ORIGINAL ARTICLE Genetic Resources (2022), 3 (6), 38–48 DOI: 10.46265/genresj.WCZG9712 https://www.genresj.org ISSN: 2708-3764 Nutritional and phenotypic variations among newly selected African eggplant (Solanum aethiopicum L.) Olawale Olusesan. Oguntolu, Christian Okechukw. Anyaoha *, Victor Anozie Chikaleke and Joseph Akindojutimi Temidayo Olofintoye National Horticultural Research Institute, P.M.B 5432, Jericho Reservation Area, Idi Ishin, Ibadan, Oyo State, Nigeria Abstract: African eggplant (Solanum aethiopicum L.) is an important but underutilized leafy and fruit vegetable. Systematic characterization of available eggplant accessions for morphological and nutritional traits is paramount to their genetic improvement. This study characterized the diversity among selected S. aethiopicum accessions from Nigeria to identify promising genotypes for future eggplant breeding activities in the region. Twenty new purified African eggplant accessions collected from farmers’ fields were characterized using morphological and nutritional descriptors. The accessions varied significantly in qualitative, quantitative and nutritional parameters. Top performers for selected yield-contributing traits and nutritional parameters were NHEPA54, NHEPA39-1, NHEAP10, NHEPA10, NHEPA1, NHEPA56, NHEPA23 for vitamin C, iron, calcium, days to flowering, number of branches, plant height at maturity and number of fruits per plant respectively. The first four principal components accounted for 72.42% of total variability. The first principal component with the largest variation (28.77%) was loaded with number of branches, plant height at maturity, number of fruits per cluster, number of fruits per plant, and fruit width. A significant positive association was exhibited between iron and yield-increasing traits such as number of fruits per plant (r = 0.532) and number of fruits per cluster (r = 0.551). Plant height at maturity positively correlated with vitamin C (r = 0.492) indicating predictable success in selecting top-performing eggplant genotypes combining high-yield potential and nutritional content. Top-performing eggplant genotypes identified in this study could be deployed as donors for a hybridization programme to develop new eggplant varieties with higher yield potential and improved nutritional quality. Keywords: Diversity, accessions, breeding, principal component, variability, correlation Citation: Oguntolu, O. O., Anyaoha, C. O., Chikaleke, V. A., Olofintoye, J. A. T. (2022). Nutritional and phenotypic variations among newly selected African eggplant (Solanum aethiopicum L.). Genetic Resources 3 (6), 38–48. doi: 10.46265/genresj.WCZG9712. © Copyright 2022 the Authors. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Introduction The African eggplant (Solanum aethiopicum L.) is one of the important indigenous fruit vegetables widely grown and consumed across most regions of tropical Africa. It is the third most consumed fruit vegetable after tomato, pepper and onion both in quantity and value in the region (Osei et al, 2010). Mature fruits of African eggplant are eaten fresh, with fried groundnuts or used to prepare special delicacies called ’African salad’ in southern Nigeria (Igwe et al, 2003). A significant increase has been observed in its production across ∗Corresponding author: Christian Okechukw. Anyaoha (kriskoty@yahoo.com) sub-Saharan Africa from 606,672 tonnes in 1994 to 2,079,920 tonnes in 2018 (FAO, IFAD, UNICEF, WFP and WHO, 2018). Eggplant is considered amongst the healthiest fruit vegetables for its low calories and high concentration of various macro and micro minerals essential for maintaining good health (Docimo et al, 2016). They are rich sources of fibres, vitamins (A, B1, B2, B6, B12, C, D), magnesium, calcium and iron even though potassium is the most abundant mineral ranging from 200 to 600mg/100g of fresh matter (Kowalski et al, 2003; Nyadanu and Lowor, 2015; Nimenibo and Omotayo, 2019). The crop has been reported to play an essential role in meeting the nutritional needs of the Igbo-speaking tribe in southern Nigeria Received: 14.9.2021 Accepted: 05.07.2022 Published online: 07.09.2022 https://www.genresj.org https://www.dx.doi.org/10.46265/genresj.WCZG9712 https://www.genresj.org https://www.dx.doi.org/10.46265/genresj.WCZG9712 mailto:kriskoty@yahoo.com Genetic Resources (2022), 3 (6), 38–48 Genetic variation of African eggplant 39 where consumption of fresh fruits might be of great benefit to glaucoma patients and to prevent heart disease (Igwe et al, 2003; Denkyirah, 2013). S. aethiopicum is used in the management and treatment of diarrhoea and hypertension (Adeniji and Aloyce, 2012). The high yield and nutritive value of the leaves and fruits complemented with resistance to pests and diseases endear the crop to consumers, farmers and researchers (Bonsu et al, 1998; Toppino et al, 2008; Taher et al, 2019). Eggplants belong to the Solanaceae family, which encompasses three closely related cultivated species endemic to Afro-Eurasia. Two sections exist at the subgenus level, namely Melongena and Oliganthes sections. The section Melongena comprises two species (S. melongena and S. macrocarpon) while the Oliganthes group has only one species (S. aethiopicum). S. aethiopicum has been grouped into four different ecotypes or cultivars including Aculetum, Gilo, Kumba and Shum groups as revealed by similarities in genotypic characterization through varied phenotypes (Sharmin et al, 2011). Aculetum is mostly used as ornamental, Gilo is used for its fruits, Kumba is for both fruits and leaves while Shum is used for its leaves (Lester and Daunay, 2003). Consumer preferences for an African eggplant culti- var are based on a number of traits including fruit size, form, fruit colour and taste (sweet or bitter). Morpholog- ical characterization using conventional descriptors has proved useful for describing and establishing relation- ships among cultivar groups and accessions in scarlet eggplants (Adeniji et al, 2013). The enormous morpho- logical variability present in the eggplant family, despite being characterized by a narrow genetic base, might be attributed to new segregants emanating from natural hybridization and backcrossing (Meyer et al, 2012). Despite their socioeconomic significance and their role in meeting the nutritional needs of the ever- increasing population across sub-Saharan Africa, these heirloom and indigenous adapted cultivars are becom- ing less popular, and the efforts to improve them for traits of interest to farmers and end-users are scarce (Bationo-Kando et al, 2015). Continuous planting and selection of many diverse cultivars of S. aethiopicum by small-scale farmers as well as the existence of germplasm collections have helped to conserve the majority of desired traits within families over the years. The long period of selection by these poorly resourced farmers has resulted in a number of landraces exhibiting different variants with unique traits such as earliness, colour, size and taste. In essence, the African eggplant has long been neglected by formal crop improvement programmes except in breeding programmes where it is used as a source of specific traits. Furthermore, they are considered neglected and underutilized crops since their nutritional and economic potentials are mostly underexploited (Padulosi et al, 2019). Systematic characterization of African eggplant acces- sions using morphological and nutritional traits is an important prerequisite toward their conservation and use in further studies and genetic improvement in the region (AVRDC, 2003). Unfortunately, minimal efforts have been directed to identify and select promising genotypes with a good combination of desired agro- nomic and nutritional qualities that could be used as parental materials for hybridization or released as new open-pollinated varieties. The process of germplasm characterization and trait screening to identify and select desired gene combinations can be challenging. This study characterized newly selected and purified eggplant accessions collected in Nigeria to ascertain their mineral composition and to identify promising elite lines with the best combination of desired agronomic traits that could further be deployed for eggplant genetic improvement programmes. Materials and methods Experimental materials Twenty new eggplant accessions (Table 1) were selected from the 2019 characterization of germplasm collections from farmers’ fields across the south-west and north- central regions of Nigeria based on observable traits under field conditions. These materials have gone through two cycles of selection and selfing. Experimental design and conditions The experiment was conducted at the experimental field of the National Horticultural Research Institute (NIHORT), Ibadan, Oyo State, Nigeria. NIHORT is located in the humid forest-savannah transition zone (210m above sea level, 7◦ 30′ N, 3◦ 54′ E) with a bimodal annual rainfall pattern of about 120–128 rainy days amounting to 1,200–1,400mm. Pan evaporation is between 1,550–1,600mm. The wet season is from March through October and the dry season from November through February with an annual maximum temperature ranging between 27◦C and 34◦C and an annual minimum temperature of 20–23◦C (Ogungbenro and Morakinyo, 2014). The eggplant accessions were first raised in a nursery and transplanted to the field after 35 days using a randomized complete block design with three replications. The plot size was 2 x 1m with a spacing of 0.5 x 0.6m between and within rows having 10 plants per plot. Manual weeding was carried out to reduce the competitiveness of soil nutrients. Fertilizer was not applied while insecticides (Cypermethrin) were used at the rate of 200ml/20l of water when needed to reduce damage caused by insects. Phenotypic characterization Phenotypic data collection was carried out on 5 uniform tagged plants out of 10 plants from each plot for the 20 accessions using 12 quantitative (number of branches, number of days to flowering, number of days to 50% 40 Oguntolu et al Genetic Resources (2022), 3 (6), 38–48 flowering, plant height at maturity, number of fruits per cluster, number of harvested fruits, weight of harvested fruits, petiole length, fruit length, fruit width, stem girth, pedicel length) and 10 qualitative traits (fruit colour, stem colour, petiole colour, leaf hairs, sepal colour, fruit colour, fruit shape, fruit position, fruit-end shape, presence/ absence of stripes). Physiochemical variables were iron, vitamin C and calcium, using the descriptor list for eggplant by the International Board for Plant Genetic Resources (IBPGR, 1990). Table 1. Status, collection source and states of Solanum aethiopicum L. accessions collected in Nigeria Accessions Status Source States 1 NHEPA01 Farmers cultivar Local Market Ogun 2 NHEPA03 Farmers cultivar Local Market Ogun 3 NHEPA10 Farmers cultivar Local Market Ogun 4 NHEPA12 Farmers cultivar Farmers Kogi 5 NHEPA17 Farmers cultivar Farmers Kogi 6 NHEPA19 Farmers cultivar Farmers Kogi 7 NHEPA23 Farmers cultivar Farmers Kogi 8 NHEPA35 Farmers cultivar Farmers Kogi 9 NHEPA36 Farmers cultivar Farmers Kogi 10 NHEPA38 Farmers cultivar Farmers Kogi 11 NHEPA39-1 Farmers cultivar Farmers Kogi 12 NHEPA39-2 Farmers cultivar Farmers Kogi 13 NHEPA39-3 Farmers cultivar Farmers Kogi 14 NHEPA51 Farmers cultivar Farmers Kaduna 15 NHEPA52 Farmers cultivar Farmers Kaduna 16 NHEPA53 Farmers cultivar Farmers Kaduna 17 NHEPA54 Farmers cultivar Farmers Kaduna 18 NHEPA55 Farmers cultivar Farmers Kaduna 19 NHEPA56 Farmers cultivar Farmers Kaduna 20 YALO Farmers cultivar Green seed company Oyo Calcium, iron and vitamin C determination Fruit samples were dried in an oven at 600◦C for 4 hours. Ashes and crucibles were previously decontaminated with a solution of 10% nitric acid at rest for a night and rinsed. Then, 10ml of 5% nitric acid was added to the sample, and this mixture was heated until complete dissolution of the ash which was then filtered. After the sample had reached room temperature, the solution was put into a 25ml volumetric flask and the volume supplemented with deionized water. The determination of calcium and iron contents was performed according to AOAC METHOD 2005 using an atomic absorption spectrophotometer flame (BULKS SCIENTIFIC® model AA 240). Calibration curves for each element were plotted using standard mineral diluted with deionized water. All analyses were performed in triplicate; the results were expressed in milligrams per 100g (mg/100g) of sample on a dry basis. The amount of vitamin C in analyzed samples was determined by titration using the method described by Mondal et al., (1995). About 0.5g of sample were soaked for 10 minutes in 40ml metaphosphoric acid- acetic acid (2%, w/v). The mixture was centrifuged at 3,000rpm for 20 minutes and the supernatant obtained was diluted and adjusted with 50ml of bi-distilled water. Ten (10)ml of this mixture was titrated to the endpoint with dichlorophenol-indophenol (DCPIP) 0.5g/l (AOAC. 1990). Statistical analysis Analysis of variances (ANOVA) was calculated using Plant Breeding Tools (ver.1.1.0, http://bbi.irri.org/prod uct) to determine significant variations in quantitative characters among the eggplant genotypes. The estimate of co-efficient of variation (CV) was calculated using the standard formulae (Burton, 1952) and expressed in percentage. Inter-species diversity pattern was ana- lyzed through Ward’s minimum variance while correla- tion, dendrogram clustering and principal components analysis (PCA) were carried out using STAR software. Results The frequency distribution of qualitative traits observed in all 20 accessions is presented in Table 2. All the genotypes (100%) exhibited green stems, petioles and sepals. At the reproductive stage, 48.48% of the fruits had a white colour, 45.45% expressed lemon green while 3.03% were light green and 3.03% exhibited deep green fruit colour. Three prominent fruit shapes were observed: oval (51.51%), long (30.30%) and round (18.18%). All accessions exhibited perpendicular fruit position with 69.69% and 30.30% of the populations having pointed and flat ends respectively. The fruits of selected eggplant accessions are presented in Figure 1. Table 3 lists the descriptive statistics measures of spread: mean, range, standard deviation and coefficient of variation (CV). The partitioning of the means revealed high significant variations for all traits at P ≤ 0.01. For most traits, higher variations in terms of range and CV were observed in the nutritional data compared to the phenotypic data. The highest CV was recorded for calcium (43.91%) followed by average Genetic Resources (2022), 3 (6), 38–48 Genetic variation of African eggplant 41 Table 2. Qualitative traits of 20 Solanum aethiopicum accessions Traits Modality Frequency (%) 1 Stem colour Green 100 2 Petiole colour Green 100 3 Leaf hairs Very few 100 4 Sepal colour Green 100 5 Fruit colour White 48.48 Lemon green 45.45 Light green 3.03 Deep green 3.03 6 Fruit shape Oval 51.51 Long 30.3 Round 18.18 7 Fruit position Direct 100 8 Fruit end Pointed 69.69 Flat 30.3 9 Stripe presence Present 100 Figure 1. Sample of fruits for selected eggplant accessions, where V04, V10, V12, V17, V19, V24, V29 and V35 rep- resent NHEPA4, NHEPA10, NHEPA12, NHEPA17, NHEPA19, NHEPA24, NHEPA29 and NHEPA35 respectively. number of fruits per plant (37.13%) and average yield per plant (32.21%). The top performers for selected yield-contributing traits and nutritional parameters are NHEPA54, NHEPA39-1, NHEPA10, NHEPA10, NHEPA1, NHEPA56, NHEPA23, for vitamin C, iron, calcium, days to flowering, number of branches, plant height at maturity and number of fruits per plant, respectively, while NHEPA54 was outstanding for high yield potential and vitamin C content. Accession NHEPA10 was the top performer for calcium and days to flowering while average number of fruits per cluster had NHEPA17, NHEPA19, NHEPA23 as the top performers. Principal component analysis (PCA), which is a statistical technique used to emphasize variation and bring out strong patterns in data sets, was performed to show the traits that best contributed to the observed genetic variation. The eigenvalues and proportion of accounted variance for each variable are shown in Table Figure 2. Distribution of 20 eggplant accessions for the first two principal components based on 14 quantitative traits. DTF, days to flowering; NoB, number of branches; PH, plant height at maturity; NoFPC, average number of fruits per cluster; NoF, average number of fruits per plant; YLD, average yield per plant; FL, fruit length; FWD, fruit width; SD, stem diameter; Pet.L, petiole length; Ped.L, pedicel length. Numbers 1-20 represent genotypes: 1, NHEPA01; 2, NHEPA03; 3, NHEPA10; 4, NHEPA12; 5, NHEPA17; 6, NHEPA19; 7, NHEPA23; 8, NHEPA35; 9, NHEPA36; 10, NHEPA38; 11, NHEPA39-1; 12, NHEPA39-2; 13, NHEPA39-3; 14, NHEPA51; 15, NHEPA52; 16, NHEPA53; 17, NHEPA54; 18, NHEPA55; 19, NHEPA56; 20, Yalo. 4. PC1 had an eigenvalue of 4.027 while PC2, PC3 and PC4 had eigenvalues of 2.889, 1.861 and 1.363, respectively. The first four principal component axes (PCA) accounted for 28.77%, 20.64%, 13.29%, 9.73% of the total variation individually and, cumulatively, 72.42% of the total variability while the first two PCs contributed 49.41% (Figure 2). The first PC axis, which accounted for the highest proportion (28.77%) of the variability, was dominated by traits with relatively high factor scores (> 2.60) corresponding to number of branches, plant height at maturity, number of fruits per cluster, number of fruits per plant, and fruit width. The second PC axis was dominated by days to flowering, fruit length, stem diameter, petiole length and pedicel length. Also, the third PC axis was dominated by average yield per plant, fruit length, stem diameter, petiole length and pedicel length while the fourth PC axis was dominated by days to flowering, number of branches, fruit length, fruit width and petiole length. Correlations between pairs of quantitative variables are recorded in Table 5. There was no significant association between the nutritional parameters except for a negative moderate significant association between vitamin C and iron (r = -0.50, P < 0.05). Iron content correlated positively with number of branches (r = 42 Oguntolu et al Genetic Resources (2022), 3 (6), 38–48 Ta bl e 3. N ut ri ti on al an d ph en ot yp ic da ta fo r re le va nt yi el d- co nt ri bu ti ng tr ai ts on 20 A fr ic an eg gp la nt ac ce ss io ns , in cl ud in g de sc ri pt iv e st at is ti cs m ea su re s of sp re ad . M ea ns va ry si gn ifi ca nt ly fo r al lt ra it s at P = 0. 05 .D TF ,d ay s to flo w er in g; N oB ,n um be r of br an ch es ;P H ,p la nt he ig ht at m at ur it y; N oF PC ,a ve ra ge nu m be r of fr ui ts pe r cl us te r; N oF ,a ve ra ge nu m be r of fr ui ts pe r pl an t; YL D ,a ve ra ge yi el d pe r pl an t; FL ,f ru it le ng th ; FW D ,f ru it w id th ; St em D ,s te m di am et er ; Pe t. L, pe ti ol e le ng th ; Pe d. L, pe di ce ll en gt h; C V, co ef fic ie nt of va ri at io n; SD , st an da rd de vi at io n. N u tr it io n al da ta (m g/ 10 0g ) Ph en ot yp ic da ta A cc es si on s V it .C Ir on C al ci u m D T F N oB PH (c m ) N oF PC N oF Y LD (g ) FL (m m ) FW D (m m ) St em D (m m ) Pe t. L (m m ) Pe d. L (m m ) N H EP A 01 3. 11 0. 43 7. 73 10 9. 12 7. 13 57 .6 6 2. 70 58 .5 5 13 70 .1 0 65 .9 0 37 .3 7 19 .5 0 23 .0 8 20 .0 1 N H EP A 03 2. 02 0. 47 4. 47 10 4. 00 5. 79 48 .5 8 4. 98 74 .6 1 16 99 .1 7 67 .3 1 32 .4 5 11 .8 4 18 .5 7 15 .1 7 N H EP A 10 2. 90 0. 50 14 .2 4 97 .9 1 4. 39 46 .3 1 4. 49 81 .0 3 13 20 .7 2 58 .5 4 22 .7 3 10 .4 4 19 .9 6 19 .0 6 N H EP A 12 2. 90 0. 25 8. 88 10 3. 04 5. 01 54 .5 8 4. 49 81 .6 8 16 73 .4 1 50 .4 9 36 .3 2 12 .5 7 20 .2 4 15 .0 6 N H EP A 17 3. 15 0. 43 4. 86 10 9. 44 4. 70 50 .6 9 5. 79 65 .2 9 17 26 .4 8 45 .3 7 32 .6 6 17 .5 8 14 .7 2 14 .8 6 N H EP A 19 3. 99 0. 50 10 .6 9 10 6. 56 5. 48 52 .4 7 5. 79 76 .3 8 15 22 .6 0 44 .9 1 33 .0 5 13 .7 6 12 .9 3 11 .7 8 N H EP A 23 4. 81 0. 46 4. 42 10 7. 52 6. 42 58 .8 0 5. 79 11 1. 71 17 31 .0 5 45 .6 2 26 .8 4 19 .1 2 13 .9 6 12 .5 8 N H EP A 35 3. 78 0. 25 7. 53 10 0. 79 3. 92 60 .0 9 3. 34 36 .8 7 16 66 .9 1 78 .1 8 41 .8 8 13 .0 7 26 .6 3 22 .5 4 N H EP A 36 4. 24 0. 18 5. 62 10 4. 96 5. 48 60 .4 2 3. 51 71 .5 6 17 47 .0 9 57 .1 6 31 .4 9 15 .5 5 18 .0 9 15 .8 4 N H EP A 38 3. 15 0. 42 4. 10 10 4. 00 6. 26 57 .3 4 4. 16 78 .1 4 16 82 .9 5 54 .8 1 32 .1 1 14 .5 6 19 .4 2 15 .1 8 N H EP A 39 -1 3. 53 0. 51 12 .7 7 10 2. 08 6. 16 52 .8 0 4. 65 77 .6 6 20 19 .6 8 76 .4 4 30 .4 2 16 .0 2 11 .5 1 17 .7 7 N H EP A 39 -2 3. 92 0. 43 6. 85 10 2. 72 5. 84 62 .5 2 4. 11 45 .5 4 13 09 .3 4 51 .0 3 64 .2 1 12 .7 7 15 .5 0 14 .4 3 N H EP A 39 -3 5. 80 0. 33 11 .2 7 10 3. 36 5. 32 61 .1 2 4. 21 34 .9 4 98 9. 17 53 .5 2 60 .3 0 14 .9 1 13 .6 0 14 .1 2 N H EP A 51 4. 28 0. 36 7. 53 99 .1 9 5. 01 60 .6 9 2. 48 36 .2 2 20 35 .7 1 47 .5 6 38 .8 4 12 .2 6 15 .0 6 14 .0 4 N H EP A 52 4. 84 0. 21 5. 95 10 5. 28 4. 80 60 .8 0 3. 13 35 .2 6 16 66 .9 1 56 .5 2 45 .6 2 16 .1 1 21 .6 0 12 .1 0 N H EP A 53 4. 95 0. 37 5. 45 10 3. 68 3. 66 62 .5 2 2. 59 47 .1 5 24 20 .5 5 54 .4 2 51 .0 3 18 .1 1 16 .0 1 14 .2 6 N H EP A 54 6. 01 0. 25 3. 31 10 4. 00 4. 70 58 .5 3 3. 56 53 .8 9 28 05 .3 9 75 .1 1 36 .0 8 15 .3 7 15 .6 8 13 .2 1 N H EP A 55 2. 58 0. 45 7. 04 10 2. 08 5. 43 59 .0 6 3. 24 64 .8 1 15 22 .6 0 56 .9 4 59 .6 1 14 .0 7 21 .0 2 13 .7 8 N H EP A 56 3. 50 0. 36 4. 29 10 4. 96 4. 70 66 .9 6 3. 56 44 .2 6 23 35 .0 3 37 .0 3 51 .9 4 16 .5 8 20 .9 9 14 .3 4 YA LO 5. 05 0. 21 4. 11 10 4. 00 3. 61 57 .0 1 1. 88 21 .4 5 21 9. 29 47 .0 8 70 .8 6 11 .8 7 16 .1 8 16 .3 0 M ea n 3. 93 0. 37 7. 06 10 3. 93 5. 19 57 .4 5 3. 92 59 .8 5 16 73 .2 1 56 .2 41 .7 9 14 .8 17 .7 4 15 .3 2 M in 2. 02 0. 18 3. 31 97 .9 1 3. 61 46 .3 1 1. 88 21 .4 5 21 9. 29 37 .0 3 22 .7 3 10 .4 4 11 .5 1 11 .7 8 M ax 6. 01 0. 51 14 .2 4 10 9. 44 7. 13 66 .9 6 5. 79 11 1. 71 28 05 .3 9 78 .1 8 70 .8 6 19 .5 0 26 .6 3 22 .5 4 C V 27 .2 3 29 .7 3 43 .9 1 2. 78 17 .9 2 8. 95 28 .8 3 37 .1 3 32 .2 1 20 .1 2 32 .3 0 17 .2 3 21 .7 6 17 .6 2 SD 1. 07 0. 11 3. 10 2. 89 0. 93 5. 14 1. 13 22 .2 2 53 8. 91 11 .3 1 13 .5 2. 55 3. 86 2. 70 Genetic Resources (2022), 3 (6), 38–48 Genetic variation of African eggplant 43 Table 4. Eigenvalues and the proportion of accounted variance for each trait across 20 accessions of eggplant for the first four principal components (PC). DTF, days to flowering; NoB, number of branches; PH, plant height at maturity; NoFPC, average number of fruits per cluster; NoF, average number of fruits per plant; YLD, average yield per plant; FL, fruit length; FWD, fruit width; StemD, stem diameter; Pet.L, petiole length; Ped.L, pedicel length. Variables PC1 PC2 PC3 PC4 Vitamin C 0.2777 -0.2423 0.1295 -0.3933 Iron -0.3759 0.0049 0.1498 0.0992 Calcium -0.1837 0.3163 0.2492 -0.1366 DTF -0.0896 -0.4322 -0.1783 0.3571 NoB -0.2992 -0.1570 -0.1479 0.2603 PH 0.3553 -0.2406 -0.1429 0.0086 NoFPC -0.4063 -0.1331 0.1653 -0.0500 NoF -0.4371 -0.0926 -0.0639 -0.0384 YLD -0.0591 -0.1850 -0.3516 -0.5664 FL -0.0488 0.2823 -0.4034 -0.3045 FWD 0.3804 -0.0242 0.2446 0.2843 StemD -0.0412 -0.4246 -0.3557 0.0549 Pet.L 0.1243 0.2639 -0.4563 0.3129 Ped.L -0.0135 0.4255 -0.3351 0.1488 Proportion of Variance 0.2877 0.2064 0.1329 0.0973 Cumulative Proportion 0.2877 0.494 0.6269 0.7242 EigenValues 4.0274 2.8891 1.8605 1.3625 0.487, P < 0.05), average number of fruits per plant (r = 0.532, P < 0.05) and number of fruits per cluster (r = 0.551, P < 0.05). Plant height had a negative but moderate significant association with iron (r = -0.461, P < 0.05) and calcium (r = -0.407, P < 0.05) but was positively correlated with vitamin C (r = 0.492, P < 0.05). The strongest and most persistent correlation was recorded for association between fruit width and plant height (r = 0.574, P < 0.01) and number of fruits per plant (r = -0.737, P < 0.01); stem diameter with days to flowering (r = 0.734, P < 0.01). A positive significant correlation was observed between pedicel length and fruit length (r = 0.561, P < 0.05), and between pedicel length and petiole length (r = 0.528, P < 0.05). Based on variation in the phenotypic parameters the 20 eggplant accessions were clustered into four unique groups (Figure 3). Clusters I and II contained seven and three accessions, respectively, while clusters III and IV both had five accessions. Means of variables, ranges and standard deviation for each cluster are presented in Table 6. Cluster III was unique in having accessions with high vitamin C content and high yield potential while clusters II was characterized by accessions with high iron content, an increased number of fruits per plant and a higher number of fruits per cluster. Clusters I and IV were characterized by early maturing accessions dominated by top-performing accessions in fruit-related traits (fruit length and fruit width respectively). Discussion The success of genetic improvement programmes in enhancing desired traits of interest to farmers and breeders depends on the magnitude of genetic variability available in the germplasm and the extent to which the desirable traits are heritable. The high significant variation observed for most qualitative and quantitative traits considered in this study establishes the feasibility of imposing selections towards the improvement of desired traits of interest in African eggplant. Frequency distribution among the qualitative traits with a preponderance of fruits characterized by white to cream colours and lemon green suggests that the majority of the accessions belong to the S. aetihiopicum group. This supports earlier reports by Osei et al (2010) that eggplant accessions belonging to S. aethiopicum had mixtures involving cream white to light yellow fruits; thus, fruit colour combined with fruit shape might be considered a strong phenotypic marker in characterizing eggplant taxa in Africa. The high CVs and range for some of the quantitative characters could be attributed to genetic variations from natural crossings and ecogeographical factors. The maximum and minimum mean values could present a rough estimate of the variation in magnitude of variability present among genotypes. Traits such as average number of fruits per plant and fruit width that exhibited a high range of variation had more scope for improvement in the eggplant population. The principal component analysis identified traits that contributed the most to observed variations within a group of entries (Sneath and Sokal, 1973; Grittins, 1975). The first four principal component axes in the current study accounted for 72.42% of the total variability measured. The first principal component analysis had the highest discriminating ability (contributing 28.77% out of 79.47% of variability from the first four axes) and was dominated by traits with relatively high factor scores (> 2.60) corresponding to number of branches, plant height at maturity, number of fruits per cluster, number of fruits per plant, and fruit width. This is in agreement with Clifford and Stephen (1975) who reported that the first principal component axis was the most important in reflecting the variation patterns among accessions and that the characters highly associated with these should be used in differentiating the accessions. Furthermore, this is in line with the findings of Iezzoni and Pritts (1991) and Chikaleke (2018) who reported that the implication of principal components can be accessed from the contribution of the different variables to each principal component (PC). Correlation analysis is used to identify the relation- ship between variables (Anshori et al, 2018) and to facil- itate the identification of elite traits to rely on in selec- tion exercises of a breeding programme. The positive significant association between iron content, number of branches, number of fruits per cluster and number of fruit per plant; number of fruit per plant with number 44 Oguntolu et al Genetic Resources (2022), 3 (6), 38–48 Ta bl e 5. C or re la ti on co ef fic ie nt s fo r nu tr it io na la nd ph en ot yp ic pa ra m et er s of th e2 0 eg gp la nt ac ce ss io ns ev al ua te d. *, si gn ifi ca nt at P ≤ 0. 05 ,* *, si gn ifi ca nt at P ≤ 0. 01 ,D TF ,d ay s to flo w er in g; N oB ,n um be r of br an ch es ;P H ,p la nt he ig ht at m at ur it y; N oF PC ,a ve ra ge nu m be r of fr ui ts pe r cl us te r; N oF ,a ve ra ge nu m be r of fr ui ts pe r pl an t; YL D ,a ve ra ge yi el d pe r pl an t; FL ,f ru it le ng th ;F W D ,f ru it w id th ;S te m D ,s te m di am et er ;P et .L ,p et io le le ng th ;P ed .L ,p ed ic el le ng th . Tr ai ts V it .C Ir on C al ci u m D T F N oB PH N oF PC N oF Y LD FL FW D St em D Pe t. L Ir on -0 .5 00 * C al ci um -0 .1 62 0. 37 7 D TF 0. 06 4 0. 02 8 -0 .4 46 N oB -0 .3 08 0. 48 7* 0. 09 1 0. 37 0 PH 0. 49 2* -0 .4 61 * -0 .4 07 * 0. 05 1 -0 .1 00 N oF PC -0 .2 79 0. 55 1* 0. 22 5 0. 29 3 0. 37 0 -0 .5 37 * N oF -0 .4 18 0. 53 2* 0. 15 1 0. 20 0 0. 54 5* -0 .5 10 * 0. 73 0* * YL D 0. 06 3 0. 04 0 -0 .2 33 -0 .0 01 -0 .0 07 0. 21 3 0. 08 6 0. 19 3 FL -0 .0 45 -0 .0 50 0. 18 1 -0 .2 60 0. 09 7 -0 .2 29 -0 .1 27 0. 00 5 0. 21 7 FW D 0. 31 9 -0 .3 16 -0 .2 00 -0 .0 85 -0 .3 39 0. 57 4* * -0 .5 54 * -0 .7 37 ** -0 .4 10 -0 .2 55 St em D 0. 23 9 0. 06 5 -0 .3 03 0. 73 4* * 0. 33 6 0. 35 0 0. 06 8 0. 17 4 0. 36 3 -0 .0 58 -0 .1 25 Pe t. L -0 .3 94 -0 .3 15 -0 .1 42 -0 .1 23 -0 .1 33 0. 15 4 -0 .3 87 -0 .1 86 -0 .0 32 0. 25 6 0. 02 9 -0 .0 99 Pe d. L -0 .3 40 -0 .0 13 0. 31 3 -0 .2 75 -0 .0 61 -0 .2 12 -0 .2 36 -0 .0 95 -0 .2 12 0. 56 1* -0 .1 47 -0 .1 47 0. 52 8* Genetic Resources (2022), 3 (6), 38–48 Genetic variation of African eggplant 45 Figure 3. Cluster dendrogram showing the relationships among the 20 eggplant accessions with cluster tree cut value at 7.3. of branches and number of fruits per cluster; fruit width and plant height at maturity; stem diameter and days to flowering; pedicel length with fruit length and with petiole length will facilitate selection of eggplant acces- sions with a good combination of these traits. The signif- icant positive correlation displayed by these traits is in agreement with Dhaka and Soni (2013) who reported a positive significant association between yield and yield- related traits. However, where the traits had significant negative correlation coefficients (vitamin C and iron content, plant height at maturity and iron content, plant height at maturity and calcium content, number of fruits per cluster and plant height at maturity, number of fruits per plant and plant height at maturity, fruit width with number of fruits per cluster and with number of fruits per plant) indicates that an increase in one trait might lead to a decrease in the other trait or vice versa. This is in agreement with Mazer et al (1999) and Nyadanu and Lowor (2015) who reported that traits with significant inverse relationships could be improved independently among eggplant accessions in Ghana. However, select- ing tall plants in this eggplant population might result in an indirect selection for low calcium content, while favouring increased iron content will lead to selecting genotypes with low vitamin C. Selection pressure can be deployed for an increased number of fruits per plant to simultaneously increase iron content and number of branches. Similar observations were reported by Arivala- gan et al (2013) and Nyadanu and Lowor (2015) in their earlier works on mineral composition and morphological characterization of eggplant. The cluster analysis emphasized further the relative contribution of various quantitative parameters to the total variability. The grouping of accessions in each cluster based on quantitative descriptors could be attributed to the fact that these accessions share some similarities. The high-yielding accessions in cluster III (NHEPA54) expressing high vitamin C and iron content could be deployed as progenitors to combine with eggplant genotypes from cluster I and create a new gene combination with improved calcium content and yield potential. Creating new eggplant varieties with high-yield potential and increased vitamins and minerals (iron, calcium and vitamin C) will not only increase farmers’ income but will also help to reduce health challenges associated with hidden hunger among the 46 Oguntolu et al Genetic Resources (2022), 3 (6), 38–48 Table 6. Means and standard deviations for various traits in different clusters. The clusters with the highest values for each trait are highlighted in bold font. DTF, days to flowering; NoB, number of branches; PH, plant height at maturity; NoFPC, average number of fruits per cluster; NoF, average number of fruits per plant; YLD, average yield per plant; FL, fruit length; FWD, fruit width; StemD, stem diameter; Pet.L, petiole length; Ped.L, pedicel length. Trait Cluster Min Max Mean StdDev Trait Cluster Min Max Mean StdDev Vit.C I 2.02 3.78 3.06 0.56 NoF I 36.87 81.68 69.79 16.50 Vit.C II 3.15 4.81 3.98 0.83 NoF II 65.29 111.71 84.46 24.24 Vit.C III 3.50 6.01 4.71 0.93 NoF III 35.26 71.56 50.42 13.58 Vit.C IV 2.58 5.80 4.33 1.22 NoF IV 21.45 64.81 40.59 16.03 Iron I 0.25 0.51 0.40 0.11 YLD I 1,320.72 2,019.68 1,633.28 233.03 Iron II 0.43 0.50 0.46 0.04 YLD II 1,522.60 1,731.05 1,660.04 119.05 Iron III 0.18 0.37 0.27 0.09 YLD III 1,666.91 2,805.39 2,194.99 480.25 Iron IV 0.21 0.45 0.36 0.10 YLD IV 219.29 2,035.71 1,215.22 674.45 Calcium I 4.10 14.24 8.53 3.84 FL I 50.49 78.18 64.52 10.53 Calcium II 4.42 10.69 6.66 3.50 FL II 44.91 45.62 45.30 0.36 Calcium III 3.31 5.95 4.92 1.10 FL III 37.03 75.11 56.05 13.50 Calcium IV 4.11 11.27 7.36 2.56 FL IV 47.08 56.94 51.23 4.14 DTF I 97.91 109.12 102.99 3.44 FWD I 22.73 41.88 33.33 6.08 DTF II 106.56 109.44 107.84 1.47 FWD II 26.84 33.05 30.85 3.48 DTF III 103.68 105.28 104.58 0.69 FWD III 31.49 51.94 43.23 9.10 DTF IV 99.19 104.00 102.27 1.86 FWD IV 38.84 70.86 58.76 12.00 NoB I 3.92 7.13 5.52 1.13 StemD I 10.44 19.50 14.00 3.02 NoB II 4.70 6.42 5.53 0.86 StemD II 13.76 19.12 16.82 2.76 NoB III 3.66 5.48 4.67 0.65 StemD III 15.37 18.11 16.34 1.10 NoB IV 3.61 5.84 5.04 0.85 StemD IV 11.87 14.91 13.18 1.28 PH I 46.31 60.09 53.91 5.03 Pet.L I 11.51 26.63 19.92 4.62 PH II 50.69 58.80 53.99 4.26 Pet.L II 12.93 14.72 13.87 0.90 PH III 58.53 66.96 61.85 3.19 Pet.L III 15.68 21.60 18.47 2.74 PH IV 57.01 62.52 60.08 2.11 Pet.L IV 13.60 21.02 16.27 2.82 NoFPC I 2.70 4.98 4.12 0.81 Ped.L I 15.06 22.54 17.83 2.89 NoFPC II 5.79 5.79 5.79 0 Ped.L II 11.78 14.86 13.07 1.60 NoFPC III 2.59 3.56 3.27 0.42 Ped.L III 12.10 15.84 13.95 1.40 NoFPC IV 1.88 4.21 3.18 1.01 Ped.L IV 13.78 16.30 14.53 1.01 rural and urban populace in the region. Selection and hybridization of genotypes from clusters I and IV such as NHEPA35 and Yalo will produce new segregants characterized by bigger fruits with increased iron concentration. Conclusion This study successfully characterized 20 eggplant accessions for phenotypic and nutritional traits of interest and identified top-performing new eggplant accessions with unique traits that could be deployed in crosses to facilitate the step-wise creation of new eggplant varieties with the best combination of desired traits. Furthermore, selection in favour of yield- increasing traits such as number of fruits per plant and number of fruits per cluster that showed a significant positive correlation with iron, will lead to selecting genotypes with increased iron content and higher yield potential simultaneously. Top-performing accessions for iron (NHEPA39-1), calcium (NHEPA39-1) and vitamin C content (NHEPA54) identified in this study should be deployed for hybridization to create new eggplant varieties with improved nutritional content. Author contributions Olawale Olsesan Oguntolu: study design, execution, drafting. Christian Okechukw Anyaoha: study design, data analysis and interpretation, drafting, revision. Victor Anosie Chikaleke: drafting and revision. Olofintoye Temidayo Joseph A, study design and execution. Conflict of interest statement The authors declare no conflict of interest. All authors approved the final manuscript. Acknowledgements The authors are grateful to the Internal Management of National Horticultural Research Institute (NIHORT) Genetic Resources (2022), 3 (6), 38–48 Genetic variation of African eggplant 47 Ibadan, Oyo state Nigeria for their support in carrying out this research work. References Adeniji, O. T. and Aloyce, A. (2012). Farmer’s knowledge of horticultural traits and participatory selection of African eggplant varieties (S. aethiopicum) in Tanzania. Tropicultura 30(3), 185–191. url: http: //www.tropicultura.org/text/v30n3/185.pdf. Adeniji, O. T., Kusolwa, P. M., and Reuben, S. (2013). Morphological descriptors and micro satellite diversity among scarlet eggplant groups. African Crop Science Journal 21(1), 37–49. Anshori, M. F., Purwoko, B. S., Dewi, I. S., S, W, Ardie, and Suwarno, W. B. (2018). Determination of selection criteria for screening of rice genotypes for salinity tolerance. J. Breed. Genet 50, 279–294. Arivalagan, M., Bhardwaj, R., Gangopadhyay, K. K., Prasad, T. V., and Sarkar, S. K. (2013). Mineral composition and their genetic variability analysis in eggplant (Solanum melongena L.) germplasm. Journal of Applied Botany and Food Quality 86, 99– 103. doi: https://doi.org/10.5073/JABFQ.2013.086. 014 AVRDC (2003). AVRDC report 2002. AVRDC Publication No. 03-563. Bationo-Kando, P., Sawadogo, B., Nanema, K., Kiebre, Z., Sawadogo, N., Traore, R. E., Sawadogo, M., and Zongo, J. (2015). Characterization of Solanum aethiopicum (Kumba group) in Bukina Faso”. Inter- national Journal of Science and Nature 6(2), 169–176. doi: https://doi.org/10.5897/JPBCS2018.0755 Bonsu, K. O., Owusu, E. O., Nkansah, G. O., Oppong- Konadu, E., and Adu-Dapaah, H. (1998). Character- ization of hot pepper (Capsicum spp) germplasm in Ghana. In Proceedings of the 1st Biennial Agricultural Research Workshop. Burton, G. W. (1952). Quantitative inheritance in grasses. In Proceedings of the 6th International Grassland Congress, 227-283. Chikaleke, V. A. (2018). Reproductive biology and genetic components of yield in eggplant (Solanum species). Ph.D. thesis, Department of Plant Breeding and Seed Technology, College of Plant Science and Crop Production, Federal University of Agriculture, Abeokuta. Denkyirah, E. K. (2013). Variation in Floral Morphology, Fruit Set and Seed Quality of Garden Egg (Solanum Aethiopicum Var Gilo) Germplasm in Ghana. Ph.D. thesis, University of Ghana. Dhaka, S. K. and Soni, A. K. (2013). Genotypic and phenotypic correlation study in brinjal genotypes. Annals Pl. Soil Res 16, 53–56. Docimo, T., Francese, G., Ruggiero, A., Batelli, G., De Palma, M., Bassolino, L., Toppino, L., Rotino, G. L., Mennella, G., and Tucci, M. (2016). Phenylpropanoids accumulation in eggplant fruit: characterization of biosynthetic genes and regulation by a MYB transcrip- tion factor. Frontiers in Plant Science 6, 1233–1233. doi: https://doi.org/10.3389/fpls.2015.01233 FAO, IFAD, UNICEF, WFP and WHO (2018). The State of Food Security and Nutrition in the World 2018. Building climate resilience for food security and nutrition (Rome: FAO). url: https://www.fao.org/3/ I9553EN/i9553en.pdf. Grittins, R. (1975). The application of ordination techniques. Multivariate statistical methods 102-131. IBPGR (1990). Descriptors for eggplant (International Board for Plant Genetic Resources). url: https://hdl. handle.net/10568/72874. Iezzoni, A. F. and Pritts, M. P. (1991). Application of principal component analysis to horticultural research. Hort Science 26(4), 334–338. doi: https: //doi.org/10.21273/HORTSCI.26.4.334 Igwe, S. A., Akunyili, D. N., and Ogbogu, C. (2003). Effects of Solanum melongena (garden egg) on some visual functions of visually active Igbos of Nigeria. Journal of Ethnopharmacology 86(2-3), 135–138. doi: https://doi.org/10.1016/S0378-8741(02)00364-1 Kowalski, R., Kowalska, G., and Wiercinski, J. (2003). Chemical composition of fruits of three eggplant [Solanum melongena L.] cultivars. Folia Horticulturae 15, 89–95. Lester, R. and Daunay, M. C. (2003). Rudolf Mansfeld and plant genetic resources; Scriften uz Genetischen Ressourcen (DEU) Sympossium dedicated to the 100th birthday of Rudolf Mansfeld, ed. Knupffer, H., and Ochsmann, J., volume 22 137-152. Mazer, S. J., Delesalle, V. A., and Neal, P. R. (1999). Responses of floral t raits t o s election o n primary sexual investment in Spergularia marina: the battle between the sexes 53, 717–731. doi: https://doi.org/ 10.1111/j.1558-5646.1999.tb05366.x Meyer, R. S., Karol, K. G., Little, D. P., Nee, M. H., and Litt, A. (2012). Phylogeographic relationships among Asian eggplants and new perspectives on eggplant domestication. Molecular phylogenetics and evolution 63(3), 685–701. doi: https://doi.org/10. 1016/j.ympev.2012.02.006 Nimenibo, U. and Omotayo, R. (2019). Comparative proximate, mineral and vitamin composition of Solanum aethiopicum and Solanum melongena. NISEB Journal 3, 1–17. Nyadanu, D. and Lowor, S. T. (2015). Promoting competitiveness of neglected and underutilized crop species: comparative analysis of nutritional compo- sition of indigenous and exotic leafy and fruit veg- etables in Ghana. Genetic Resources and Crop Evo- lution 62(1), 131–140. doi: https://doi.org/10.1007/ s10722-014-0162-x5. Ogungbenro, S. B. and Morakinyo, T. E. (2014). Rainfall distribution and change detection across climatic zones in Nigeria. Weather and Climate Extremes 5-6, 1– 6. doi: https://doi.org/10.1016/j.wace.2014.10.002. Osei, M. K., Banful, B., Osei, C. K., and Oluoch, M. O. (2010). Characterization of African Eggplant for http://www.tropicultura.org/text/v30n3/185.pdf http://www.tropicultura.org/text/v30n3/185.pdf https://doi.org/10.5073/JABFQ.2013.086.014 https://doi.org/10.5073/JABFQ.2013.086.014 https://doi.org/10.5897/JPBCS2018.0755 https://doi.org/10.3389/fpls.2015.01233 https://www.fao.org/3/I9553EN/i9553en.pdf https://www.fao.org/3/I9553EN/i9553en.pdf https://hdl.handle.net/10568/72874 https://hdl.handle.net/10568/72874 https://doi.org/10.21273/HORTSCI.26.4.334 https://doi.org/10.21273/HORTSCI.26.4.334 https://doi.org/10.1016/S0378-8741(02)00364-1 https://doi.org/10.1111/j.1558-5646.1999.tb05366.x https://doi.org/10.1111/j.1558-5646.1999.tb05366.x https://doi.org/10.1016/j.ympev.2012.02.006 https://doi.org/10.1016/j.ympev.2012.02.006 https://doi.org/10.1007/s10722-014-0162-x5. https://doi.org/10.1007/s10722-014-0162-x5. https://doi.org/10.1016/j.wace.2014.10.002. 48 Oguntolu et al Genetic Resources (2022), 3 (6), 38–48 Morphological Characteristics. Journal of Agricultural Science and Technology 4, 33–38. Padulosi, S., Phrang, R., and Rosado-May, F. J. (2019). Soutenir une agriculture axée sur la nutrition grâce aux espèces négligées et sous-utilisées: Cadre opérationnel. Bioversity International and IFAD, Rome, Italy, 978-92. Sharmin, D., Khalil, M. I., Begum, S. N., and Meah, M. B. (2011). ”Molecular Characterization of eggplant crosses by using RAPD Analysis”. International Journal of Sustainability and Crop Production 6(1), 22–28. Sneath, P. M. and Sokal, P. P. (1973). Numerical taxonomy. The principle and practice of numerical classification (Freeman; San Francisco) . Taher, D., Rakha, M., Ramasamy, S., Solberg, S., and Schafleitner, R . ( 2019). S ources o f R esistance for Two-spotted Spider Mite (Tetranychus urticae) in Scarlet (Solanum aethiopicum L.) and Gboma (S. macrocarpon L.) Eggplant Germplasms. HortScience horts 54, 240–245. doi: https://doi.org/10.21273/ HORTSCI13669-18 Toppino, L., Vale, G., and Rotino, G. L. (2008). Inheritance of Fusarium wilt resistance introgressed from Solanum aethiopicum Gilo and Aculeatum groups into cultivated eggplant (S. melongena) and development of associated PCR-based markers. Molecular Breeding 22, 237–250. doi: https://doi.org/ 10.1007/s11032-008-9170-x https://doi.org/10.21273/HORTSCI13669-18 https://doi.org/10.21273/HORTSCI13669-18 https://doi.org/10.1007/s11032-008-9170-x https://doi.org/10.1007/s11032-008-9170-x Introduction Materials and methods Experimental materials Experimental design and conditions Phenotypic characterization Calcium, iron and vitamin C determination Statistical analysis Results Discussion Conclusion Author contributions Conflict of interest statement