Marker-Trait association for fruit characters in mango (Mangifera indica L.) cultivars B. Padmakar1, M.R.Dinesh2, K.V. Ravishankar1* 1Division of Biotechnology, 2Division of Fruit Crops ICAR-Indian Institute of Horticultural Research Hessaraghatta Lake Post, Bengaluru 560089, Karnataka, India *Corresponding author: E-mail: kvravi@iihr.res.in, Fax: +91-80-28466291 Ph: +91-80-28466420 Extn: 406 (Off.), +91-9480050523 (Mob.) ABSTRACT In the present study, putative marker-trait associations were identified within a core collection of mango cultivars by simple-sequence-repeat marker based association study. A panel of 48 mango var ieties which represented the core collection of the South-West region of India, were characterized at the molecular level using 31 simple sequence repeat markers. Morphological characterization included important fruit characteristics viz., fruit weight, total soluble solids (TSS), pulp content and acidity. The study on population structure revealed two sub-groups in the core collection. Association analysis, computed by Gene ral Line ar Model (GLM), using TASSEL resulted in the identification of seven markers being associated with the trait titrable acidity where as one marker each of the traits fruit weight and TSS. These trait- specific markers were highly significant at p<0.05 and explained a good amount of phenotypic variation by exhibiting substantial R2 values ranging from 0.71 to 0.86 for acidity, 0.61 for TSS and 0.59 for fruit weight. This is the first report on marker-trait associations (MTA) in mango. Key words: Marker-Trait association, fruit characters, mango, Mangifera indica L. INTRODUCTION The genus Mangifera belongs to the family Anacardiaceae, and Mangifera indica L. (mango) is the most important species in this genus for commercial fruit production. Mango has been under cultivation in India for more than 4000 years (De Candolle, 1884; Mukherjee, 1951). India is considered as the world’s richest diversity centre for mango. The narrow genetic base of modern crop cultivars is the most serious obstacle to sustain and improve crop productivity due to the rapid vulnerability of genetically uniform cultivars by potentially new biotic and abiotic stresses (Van Esbroeck et al, 1999). Plant genetic resources comprising of wild plant species, modern cultivars and their crop wild relatives, which are the important reservoirs of natural genetic variations, originated from a number of historical genetic events as a response to environmental stresses and selection through crop domestication (Ross-Ibarra and Gaut, 2007; Meilleur and Hodgkin, 2004). An efficient exploiting of these ex situ c onserved genetic diversities is vital to overcome future problems a ssoc iated with the narrowness of the genetic base of modern cultivars. Association mapping was first introduced into plant genetics in 2001 (Thornsberry et al, 2001). This depends on the population structure and linkage disequilibrium (LD) pattern in plants (Flint-Garcia et al, 2003). Many important crops have a long and complex domestication and breeding history, together with the limited gene flow in most wild plants and populations exist as complex population structures (Sharbel et al, 2000; Flint-Garcia et al, 2003). When performing association a nalysis based on these popula tions without considering the effects of population structure, spurious association between genotype and phenotype variation may be detected because of the unequal allele frequency distribution between subgroups (Knowler et al, 1988). Recently, J. Hortl. Sci. Vol. 11(2): 170-178, 2017 171 with the development of statistics, independent markers that are distributed through whole genome are successfully used to detect population structures (Pritchard et al, 2000a, b). The resolution of association mapping depends on the extent and distribution of LD ac ross the ge nome within a given popula tion (Remington et al, 2001). In mango, information on genetic diversity, population structure, and LD is very meagre. Hence, the objectives of our present research were to assess the genetic diversity in the germplasm collection, to investigate the population structure of the germplasm and, fina lly, to detect the putative ma rker-trait associations. MATERIAL AND METHODS Plant material A set of 48 mango cultivars, from a pool of 269 cultivars of the Indian peninsular region were selected as core-collection, which are being maintained in the field genebank collection of the Indian Institute of Horticultural Research, Bengaluru, India, were taken for the study. The total genomic DNA was extracted from leaf mate ria l by modified CTAB me thod (Ravishankar et al, 2000). The DNA quantification was c arr ied out using a Gene qua nt UV- Spectrophotometer (GE Health Care Bio-sciences Ltd) a nd inte grity wa s e xa mine d by aga rose gel electrophoresis (1.0%). Morphological characterization The genotypes were characterized for traits such as fruit weight (g), TSS (ºB), acidity (%) and pulp content (%). Mature, naturally ripened fruits were used for recording observations on fruit weight, TSS, acidity and pulp content. The mean of five randomly chosen fruits in each variety was used for analysis. TSS was determined using a refractometer. Pulp content was calculated using the following formula: {Total fruit wt (g) - [Stone wt (g) + Peel wt (g)]} x 100/Total fruit wt (g) Titrable acidity was determined by titration in terms of citric acid with NaOH 0.1N where 10ml of the sample was taken and dissolved in water to make up the volume to 100ml and filtered through muslin cloth. 10ml of the aliquot was titrated against 0.1N NaoH using a few drops of 1% phenolphthalein indicator till the pink colour persisted as an end-point (Ranganna, 1977). Acidity was calculated as per cent anhydrous citric acid as per the formula given below: Titre × Normality of alkali × Volume made up × Eq. wt. of acid × 100 % Acidity = -------------------------------------------------------- Volume of sample taken for estimation × Wt./ Volume of sample × 1000 Molecular characterization A set of 31 SSR loci containing 20 SSRs developed in our laboratory (Ravishankar et al, 2011) was used for genotyping the core mango germplasm. The remaining 11 SSR markers were selected from other studies (Schnell et al, 2005; Duval et al, 2005; Viruel et al, 2005). PCR amplification was performed using labelled forward primers with fluorophores FAM and HEX at their 5’ end. PCR reaction conditions were e mploye d a s pe r Ra vis hanka r e t al ( 2011). Amplification products were initially screened on 3% Agarose gel electrophoresis and, then, HEX and FAM amplified products were pooled. Pooled PCR products were separated to determine product-size by capillary electrophoresis using an automated DNA Sequencer ABI 3730 DNA analyzer (Applied Biosystems, Foster City, CA) at the facility at ICRISAT, Hyderabad. Existing genetic diversity within the core collection was assessed by analyzing the following parameters: number of alleles per locus (A), observed he te roz ygosity ( Ho, dire ct count), expe c ted heterozygosity (He=1 pi2 where, pi is the frequency of the ith alle le ) (Ne i 1973) and Polymorphic Information Content (PIC) was arrived at as per Botstein et al (1980). This was done using CERVUS 3.0.3 software (Kalinowski et al, 2007). Additionally, genetic relationship among genotypes was calculated by computing the dissimilarities through simple ma tc hing c o-e ffic ient a nd a de ndr ogra m wa s constructed using Ward’s Minimum Variance method and DARWin 5.0 software (Perrier et al, 2003). Population structure analysis STRUCTURE 2.3.1 (Pritchard et al, 2000a) was used for evaluating the structure, i.e., to identify the admixture among cultivars, and, to predict the number of populations present among the 269 cultivars under study. The number of subgroups (K) was set from 2 J. Hortl. Sci. Vol. 11(2): 170-178, 2017 Marker-Trait association in mango 172 to 10. For each K, six runs were performed separately by setting the burn-in length and MCMC iterations to 100,000. The model choice criterion implemented in the structure to detect true K is an estimate of the posterior probability of data for a given K, Pr(X | K) (Pritchard et al, 2000a). This value, called ‘Ln P(D)’ in structure output, is obtained by first computing the log likelihood of data at each step of the MCMC. From this K, which is the second order rate of change of likelihood, the function with respect to K was computed and plotted against K, for detecting the number of true populations (Evanno et al, 2005). Structure harvester (Earl, 2009), which harvests data from STRUCTURE results and generates K (Evanno et al, 2005), was used to predict the true number of populations. Cultivars with membership probabilities e” 0.8 were assigned to the corresponding sub-group and lines while membership probabilities < 0.8 were assigned to a ‘mixed’ sub-group. Association analysis With available genotyping, phenotyping and structure-based Q-matrix data, association analysis was computed using the General Linear Model (GLM) module by selecting F-test with 1000 permutations, with software Trait Analysis by Association, Evolution and Linkage (TASSEL) 2.10 (Bradbury et al, 2007). RESULTS AND DISCUSSION Morphological characterization Fruit weight ranged from 20g to 898g, with a mean of 288.9g per fruit. TSS values (ºBrix) ranged from 8.85 to 23.9, with a mean value of 17.6. Per cent titrable acidity values ranged from 0.12 to 6.02, with a mean value of 0.714. Pulp content in the fruit ranged from 26.6% to 79.8%, with a mean value of 65.07% (Table 1), which showed considerable variation for various traits among the varieties studied. A wide variability, existing for different traits in mango varieties from different regions, has been reported earlier (Lakshminarayana, 1980). Molecular characterization A total of 31 SSR loci, randomly distributed across the genome, were used for evaluating genetic diversity in the core mango germplasm consisting of 48 cultivars. All the 31 SSR loci studied were polymorphic across the 48 cultivars, and a total of 380 alleles were detected (Table 2). Average number of alleles per locus was 12.25, ranging from 3 to 22. Mean PIC value was 0.768, with a range of 0.516–0.936. Choice of germplasm is one of the key fac tors determining resolution in association mapping. For de tec ting more number of allele s, the selec ted germplasm should theoretically include all the genetic variation available in a particular species, because, a divers e ge rmpla sm include s a more extensive recombination in its history, and allows for high level of resolution. In the present study, 31 SSR loci, likely distributed randomly across the genome, were used for detecting genetic diversity in a germplasm containing 48 mango cultivars. A total of 380 alleles, with an average of 12.25 alleles per locus, were detected in the entire population; the average PIC was 0.768. The genetic diversity detected was much higher than that reported by Singh and Bhat (2009), He being 0.651 and PIC 0.483. The main reasons for this difference are the germplasm under study and the SSRs used. The high genetic diversity detected in our study was mainly due to the broad range of germplasm used and more number of dinucleotide-type SSRs used, which are considered to have higher mutation rates than the other types (Vigouroux et al, 2002). The dendrogram developed (Fig. 1) clustered the mango varieties into two main groups, which were further sub-grouped into several sub-clusters based on their genetic relationship. These groupings are incongruent with results of the structure. Fig. 1. Dendrogram of 48 mango cultivars generated through Ward’s Minimum Variance method using dissimilarity ma- trix, computed by simple-matching coefficient through DARWin software J. Hortl. Sci. Vol. 11(2): 170-178, 2017 Padmakar et al 173 Cultivar Fruit weight (g) TSS (OBrix) Acidity Pulp content (%) Akhadya 203.70 19.60 0.32 60.10 Allanpurbaneshan 360.00 17.90 0.25 73.19 Alphonso 246.20 19.00 0.32 66.90 Appemidi 111.00 15.50 2.88 63.06 Aryasamaj 281.00 14.90 0.19 69.61 Asiquot 465.00 18.00 0.38 76.93 Bandarbandal 481.20 19.50 0.25 76.66 Banganapalli 440.00 18.50 0.12 61.70 Bombaygreen 350.00 21.50 0.32 65.00 Chandrakaran 56.20 23.70 0.80 26.60 Chettalli 436.50 11.10 0.19 79.80 Cipia 387.00 18.70 0.19 68.09 Dwarfrumani 201.00 14.50 0.12 76.83 Goakodur 183.00 13.20 0.83 67.38 Gulabkhas 231.00 20.00 0.19 75.46 Guruvam 225.50 15.20 0.38 64.10 Halsage 99.00 8.85 3.78 52.30 Hamlet 898.00 14.30 2.43 69.00 Himayatpasand 492.50 20.70 0.12 63.30 Holekoppodaappe 81.90 9.40 6.02 45.06 Hydersaheb 242.50 18.80 0.25 58.20 Jeerige 177.00 16.00 1.47 63.61 Kadikai 292.70 15.90 0.70 75.65 Kalapadi 150.00 19.50 0.44 31.90 Kanaappe-1 22.60 22.10 1.21 46.46 Karol 202.00 22.00 0.64 66.60 Khuddus 178.00 20.40 0.25 69.37 Lazzatbaksh 20.00 13.80 0.13 64.20 Mahamoozda 245.00 20.70 0.13 59.10 Malaimisri 248.00 14.70 0.19 70.60 Malgesh 271.50 14.70 0.25 69.31 Moreh 237.00 17.50 0.51 61.44 Muffarai 351.50 23.90 0.76 70.35 Mulgoa 362.50 20.80 0.27 64.40 Mutwarpasand 837.50 12.60 0.64 77.00 Navneetham 440.00 20.70 0.38 75.07 Neelum 256.00 20.00 0.40 57.00 Nekkare 170.00 15.50 0.38 43.20 Papayarajugoa 467.50 20.80 0.32 75.90 Peddarasam 463.70 14.80 0.12 71.90 Rangoongoa 320.00 20.00 0.61 71.60 Ratnagirialphonso 220.00 20.00 0.32 74.34 Rumani 200.00 19.20 0.25 75.40 Sardar 236.20 19.20 0.14 59.70 Shendriya 225.50 19.00 0.38 64.92 Shidadakkeappe 171.70 10.80 2.63 66.98 Totapuri 471.00 17.50 0.25 70.50 Vellaikulamban 161.00 18.70 0.19 67.60 J. Hortl. Sci. Vol. 11(2): 170-178, 2017 Marker-Trait association in mango Table 1. List of traits and their morphological characteristics within the core collection 174 Polymorphic Observed Expected Information Locus No. of alleles heterozygosity (Ho) heterozygosity (He) Content (PIC) MiIIHR03 4 0.543 0.666 0.59 MiIIHR04 11 0.532 0.792 0.754 MiIIHR05 9 0.63 0.699 0.653 MiIIHR12 6 0.646 0.686 0.631 MiIIHR13 8 0.646 0.825 0.79 MiIIHR15 19 0.596 0.888 0.868 MiIIHR17 22 0.708 0.937 0.923 MiIIHR18 15 0.25 0.789 0.767 MiIIHR19 20 0.688 0.902 0.883 MiIIHR23 20 0.354 0.912 0.895 MiIIHR24 17 0.479 0.835 0.808 MiIIHR26 22 0.479 0.949 0.936 MiIIHR28 13 0.667 0.845 0.816 MiIIHR30 15 0.729 0.901 0.882 MiIIHR31 16 0.563 0.835 0.812 MiIIHR32 17 0.688 0.886 0.868 MiIIHR34 14 0.75 0.859 0.835 MiIIHR35 12 0.583 0.874 0.85 MiIIHR36 21 0.75 0.9 0.883 MiIIHR37 18 0.604 0.87 0.847 MiSHRS1 13 0.5 0.873 0.835 MiSHRS4 12 0.393 0.877 0.846 MiSHRS18 8 0.595 0.699 0.644 MiSHRS37 9 0.333 0.702 0.668 MiSHRS48 3 0.375 0.642 0.516 MiCR008 4 0.25 0.758 0.658 MiCR009 7 0.649 0.778 0.729 MiCR018 5 0 0.758 0.677 MiCR030 10 0.435 0.859 0.823 LMMA7 4 0.625 0.708 0.616 LMMA11 6 0.13 0.577 0.533 Population structure In order to understand the genetic structure of the population under study, a model-based approach in the STRUCTURE software was used to subdivide ea c h cultiva r to the c or res ponding s ubgroup. STRUCTURE software was run for the number of fixed subgroups K from 2 to 10, and six runs were performed for each K. As the STRUCTURE software overestimates the number of subgroups (Pritchard and Wen, 2004), and it is difficult to choose the ‘‘correct’’ K from the Ln probability of data, Ln P(D). Thus, the true number of populations in the present study has been predicted by computing the K (Evanno et al, 2005), which is the second order rate of change of the likelihood function with respect to K, using Structure Harvester (Earl, 2009). The resulting plot (Fig. 2) between K and K clearly showed the number of populations of K=2 is true. The structure derived subgroups were corresponded as P1 and P2. P1 sub- group was the largest with 25 cultivars, followed by P2 sub-group having 19 cultivars. Additionally, 4 cultivars that had < 0.8 membership in each of the two J. Hortl. Sci. Vol. 11(2): 170-178, 2017 Padmakar et al Table 2. List of SSR markers used and their characteristics 175 Fig. 2. Graph of K (assumed population number) versus DK (second-order rate of change of likelihood function with respect to K) predicting the probability that K=2 is true (Obverlap of words in the diagram are due to immutable software and may be ignored) sub-groups, and had a mixture of the two sub-groups, were assigned to a mixed sub-group. Out-breeding and the wide range of agro- climatic conditions prevalent in this country continue to contribute to diversity in this crop that has a complex ge ne tic ba ckground. There fore , unde rstanding population structure and relationships in mango germplasm is of significant importance for mango improvement and association analysis. Evaluation of population structure is of considerable interest, as, it is a prerequisite for answering several other questions suc h a s e stimation of migra tion, ide ntifying conservation units, and, specifying phylo-geographical patterns (Manel et al, 2005). For statistical inference, model-based approaches such as Bayesian clustering are more suitable (Pritchard et al, 2000a or 2000b). In the present study, analysis of K (which is the second- order rate of change of likelihood function with respect to K) clearly predicted the probability of a number of populations of K=2 as true. Association analysis In pla nt syste ms, linka ge ma pping ha s traditionally been the most commonly employed method to explain natural phenotypic variation in terms of simple J. Hortl. Sci. Vol. 11(2): 170-178, 2017 Marker-Trait association in mango 176 changes in DNA sequence: experimental crosses are made to generate a family with known relatedness, and, attempts are made to identify co-segregation of genetic markers and phenotypes within this family. In vertebrate systems, association mapping (also known as linkage disequilibrium mapping) is increasingly being used as the mapping method of choice. Association mapping involves looking for genotype-phenotype correlation in unrelated individuals, and, is often more rapid and cost-effective than traditional linkage mapping. Association mapping is not a controlled experiment, but rather, a natural experiment. Genotype and phenotype data are collected from a population in which relatedness is not controlled by the experimenter, and c orrelations be twee n ge ne tic marke rs a nd phenotype are sought within the population. This open- system-design provides higher mapping resolution than the closed system of controlled crosses; but it is difficult to infer as to when and where the recombination occurred. Unlike in vertebrates where controlled crosses may be expensive or impossible, the scientific community in plants can exploit advantages of both controlled crosses and association mapping to increase statistical power and mapping resolution. Besides, this strategy contributes to detection of genetic basis of variation exhibited in various traits of economic importance. J u s t a f e w r e po rt s a r e a va il a bl e on association-mapping-based identification of marker- trait associations in plants such as maize (Harjes et al, 2008;Kump et al, 2011; Tian et al , 2011), Arabidopsis (Brachi et al, 2010), barley (Ramsay et al, 2011), wheat (Zhang et al, 2010; Letta et al, 2013) and rice (Huang et al, 2011). In the case of pe re nn ia l t re e c rop s (whe r e de ve lopme nt of ma pping popula t ions is a ma jor c ons tra in t), association ma pping is considered as a pivotal strategy for diss ecting complex tra its a nd for identifying genes responsible for trait-variations. Therefore, in the present study, we selected a diverse panel of 48 mango cultivars as a core from a set of 269 cultivars. The core was exploited for phenotyping, genotyping and for also identifying population structure. Data thus generated was used for identifying putative marker-trait associations. Of the 31 S SR m a rke r s s c r e e ne d, ni ne sh owe d significant association with three traits (Table 3); se ve n we re a ss oc iated with the tra it, a cidity; whereas, there was one marker for fruit weight, and one for TSS. These trait-specific markers were hi ghl y s ign if ic a nt a t p< 0. 0 5 a nd e xp la i ne d considerable amount of phenotypic variation by exhibiting substantial R2 values, ranging from 0.71 to 0.86 for acidity, 0.61 for TSS and 0.59 for fruit Padmakar et al Table 3. List of Identified Marker-Trait Associations and their characteristics Trait Locus p-value R2_Marker_value Acidity MiIIHR17 9.9 X 10-4 0.885 Acidity MiSHRS1 0.004 0.864 Acidity MiIIHR36 9.9 X 10-4 0.841 Acidity MiIIHR30 0.033 0.781 Acidity MiIIHR23 0.025 0.751 Acidity MiIIHR24 9.9 X 10-4 0.712 Acidity MiIIHR12 9.9 X 10-4 0.533 Fruit weight MiIIHR04 0.047 0.593 TSS MiIIHR37 0.048 0.614 weight. We could not conduct linkage disequilibrium and linkage decay studies, as no linkage map is available for mango yet. In a recent study in peach and nectarine cultvars, significant association was observed between markers and pomological traits (Forca da et al, 2013). T he marke r BPPCT015 showed significant association with harvest date, flavonoid and sorbitol content, Also, two genotypes of CPPCT028 showed association with harvest date, total phenolics and total sugars. To the best of our knowledge, this is the first report on identification of marker-trait association using association studies in mango. Thus, ide ntification of trait-specific markers can play a vital role in marker-assisted s e l e c ti on (M AS ) fo r a c c e le r a t in g ma n go improvement programs. ACKNOWLEDGEMENT The authors acknowledge financial support from Department of Biotechnology, New Delhi, for studies on markers. J. Hortl. Sci. Vol. 11(2): 170-178, 2017 177 REFERENCES Kalinowski, S.T., Taper, M.L. and Marshall, T.C. 2007. Revising how the computer program CERVUS accommoda tes genotyping e rror increases success in paternity assignment. Mol. Ecol., 16:1099-1006 Knowler, W.C., Williams, R.C., Pettitt, D.J. and Steinberg, A.G. 1988. 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