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Indian Journal of Pure & Applied Biosciences (IJPAB)
Year : 2020, Volume : 8, Issue : 3
First page : (228) Last page : (235)
Article doi: : http://dx.doi.org/10.18782/2582-2845.7925
Genetic Diversity Studies for Yield and Yield Component Traits using Principal Component Analysis in Rice (Oryza sativa L.)
K. Sudeepthi1*, T. Srinivas2, B.N.V.S.R. Ravi Kumar3, Jyothula D.P.B.4 and Sk. Nafeez Umar5
1Research Scholar, 2Professor and Head, Department of Genetics & Plant Breeding,
5Assistant Professor, Department of Statistics and Computer Application,
Agricultural College, Bapatla, 522101, Andhra Pradesh, India
3Senior Scientist, Department of Genetics & Plant Breeding,
Regional Agricultural Research Station, Maruteru, 534122, Andhra Pradesh, India
4Associate Professor, Department of Genetics & Plant Breeding,
Administrative office, LAM, Guntur, 522034, Andhra Pradesh, India
*Corresponding Author E-mail: sudeepthisep3@gmail.com
Received: 19.01.2020 | Revised: 24.02.2020 | Accepted: 3.03.2020
ABSTRACT
The present investigation was carried out to assess the diversity among 107 rice genotypes with regards to yield and yield component traits. Principal component analysis was utilized to evaluate the variation and to estimate the relative contribution of various traits for total variability. Results revealed three principal components with eigen value > 1. These components contributed for a total variability of 69.412 per cent. Component 1 (PC 1) had contributed maximum of 33.594 per cent while PC 2 had contributed to 22.566 per cent and PC 3 had contributed to 13.251 per cent towards the total variability. The characters, namely, panicle length, days to 50 per cent flowering, ear bearing tillers per plant, plant height and test weight were observed to explain maximum variance in PC 1. The results of 2D scatter diagram revealed RTCNP 9 and MTU 1006 genotypes to be most diverse. Hybridization of these diverse genotypes is therefore predicted to result in desirable transgressive segregants.
Keywords: Genetic Divergence, Principal Component, Rice, Yield
Full Text : PDF; Journal doi : http://dx.doi.org/10.18782
Cite this article: Sudeepthi, K., Srinivas, T., Ravi Kumar, B.N.V.S.R., Jyothula, D.P.B., & Umar, S.N. (2020). Genetic Diversity Studies for Yield and Yield Component Traits using Principal Component Analysis in Rice (Oryza sativa L.), Ind. J. Pure App. Biosci. 8(3), 228-235. doi: http://dx.doi.org/10.18782/2582-2845.7925
INTRODUCTION
ice is one of the most extensively cultivated cereal crops in the world and a primary source of food for more than half of the world's population. Among the rice growing countries, India has largest area in the world i.e. 43.86 million hectares and ranks second in production with 99.24 million tonnes and productivity of 2.49 t/ha next to wheat (Ministry of Agriculture, Government of India, 2018-19). With ever increasing population and plateauing trend in the yield coupled with declining natural resources like land and water, development of new high yielding rice varieties has become essential.
In this context, multivariate analysis tools such as principal component analysis (PCA) and cluster analysis have been reported to be effective for evaluating the phenotypic diversity in addition to identifying genetically distant clusters of genotypes and selecting important traits contributing to the total variation in the genotypes. These analyses provide information that could help in better selection of parental genotypes with specific traits and in devising breeding strategies for trait improvement. Principal component analysis (PCA) allows natural grouping of the genotypes and is precise indicator of differences among genotypes. The main advantage of using PCA over cluster analysis is that each genotype can be assigned to one group only (Mohammadi, 2002). Principal component analysis (PCA) was used to identify redundancy of the genotypes with similar characters and their elimination (Adams, 1995), while two-way cluster analysis is useful for identification and separation of core subset of genotypes with distinct phenotypic traits. Principal component analysis (PCA) allows natural grouping of the genotypes and is precise indicator of differences among genotypes. Principal component analysis (PCA) and two-way cluster analysis are therefore two important statistical programs that aid in selecting elite genotypes. The present investigation was undertaken in this context to study the nature and magnitude of genetic diversity among 107 rice genotypes for yield and yield component traits using Principal Component Analysis (PCA).
MATERIALS AND METHODS
Experimental material for the present investigation comprised of 107 elite rice genotypes collected from Regional Agricultural Research Station (RARS), Maruteru; Agricultural Research station (ARS), Bapatla; and erstwhile, ARS, Pulla; Andhra Pradesh, India, in addition to germplasm obtained from International Rice Research Institute (IRRI), Phillippines (Table 1). These genotypes were sown during Kharif 2017 at RARS, Maruteru in a randomized block design with two replications. For transplanting, nursery was raised separately and 28 days old seedlings were transplanted in the main field with a spacing of 20×15 cm. Standard agronomic practices were followed to raise good crop. Observations were recorded on five randomly selected plants for grain yield per plant (g) and yield component characters, namely, days to 50 per cent flowering, plant height (cm), number of ear bearing tillers per plant, panicle length (cm), total number of grains per panicle, spikelet fertility (%) and test weight (g). However, days to 50 per cent flowering was recorded on plot basis. In contrast, observations for test weight were obtained from a random grain sample drawn from each plot in each genotype and replication. Principal component analysis was carried out using the software WindowStat Version 8.5.
RESULTS AND DISCUSSION
The results on analysis of variance (ANOVA) for yield and yield component traits revealed highly significant differences among the genotypes for all the characters studied, indicating the exsistence of sufficient variation among the genotypes and therefore opportunity for plant breeder to undertake further breeding activities like hybridization program. In the present study, first four principal components contributed to 81.328 per cent towards the total variability (Table 2). The first principal component (PC 1) contributed 33.594 per cent towards variability. The characters, namely, panicle length (0.478), days to 50% flowering (0.458), ear bearing tillers per plant (0.427), plant height (0.369), test weight (0.367) and grain yield per plant (0.312) explained maximum variance in this component. The second principal component (PC 2) contributed to 22.566 per cent of total variance. The characters namely spikelet fertility (0.600), grains per panicle (0.512), ear bearing tillers per plant (0.201), days to 50% flowering (0.140), panicle length (0.136) explained maximum loadings in this component. The third principal component was characterized by 13.251 per cent contribution towards the total variability. Characters, namely, ear bearing tillers per plant (0.357) and days to 50% flowering (0.319) explained maximum variance in this component. The fourth principal component was characterized by 11.915 per cent contribution towards the total variability. Characters namely, test weight (0.360), days to 50% flowering (0.359), ear bearing tillers per plant (0.316), spikelet fertility, grains per panicle showed maximum variance in this component.
The PCA analysis thus identified maximum contributing traits towards the exsisting variability as panicle length, days to 50% flowering, ear bearing tillers per plant, plant height and test weight. Similar results were reported earlier by Tiruneh et al. (2019) for panicle length, Prafull et al. (2015) for days to 50% flowering, Pachuari et al. (2015) for tillers per plant, Shaibu and Uguru (2017) for plant height and Ahmed et al. (2016) for test weight. The PCA scores for 107 rice genotypes in the first three principal components were computed and were considered as three axes as X, Y and Z and squared distance of each genotype from these three axes were calculated (Table 3). These three PCA scores for 107 genotypes were plotted in graph to get two dimensional scatter diagram (Fig. 1). A perusal of these results revealed genotypes number 74 (RTCNP 9) and 6 (MTU 1006) to be most diverse. Hybridization of these diverse genotypes is therefore predicted to result in desirable transgressive segregants.
Table 1: Details of the material studied
S.No. |
Centre of Collection |
Genotypes |
1 |
Maruteru, Andhra Pradesh, India |
MTU 1001, MTU 1006, MTU 1010, MTU 1031, MTU 1032, MTU 1061, MTU 1064, MTU 1071, MTU 1075, MTU 1078, MTU 1112, MTU 1121, MTU 1140, MTU 1153, MTU 1156, MTU 1166, MTU 1184, MTU 1187, MTU 1194, MTU 1210, MTU 1224, MTU 1226, MTU 1229, MTU 2067, MTU 2077, MTU 2716, MTU 3626, MTU 4870, MTU 5182, MTU 5249, MTU 5293, MTU 7029, RTCNP 1, RTCNP 3, RTCNP 4, RTCNP 5, RTCNP 6, RTCNP 7, RTCNP 8, RTCNP 9, RTCNP 10, RTCNP 12, RTCNP 13, RTCNP 14, RTCNP 15, RTCNP 17, RTCNP 18, RTCNP 20, RTCNP 21, RTCNP 23, RTCNP 28, RTCNP 29, RTCNP 31, RTCNP 33, RTCNP 34, RTCNP 35, RTCNP 36, RTCNP 37, RTCNP 38, RTCNP 39, RTCNP 40, RTCNP 41, RTCNP 42, RTCNP 43, RTCNP 44, RTCNP 45, RTCNP 46, RTCNP 47, RTCNP 48, RTCNP 49, RTCNP 50, RTCNP 52, SM-1, SM-2, SM-3, SM-4, SM-6, SM-7, SM-8, SM-9, SM-10, SM-11, SM-13, SM-14, SM-15, SM-16, SM-17, SM-18, SM-19, SM-23, SM-24, SM-25, SM-26, SM-27, SM-28, SM-29, SM-30, SM-31, SM-3-1 |
2 |
Bapatla, Andhra Pradesh, India |
BPT 2231, BPT 3291, BPT 5204 |
3 |
Pulla, Andhra Pradesh, India |
PLA-1100 |
4 |
IRRI, Phillipines |
FL 478, NONA BOKRA, POKKALI |
Table 2: Eigen values, proportion of the total variance represented by first four principal components, cumulative per cent variance and component loading of different characters in rice for
yield and yield component traits
PC 1 |
PC 2 |
PC 3 |
PC 4 |
|
Eigen Value (Root) |
2.687 |
1.805 |
1.060 |
0.953 |
% Var. Exp. |
33.594 |
22.566 |
13.251 |
11.915 |
Cum. Var. Exp. |
33.594 |
56.161 |
69.412 |
81.328 |
Days to 50% Flowering |
0.458 |
0.140 |
0.319 |
0.359 |
Plant Height (cm) |
0.369 |
-0.036 |
0.085 |
-0.721 |
Ear bearing tillers per plant |
0.427 |
0.201 |
0.357 |
0.316 |
Panicle length (cm) |
0.478 |
0.136 |
0.043 |
-0.348 |
Grains per panicle |
0.111 |
0.512 |
-0.528 |
0.021 |
Spikelet Fertility (%) |
0.000 |
0.600 |
-0.312 |
0.030 |
Test Weight |
0.367 |
-0.345 |
-0.371 |
0.360 |
Grain yield per plant |
0.312 |
-0.423 |
-0.498 |
-0.006 |
Table 3: PCA scores for 107 rice genotypes
S.No. |
Genotype |
PC I |
PC II |
PC III |
1 |
BPT 5204 |
54.947 |
27.892 |
0.441 |
2 |
BPT 3291 |
53.933 |
22.749 |
1.057 |
3 |
BPT 2231 |
55.129 |
25.017 |
-0.061 |
4 |
FL 478 |
51.428 |
15.818 |
-3.832 |
5 |
MTU 1001 |
58.887 |
20.84 |
-0.59 |
6 |
MTU 1006 |
49.866 |
20.487 |
0.903 |
7 |
MTU 1010 |
55.543 |
22.784 |
-1.377 |
8 |
MTU 1031 |
61.824 |
23.985 |
2.972 |
9 |
MTU 1032 |
61.189 |
24.113 |
2.211 |
10 |
MTU 1061 |
62.1 |
25.679 |
2.124 |
11 |
MTU 1064 |
61.763 |
23.416 |
3.002 |
12 |
MTU 1071 |
64.535 |
22.986 |
3.391 |
13 |
MTU 1075 |
60.002 |
24.789 |
-0.141 |
14 |
MTU 1078 |
59.374 |
25.818 |
2.739 |
15 |
MTU 1112 |
62.958 |
24.276 |
5.428 |
16 |
MTU 1121 |
58.914 |
25.332 |
-3.622 |
17 |
MTU 1140 |
61.372 |
26.087 |
-0.813 |
18 |
MTU 1153 |
53.023 |
22.977 |
-4.923 |
19 |
MTU 1156 |
54.924 |
23.17 |
-4.673 |
20 |
MTU 1166 |
63.059 |
24.826 |
2.489 |
21 |
MTU 1184 |
62.413 |
26.983 |
3.278 |
22 |
MTU 1187 |
59.916 |
25.674 |
-1.074 |
23 |
MTU 1194 |
61.206 |
24.091 |
2.933 |
24 |
MTU 1210 |
57.954 |
25.811 |
0.503 |
25 |
MTU 1224 |
55.896 |
29.132 |
1.361 |
26 |
MTU 1226 |
66.769 |
25.978 |
1.413 |
27 |
MTU 1229 |
63.63 |
31.279 |
4.179 |
28 |
MTU 2067 |
62.282 |
24.107 |
2.084 |
29 |
MTU 2077 |
58.286 |
25.294 |
-1.055 |
30 |
MTU 2716 |
58.544 |
23.407 |
1.004 |
31 |
MTU 3626 |
52.307 |
17.621 |
-3.148 |
32 |
MTU 4870 |
60.163 |
24.472 |
-0.021 |
33 |
MTU 5182 |
59.161 |
22.362 |
-1.149 |
34 |
MTU 5249 |
56.261 |
22.43 |
-0.408 |
35 |
MTU 5293 |
59.672 |
23.302 |
0.051 |
36 |
MTU 7029 |
56.524 |
23.566 |
-1.544 |
37 |
NONABOKRA |
54.563 |
18.251 |
-3.399 |
38 |
PLA-1100 |
60.495 |
23.878 |
1.543 |
39 |
POKKALI |
56.338 |
23.957 |
1.868 |
40 |
SM1 |
58.579 |
25.067 |
-3.257 |
41 |
SM2 |
57.058 |
24.333 |
0.355 |
42 |
SM 3 |
56.016 |
25.4 |
-2.508 |
43 |
SM 4 |
62.06 |
26.622 |
2.98 |
44 |
SM 6 |
62.246 |
26.576 |
-0.281 |
45 |
SM 7 |
58.92 |
25.016 |
-2.278 |
46 |
SM8 |
58.398 |
26.312 |
-0.451 |
47 |
SM 9 |
57.857 |
26.774 |
-0.699 |
48 |
SM 10 |
60.356 |
23.371 |
-0.182 |
49 |
SM 11 |
59.032 |
26.07 |
-0.048 |
50 |
SM 13 |
59.478 |
22.535 |
1.498 |
51 |
SM 14 |
57.978 |
27.103 |
0.612 |
52 |
SM 15 |
58.217 |
20.717 |
1.865 |
53 |
SM16 |
58.833 |
23.989 |
1.156 |
54 |
SM 17 |
58.542 |
25.6 |
-0.212 |
55 |
SM 18 |
58.57 |
23.591 |
0.199 |
56 |
SM 19 |
58.188 |
23.387 |
0.34 |
57 |
SM 23 |
60.821 |
26.805 |
-0.747 |
58 |
SM 24 |
60.775 |
23.842 |
0.243 |
59 |
SM 25 |
61.082 |
29.331 |
-1.594 |
60 |
SM 26 |
64.244 |
29.075 |
0.403 |
61 |
SM 27 |
57.445 |
27.476 |
-0.673 |
62 |
SM 28 |
62.44 |
27.411 |
-0.614 |
63 |
SM 29 |
59.452 |
26.634 |
-0.444 |
64 |
SM 30 |
61.823 |
25.593 |
-3.08 |
65 |
SM 31 |
58.077 |
27.751 |
0.979 |
66 |
SM 3-1 |
53.257 |
27.37 |
-3.582 |
67 |
RTCNP1 |
59.986 |
27.76 |
3.293 |
68 |
RTCNP 3 |
64.688 |
22.978 |
8.503 |
69 |
RTCNP 4 |
66.203 |
24.73 |
4.957 |
70 |
RTCNP5 |
61.956 |
31.393 |
-2.706 |
71 |
RTCNP 6 |
64.817 |
20.771 |
8.891 |
72 |
RTCNP 7 |
66.808 |
26.723 |
5.974 |
73 |
RTCNP 8 |
71.77 |
30.499 |
1.529 |
74 |
RTCNP 9 |
73.203 |
21.835 |
3.456 |
75 |
RTCNP 10 |
62.883 |
27.034 |
0.229 |
76 |
RTCNP 12 |
67.565 |
25.65 |
3.012 |
77 |
RTCNP 13 |
66.944 |
25.925 |
5.621 |
78 |
RTCNP 14 |
68.807 |
23.039 |
3.431 |
79 |
RTCNP 15 |
67.776 |
23.877 |
2.111 |
80 |
RTCNP 17 |
68.531 |
25.968 |
0.978 |
81 |
RTCNP 18 |
65.107 |
25.066 |
2.184 |
82 |
RTCNP 20 |
67.748 |
28.518 |
3.937 |
83 |
RTCNP 21 |
68.106 |
26.066 |
5.306 |
84 |
RTCNP 22 |
69.867 |
22.467 |
2.51 |
85 |
RTCNP 23 |
66.049 |
26.795 |
1.076 |
86 |
RTCNP 28 |
66.33 |
27.617 |
-1.835 |
87 |
RTCNP 29 |
60.406 |
27.907 |
-0.789 |
88 |
RTCNP 31 |
67.343 |
26.235 |
3.253 |
89 |
RTCNP 33 |
60.696 |
28.291 |
2.01 |
90 |
RTCNP 34 |
66.009 |
24.415 |
3.129 |
91 |
RTCNP 35 |
67.191 |
29.339 |
4.054 |
92 |
RTCNP 36 |
62.107 |
25.425 |
2.638 |
93 |
RTCNP 37 |
68.636 |
25.046 |
1.036 |
94 |
RTCNP 38 |
66.475 |
27.177 |
-2.336 |
95 |
RTCNP 39 |
64.858 |
27.475 |
4.733 |
96 |
RTCNP 40 |
66.135 |
23.096 |
3.427 |
97 |
RTCNP 41 |
62.15 |
28.173 |
0.783 |
98 |
RTCNP 42 |
58.521 |
27.793 |
1.363 |
99 |
RTCNP 43 |
57.055 |
21.9 |
-3.659 |
100 |
RTCNP 44 |
60.501 |
29.33 |
1.439 |
101 |
RTCNP 45 |
65.431 |
27.911 |
4.514 |
102 |
RTCNP 46 |
67.512 |
24.62 |
1.101 |
103 |
RTCNP 47 |
63.468 |
26.209 |
-1.323 |
104 |
RTCNP 48 |
65.215 |
31.771 |
-4.081 |
105 |
RTCNP 49 |
63.605 |
28.416 |
0.499 |
106 |
RTCNP 50 |
65.777 |
22.603 |
0.982 |
107 |
RTCNP 52 |
65.207 |
34.819 |
-9.138 |
Acknowledgment
The first author is thankful to University Grants Commission, Government of India, for providing financial support in the form of fellowship for the study
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