Association Mapping for Five Agronomic Traits in the Common Bean (Phaseolus vulgaris L.) Abstract Seda NEMLI1, Tansel Kaygisiz Asciogul2, Hilal Betul KAYA1, Abdullah Kahraman3, Dursun Eşiyok2, Bahattin TANYOLAC1* 1 Ege University - Department of Bioengineering, Bornova-Izmir 35100, Turkey 2 Ege University Department of Horticulture, Izmir 35100, Turkey 3 Harran University Department of Field Crops, S Urfa 63300, Turkey Abstract Background: The common bean is the most important grain legume and a major source of protein in many developing countries. We analyzed the following traits: pod fiber (PF), seeds per pod (SPP), plant type (PT), growth habit (GH), and days to flowering (DF) for a set of diverse common bean accessions and determined whether such traits were associated with AFLP (Amplified Fragment Length Polymorphism), SSR (Simple Sequence Repeat) and SNP (Single Nucleotide Polymorphism) markers. Results: In this study, 66 common bean genotypes were used and genotyped with 233 AFLP, 105 SNP and 80 SSR markers. The association analysis between markers and five traits was performed using a General Linear Model (GLM) in Trait Analysis by aSSociation, Evolution and Linkage (TASSEL). The population structure was determined using the STRUCTURE software, and 7 groups (K=7) were identified among genotypes. The associations for such traits were identified and quantified; 62 markers were associated with the five traits. Conclusion: This study demonstrated that  association mapping (AM) using a reasonable number of markers, distributed across the genome and with the appropriate number of individuals harboured to detect DNA markers linked to the traits of PF, SPP, PT, GH, and DF in common bean. Key Words: Association mapping, common bean, days to flowering, growth habit, plant type, pod fiber This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1002/jsfa.6664

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INTRODUCTION The common bean (Phaseolus vulgaris L.; 2n=2x=22) is predominantly self-pollinated and has the simplest non-duplicated genome among food legumes. It is the world’s most important food legume for the human diet.1 Its nutritional composition includes complex carbohydrates, vegetable protein, important vitamins and minerals and small fat levels.2 The common bean primarily originates from Mexico to Argentina in the midaltitude Neotropical and Subtropical regions. The world production for beans is 17,662,028 tons; with a 587,967 tons annual production, Turkey ranks third in common bean production worldwide.3 However, productivity in a variety of beans remains low in developing countries. It is important to identify DNA markers associated with agronomic characteristics, such as PF, SPP, PT, GH, and DF to better understand their function and improve such traits in common bean breeding.4 Significant correlations have been detected among the aforementioned agronomic traits in common bean genotypes.5 GH is a key parameter for differentiating between wild and domesticated common beans.6 The common bean is classified into four GHs using a combination of traits, such as stem strength and climbing ability 7,8; these groups are categorized using apical termination in the stem and branches, such as determinate bush, indeterminate bush, indeterminate semi climber and indeterminate climber. Different breeding strategies are used for particular GHs in different common bean gene pools.9 An additional important factor is DF, which is related to GH. DF is required for physiological maturity, which increases with the plants’ climbing ability.10 For example, the wild bean flowers earlier, and the flowering duration is longer than for the domesticated bean.5 Furthermore, GH is associated with SPP and is distinctive to the Mesoamerican and Andean gene pools. New plant types can be adjusted for breeding programs. In beans and certain other legumes, PF is perhaps one of the most important parameters for economic quality and physiological value because the fiber content and type are undesirable characteristics in the fresh bean market. The challenge for breeding processed cultivars is to select for straight pods, while maintaining low fiber content.11 Thus, discerning a greater number of quantitative trait loci (QTL) that control such traits may also yield specific favorable alleles for each bean cultivation environment. Determining the genetic basis for economically important complex traits is a major goal for plant breeding; such traits are controlled by multiple genes, and QTL mapping has been investigated specifically to segregate populations. Such populations include different progeny This article is protected by copyright. All rights reserved 

types, such as F2 or F3 generations, backcross populations (BC), double haploid (DH), and recombinant inbred lines (RIL), which are then phenotyped to analyze segregation for such traits in different environments. Such populations are also restricted and specific to the biparental population used to identify the QTLs. Genetic variation among the F2 progeny depends on the level of genetic variation between the two parents, which may yield relatively a low mapping resolution12 due to a lower number of meiotic events after experimental crossing.13 On the other hand, generating a segregating population requires much time, especially in RIL. An alternative strategy to QTL mapping is AM which overcomes the limitations to QTL mapping.13 AM detects correlations between genotypes and phenotypes in unrelated individuals or a natural germplasm collection through linkage disequilibrium (LD),14 which tends to be maintained over many generations between such loci, which are non-random associations of alleles at separate loci.13,15 Unlike QTL mapping, AM takes advantage of natural genetic diversity in different populations in several ways, which yields a higher mapping resolution.16,17 AM does not require that specific genetic populations are generated, which is time-consuming and expensive.18 In addition, this approach can detect higher numbers of polymorphic markers and increase mapping resolution.19 AM was first developed in human studies to understand genetic control for diseases 20,21

and has recently been used in a wide range of studies that involve model and crop plants.22

AM has been a research objective for plants beginning with the model organism Arabidopsis. 23 Moreover, AM has been used in many major crops, such as maize,24-27 wheat,

28,29

rice,30-32

barley,33 sorghum,34,35 potato,36 sugarcane37 and soybean.38 Recently, Shi et al.39 identified 18, and 22 markers significantly associated with common bacterial blight (CBB), which were evaluated 14, and 21 days after inoculation (DAI), respectively, for the common bean. Fourteen markers were significant for both time points. In this study, the markers UBC420, SU91, g321, g471, and g796 were highly significant (p ≤ 0.001) for these time points in 395 dry bean lines from different market classes using 132 SNP. Galeano et al.40 reported several significant associations with yield components that were identified using SNP and SSR markers for 93 common bean genotypes. The objective for this research was to evaluate AM and identify DNA markers associated with PF, SPP, PT, GH, and DF traits for the common bean. We analyzed the population structure levels in the genotypes to understand the AM study feasibility and resolution for the common bean.

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EXPERIMENTAL

Plant materials We examined 66 common bean genotypes; 16 were from different countries, while the remaining 50 were from Turkey (Table 1).

Phenotypic evaluation The beans with the various genotypes were planted in a 60 x 10 cm rectangle with row and intra-row spacing, respectively, using a randomized complete block design with three replicates in 2011, and 2012 at the Experimental Field of the Department of Horticulture at the Ege University, Izmir, Turkey. Five traits (PF, SPP, PT, GH, and DF) were analyzed for the study herein. The days from sowing to the first open flower were recorded as the DF.41 The SPPs from 5 plants for each replication were counted, and the mean was calculated for each replication. PF was visually determined as absent (1) or present (2).42 The PT, and GH analyses were evaluated in accordance with The International Board for Plant Genetic Resources (IBPGR) criteria (Table 2).

DNA extraction The seeds were germinated in a small pod. Small, young seedling leaves were harvested and placed in Eppendorf tubes on ice then stored at - 80°C until use. Leaf tissues from each individual were ground to a fine powder in liquid nitrogen with a mortar and pestle. Genomic DNA isolation was performed in accordance with the cetyl trimethyl-ammonium bromide (CTAB) method in Saghai-Maroof et al.43 The DNA was eluted in 100 DL of elution buffer (10 mmol L−1 Tris-HCl, 0.5 mmol L−1 EDTA, pH8). The quality was determined through electrophoresis with a 10 g L−1 agarose gel and compared with a 1-kb DNA ladder standard (Thermo Sci. Co, Lafayette, CO, USA). The DNA concentration for each sample was determined spectrophotometrically by measuring the absorbance at 260–280 nm using a Nanodrop ND-1000 (Thermo Sci. Co, Lafayette, CO, USA). Finally, the stock DNA was diluted to a 20 ng μL−1 final concentration for the SSR, and SNP analyses and 40 ng μL−1 for the AFLP analyses.

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Molecular marker analysis An AFLP analysis for 9 primer combinations was performed in accordance with the LiCor AFLP kit (catalog number: 830-06195 AFLP 2-DYE Selective Amplification Kit) as described in Vos et al.44 We used the SSR markers identified by Yu et al.,45 Gaita′n-Soli′s et al.46 and Blair et al.47 and the SNP markers identified by Galeano et al.48 and Galeano et al.49 The forward SSR and SNP primer sequences were modified by adding an M13 tail (CACGACGTTGTAAAACGAC) to the 5’ end, and the M13 primers were labeled with two different fluorescent dyes, IRD 700, and IRD 800, to universally label the PCR products. The SSR and SNP PCR amplification experiments were conducted in accordance with the procedures described by Maccaferri et al.50 The PCR products (AFLP, SSR, and SNP) were loaded in a 80 g L−1 denaturing polyacrylamide gel in 1X TBE (Tris-borate-EDTA) buffer; 1500 V and 40 mA were used. To further identify polymorphisms, the PCR products were analyzed using a LiCor 4300s DNA Analyzer. Image processing for the SSR, SNP, and AFLP fragments was performed using the SAGA software (LiCOR Biosciences, Lincoln, NE, USA). Each polymorphic AFLP, SSR, and SNP band was scored visually as present (1) or absent (0) across all 66 genotypes for each primer pair, and the values were used to compile a binary data matrix.51 Polymorphism information content (PIC) values were used to estimate the discriminating power of each marker and calculated as described by Anderson et al.52 using the equation PIC = 1- Σ pi2, where pi is the proportion of the population with the ith allele, as calculated for each AFLP, SSR, and SNP marker.

Diversity and population structure The population structure among common bean genotypes was analyzed using Bayesianclustering through a Markov Chain Monte Carlo (MCMC) approach (the model-based clustering algorithm) with the STRUCTURE software version 2.2.53 In this model, a certain number of populations (K) were assumed; each was characterized by a set of allele frequencies at each locus. The numbers of subgroups (K) selected were 1 to 10 based on models characterized by admixed and correlated allele frequencies. For each experiment, the number of iterations and burn-in period iterations were both 100,000. For each K value, the experiments with the greatest posterior data probabilities were considered. The true value for K was determined through an ad

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hoc quantity based on the second order rate of change for the likelihood function with respect to K (∆K).54 ∆K = m (|L (K + 1) ∆2 L (K) + L (K ∆ 1)|)/ s [L (K)], where L (K) is Ln P(D), which is the posterior probability for data with a given K, Pr (X|K) using the STRUCTURE software output; s[L(K)] is the standard deviation for L(K); and m is the mean for the parentheses. UK shows a clear peak at the true value for K. The Q values that correspond to the greatest UK were adopted for the AM analysis.

Association analysis Marker genotype data from AFLP, SSR, and SNP markers were used in the analysis. AM for PF, SPP, PT, GH, and DF was performed in accordance with a GLM method based on the Q matrix and using the TASSEL software. The population structure Q matrix was used to describe the subpopulation parentage for each line in the analysis; the percentages were identified by running the STRUCTURE software at ∆K=7. The p- 1 value (p-value ≤ 0.01) was used to determine whether a QTL is associated with the marker, and the r2 (r2 ≥ 0.1) marker was used to evaluate the magnitude of the QTL effects.31,55 RESULTS AND DISCUSSION

Phenotypic evaluation The frequency distributions for the 5 traits in the 66 common bean genotypes are depicted in Figure 1. Among the 66 genotypes, 55% had no fiber in the pod, whereas 45% had fiber in the pod. The PT, and GH frequency distributions yielded bimodal distribution patterns. For the GH results, 49% of the genotypes were a determinate bush, 16% were an indeterminate semi-climber, and 35% were an indeterminate climber. An indeterminate bush type was not detected among the genotypes. Similarly, for plant type, 49% of the genotypes were a determinate bush, and 51% were indeterminate based on a 1 to 7 scale. The SPP ranged from 2 to 9 with a normal distribution pattern. For DF, interestingly, almost all of the genotypes (98%) were early flowering (from 25 to 35 days), while only 1 genotype was late flowering (84 days).

Molecular marker analysis

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Seventy-nine primer pairs, including 9 AFLP, 22 SSR, and 48 SNP, were used to detect marker-phenotype correlations among 66 common bean genotypes. We detected 418 polymorphic amplification products among the common bean genotypes (Table 3). Eighty polymorphic SSR alleles were detected from 22 SSR primer pairs selected for each chromosome based on the common bean genetic linkage map from Yu et al.,45 Gaita′n-Soli′s et al.46 and Blair et al.47 The average number of alleles per SSR was 4 with the range 1 (BMd1, and BMd27) to 7 alleles (BM181, and BM210). This value is lower than 7.8, which was detected for over 129 SSR markers from 44 common bean genotypes56 and 5.5 for over 166 SSR loci from 259 common bean genotypes.57 Burle et al.58 reported 7 alleles on average, which ranged from 2 to 37, from 279 geo-referenced common bean landraces in Brazil. The different allele numbers may be due to the different motifs used in the SSR primers and the more diverse genotypes used in the experiments. Cabral et al.59 found that the number of alleles per locus varied from 2 to 4 with a 2.23 mean among 16 SSR primers used for 57 dry bean genotypes. Similar numbers for alleles per locus were also detected by Hanai et al.60 Among the SSR primers, 2 types of SSRs were used based on gene-based, and genomic sequences, respectively. Gene-based SSRs had a smaller average number of alleles (3.5 alleles) than genomic SSR (4 alleles), such as Bmd33, Bmd36, Bmd38, Bmd39 and Bmd42. Similarly, Blair et al.56 found that genomic SSR had a higher average number of alleles (9.2) than gene-based SSR (6.0) using different SSR primers in the common bean, likely because genomic-based SSR show more repetitive regions in the genome.56 The 9 AFLP primer pairs revealed 233 polymorphic markers across the 66 genotypes with 26 markers per primer pair on average. The band sizes for the study herein ranged between 100, and 900 bp. The average number of polymorphic markers per primer pair varied widely among the markers, ranging from 3 (M-ACG/E-ACA) to 57 (M-CAA/E-AAC) with 26 markers on average. Depending on the population structure, primer combination, equipment used for separation and band visualization, the literature includes different reports with 11, and 18 markers per AFLP primer combination on average,61,62 respectively. However, we detected 57 markers from the M-CAA/E-AAC primer combination; though, Svetleva et al.61 detected 22 markers from the same primer combination. These results differ likely because the common bean germplasm used herein includes more diverse genotypes than in Svetleva et al.61

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Using 48 SNP primer pairs, 105 polymorphic markers were detected. The number of markers per primer varied from 1 (SNP3, SNP5, SNP7, SNP8, SNP9, SNP11, SNP16, SNP18, SNP20, SNP22, SNP24, SNP26, SNP27, SNP28, SNP29, SNP30, SNP38, SNP39, SNP42, SNP44, SNP48, SNP49, and SNP50) to 12 (SNP2) with a 2 markers per primer mean. PIC is an index that reflects the utility of a genetic marker based on the number of alleles and their frequencies.63,64 The average PIC for AFLP markers was 0.51, and ranged from 0.37 for MCAA/E-AGC to 0.63 for M-CAA/E-AAG. Sustar-Vozlic et al.65 reported that the polymorphism percentage ranged from 58% to 83% with a 71% average using 10 AFLP primers for the common bean. The PIC values ranged from 0.06 to 0.89 with a 0.34 average for SSR markers, and from 0.03 (SNP2) to 0.97 (SNP15) with a 0.65 average for SNP markers. Blair et al.56 calculated 0.59 as the PIC value for gene-based SSR, while greater size differences between the largest and smallest allele were observed for the genomic SSRs compared with the gene-based microsatellites (0,44). Similarly, Blair et al.66 showed that the average genomic SSR PIC value (0.75) was greater than for gene-based SSRs (0.51). Thus, such results show that gene-based SSRs are less polymorphic than genomic SSRs. The observed SNP-PIC value was greater than the AFLP, and SSR marker data; this value was also greater than reported from other common bean studies.67,68 These results indicate that SNP markers are an effective marker system for distinguishing common bean genotypes.

Diversity and Population structure Understanding the population structure, which can strongly influence linkage disequilibrium, provides a robust analysis for understanding the common bean genotype origins. For population structure, the first analyses were conducted for spurious associations using the software STRUCTURE.69 In the STRUCTURE analysis, the number of groups (K) varied from 1 to 10. Using the ∆K criteria from Evanno et al.54 the K value was generated at ∆K=7, which defines the most appropriate number of groups (Figure 2). These results show that 7 distinct gene pools were identified among

66

common bean varieties (Figure 3). According to the

STRUCTURE results, Groups I, IV, V, and VI each included only one genotype: #32 (Horan), #9 (Kula barbunya), #3 (Seker barbun), and #29 (Taze), respectively. Two genotypes, #51 (Kuzga), and #52 (Flora), formed Group II. Group III contained 42 genotypes: #1-2, 5-8, 10-15, 19-28, 30-

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31, 36- 37, 39-43, 46-49, 54, 57, 61- 62, and 65-66. Group VII included 18 genotypes: #4 (Alacali Ayse), 17 (Kuru fasulye), 18 (Kuru fasulye), 33 (Mora), 34 (Ispir), 35 (Arba), 38 (Alman sarikiz), 44 (Emergo155), 45 (Purple teepe 141), 50 (Dolic hos), 53 (Lima), 55 (Cobra), 56 (Algarve), 58 (Alman ayse 5), 59 (Limka), 60 (No:209), 63 (E-Z Pick), and 64 (Fortex) (Figure 3). The genetic dissimilarities were estimated based on the results for the 418 polymorphic markers using a 0/1 matrix with the NTSYS-pc version 2.1 software. The greatest genetic dissimilarity values (93%) were observed between genotypes #52 (Flora), and #32 (Horan), which indicates that such cultivars were highly distinct. The lowest genetic dissimilarity (0.09%) was detected between genotypes #11 (Melka), and #12 (Bandirma). A wide range of dissimilarity coefficients (0.09 to 0.93) was detected among the common bean genotypes used herein. This rate is similar to reports from Maras et al.62 and Kumar et al.70 on common bean diversity. Maras et al.62 found that the total variance percentage was between 0, and 75 for common bean genotypes using SSR markers. Kumar et al.70 reported a good genetic diversity range, 0.18–0.76, using AFLP markers. These results indicate significant molecular differences between genotypes.

Association mapping The study herein is the first to evaluate AM for genes related to the traits PF, PT, and GH for common bean genotypes. For the AM analyses, the GLM method was used. Two previous studies investigated AM for the common bean.39,40 Galeano et al.40 studied DF, days to maturity, pods per plant, SPP, seeds per plant, empty pod%, average pod length, 100 seeds weight, and grain yield; they also used GLM. The GLM method has been used for other crops.29,71,72 GLM was used because it yields more significant p- values, and, therefore, more associations. Most of the QTL were detected in balanced populations derived from single crosses (e.g., F2 or RIL).47,66,73-76 Such populations requires time for development, whereas AM does not require structurally related individuals.77 A diverse natural population that represents all alleles can be used for AM analyses. The AM power is highly dependent on the number of genotypes used.31,40 Sixty-six common bean genotypes from different geographic regions were used herein to identify genetic markers associated with agronomic traits. In previous studies, similar numbers of genotypes were used by Roy et al.78 (using 55 wheat genotypes), and Simko et al.79 (using 68

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lettuce genotypes). Therefore, the diversity for 66 genotypes, which is a medium-size population, is adequate and provides a valuable resource for AM analysis based on a saturated genome map for the common bean. Sixty-two marker-trait associations were identified for the 5 traits (Table 4). Five markers (SNP1, M-CAA/E-AAC, M-CAA/E-AAG, M-GAT/E-ACT and M-GAT/E- AGT) had common associations for 4 traits, including PF, PT, GH and DF. Furthermore, certain markers were only associated with 1 trait; for example, SSR-Bmd1 with PF, SSR- Bmd45 with SSP, and SNP6 and SNP50 with GH. In general, 13 markers (5 SSR, 3 SNP, and 5 AFLP) were significant for PF; among such markers, SSR-Bmd39, and PFwere associated with the greatest value (p-values = 0.00263, r2 = 0.15). The SSR-Bmd45 and M-CAA/E-ACG markers were associated with SPP. PT was associated with 11 markers; 4 were SSR markers, and 1 was a SNP marker. In addition, the 6 AFLP markers correlated with PT. Thirteen markers were associated with GH; among such markers, 5 were SSR markers, 3 were SNP markers, and 5 were AFLP markers. M- GAT/E-ACT showed the strongest association with GH (p-values = 0.00112, r2 = 0.18) among the markers associated with GH. Twenty-one significant marker-trait associations involving 4 SSR markers, 12 SNP, and 5 AFLP markers were related to DF. Among such markers, 18 were consistent with a high correlation (r2 = 0.88, p-values = 4X10-12); 3 were SSR markers, BM210, BM151, and BM157; 10 SNP markers were significantly associated with DF, and the remaining 5 AFLP markers were M-CAA/E- AAC, M-CAA/E-AAG, M-CAA/E-AGG, M-GAT/E-ACT, and MGAT/E-AGT. Several QTL reports have been generated for DF,66 GH,80,81 PF82 and SPP40 using the common bean. Such reports showed that 3 SNP markers (SNP1, SNP16, and SNP32) were significantly associated with PF. Previously, the SNP1 marker was mapped onto LG 1 by Galenoa et al.40 SNP16, and SNP32 were significantly associated with PF herein; but the markers were not previously been mapped on a linkage groups nor were detected as a significant QTL in a linkage or association study. The 3 SNP markers explained 36% of the total variation for PF. Five SSRs (4 mapped and one unmapped), and 5 unmapped AFLP markers were also significantly associated with PF, and certain SSR markers that were significantly associated with PF have already been mapped for the common bean genome (Bmd1, and BM181 on LG 3, Bmd42 on LG 10, and Bmd46 on LG 9).40 The 5 SSR markers explained 66% of the total variation. We detected significant associations between GH, and the SSR marker BM157, which is located on linkage group 10;40 BM170, which is located on linkage group 6 by Galeano et al.40

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and finally, the 3 SSR markers BM160, BM183, and BM210, which are located on linkage group 7.40 Those markers explained 76% of the total variation for GH. Further, SNP1, which is located on LG 140 was associated with GH. The SNP1 marker explained 13% of the total variation. Chavarro and Blair8 found that the fin locus, which is related to GH, was located at LG 1 between the SSR markers BMd10, and BMd201. Previous studies have shown that the same region is associated with the fin locus.4,83 Because the marker SNP1 is associated with GH herein, this marker may be located in the same region as indicated in Kwak et al.,4 Chavvaro and Blair8 and Paneda et al.83 Notably, for SPP, weaker associations were detected compared with the other traits investigated herein. One SSR marker (Bmd45), and an unmapped AFLP (M- CAA/E-ACG) marker were significantly associated with SPP. On the other hand, we did not detect an association between the SNP markers, and SPP. On the other hand, Galeano et al.40 did not use the SSR marker Bmd45. However, interestingly, Galeano et al.40 linked the SSR marker BM160 on LG 7 to SPP. BM160 was also associated with GH, and PT herein. BM160 has 4 alleles, which may be linked to traits with genes distributed throughout the genome. For DF, 22 associations were detected herein; such association included the SSR marker BM210, which was mapped on LG 7; BM151 was located on LG 8; and BM157 was mapped to LG 10 by Galeano et al.40 Such associations are consistent with the QTL reported by Chavvaro and Blair8 and Blair et al.66 Chavvaro and Blair8 indicated that DF was linked to a location near the fin gene on linkage group 1. Blair et al.66 also reported that BM185 on LG 7, and BM138 on LG 5 were significantly associated with DF in the common bean RIL population. DF was linked to 12 SNP markers herein; certain such SNP markers were previously mapped by Galeano et al.,40 but most were unmapped. Four such markers (SNP1 on LG 1, SNP39 on LG 11, SNP41 on LG 2, and SNP43 on LG 8) were previously mapped on the common bean by Galeano et al.40 Similar results were also observed for this trait in the 142 F2 common bean lines.84 Taran et al.84 mapped the QTL for DF located at LG 11. SNP39, which we associated with DF herein, may be the same chromosomal region that Taran et al.84 detected. In this study, we analyzed 418 DNA markers for associations using various marker systems, such as AFLP, SSR, and SNP, in 66 common bean genotypes. We attempted to fully include the different parts of the genome using the different types of markers in common bean. Therefore, these markers are useful for different QTL mapping reports. Galeano et al.40 used

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SNP, and SSR markers in their association studies on the common bean. Blair et al.66 generated AFLP, RAPD, and SSR markers to construct a genetic map and for QTL analyses using the common bean. Additional studies have included only one type of marker. For instance, Abdurakhmonov et al.85 studied AM for fiber quality traits in exotic G. hirsutum L. germplasm using only SSR markers, and Agrama et al.31 identified SSR markers associated with yield. Although SSR markers are highly polymorphic, they may not be sufficiently frequent for association studies.86 Herein, we detected at least 1 association between markers, and traits for each linkage group, except linkage groups 4, and 5. Herein, 22 SSR markers (17 gene-based, and 5 genomic SSR) were used and 10 gene-based SSR, and 3 genomic SSR markers were significantly associated with 5 agronomical traits. We also detected markers associated with more than 1 trait. For example, SNP1, and MCAA/E-AAC were associated with DF, PT, GH, and PF (Table 4). Similarly, Hou et al.72 found that certain markers were associated with more than a single trait; for example, satt001 was associated with sucrose content, 100-pod fresh weight, and 100- seed fresh weight in soybean; and satt588 was associated with free amino acid content, 100-pod fresh weight, and 100-seed fresh weight. Galeano et al.40 observed that BSn66_SNP2 in LG 2 was associated with days to maturity, empty pod%, pods per plant, seeds per pod, seed per plant, and yield; and BSn44_2 at the LG 3 locus was associated with DF, days to maturity, 100 seeds weight, pod length average, seeds per pod, seed per plant, and yield. Such findings may be due to either pleiotropic effects or linked genes that control the traits, and it is helpful to understand the underlying genetic effect of multiple trait associations.87-89 Bmd1 and Bmd45 were linked to PF, and SPP, respectively. Further, Bmd1 contributed 11.63% to increased grain yield, and BMd45 reduced yield in the common bean.90 The SSR markers (BM160, BM170, BM183, and BM210) associated with PT, and GH tended to cluster together, especially for linkage groups 6, and 7.40 Galeano et al.40 used the same markers for mapping studies using the common bean; they observed associations between BM160, and DF, empty pod %, pods per plant, seed per plant, and seeds per pod. BM183 was associated with empty pod %, and seeds per plant. On the other hand, Teixeira et al.91 reported that BM210, which was mapped to linkage group 7, was the most outstanding marker for QTL mapping using the angular leaf spot reaction. However, they did not investigate PT, and GH.

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We also detected significant associations for the unmapped SSR, and SNP as well as all of the AFLP markers used herein, which were not previously associated with traits or linkage groups. For example, the markers Bmd39, SNP16, SNP32, M-CAA/E- AAC, and M-CAA/EACG are new markers that should be mapped as QTL and chromosomal regions in the common bean genome. In summary, for the first time, our results demonstrate the significant potential for AM using PT, PF, and GH for 66 common bean genotypes with AFLP, SSR, and SNP markers. Because they are multi-allelic, these types of markers are also useful for following inheritance patterns for individual QTL incorporated in backcross breeding programs. Sixty-two marker associations were detected between the loci and 5 phenotypic traits with the significance level 0.01 and spread throughout the common bean genome. These results may have important implications for detecting the maximum number of well-defined marker-trait associations for molecular breeding in the common bean. The DNA markers we identified SSRs, and SNPs by AM analysis were proved with previously identified QTL analysis with this study. Until now, the number of QTL studies for agronomic characteristics has been limited; thus, AM studies have used relatively simple methodology and only moderately examined genotypes for the common bean. The association results herein may provide markers that are useful for common bean genetics, trait selection, breeding applications that include crossing and genetically dissecting novel traits for the well-characterized common bean. Further, the markers detected herein will be interesting for future association studies, wherein marker-trait associations are compared. Further experiment to verify functional effect of the gene is underway.

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Table 1. A list of the 66 P. vulgaris genotypes subjected to analysis Code number (#)

Location

1

Golcuk/Tu rkey

2

Bozdag/Tu rkey

Name of the variet y Surm eli barbu nya Alaca li barbu nya

3

Golcuk/Tu rkey

Seker barbu n

4

Golcuk/Tu rkey

Alaca li Ayse

5

Golcuk/Tu rkey

Ege barbu nya

6

Golcuk/Tu rkey

7

Bozdag/Tu rkey

8

Golcuk/Tu rkey

9

Bozdag/Tu rkey

10

Kirklareli/ Turkey

11

Kirklareli/ Turkey

12

Bandirma/ Turkey

13

Bandirma/ Turkey

14

Bandirma/ Turkey

Elind ar

Code number (#)

Location

18

Golcuk/Tur key

19

Kirklareli/ Turkey

20

Kirklareli/ Turkey

21

Bandirma/ Turkey

22

Bandirma/ Turkey

23

Bandirma/ Turkey

Yerli barbu nya

24

Kirklareli/ Turkey

Ayse kadin

25

Yalova5/T urkey

26

Yalova17/ Turkey

27

Gino/Turke y

Melk a

28

Sarikiz/Tur key

Bonc uk

29

Selcuk/Tur key

30

Tokat/Turk ey

31

Tire/Turke y

Kula barbu nya Ak

Sariki z fasuly e Ayse kadin

Nam e of the vari ety

Code number (#)

Location

Beyo n

35

Isparta/Tur key

Horo z

36

Isparta/Tur key

37

Karadeniz/ Turkey

38

Turkey

39

Sarikiz/Tur key

40

Turkey

Man da Fasu lye Bonc uk Ayse Hata y Otur ak Gino

Sarik iz

41

Turkey

Yalo va 5

42

Turkey

43

Bulgaria

44

Turkey

45

Germany

46

Germany

Gunl uk

47

Turkey

Piya zlik

48

Turkey

Yalo va 17 Gino 10 Dilm e sarik iz Taze

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Name of the variety

Nam e of the varie ty

Code number (#)

Locatio n

52

India

Taze fasulye

53

USA

Alman ayse

54

England

Alman sarikiz

55

England

Cobr a

56

England

Algar ve

57

Turkey

Magn um

Arba

Meksik a fasulye si Kuru fasulye 13

Volare

Flora

Lima

Maxi

Alma n ayse 5

58

Bursa/T urkey

59

Netherla nds

60

Netherla nds

Emerg o155

61

USA

Maxi bell

Purple teepe 141

62

USA

Provi der

63

USA

E-Z Pick

Roma 2

64

USA

Forte x

Roma 42

65

Turkey

Arya

Mergse ed Helda

Akkiz

Limk a No:2 09

15

Bandirma/ Turkey

16

Bandirma/ Turkey

17

Golcuk/Tu rkey

Sari seker

32

Antalya/Tu rkey

Kayn arca

33

Tekirdag/T urkey

Kuru fasuly e

34

Karadeniz/ Turkey

Hora n

49

Turkey

Mor a

50

India

51

Netherland s

Ispir

Admir es 3060

Turkey

66

Dolic hos

Kuzga

Table 2. List of traits analyzed Trait type 

Code 

Pod fibre  Seeds per pod  Plant type 

PF  SPP  PT 

Growth habit 

GH 

Days to  flowering 

DF 

Trait descripition  42

1.Fibrous 2. Non fibrous     1.determinate bush 2.indeterminate bush erect  branches 3. indeterminate bush prostrate branches  4. indeterminate with semi‐climbing main stem and  branches 5. indeterminate with moderate climbing  ability and pods distributed evenly up the plant 6.  indeterminate with aggressive climbing ability and  pods mianly on the upper nodes of plant 7.other  (IBPGR)  1.determinate bush 2.indeterminate bush  3.indeterminate semi‐climber or prostrate  4.indeterminate climber (IBPGR)  Days from sowing to appearance of first open flower  41  

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Scale  1‐2  Number  1‐7 

1‐4 

Days 

Melu k

Table 3. List of AFLP primer combinations, SSR and SNP primers used with number of polymorphic markers and PIC value

Marker AFLP M-CAA/E-AAC M-CAA/E-AGC M-CAA/E-AAG M-CAA/E-AGG M-GAT/E-ACA M-GAT/E-ACT M-GAT/E-AAG M-GAT/E-AGT M-ACG/E-ACA SSR BMd45 BMd1 BMd50 BMd37 BM170 BM181 BM160 BM210 BM183 BM151 BMd33 BMd27 BMd42 BM22 BM157 BM184 BMd36 BMd38 BMd39 GATS11 BMd46 BM114 SNP SNP1 SNP2 SNP3 SNP4 SNP5 SNP6 SNP7 SNP8

Polymorphic band number

PIC

57 39 34 46 5 27 5 17 3

0.58 0.37 0.63 0.49 0.43 0.61 0.47 0.45 0.62

2 1 2 2 5 7 4 7 4 4 4 1 5 2 6 6 4 2 2 2 3 5

0.51 0.89 0.47 0.40 0.18 0.13 0.06 0.19 0.25 0.23 0.26 0.76 0.18 0.50 0.16 0.29 0.25 0.48 0.94 0.08 0.26 0.14

7 2 1 12 1 5 1 1

0.40 0.03 0.93 0.06 0.96 0.33 0.96 0.93

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Marker SNP9 SNP10 SNP11 SNP13 SNP15 SNP16 SNP17 SNP18 SNP19 SNP20 SNP21 SNP22 SNP23 SNP24 SNP25 SNP26 SNP27 SNP28 SNP29 SNP30 SNP31 SNP32 SNP33 SNP34 SNP35 SNP36 SNP37 SNP38 SNP39 SNP40 SNP41 SNP42 SNP43 SNP44 SNP45 SNP46 SNP47 SNP48 SNP49 SNP50 TOTAL

Polymorphic band number

PIC

1 2 1 2 2 1 2 1 2 1 4 1 2 1 3 1 1 1 1 1 3 2 3 3 2 2 3 1 1 2 2 1 3 1 3 3 6 1 1 1

0.84 0.53 0.92 0.96 0.97 0.93 0.47 0.93 0.46 0.87 0.33 0.89 0.84 0.93 0.32 0.95 0.92 0.93 0.92 0.89 0.31 0.47 0.28 0.29 0.43 0.46 0.31 0.93 0.92 0.45 0.47 0.74 0.31 0.95 0.29 0.30 0.41 0.96 0.93 0.92

418

Table 4. AM result of some agronomic traits and markers using the GLM.

Code number (#)

Trait

Marker

LG (Linkage group according to Galeano et al.40

1

Pod Fibre

SSR Bmd1

3

2

Pod Fibre

SSR Bmd39

6

3

Pod Fibre

SSR Bmd42

4

Pod Fibre

5

Pod Fibre

6

Locus position (cM)*

marker F

marker p

marker r2

261

**

0,00564

0,13

-

**

0,00263

0,15

10

1

**

0,00534

0,13

SSR Bmd46

9

160

**

0,00867

0,12

SSR BM181

3

99

**

0,00564

0,13

Pod Fibre

SNP1

1

69

**

0,00921

0,12

7

Pod Fibre

SNP16

6

-

**

0,00828

0,12

8

Pod Fibre

SNP32

5

-

**

0,00904

0,12

9

Pod Fibre

M-CAA/E-AAC

Unmapped

-

**

0,00621

0,13

10

Pod Fibre

M-CAA/E-AAG

Unmapped

-

**

0,00828

0,12

11

Pod Fibre

M-GAT/E-ACT

Unmapped

-

**

0,00828

0,12

12

Pod Fibre

M-GAT/E-AGT

Unmapped

-

**

0,00564

0,13

13

Pod Fibre

M-ACG/E-ACA

Unmapped

-

**

0,00828

0,12

1

Plant Type

SSR BM160

7

9

**

0,00314

0,15

2

Plant Type

SSR BM170

6

119

**

0,00675

0,13

3

Plant Type

SSR BM183

7

18

**

0,00964

0,12

4

Plant Type

SSR BM210

7

127

**

0,00345

0,15

5

Plant Type

SNP1

1

69

**

0,00666

0,13

6

Plant Type

M-CAA/E-AAC

Unmapped

-

**

0,00160

0,17

7

Plant Type

M-CAA/E-ACG

Unmapped

-

**

0,00110

0,18

8

Plant Type

M-CAA/E-AAG

Unmapped

-

**

0,00138

0,17

9

Plant Type

M-CAA/E-AGG

Unmapped

-

**

0,00136

0,17

10

Plant Type

M-GAT/E-ACT

Unmapped

-

**

0,00110

0,18

11

Plant Type

M-GAT/E-AGT

Unmapped

-

**

0,00992

0,11

1

Growth Habit

SSR BM157

10

46

**

0,00145

0,17

2

Growth Habit

SSR BM160

7

9

**

0,00261

0,15

3

Growth Habit

SSR BM170

6

119

**

0,00521

0,13

4

Growth Habit

SSR BM183

7

18

**

0,00208

0,16

5

Growth Habit

SSR BM210

7

127

**

0,00287

0,15

6

Growth Habit

SNP1

1

69

**

0,00546

0,13

7

Growth Habit

SNP6

2

202

**

0,00898

0,12

8

Growth Habit

SNP50

Unmapped

-

**

0,00954

0,12

9

Growth Habit

M-CAA/E-AAC

Unmapped

-

**

0,00175

0,16

10

Growth Habit

M-CAA/E-ACG

Unmapped

-

**

0,00144

0,17

11

Growth Habit

M-CAA/E-AAG

Unmapped

-

**

0,00114

0,18

12

Growth Habit

M-CAA/E-AGG

Unmapped

-

**

0,00133

0,17

13

Growth Habit

M-GAT/E-ACT

Unmapped

-

**

0,00112

0,18

14

Growth Habit

M-GAT/E-AGT

Unmapped

-

**

0,00236

0,16

1

Seed per pod

SSR Bmd45

1

69

**

0,00976

0,12

2

Seed per pod

M-CAA/E-ACG

Unmapped

-

**

0,00195

0,16

1

Days to flowering

SSR BM38

Unmapped

-

**

0,00133

0,17

2

Days to flowering

SSR BM151

8

124

**

4x10-12

0,88

3

Days to flowering

SSR BM157

10

46

**

4x10-12

0,88

4

Days to flowering

SSR BM210

7

127

**

4x10-12

0,88

5

Days to flowering

SNP1

1

146

**

4x10-12

0,88

6

Days to flowering

SNP24

Unmapped

-

**

4x10-12

0,88

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7

Days to flowering

SNP30

Unmapped

-

**

0,00196

0,16

8

Days to flowering

SNP31

Unmapped

-

**

4x10-12

0,88

9

Days to flowering

SNP32

Unmapped

-

**

4x10-12

0,88

10

Days to flowering

SNP33

Unmapped

-

**

4x10-12

0,88

11

Days to flowering

SNP34

Unmapped

-

**

4x10-12

0,88

12

Days to flowering

SNP39

11

15

**

0,00177

0,16

13

Days to flowering

SNP41

2

5

**

4x10-12

0,88

14

Days to flowering

SNP43

Unmapped

-

**

4x10-12

0,88

15

Days to flowering

SNP45

Unmapped

-

**

4x10-12

0,88

-12

0,88

16

Days to flowering

SNP46

Unmapped

-

**

4x10

17

Days to flowering

M-CAA/E-AAC

Unmapped

-

**

4x10-12

0,88

18

Days to flowering

M-CAA/E-AAG

Unmapped

-

**

4x10-12

0,88

-12

19

Days to flowering

M-CAA/E-AGG

Unmapped

-

**

4x10

0,88

20

Days to flowering

M-GAT/E-ACT

Unmapped

-

**

4x10-12

0,88

21

Days to flowering

M-GAT/E-AGT

Unmapped

-

**

4x10-12

0,88

22

Days to flowering

M-ACG/E-ACA

Unmapped

-

**

0,00588

0,13

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Figure legends Figure 1. Histogram for PF (a), PT (b), GH (c), SPP (d) and DF (e).

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Figure 2. STRUCTURE estimation of the number of populations for K ranging from 1 to 10 by delta K values (UK).

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Figure 3. Barplot showing genetic diversity structure for 66 common bean genotypes using the program STRUCTURE (version 2.2) for K=7. Each subgroup is represented by a different color (pink, green, blue, jade, yellow, red and amber respectively).

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