASIIHR64 | A | A | A | A | A |
| 65 | ASIIHR65 | A | A | A | NA | NA |
| 66 | ASIIHR66 | A | A | A | A | A |
| 67 | ASIIHR67 | A | A | A | A | A |
| 68 | ASIIHR68 | A | NA | A | NA | NA |
| 69 | ASIIHR69 | A | A | A | A | A |
| 70 | ASIIHR70 | A | A | A | A | A |
| Transferability % | 94.2 | 95.7 | 97.1 | 90.00 | 80.00 |
The SSR markers showed high PIC values, ranging from 0.78 to 0.96, which were notably higher than those reported in earlier studies using SSRs from A. cherimola in A. squamosa (Anuragi et al., 2016). For example, Anuragi et al. (2016) examined molecular diversity among 20 A. squamosa genotypes using 12 A. cherimola SSR markers, which had PIC values ranging from 0.169 to 0.694. Thus, species-specific SSR are more efficient in assessing the diversity. Similarly, Thanachseyan et al. (2017) studied 34 accessions of A. muricata using 8 A. cherimola SSR markers, reporting an average PIC value of 0.0131, which is much lower than our findings. Thus, it appears that analysis employing species-specific SSR markers gives a better indication of diversity and heterozygosity in the population.
Table 4.
AMOVA analysis of genetic variances within and among populations of 40 custard apple accessions.
| Source of variation | df | SS | MS | Estimated variance | % Variation |
|---|---|---|---|---|---|
| Among populations | 5 | 213.769 | 42.74 | 0.021 | 0 |
| Among individuals | 34 | 1447.244 | 42.56 | 9.870 | 30 |
| Within individuals | 40 | 913.00 | 22.825 | 22.825 | 70 |
| Total | 100 |
In this study, all 70 SSR markers were highly polymorphic, with PIC values exceeding 0.5 (Table 3), indicating a high level of genetic diversity among the 40 Annona genotypes. This was further supported by the high Ho values, which ranged from 0.250 to 1.000, and He values, which ranged from 0.781 to 0.978 (Table 3). These results demonstrate the effectiveness of speciesspecific SSR markers for assessing genetic diversity in A. squamosa. Additionally, the Ho being higher than expected suggests the presence of at least two isolated populations among the genotypes studied.
Among the markers, three SSRs (ASIIHR19, ASIIHR31 and ASIIHR35) showed the highest number of alleles (46), all of which were derived from dinucleotide perfect repeat motifs (CA)8, (CT)8 and (GA)8, respectively. Previous studies (Liu et al., 2016 in kiwifruit; Biswas et al., 2014 in sweet orange; Chapman, 2019 in lablab) have also reported that microsatellite markers with di-nucleotide repeat motifs exhibit a significantly higher number of alleles compared with those with tri-, tetra- and penta-nucleotide repeats, likely due to the ease of mutation via DNA slippage during replication.
Furthermore, seven markers (ASIIHR04, ASIIHR19, ASIIHR31, ASIIHR34, ASIIHR35, ASIIHR40 and ASIIHR47) with low PI values ranging from 0.0022 to 0.0037 were found to be suitable for DNA fingerprinting of A. squamosa genotypes (Table 3). SSR markers with low PI values are highly useful for DNA fingerprinting (Ravishankar et al., 2017a, 2017b), making these markers ideal for the identification of custard apple varieties.
The dendrogram analysis classified the A. squamosa genotypes into two main groups, further subdivided into sub-clusters based on genetic relatedness (Figure 1). Low bootstrap values observed here may be due to only too few SSR markers employed to estimate meaningful genetic differentiation or from a weak population structure due to recent divergence or gene flow. The later explanation is plausible given the recent introduction of Annona species to the Indian subcontinent. This clustering aligns with earlier studies, such as Anuragi et al. (2016), where 20 A. squamosa genotypes were grouped into 7 clusters using 12 A. cherimola SSR markers. The high genetic diversity observed in this study is likely due to the collection of germplasm from diverse locations and species-specific markers used, reflecting the presence of a broad genetic base in A. squamosa. A similar pattern of high diversity was reported for A. cherimola (Escribano et al., 2007) and A. senegalensis (Kwapata et al., 2007).
A Bayesian model-based analysis further supported the clustering results, grouping the 40 A. squamosa genotypes into two major clusters (Figure 2A). The ΔK value derived from Evanno’s algorithm predicted K = 2, consistent with the dendrogram results. Cluster I comprised genotypes from Andhra Pradesh, Tamil Nadu, Maharashtra, Karnataka, the USA and Taiwan, while Cluster II comprised genotypes from Andhra Pradesh, Telangana, Odisha and the USA.
Cross-species amplification of SSRs was highly successful, with a transferability rate of 80.0%–97.1% across five Annona species (Table 3). This high transferability is likely due to the conserved nature of flanking sequences around microsatellites in Annona species. Previous studies, such as those by Anuragi et al. (2016) and Kwapata et al. (2007), reported similar results, with SSR markers showing high cross-species transferability among different Annona taxa.
Although SSR development is expensive, once established, these markers are cost-effective and timeefficient for genetic analysis. In A. squamosa, SSRs have not been extensively used, but their high polymorphism suggests that they are valuable for assessing genetic diversity. Most of the genetic variation in this study was due to differences within populations, likely because A. squamosa is a recently introduced cultivated species. The AMOVA results, with an Fst value of 0.065, indicate significant genetic differentiation among the 40 custard apple genotypes, reflecting the coexistence of different genotypes within the same region (Ravishankar et al., 2015).
CONCLUSIONS
NGS proved to be an efficient method for identification and development of microsatellite markers. Compared with previous studies, this study revealed relatively higher heterozygosity and PIC values, emphasising the usefulness of species-specific SSR markers. The high PIC, polymorphism and Ho values indicate that the SSR markers developed in this study are effective for genetic diversity analysis in A. squamosa. These markers also demonstrated high transferability to closely related species, including A. cherimola, A. reticulata, A. glabra, A. muricata and A. atemoya. The microsatellite markers generated in this study will be valuable for genetic diversity studies, mapping, development of linkage map and other crop improvement programmes, like gene discovery, in A. squamosa.
ACKNOWLEDGEMENTS
We would like to thank the Indian Council of Agricultural Research-Indian Institute of Horticultural Research (ICAR-IIHR), Hesaraghatta Lake Post, Bangalore for providing facilities.
Notes
[5] Conflicts of interest CONFLICT OF INTEREST
The authors report that there are no competing interests to declare. AI tool Kimi has been used to correct English language only. Sequence data has been submitted to NCBI (Bioproject PRJNA682654).
SUPPLEMENTARY MATERIALS
Supplementary Table 1.
List of 40 custard apple (A. squamosa) genotypes/cultivars belonging to A. squamosa and different species of Annona used in the study.
| Sl. No. | Cultivars/accessions No. | Place of collection | State |
|---|---|---|---|
| 1. | Balanagar | Sangareddy | Telangana |
| 2. | Raidurg | Ananthapur | Andhra Pradesh |
| 3. | Taiwan | Taiwan | Taiwan |
| 4. | Arka_Sahan (IC No. 0632061) | IIHR | Karnataka |
| 5. | NMK_1 | Garmale | Maharashtra |
| 6. | APK_1 | Aruppukottai | Tamil Nadu |
| 7. | WASHINGTON_05 | Florida | USA |
| 8. | Barbados | Florida | USA |
| 9. | Washington_97 | Florida | USA |
| 10. | 19/26 (IC no. 0632061) | IIHR, Bangalore | Karnataka |
| 11. | Arka Neelanchal Vikram | Bhubaneswar | Odisha |
| 12. | Mammoth | Florida | USA |
| 13. | Red_sitaphal | Sangareddy | Telangana |
| 14. | 6_8 | Nalakadoddi | Andhra Pradesh |
| 15. | 2_1 | Vengalampalli | Andhra Pradesh |
| 16. | 3_1 | Vengalampalli | Andhra Pradesh |
| 17. | 1_1 | Vengalampalli | Andhra Pradesh |
| 18. | 8_8 | Pythota | Andhra Pradesh |
| 19. | 8_9 | Pythota | Andhra Pradesh |
| 20. | 2_2 | Vengalampalli | Andhra Pradesh |
| 21. | 27_1 | Jambugumpala | Andhra Pradesh |
| 22. | 5_8 | Molakalmuru | Karnataka |
| 23. | 3_2 | Vengalampalli | Andhra Pradesh |
| 24. | 5_1 | Molakalmuru | Karnataka |
| 25. | 8_18 | Pythota | Andhra Pradesh |
| 26. | 1_10 | Vengalampalli | Andhra Pradesh |
| 27. | 8_16 | Pythota | Andhra Pradesh |
| 28. | 11_8 | Yercaud | Tamil Nadu |
| 29. | 3_3 | Vengalampalli | Andhra Pradesh |
| 30. | 4_11 | Molakalmuru | Karnataka |
| 31. | 8_17 | Pythota | Andhra Pradesh |
| 32. | 1_5 | Vengalampalli | Andhra Pradesh |
| 33. | 2_13 | Vengalampalli | Andhra Pradesh |
| 34. | 4_1 | Molakalmuru | Karnataka |
| 35. | 2_10 | Vengalampalli | Andhra Pradesh |
| 36. | 4_10 | Molakalmuru | Karnataka |
| 37. | 6_10 | Nalakadoddi | Andhra Pradesh |
| 38. | 4_12 | Molakalmuru | Karnataka |
| 39. | 8_7 | Pythota | Andhra Pradesh |
| 40. | Rolenia | Moodubidire | Karnataka |
| 41. | A. cherimola | Sangareddy | Telangana |
| 42. | A. reticulata | Sangareddy | Telangana |
| 43. | A. glabra | Sangareddy | Telangana |
| 44. | A. muricata | Sangareddy | Telangana |
| 45. | A. atemoya | Sangareddy | Telangana |
Supplementary Table 2.
Summary of sample Balanagar assembled genome.
| Plant WGS | Balanagar |
|---|---|
| Contigs generated | 1388525 |
| Maximum contig length | 21462 |
| Minimum contig length | 100 |
| Average contig length | 466.126 |
| Total contigs length | 647227553 (647.2 MB) |
| Total number of non-ATGC characters | 54429 |
| Percentage of non-ATGC characters | 8.40956E-05 |
| Contigs >100 b: | 1379816 |
| Contigs >500 b: | 350135 |
| Contigs >1 Kb: | 170254 |
| Contigs >10 Kb: | 19 |
| Contigs >100 Kb: | 0 |
| n50 value: | 954 |
| n90_value: | 151 |
| kmer length | 89 |
| Total number of scaffolds | 58528 |
| Total number of identified SSRs | 1790080 |