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ABSTRACT - A set of 45 maize inbreds maintained at the. Millet Breeding Station, Centre for Plant Breeding and Ge- netics, Tamil Nadu Agricultural University, ...

Maydica 54 (2009): 113-123

GENETIC DIVERSITY ANALYSIS OF MAIZE (ZEA MAYS L.) INBREDS DETERMINED WITH MORPHOMETRIC TRAITS AND SIMPLE SEQUENCE REPEAT MARKERS M.A.B. Ranatunga1,3, P. Meenakshisundaram1,4, S. Arumugachamy2, M. Maheswaran2,* 1

Department of Plant Molecular Biology and Biotechnology, Centre for Plant Molecular Biology, and 2 Centre for Plant Breeding and Genetics, Tamil Nadu Agricultural University, Coimbatore - 641 003, Tamil Nadu, India 3 Tea Research Institute of Sri Lanka, Talawakelle, Sri Lanka 4 Plant Biotechnology Division, SBST, VIT University, Vellore - 632 016, India

Received June 29, 2009

ABSTRACT - A set of 45 maize inbreds maintained at the Millet Breeding Station, Centre for Plant Breeding and Genetics, Tamil Nadu Agricultural University, Coimbatore was subjected to genetic diversity analyses based on eight qualitative traits, 10 quantitative traits and 42 SSR primer pairs. Among the eight qualitative traits, leaf pubescence did not show any variation across the genotypes. Traits viz., silk colour at emergence, ear shape, kernel colour, grain texture and grain shape were the predominant phenotypic variants. Ten quantitative traits observed across the 43 maize inbreds showed wide variation. Hundred seed weight was maximum (36.8 g) in UMI 603 and minimum (11.1 g) in UMI 27. SSR analysis involving a set of 22 primer pairs derived from maize genome and 20 primer pairs derived from rice genome generated 132 and 181 markers with an average polymorphism information content (PIC) value of 0.83 and 0.38, respectively. Cluster analysis using 8 different qualitative traits across 43 maize genotypes resulted in grouping of genotypes into two major clusters of 19 and 24 genotypes where as cluster analysis based on 10 quantitative traits resulted in 2 major clusters, one with 39 genotypes and the other with 4 genotypes. Clustering pattern of maize genotypes based on SSR marker profiles were different from that of morphometric traits. KEY WORDS: Genetic diversity; Maize; Morphometric traits; SSR markers; Cluster analysis.

INTRODUCTION Maize (Zea mays L.) otherwise known as corn, is the only cereal crop of American origin that is

* For correspondence (e.mail: [email protected]).

cultivated in tropical and subtropical regions throughout the world. The increasing use of maize as a staple food reflects higher yields per hectare, compared with wheat, rye and barley. Since maize is cheap, it has become the dominant food and main source of dietary energy and protein for poor people, particularly those in rural and underprivileged segments of the society. There is an urgent need to promote maize breeding on priority basis by adopting various approaches to meet the increasing demand for maize grain and its products. In this context, maize hybrid breeding remains the choice of the methods considering its success over years. For exploiting the potential of hybrid breeding in maize, many maize inbreds have been developed from a limited number of elite lines and elite line synthetics, a practice that heightens the risk of decreased genetic diversity in commercial maize production fields (HALLAUER et al., 1988). Better understanding on the genetic diversity ensures the breeder in planning crosses for hybrid and line development, in assigning lines to heterotic groups, and in plant variety protection (PEJIC et al., 1998). Meanwhile, there have been frequent warnings about the genetic vulnerability of maize (GOODMAN, 1990). This made the maize breeders to realize the need for both maintaining genetic diversity and improving the management of genetic resources (GOODMAN, 1994). The developments during the past three decades in the DNA marker technology are enormous and an array of DNA markers is made available as a tool to assess the genetic diversity in plants and animals. In the present study, an attempt was made to study the extent of genetic diversity available across the maize inbreds exclusive-

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ly developed/maintained for the maize hybrid breeding in the Tamil Nadu Agricultural University using qualitative and quantitative traits and DNA based simple sequence repeat markers (SSR) having their origin from maize and rice.

MATERIALS AND METHODS Materials A total of 45 maize genotypes (37 inbreds, 2 sweet corn varieties, 1 baby corn variety and 5 accessions from CIMMYT) available at the Millet Breeding Station, Centre for Plant Breeding and Genetics (CPBG), Tamil Nadu Agricultural University (TNAU), Coimbatore formed the biological materials for the present study. The details of maize genotypes are given in Table 1. Morphometric analysis Field evaluation for morphological traits - All the 45 maize genotypes were grown for evaluation in the fields of Millet Breeding Station, CPBG, TNAU in randomized complete block design (RCBD) with two replications. Recommended agronomic practices and plant protection measures were followed. Out of 45 genotypes, two genotypes (UMI 242 and DMR SW 4) did not

germinate in the field. The field grown 43 genotypes were observed for 8 qualitative and 10 quantitative characteristics from 10 randomly selected plants from each replication. Qualitative and quantitative traits - The qualitative traits that were studied include tassel anther glume colour, silk colour at emergence, leaf colour, leaf pubescence, ear shape, kernel colour, grain texture and grain shape and the observations were recorded as per the International Plant Genetic Resources Institute (IPGRI)’s descriptor (IPBGR, 1980) on maize. The quantitative traits that were observed from 10 randomly selected plants of each replication included days to tasseling, days to silking, tassel branching, plant height, ear length, ear width, ear height, number of kernel rows, number of kernel columns and hundred seed weight. Data analysis and scoring - Qualitative multistate traits that depict an array of characters were converted into binary characters (SNEATH and SOKAL, 1973) based on the variations present in each trait. The presence of phenotypes was given the score of ‘1’ and its absence the score of ‘0’. The quantitative data gathered on different traits were standardized to zero mean and a unit variance. DNA marker analysis DNA isolation, quality and quantity check - Leaf samples for DNA isolation were collected from 10 days-old seedlings and the extraction and purification of the genomic DNA from each acces-

TABLE 1 - Maize genotypes used for genetic diversity analysis. ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Name Parentage Source Name Parentage Source ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– BC7 Selection from PAC7006 TNAU UMI395 CM 600/Suwan1 TNAU DMRSW2

Selection from JC (Sweet corn) 8

TNAU

UMI420

UMI29/UMI51

TNAU

DMRSW4

Thai composite selection

TNAU

UMI447

Hunius EV (2FS3) F5

TNAU TNAU

UMI12

Selection from CM105

TNAU

UMI482

EH459379# Dhar79R

UMI123

CM202/Selection thai

TNAU

UMI532

Not known

TNAU

UMI140

Selection from DMM101

TNAU

UMI603

UMI197/UMI191

TNAU

UMI143

Histwich

TNAU

UMI623

Shakthi/CM202

TNAU

UMI18

Selection from CM500

TNAU

UMI678

UMI133/UMI140

TNAU

UMI2

Local collection from Ariamalam

TNAU

UMI688

UMH3/UMH4

TNAU

UMI216

Selection from DMR2448

TNAU

UMI701

Maraccay7921

TNAU

UMI232

Selection from PDK9

TNAU

UMI749

Syn P200 Kissan/Huniu 27

TNAU

UMI234

(SeleP200/Kissan)/Munius

TNAU

UMI81

Bogor Com13 selection

TNAU

UMI242

Selection from M 3

TNAU

UMI830

Selection from POP145 – DMR –C5- H5 TNAU

UMI250

Comp B VI of S 58

TNAU

UMI86

Selection from Amber

UMI254

Local collection from Ernampatty

TNAU

UMI879

Selection from 86-110CG (ohS3S1C1)

TNAU

UMI27

CM105/CM104

TNAU

UMI97

EH7745 selection

TNAU

UMI303

SS4/Suwan 2

TNAU

USC11

Selection from Madhuri

TNAU

UMI318

(CM202/CM111)/Suwan 2

TNAU

CIMMYT1

Introduction from CIMMYT

CIMMYT

UMI325

(CM560/Phil Early DMR1)/Suwan2

TNAU

CIMMYT11/13 Introduction from CIMMYT

CIMMYT

UMI334

Guajira314/Suwan2

TNAU

CIMMYT4

Introduction from CIMMYT

CIMMYT

UMI340

ETO CBE Flint/Suwan2

TNAU

CIMMYT7

Introduction from CIMMYT

CIMMYT

UMI35

Phil DMR5/Taiwan composite

TNAU

CML176

(P63-12-1/P67-5-1-1) -1-2-B-B

CIMMYT

TNAU

UMI354 Amarillo del Bajiro/Suwan2 TNAU –––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––

GENETIC DIVERSITY ANALYSIS

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TABLE 2 - List of SSR primers used in the genetic diversity analysis of maize. ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Maize derived SSR primers Rice derived SSR primers ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– SSR Forward primer Reverse primer Chr. Annealing SSR Forward primer Reverse primer Chr. Annealing locus (5’ - 3’) (5’ - 3’) No. Temp. (°C) locus (5’ - 3’) (5’ - 3’) No. Temp. (°C) ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– bnlg118 cttccagccgcaaccctc ccaacaacgcggacgtga 5 56 RM9 ggtgccattgtcgtcctc acggccctcatcaccttc 1 55 bnlg391 cagatatcacagcatcagaagatca

aaaatgtaagaacttgtttgggatt

6

56

RM84

taagggtccatccacaagatg ttgcaaatgcagctagagtac

1

55

nc130

gcacatgaagatcctgctga

tgtggatgacggtgatgc

5

54

RM1135

agccaaccaagcaagatagc acacacatgtaagcctcccc

7

55

nc133

aatcaaacacacaccttgcg

gcaagggaataaggtgacga

2

54

RM1279

gggtataaaatgcgtggcac

atggatggtacgaggacgag

7

50

phi002

catgcaatcaataacgatggcgagt

ttagcgtaacccttctccagtcagc

1

60

RM2819

aatgttgctagatttaaaac

cagtaggatatcttacaacc

8

55

phi006

aggcggcgtgctgaacacct

cgcttcatctcccgtgacaatg

4

52

RM3186

gagtagaaggtgaggccacg cgaccaagagatgcttcctc

7

55

phi011

tgttgctcggtcaccatacc

gcacacacacaggacgacagt

4

60

RM3555

tggaagtttcctggcgatag

tggttggactgaaaagtccc

7

50

phi014

agatgaccagggccgtcaacgac

ccagcttcaccagcttgctcttcgtg

8

52

RM3583

tacaatttggcgacctcctc

ggatgccatgtcatcatctg

7

50

phi024

actgttccaccaaaccaagccgaga

agtaggggttggggatctcctcc

5

60

RM5405

cactctcacactcaccagcg

gtcgtctcgctctcatctcc

7

50

phi029

ttgtctttcttcctccacaagcagcgaa atttccagttgccaccgacgaagaactt

3

56

RM5499

tggagtacgacgtgatcgtg

cagaaacgggaggggatc

7

50

phi032

ctccagcaagtgatgcgtgac

9

56

RM5672

caccctacaaggaaacaagc tgcccaatatagaggcaacc

7

50

gacacccggatcaatgatggaac

phi034

tagcgacaggatggcctcttct

ggggagcacgccttcgttct

7

56

RM6081

cttccccaccctgaacacc

gatgccaaggtggtcgac

7

61

phi046

atctcgcgaacgtgtgcagattct

tcgatctttcccggaactctgac

3

54

RM6083

cgtaaaaggtcctcgtcgtc

agtagcctgctctccattgg

1

55

phi050

taacatgccagacacatacggacag

atggctctagcgaagcgtagag

10

56

RM6111

gagtcgtcgtcttcgtctcc

tctagggctagctcttcccc

7

50

phi053

ctgcctctcagattcagagattgac

aacccaacgtactccggcag

3

56

RM6128

ctctctctccccacccaatc

gagggaggaggaggtgtagg

7

50

phi056

acttgcttgcctgccgttac

cgcacaccacttcccagaa

1

56

RM6779

cacagcctctcacaagggag

aggacgaggagcaggaggag

6

50

phi059

aagctaattaaggccggtcatccc

tccgtgtactcggcggactc

10

60

RM7040

tacgtacggatgtctgcatg

agtagggccggaaatgaatg

7

50

phi062

ccaacccgctaggctacttcaa

atgccatgcgttcgctctgtatc

10

56

RM7102

ttgagagcgtttttaggatg

tcggtttacttggttactcg

7

55

phi063

ggcggcggtgctggtag

cagctagccgctagatatacgct

10

54

RM7183

agtgtttgtaggagcgccac

ctagcaggagagctaccaatc

7

55

phi064

ccgaattgaaatagctgcgagaacct acaatgaacggtggttatcaacacgc

1

56

RM8261

gacgactggatggtacgac

tgcttctcctgcaaacac

7

55

phi065

agggacaaatacgtggagacacag

9

54

cgatctgcacaaagtggagtagtc

phi073 gtgcgagaggcttgaccaa aagggttgagggcgaggaa 3 56 –––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––

sion was carried out following the protocol (GEORGE et al., 2004) made available from Asian Maize Biotechnology Network (AMBIONET) Services Laboratories, Thailand. DNA quality and quantity of each genotype was assessed by electrophoresing the DNA in 0.8 percent agarose gel with known standards. All the DNA samples were uniformly diluted to have a final concentration of 10 ng/µl. SSR analysis - A total of 42 SSR primer pairs (22 from maize and 20 from rice), synthesized from SIGMA Aldrich Inc. was used for PCR amplification of repeat sequences from the genomic DNA of each sample. The primer pairs used are given in Table 2. PCR reactions were performed using PTC 100 programmable thermal cycler from MJ Research Inc. in 15 µl volume containing 2 µl DNA, 1.2 - 4.0 pmol of each primer, 10x PCR buffer, 1.25 mM dNTPs and 0.5 units Taq polymerase. Amplifications were done under conditions of 94°C for 2 minutes followed by 30 cycles of 94°C for 30 seconds, X °C for 1 minute, 72°C for 1 minute followed by final extension at 72°C for 5 minutes. X °C refers to the annealing temperature which is specific for each of the primer pairs used as furnished in Table 2. PCR products were loaded on 5 percent denaturing polyacrylamide gels and electrophoresed in 1x TBE buffer (pH 8.3) at constant power (100 Watts) for 1 hour using Sequi Gen® GT Nucleic acid electrophoresis cell from Biorad Laboratories. The PCR

products were resolved by silver staining (PANAUD et al., 1996). Data analysis and scoring - Only clear and unambiguous bands of SSR markers were scored. Markers were scored for the presence and absence of the corresponding band among the genotypes. The scores ‘1’ and ‘0’ indicate the presence and absence of bands, respectively. The data matrix was subjected to further analysis. Polymorphism Information Content (PIC) - PIC values or expected heterozygosity scores for SSR (polyallelic) markers were calculated following the formula: Hj = 1 - ∑pi2, where pi is the frequency for the i-th allele (NEI, 1973). Cluster analysis - The three different sets of data gathered (qualitative traits, quantitative traits, and SSR markers) were subjected to cluster analysis. Sequential Agglomerative Hierarchical Non-overlapping (SAHN) clustering was performed on similarity matrices utilizing the Unweighted Pair Group Method with Arithmetic Averages (UPGMA) method. Data analysis was done using NTSYSpc version 2.02i (ROHLF, 1998). Comparison of similarity coefficient matrices - The correspondence between the qualitative traits, quantitative traits, and SSR based similarity coefficient matrices was tested based on correlation analysis and MANTEL (1967) matrix correspondence test. The Mantel matrix correspondence test was carried out using the MXCOMP procedure in NTSYSpc version 2.02i (ROHLF, 1998).

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TABLE 3 - Phenotype variants observed for eight different qualitative characteristics. –––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Observations recorded No. of Character Phenotype Score genotypes –––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Tassel anther glume color Pink 1 11 Green 2 13 Light purple 3 8 Purple 4 11 Others 99 0 –––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Silk color at emergence Green 1 21 Pink 2 6 Red 3 6 Purple 4 10 Others 99 0 –––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Leaf color Yellowish green 1 4 Light green 2 4 Green 3 16 Dark green 4 19 Others 99 0 –––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Leaf pubescence Absent 0 0 Present 1 43 –––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Ear shape Cylindrical 1 24 Cylindrical-Conical 2 10 Conical 3 6 Round 4 3 Others 99 0 –––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Kernel color White 1 5 Yellow 2 6 Purple 3 1 Variegated 4 0 Brown 5 0 Orange 6 20 Mottled 7 1 White cap 8 8 Red 9 1 Others 99 1 –––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Grain texture Flat 1 17 Beaked 2 5 Round 3 21 Others 99 0 –––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Grain shape Shrunken 1 4 Round 2 20 Indented 3 19 Pointed 4 0 Others 99 0 ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––

RESULTS Variability/Polymorphism analysis based on morphological traits Qualitative traits - Observations for eight qualitative traits taken from five randomly selected plants of 43 different maize genotypes indicated that leaf pubescence did not show any variation across the genotypes. The traits such as green silk colour at emergence, cylindrical ear shape, orange kernel colour, round grain texture and round grain shape were found to be predominant among the 43 genotypes which are evident from the number of genotypes observed under each category (Table 3). Quantitative traits - The mean, range, standard deviation and standard error (SE) mean for each of the 10 quantitative traits observed are given in Table 4. Among the 10 traits, wider range of variation was observed across the 43 different maize genotypes for the traits viz. plant height, ear height, days to tasseling and days to silking. The minimum (96.50 cm) and maximum (171.10 cm) plant height were observed in UMI 250 and UMI 12, respectively. The 100 seed weight was maximum (36.8 g) in UMI 603 and minimum (11.1 g) in UMI 27. Variability/Polymorphism analysis based on SSR markers Based on SSR markers derived from maize - The total number of markers observed among the 45 genotypes produced by 22 primer pairs derived from maize was 132. All the primer pairs generated multiple markers and the number of markers per primer pair ranged from 1 to 12 with an average of 6.00. The level of polymorphism was 100 per cent for almost all the primer pairs except for phi073. The average polymorphism percent was 99.20. The PIC value for the primer pairs ranged from 0.53 to 0.99 with an average of 0.83 (Table 5). SSR marker profile produced by the primer pair phi065 is shown in Fig. 1. Based on SSR markers derived from rice - A total of 20 SSR primer pairs derived from rice produced 181 markers. Six primer pairs viz. RM84, RM1135, RM2819, RM6081, RM6779, and RM7040 produced monomorphic markers across the 45 genotypes. The percentage polymorphism ranged from 0 to 100 with an average of 53.04. All the primer pairs produced more than two markers per primer except RM84. The PIC value for the primer pairs ranged from 0.00 to 0.96 with an average of 0.38 (Table 5). SSR marker profile generated by the primer pair RM5499 is shown in Fig. 2.

GENETIC DIVERSITY ANALYSIS

FIGURE 1 - SSR marker profile of 45 maize genotypes produced by primer pair of phi 065.

FIGURE 2 - SSR marker profile of 45 maize genotypes produced by primer pair of RM5499.

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TABLE 4 - Descriptive statistics for 10 quantitative traits observed. ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Trait Mean Range Standard Deviation SE mean ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Tassel branching 11.22 1.7-20.0 5.04 0.76 Plant height (cm) 135.60 96.5-171.1 19.85 3.03 Ear height (cm) 70.68 28.4-134.6 18.88 2.88 Days to tasseling 57.28 43-70 5.88 0.90 Days to silking 63.04 47-74 5.72 0.88 Ear length (cm) 13.28 6.6-24.4 2.80 0.43 Ear width (cm) 3.58 2.8-4.8 0.44 0.07 Number of kernel/column 12.21 9.0-16.3 1.48 0.23 Number of kernel/row 25.37 12.2-34.8 4.79 0.73 100 seed weight (g) 21.83 11.1-36.8 5.62 0.86 –––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––

TABLE 5 - Comparative statement on SSR markers obtained from maize and rice SSR primer pairs across the 45 genotypes of maize. –––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Maize primer Rice primer Marker parameter pairs pairs –––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Total number of markers 132 181 Monomorphic markers 1 85 Polymorphic markers 131 96 Number of markers per primer 6.00 9.05 Polymorphism (%) 99.20 53.04 Minimum PIC value 0.53 0.0 Maximum PIC value 0.99 0.96 Average PIC value 0.84 0.32 ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––

Comparison of SSR markers derived from maize and rice - The comprehensive details of SSR analysis using maize and rice derived primers are given in Table 5. Among the two different sources used for the generation of markers across 45 maize genotypes, the primer pairs derived from maize were found to be more informative. The average polymorphism percentage for maize derived primer pairs was 99.20 whereas for rice derived primer pairs, it was 53.04. The average PIC values were 0.84 and 0.32 for maize and rice derived primer pairs, respectively. Cluster analysis The dendrograms of test genotypes were constructed using eight qualitative traits, 10 quantitative traits, 132 maize derived SSR markers, 181 rice derived SSR markers and 313 SSR markers generated by maize and rice derived primer pairs. The results of cluster analysis are presented below.

Based on qualitative traits and quantitative traits - Cluster analysis using eight different qualitative traits across 43 maize genotypes resulted in grouping of genotypes into two major clusters of 19 and 24 genotypes. The similarity coefficient ranged between 0.22 and 0.78. The pairs of maize genotypes viz. USC 11 and UMI 603, UMI 140 and CIMMYT 4, UMI 879 and CIMMYT 1 and UMI 325 and UMI 420 in cluster I and UMI 447 and UMI 250 and UMI 688 and UMI 522 in cluster II were almost genetically similar with a similarity level of 78% between the individuals making up the pair but were distinct from each other. The dendrogram of 43 maize genotypes constructed using eight different qualitative traits is shown in Fig. 3a. The dendrogram constructed based on 10 quantitative traits across the 43 maize genotypes comprised of two major clusters, one with 39 genotypes and the other with 4 genotypes (Fig. 3b). UMI 623 and UMI 354 were the only genotypes with maximum similarity whereas others exhibited considerable variation. Based on SSR markers derived from maize genome - The similarity coefficients of 45 genotypes based on 132 SSR markers derived from maize primer pairs ranged from 0.34 to 0.72. Clustering pattern of 45 genotypes based on the SSR markers generated by maize primer pairs (Fig. 4a) was entirely different from the patterns obtained from qualitative and quantitative traits. Out of 45 genotypes, 41 were grouped into a single cluster with two subclusters of 35 and 6 genotypes. Among the 45 genotypes, CIMMYT1 and CIMMYT4 were almost genetically similar with a similarity coefficient of 0.72. Based on SSR markers derived from rice genome The similarity coefficients of 45 genotypes based on

GENETIC DIVERSITY ANALYSIS

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FIGURE 3 - Dendrogram of 43 maize genotypes based on (a) eight qualitative traits and (b) ten quantitative traits.

181 SSR markers generated using rice derived primer pairs ranged from 0.88 to 0.97. Comparing the range of similarity coefficient generated by the rice derived SSR markers with that of maize derived primer pairs indicated that the SSR markers of rice origin were not differentiating the 45 genotypes with clarity. The pattern of the dendrogram (Fig. 4b) obtained was different from the previous one (Fig. 4a). Two major clusters of 34 and 11 genotypes were observed. Among the genotypes, UMI 140 and UMI 141 were found to have a similarity coefficient of 0.97. Based on SSR markers derived from maize and rice - The similarity coefficients of 45 genotypes based on 132 SSR markers derived from maize primer pairs and 181 SSR markers generated using rice derived primer pairs ranged from 0.78 to 0.91. Combining the SSR markers of rice origin reduced the power of SSR markers of maize origin in differentiating the maize genotypes under the study. Two major clusters of 36 and 9 were obtained (Fig. 4c). Genotypes viz. CIMMYT 1, CIMMYT 4 and CIMMYT 7 were grouped together.

Comparison of clusters based on morphological markers and SSR markers - Correlation analysis carried out based on similarity coefficient values to compare the pattern of dendrograms constructed using qualitative traits, quantitative traits, SSR markers of maize derived primer pairs, SSR markers of rice derived primer pairs and SSR markers of maize + rice derived primer pairs were not comparable since the correlations observed between pairs were not significant except for SSR rice vs maize + rice. The analysis indicated that clusters produced by SSR rice vs SSR rice + maize only showed the correlation value of 0.8008, almost similar to the minimum required value of 0.80.

DISCUSSION Maize is the third important cereal crop in the world next to rice and wheat. There exists an urgent need to promote maize breeding to meet the increasing demands for maize grain and its prod-

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FIGURE 4 - Dendrogram of 45 maize genotypes based on SSR markers (a) based on 132 maize SSR markers (b) based on 181 rice SSR markers (c) based on 313 maize and rice SSR markers.

ucts. In this context, maize hybrid breeding remains the choice of methods considering its success over years. Better understanding on the genetic diversity ensures the breeder in planning crosses for hybrid and line development, in assigning lines to heterotic groups, and in plant variety protection. In the present study, a set of maize inbreds have been subjected to diversity analysis based on variations in qualitative and quantitative traits and SSR marker profiles. In the present study, the observations on eight qualitative and 10 quantitative traits across the 43 maize genotypes provided wealth of information to understand the existing diversity in breeding materials which lack detailed pedigree data. Forty three genotypes observed for eight different qualitative traits showed variation among themselves for most of the traits except leaf pubescence. However, no single phenotypic variant for the all the traits except kernel color was observed indicating less genetic diversity across the genotypes observed. The range of variability observed for the quantitative traits across the 43 genotypes was found to be significant. This was more evident for the traits viz. ear height (96.5 to 171.1 cm), number of kernels/row (12.2 to 34.8)

and 100 seed weight (11.1 to 36.8 g) indicating possibilities for grouping the maize genotypes into various groups of poor performers and good performers. Better understanding on the influence of environment on these quantitative traits would help to group the genotypes with better accuracy. Based on the phenotypic traits studied, WIETHOLTER et al. (2008) concluded that the traits that most contributed to the classification of Brazilian corn landraces were plant height, ear insertion, female flowering, male flowering and kernel row number per ear. Though quantitative traits could be better employed for grouping the maize genotypes, qualitative traits and quantitative traits with high heritability were preferred. Based on this, ABU-ALRUB et al. (2006) used kernel traits as the best descriptors for classifying Peruvian highland maize germplasm, followed by ear traits. Tassel traits were found to be less reliable descriptors for classifying the germplasm. Considering the drawbacks in using morphometric traits to have a clear-cut understanding on the genetic diversity, DNA markers were employed in the present study due to their high discriminatory power and repeatability. All the 45 maize inbreds

GENETIC DIVERSITY ANALYSIS

were genotyped with SSR markers using 42 SSR primer pairs, of which 22 were derived from maize genome and the remaining 20 were from rice genome. Twenty two primer pairs derived from maize genome produced a total of 132 markers with an average of 6 markers per primer pair. The results obtained in the present study are in accordance with the results of PEJIC et al. (1998) where in 6.8 markers per primer were noticed using 27 SSR primer pairs. WARBURTON et al. (2002) reported an average of 6.3 markers per primer using 85 SSR loci and YU et al. (2007) have reported an average of 5.34 markers per primer pair using 49 SSR primer pairs. The availability of enormous number of SSR primer pairs in other cereals such as rice (MCCOUCH et al., 2002) and wheat (GUPTA et al., 2003) and their cross transferability (KULEUNG et al., 2006; HU et al., 2007) to produce polymorphic markers improved the genotyping of crop plants. A total 20 SSR primer pairs from rice were also used across the 45 maize genotypes. A total of 181 markers were produced with an average of 9.05 markers per primer pair. However, the markers produced by rice primer pairs showed low level of polymorphism (53.04%) compared to maize genome derived primer pairs (99.20%). From the results of the present study, it is speculated that the genomic regions of maize amplified with rice genome derived primer pairs might be conserved across the genera. However this needs further confirmation with more markers covering the whole genome. Moreover, most of the primer pairs except phi002 from maize and RM84 from rice used in the present study generated multiple markers not allowing establishment of clear-cut allelic variations for the individual locus. Considering the ambiguity in the number of alleles per locus, all the markers generated were scored as dominant markers. Though SSR markers are considered as single locus, codominant and polyallelic in nature, the generation of multiple markers denied the power of informativeness in using them for genetic diversity analysis. From the present study, it is established that prior to genotyping plants with SSR primer pairs, the specificity of the markers should be established and only the primer pairs producing informative markers should be employed. In the recent past, the genetic diversity analysis in maize was carried out with SSR markers involving 260 maize inbred lines from temperate, tropical and subtropical conditions (LIU et al., 2003), five maize

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populations from Central Europe (REIF et al., 2005), 45 landraces covering the entire pre-Columbian range (VIGOUROUX et al., 2005), 18 maize populations maintained at Maize Research Institute, Serbia (IGNJATOVIC-MICIC et al., 2007) and 17 elite maize inbred lines of West and Central Africa (ADETIMIRIN et al., 2008). Dendrograms constructed using morphometric traits resulted in the establishment of clear-cut difference between the individuals indicating the extent of variation across 43 maize genotypes for both qualitative and quantitative traits. Clustering patterns based on SSR markers of maize origin, SSR markers of rice origin and SSR markers of maize + rice origin were very distinct and all the genotypes were distinctly separated from each other. The clustering pattern derived from 132 SSR markers of maize origin was the most distinct with many sub-clusters. Thus with the available resources at morphometric traits level and SSR markers level, 45 maize genotypes used in the present study were clearly differentiated from one another. The results of the present study could be a valuable source of information for future maize breeding which could be established based on the pedigree information available for each of the inbred. In the present study, dendrogram patterns derived from qualitative traits, quantitative traits and various combinations of SSR markers were strikingly different from one another. This is very much evident from poor correlation values obtained for the pairs of similarity matrices based on MANTEL’s (1967) t-test. The finding indicated the need for selecting the best marker system to derive the right pattern of clusters of the genotypes involved. This could be achieved by having a better understanding on the genome organization of maize using specific marker system. VIGOUROUX et al. (2005) analyzed diversity at 462 SSR spread throughout the maize genome and compared the diversity observed at these SSR loci in maize to that observed in its wild progenitor, teosinte. The results reveal a modest genome wide deficit of diversity in maize relative to teosinte. The relative deficit of diversity is less for SSR with dinucleotide repeat motifs than for SSR with repeat motifs of more than two nucleotides. This kind of analysis should be made before embarking on cross-transferring SSR markers having their origin from other cereal genomes. In maize, genetic diversity analysis to determine the genetic distance (GD) using various operational taxonomic units (OTUs) remains routine practice. It

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is an established fact that more the GD more is the heterotic effect. BENCHIMOL et al. (2000) analyzed tropical maize inbred lines and assigned the inbreds to different heterotic groups and established that RFLP based GD were efficient and reliable to assess and allocate genotypes to different heterotic groups. However, the study indicated that the RFLP based GD were not suitable for predicting the performance of line crosses from genetically different groups. Replacement of RFLP markers with easy to use SSR markers is expected to speedup the process of determining GD between inbreds and allocating them to different heterotic groups which may prove to be a valuable asset for a maize breeding programme as established by SENIOR et al. (1998). Analysis of genetic structure and diversity among maize inbred lines as inferred from DNA microsatellites could be a timely tool for identifying right inbreds for maize hybrid breeding. LIU et al. (2003) assayed 260 maize inbred lines for polymorphism at 94 microsatellite loci. A model-based clustering analysis placed the inbred lines in five clusters that correspond to major breeding groups plus a set of lines showing evidence of mixed origins. Clustering of inbreds based on marker data was in good agreement with the pedigree information. From the genetic diversity analysis results of present study, maize inbreds lacking their pedigree data could be identified based on their GD to make hybridization between them. Preliminary results obtained clearly indicated the clustering of maize inbreds into different groups. However, the reliability of the clustering could not be established for want of pedigree information on the individual maize inbreds, which could be overcome by using additional SSR markers having genome wide coverage and locus specificity. The results of genotyping 45 maize inbreds with SSR markers established a benchmark on the genetic diversity existing across the inbreds. Involving more markers for genotyping is expected to group the genotypes in a better manner. The SSR marker based genotyping of maize inbreds will remain as an information source for possible implementation of plant varietal protection in the near future. DNA marker profiles can be used as criteria for the prediction of heterosis in maize and also hybrid identification (PUSHPAVALLI et al., 2001). Supplementing the existing morphological descriptors with reliable and repeatable DNA based marker profiles is a must considering the ramifications in the future maize breeding in India.

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