705-TH Generating Genomic Tools for Efficient Breeding of the African Eggplant Masika BF1&2, Kamenya S1, Eldridge T2, Njuguna JN2, Stomeo F2, Asami P2, Kizito EB1, Odeny DA3 1Department of Agricultural and Biological Sciences Uganda Christian University, Kampala, Uganda 2Biosciences eastern and central Africa-International Livestock Research Institute Hub, Nairobi, Kenya 3The International Crops Research Institute for the Semi-Arid Tropics, Nairobi, Kenya INTRODUCTION The name eggplant is commonly used to refer to three closely related cultivated species; S. melongena L. (brinjal eggplant), S. macrocarpon (Gboma eggplant) and S. aethiopicum (the African eggplant, or scarlet eggplant). The African eggplant (Solanum aethiopicum L.) (2n=2x=36) is among the most popular edible leafy vegetables in African diets (Anaso 1991). They are nutrient rich and are a source of income for many women, youth and smallholder farmers in sub-Saharan Africa. Four cultivar groups of S. aethiopicum are known, namely, Gilo (C115) (Fig. 1a), Shum (C302) (Fig. 1b), Kumba and Aculeatum (Knapp et al. 2013). All the 4 groups are edible except Aculeatum, which is used as an ornamental (Plazas et al. 2014). Despite their economic importance, there are currently limited genomic resources developed for S. aethiopicum resulting in slow breeding progress and very few commercial cultivars. The Objective of the current study was to identify Single Nucleotide Polymorphisms (SNPs) in S. aethiopicum Shum and Gilo cultivar groups and establish the extent of variation within and across the two groups. Shum and Gilo are the most popular groups in Uganda (Fig. 1). Fig. 1. Pictures of the two most popular S. aethiopicum cultivar groups in Uganda at flowering. A. Oval-shaped Gilo fruits. B. Flat round Shum fruits. Photos taken by Sandra Kamenya. RESULTS AND DISCUSSION We generated at least 2 Gb of raw transcriptome data for each leaf tissue. A summary of the quality, coverage and assembly statistics are shown in Table 1. The generated transcriptome data also revealed a high degree of homozygosity within the genotypes sequenced with only 316 and 106 SNPs identified within C302 and C115 respectively. We identified more SNVs (70,941 SNPs and 7,894 INDELs) between C302 and S. melongena than those identified between C115 and S. melongena (54298 SNPs and 5136 INDELs ) (Figure 3) suggesting that C115 could be more closely related to S. melongena than C302. However, these results may be simply reflecting the amount of data generated from the two cultivar groups. Significantly less SNVs (26,852) were identified between C302 and C115 accessions, which was expected since the two cultivar groups are more closely related to each other than with S. melongena. The Ts/Tv ratio was similar across the two cultivar groups with C302 revealing a ration of 1.64 while the ratio in C115 was 1.68. Table 1: A summary of transcript data generated for each S. aethiopicum cultivar type, quality of reads and mapping rates to S. melongena reference Gilo Shum C115 Flower C115 Leaf C302 Flower C302 Leaf Raw data (Gb) 2.68 2.10 4.16 2.36 Max read length (bp) 351 288 350 312 Total no. of reads (m) 10.55 10.12 16.83 11.34 Quality bases (%) 89.5 91.2 91.4 Coverage (x) 1.32 1.01 2.04 1.13 Transcripts generated 10,379 11,676 20,824 13,626 Transcripts N50 (bp) 872 673 898 765 A B MATERIALS AND METHODS Seeds of Shum (C302) and Gilo (C115) inbred lines were planted in the glasshouse at Uganda Christian University. Fresh leaf and floral tissues were harvested from a single plant of each cultivar group, maintained in RNAlater Stabilization Solution (Thermo Fisher, Waltham, USA) and submitted to QTLomics Technologies Pvt Ltd, Bangalore, India for RNA sequencing using the Ion Proton platform (Life Technologies, Carlsbad, CA, USA). Poor quality reads and adapter sequences were removed using Fastq-mcf software (Lohse et al. 2012). Trimmed transcript reads from leaf and floral tissues from each genotype were mapped to S. melongena reference (Hirakawa et al. 2014) using Tophat (Kim et al. 2013). Single Nucleotide Variations (SNVs) were identified using Freebayes (Garrison and Marth 2012) as shown in Fig. 2. The raw SNPs were further filtered using VCFtools (Danecek et al. 2011) based on a minimum quality score of 30 and a minimum coverage of 3. Fig. 3. SNVs identified between each of the S. aethiopicum cultivar groups, Shum and Gilo, with reference to S. melongena. CONCLUSION This study presents a large number of SNVs (SNPs and INDELS) identified across and within the two most popular S. aethiopicum cultivars grown in Uganda, Shum and Gilo. Although not conclusive, our results provide an insight into the extent of diversity within S. aethiopicum cultivar groups and across S. aethiopicum and S. melongena. The markers identified here, once validated, will go a long way in improving the efficiency of breeding S. aethiopicum in Uganda and Africa as a whole. Acknowledgement The authors are thankful to the Biosciences eastern and central Africa-International Livestock Research Institute Hub for funding the research and supporting Fred Masika to participate in this conference. The authors are also grateful for the RNA-sequencing funding provided by QTLomics Technologies Pvt Ltd, Bangalore. Add or replace read groups/Mark duplicates Fig. 2. Work flow used for data analysis right from the processing of raw reads to the identification of SNVs. Raw reads Trimmed quality reads Assembled/mapped reads Call SNPs Fastq-mcf Tophat (reference-based) Picard-tools Freebayes Literature cited 1. Anaso HU. 1991. Euphytica 53: 81-85 2. Danecek P, et al. 2011. Bioinformatics 27:2156–2158 3. Garrison, E. and Marth, G. 2012. ArXiv e-prints1207.3907: 1–9 4. Hirakawa H, et al. 2014. DNA Res 21:169–181 5. Kim D, et al. 2013. Genome Biology 14: R36. 6. Knapp S, et al. 2013. PLoS ONE 8(2): e57039 7. Lohse M, et al. 2012. Nucleic Acids Res 40: W622–W627 8. Plazas M, et al. 2014. Frontiers in plant science 5,318 .