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Maize Germplasm Characterization Using Principal Component and Cluster Analysis

Solomon Mengistu*

Harar Biodiversity Center, Ethiopian Biodiversity Institute,Addis Ababa,Ethopia

*Corresponding Author:
Solomon Mengistu
Harar Biodiversity Center
Ethiopian Biodiversity Institute
Addis Ababa
Ethiopia
E-mail: [email protected]

Received date: 13/05/2021; Accepted date: 27/05/2021; Published date: 03/06/2021

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Abstract

In Ethiopian Biodiversity Institute Gene bank, huge collections of maize germplasm are not yet characterized for the magnitude of genetic variability from each other. Though knowing the contribution of individual characters is essential to focus on particular characters in cultivar development. Hence, this experiment was conducted on 92 maize accessions which were not yet characterized and 2 local checks to estimate the magnitude of genetic diversity among the genotypes and to identify the major agro-morphological characters contributing for the observed variations. The experiment was arranged in Augmented Design in seven blocks at Arsi-Negele in 2016 main cropping season. The characters used for analysis were days to flowering, plant height, ear height, ear per plant, days to maturity, ear length, kernel rows per ear, thousand grain weight and yield per plot. The 94 genotypes were grouped into four clusters where cluster I, II, III and IV comprised 30, 21, 23 and 20 genotypes respectively. Early matured and short genotypes were grouped in cluster IV, late matured in cluster II and high yielding and tall genotypes in cluster I. The principal component analysis indicated that the first principal component (PC1) had an eigenvalue of 4.4 and reflects 48.85% of the total variation, this represents the equivalent of two individual variables and the two variables that weighted higher than the other variables are plant height and ear length. The second principal component (PC2) was recorded eigenvalue of 1.63 and maintaining 18.11% of the total variation and related to diversity among genotype due to ear per plant (EPP). Moreover, principal components 3 to 9 were showed more than one eigenvalue, thus they represent equivalent of one individual variable each accounted for 0.98%, 0.78%, 0.68%, 0.35%, 0.15%, 0.03% and 0% respectively toward the variation observed among genotypes. The result ensures the existence of high genetic divergence among the studied maize genotypes.

Keywords

Escherichia coli, Biosensor, Glycerol, Adaptive Laboratory Evolution, L-serine

Introduction

Maize (Zea mays L.) Is one of the popular crops grown in the world, ranking second to wheat and followed by rice [1]. It occupies an important position in the world economy as food, feed, and industrial grain crop. It is a staple food for several million people in the developing world where they derive their protein and calorie requirements from it. Maize is among the leading cereal crops selected to achieve food self-sufficiency in Ethiopia [2]. Although, improved cultivars have been largely included in the national extension package, the national average yield of maize is only 3.45 tons/ha [3]. which is far below the world average of 5.5 tons/ha. In any crop, germplasm resource not only serves as a valuable source of useful genes but also provides a wide genetic variability. Bringing improvement over existing crop varieties is a continuous process in plant breeding. To achieve this objective, the breeder has to identify diverse parents having superior genetic variability for combining desirable characters. Hence, knowledge of sound genetic diversity is crucial for undertaking any recombination breeding program. Multivariate statistical techniques used to analyze multiple measurements on each individual and used in the analysis of genetic diversity. Among the multivariate techniques, principal component analysis (PCA) and cluster analysis had been shown to be very useful in selecting genotypes for breeding program that meet the objective of a plant breeder [4], PCA may be used to reveal patterns and eliminate redundancy in data sets [5], as morphological and physiological variations routinely occur in crop species. Cluster analysis is commonly used to study genetic diversity and for forming core subset for grouping accessions with similar characteristic into homogenous category. Cluster analysis is frequently used to classify maize accessions and can be used by breeders and geneticists to identify subsets of accessions which have potential utility for specific breeding or genetic purposes [6].Therefore, the objective of this study was aimed to estimate the magnitude of genetic diversity among the maize genotypes and to identify the major agro-morphological characters contributing for the observed variations.

Materials And Methods

The study was conducted during the year 2016 at the experimental field of Arsi-Negele, Oromia Regional State, Ethiopia. It is located at 7°21’N 38°42’E and at an elevation of 1940 m.a.s.l. It has a chromic and pellic vertisols with PH value of 5-7. The annual rainfall of the location is measured 915 mm with 27 ± 0.30ºCmean daily temperature. 92 maize accessions obtained from Ethiopian Biodiversity Institute and two local checks named as check 1 and 2 were grown at farm site. The ninety two maize accessions without replication along with two replicated checks were arranged in augmented design. Individual plot size measured 9m × 1.5m with 4 rows planted at a spacing of 75 × 30cm. Recommended doses of fertilizers were applied. The other management operations were done timely and properly to raise the crop uniformly. Twenty randomly selected plants were used for recording observations on days to flowering, plant height, ear height, ear per plant, days to maturity, ear length and kernel rows per ear, thousand grain weight, and yield per plot. The data collected for all quantitative characters were subjected to analysis of basic statistics, correlation, cluster and principal component analysis using the software statistical package for the social sciences (SPSS) 16.0 package [7].

Results

In the present study, genetic diversity was analyzed among 94 maize genotypes (Table 6) on the basis of 9 agro-morphological characters. The results of descriptive analysis (Table 1), ear height (EH) was showed the highest variation (35.52%) followed by number of ear per plant (30.39%). Conversely, the lowest variation was recorded from kernel rows per ear (6.01%) and days to maturity (9.61%).

Characters Mean Minimum Maximum Range SD CV (%)
Days to flowering 107.00 59.00 134.00 75.00 13.75 12.85
Plant height (m) 2.23 1.10 3.04 1.94 0.41 18.36
Ear height (m) 1.05 0.26 1.95 1.69 0.37 35.52
Ear per plant 1.95 0.00 3.10 3.10 0.59 30.39
Days to maturity 143.00 95.00 170.00 75.00 13.75 9.61
Ear length (cm) 15.13 10.70 19.00 8.30 1.80 11.90
Kernel rows per ear 12.28 9.80 14.40 4.60 0.74 6.01
Thousand grain weight (g) 338.37 196.00 504.00 308.00 54.47 16.10
Yield (kg/plot) 5.78 3.37 7.69 4.32 0.95 16.45

Table 1. Basic statistics for various characters of maize genotypes.

Simple correlation coefficients confirmed that yield per plot was recorded highly significant positive correlations among plant height, ear length, and thousand grain weight and maintained positive significant correlation among days to flower, ear height, days to maturity and kernel rows per ear. Likewise, ear per plant was recorded insignificant negative correlations with days to flowering, plant height and ear height (Table 2). Similarly [8], found highly significant positive correlation of grain yield with cob diameter and thousand kernels weight and significant positive correlation with plant height.

  DF PH EH EPP DM EL KRPE TGW YPP
DF 1.00 0.59218** 0.59323** -0.092 1.00000** 0.43338** 0.18989 0.22982* 0.25984*
PH 1.00 0.96212** -0.009 0.59218** 0.68246** 0.37618* 0.47818** 0.41423**
EH 1.00 -0.018 0.59323** 0.62418** 0.31869* 0.41280** 0.37949*
EPP   1.00 -0.09127 0.23183* 0.13729 0.17099 0.0399
DM     1.00 0.43338** 0.18989 0.22982* 0.25984*
EL       1.00 0.42315** 0.57258** 0.45637**
KRPE         1.00 0.36272 0.31602*
TGW               1.00 0.83037**
YPP                 1.00

Table 2. Phenotypic correlation coefficients for different traits on maize genotypes.

Principal component analysis

The nine components which had eigenvalues equal to or greater than one were retained as meaningful interpretation (Table 3). The principal component analysis indicated that the first principal component (PC1) had an eigenvalue of 4.4 and reflects 48.85% of the total variation (Table 3), this represents the equivalent of three individual variables and the three variables that weighted higher than the other variables are plant height (PH), days to maturity and ear length (EL). The second principal component (PC2) was recorded eigenvalue of 1.63 and maintaining 18.11% of the total variation and related to diversity among genotype due to ear per plant (EPP). Moreover, principal components 3 to 9 were showed more than one eigenvalues, thus they represent equivalent of one individual variable each accounted for 0.98%, 0.78%, 0.68%, 0.35%, 0.15%, 0.03% and 0% respectively towards the total variation (Table 4).

Eigenvalue 4.40 1.63 0.98 0.78 0.68 0.35 0.15 0.03 0.00
% of total variance 48.85 18.14 10.93 8.63 7.59 3.85 1.66 0.35 0.00
Cumulative variance % 48.85 66.99 77.92 86.55 94.14 97.99 99.65 100 100

Table 3. Principle component analysis of different characters on maize genotypes.

Character PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9
DF 0.36 -0.43 0.07 0.29 0.30 0.00 -0.04 0.00 0.71
PH 0.43 -0.09 0.06 -0.27 -0.38 0.22 -0.06 -0.73 0.00
EH 0.41 -0.13 0.07 -0.25 -0.43 0.33 0.07 0.67 0.00
EPP 0.03 0.40 0.76 0.42 -0.03 0.28 0.06 -0.02 0.00
DM 0.36 -0.43 0.07 0.29 0.30 0.00 -0.04 0.00 -0.71
EL 0.38 0.18 0.21 -0.05 -0.17 -0.85 0.17 0.05 0.00
KRPE 0.24 0.27 0.18 -0.62 0.66 0.11 0.00 0.03 0.00
TGW 0.32 0.44 -0.31 0.23 0.00 0.01 -0.73 0.09 0.00
YPP 0.30 0.38 -0.48 0.27 0.10 0.18 0.65 -0.06 0.00

Table 4. The principal component of traits used for cluster analysis.

The PC3 showed high weights in ear per plant (EPP) and probably reflecting yield. The fifth principal component (PC5) kernel rows per ear (KRPE) had the largest weight, thus reflecting yield. The seventh principal component (PC7) was showed high value on yield per plot (YPP). Eighth principal component (PC8) had weighted high value of ear height (EH); this is probably reflecting the plant structure. Moreover, the ninth principal component (PC9) was recorded highest value on days to flowering (DF), thus reflecting flower development. In this study the principal component analysis was categorized the total variance into nine (9) principal components and contributing maximum towards the total diversity. Similarly, [9,10] reported important contribution of the first pcs in the total variability while studying various traits. Principal component analysis (PCA) is usually used in plant sciences for the reduction of variables and grouping of genotypes. Several authors suggested first principal component (PC) scores as input variables for the clustering process (Figure 1) [11].

maize

Figure 1: Distribution of maize accessions for the first two principal components (PC1&PC2).

Cluster analysis

Clustering pattern of maize accessions under this experiment reveals that the maize germplasm showed considerable genetic diversity among them by occupying four different clusters (Table 5). These maize germplasm were grouped based on mainly day to flowering, plant height, ear height, ear per plant, days to maturity, ear length, kernel rows per ear, thousand grain weights and yield as variables. Ninety four maize genotypes were grouped into 4 clusters based on various agro-morphological characters. Cluster I to IV were comprised 30, 21, 23 and 20 maize genotypes respectively (Table 6 ).Thus, Cluster I (Table 5) was maintained maximum plant height (2.42 m), ear height (1.21 m), ear per plant (2.25), ear length (16.21 cm), kernel rows per ear (12.61) and yield (6.58 kg/plot). Cluster II was showed late days to flowering (120.9 days) and maturing nature (156.9 days). Cluster III also maintained higher yield (5.88 kg/plot). Moreover cluster IV was showed relatively early maturing characters (133.5 days) but had minimum values of plant height (1.84 m), ear height (0.74 m), ear length (13.34 cm), kernel row per ear (11.82), and low yield (4.5 kg/plot). Similarly, hierarchical cluster analysis has been suggested for classifying entries of germplasm collections based on degree of similarity and dissimilarity [12]. A combination of cluster and principal component analysis has been used to classify maize (Zea mays L.) Accessions [13].

Cluster name
Character I II III IV
DF 109.2 120.9 99.7 97.5
PH 2.42 2.4 2.17 1.84
EH 1.21 1.2 0.97 0.74
EPP 2.25 1.75 1.71 2
DM 145.2 156.9 135.7 133.5
EL 16.21 15.56 14.9 13.34
KRPE 12.61 12.3 12.25 11.82
TGW 396.93 330.19 338.09 259.45
YPP 6.58 5.74 5.88 4.5

Table 5. Cluster means values for different agro- morphological characters of 94 genotypes.

Cluster name No. Of Genotypes Name of accessions in each cluster
I 30 ACC-9994  ACC-16226 ACC-16233 ACC-16570 ACC-16571 ACC-18106
ACC-18108 ACC-18112 ACC-18121 ACC-24297 ACC-9183  ACC-9987 
ACC-9995  ACC-9996  ACC-9998  ACC-9999  ACC-15325 ACC-15326
ACC-15460 ACC-15466 ACC-15467 ACC-16276 ACC-16278 ACC-16279
ACC-16559 ACC-241584 ACC-18113 ACC-237657 Check_1   Check_2  
II 21 ACC-9187  ACC-9190  ACC-9191  ACC-16012 ACC-16025 ACC-16236
ACC-16261 ACC-16269 ACC-18096 ACC-18098 ACC-18100 ACC-18103
ACC-228786 ACC-237684 ACC-239668 ACC-9181  ACC-10000 ACC-15324
ACC-15327 ACC-15455 ACC-24308      
III 23 ACC-9993  ACC-16023 ACC-16234 ACC-16582 ACC-98098 ACC-237597
ACC-239645 ACC241616 ACC-9188  ACC-9192  ACC-9194  ACC-9984 
ACC-9985  ACC-9986  ACC-9997  ACC-15247 ACC-15328 ACC-15462
ACC-15463 ACC-16020 ACC-16561 ACC-16562 ACC-18114  
IV 20 ACC-16262 ACC-16563 ACC-16567 ACC-18104 ACC-239620 ACC-9182 
ACC-9193  ACC-9195  ACC-9988  ACC-9989  ACC-9990  ACC-9991 
ACC-9992  ACC-15456 ACC-15457 ACC-15458 ACC-15459 ACC-15461
ACC-16021 ACC-18122        

Table 6. Clustering pattern of the 94 accessions based on agro-morphological characters.

The tree diagram comprising 4 main cluster groups and each of them further subdivided into sub-clusters (Figure 2).The information regarding association among various traits is an important part for the initiation of any breeding programme and gives good chance for the selection of superior genotypes having desirable traits simultaneously [14].

Dendrogram

Figure 2: Dendrogram classifying the 94 genotypes based on agro-morphological characters.

Conclusion

Different methods are available to assessing genetic variability among and within crop species. In the present study, principal component and cluster analysis techniques were employed to examine the amount of genetic variability present in a set of 94 maize accessions. Thus, it can be inferred from the present investigation that high level of genetic variability was present in agronomic and morphological traits like days to flowering, plant height, ear height, ear per plant, days to maturity, ear length, kernel rows per ear, thousand grain weight and yield per plot in the tested germplasm. Therefore, promising maize genotypes with more genetic divergences were identified. The identification of high level of genetic variability during the current study could be indicated for germplasm characterization, conservation and further improvement in maize breeding program.

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