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Genetic divergence and genetic gain in bread wheat through selection practices

Authors:
  • Centre for Climate Change and Environmental Research

Abstract

Genetic diversity is essential to meet the diversified goals of plant breeding such as producing cultivars with increased yield, wider adaptation, desirable quality, pest and disease resistance. In this study genetic diversity and selection index of 24 genotypes of bread wheat were evaluated. The performance of 24 wheat genotypes showed that there were significant variations for the characters suggesting the presence of genetic variability among the genotypes. The genotypes were grouped into five clusters viz. I, II, III, IV and V based on Mahalanobis' D 2 statistics. Cluster I and II were the largest group containing six genotypes and the rest three clusters contained four, five and three genotypes respectively. The genotyps belonging to the same group had smaller D 2 -value than between those belonging to different clusters. Study on selection indices through discriminate function showed that Anza ranked as the best followed by the genotypes Rawal, PBW-373 and Kheri and suggests that these highest scoring genotypes might be recommended for farmers' cultivation for better yield and it would be expected genetic gain upto 49.77% through selection practices based on the characters studied. Therefore, a crossing programme could be made among the genotypes belonging in cluster I and cluster V will provide maximum heterotic combination, especially for yield of bread wheat. Alternatively, among the studied genotypes Anza could be cultivated for better performance.
J. Bangladesh Agril. Univ. 9(1): 1–4, 2011 ISSN 1810-3030
Genetic divergence and genetic gain in bread wheat through selection
practices
M. Ferdous, U. K. Nath and A. Islam
Department of Genetics and Plant Breeding, Bangladesh Agricultural University, Mymensingh
E-mail:m.ferdous122@yahoo.com
Abstract
Genetic diversity is essential to meet the diversified goals of plant breeding such as producing cultivars with
increased yield, wider adaptation, desirable quality, pest and disease resistance. In this study genetic diversity and
selection index of 24 genotypes of bread wheat were evaluated. The performance of 24 wheat genotypes showed
that there were significant variations for the characters suggesting the presence of genetic variability among the
genotypes. The genotypes were grouped into five clusters viz. I, II, III, IV and V based on Mahalanobis’ D2 statistics.
Cluster I and II were the largest group containing six genotypes and the rest three clusters contained four, five and
three genotypes respectively. The genotyps belonging to the same group had smaller D2- value than between those
belonging to different clusters. Study on selection indices through discriminate function showed that Anza ranked as
the best followed by the genotypes Rawal, PBW-373 and Kheri and suggests that these highest scoring genotypes
might be recommended for farmers’ cultivation for better yield and it would be expected genetic gain upto 49.77%
through selection practices based on the characters studied. Therefore, a crossing programme could be made among
the genotypes belonging in cluster I and cluster V will provide maximum heterotic combination, especially for yield of
bread wheat. Alternatively, among the studied genotypes Anza could be cultivated for better performance.
Keywords: Genetic divergence, Genetic gain, Selection indices, Bread wheat
Introduction
In Bangladesh wheat (Triticum aestivum) is the second important cereal crop next to rice and gaining
popularity day-by-day. In terms of food value wheat is more nutritive than rice. It is the most widely
adapted crop all over the world. About 2 million farmers of Bangladesh have benefited from wheat
cultivation; about 600,000 people are employed for a period of 120 man-days during the wheat season,
and 20 million tons of wheat has been produced in a period of last 20 years (Banglapedia, 2004). To feed
the ever increasing population in the country, the need for more wheat will continue. There are many
possibilities to increase wheat yields in Bangladesh through developing new high yielding varieties and by
adoption of proper package of technology.
Genetic diversity is the basic for genetic improvement. It is widely accepted that information on
germplasm diversity and genetic relatedness among elite breeding materials are fundamental elements in
plant breeding (Mukhtar et al., 2002). Genetic diversity is very important factor for any hybridization
program aiming at genetic improvement of yield especially in self pollinated crops (Joshi and Dhawan,
1966). Different methods have been used to assess genetic diversity. This can be obtained from pedigree
analysis, morphological traits or using molecular markers. With the development of advanced biometrical
method such as multivariate analysis based on Mahalanobis' (1936) D2 statistics and Ward's non
hierarchical squared Euclidean distance method have become popular to quantify magnitude of diversity
among germplasm for their evaluation in respect of breeding program. For improving yield, selection
index is also practiced on the basis of different yield contributing traits. Therefore, this piece of work has
been performed to access genetic diversity and expecting higher genetic gain to improve yield in bread
wheat.
Materials and Methods
Twenty four bread wheat genotypes viz Sonalika, Kalaynsona, Pavon, Ananda, Sawgat, Protiva, Sourab,
Gourab, Satabdi, Sufi, Bijoy, Prodip, Kheri, Anza, Rawal, Raj-3765, HD-1553, GW-322, PBW-373, CB-38,
CB-43, CB-50, CB-51 and CB-53 were selected for this study. The experiment was carried out at the
experimental farm, Department of Genetics and Plant Breeding, Bangladesh Agricultural University,
2 Genetic divergence and genetic gain in bread wheat
Mymensingh during the period from November 2008 to March 2009. The experiment was set up in
randomized complete block design (RCBD) with three replications. Five plants from each plot were
randomly selected to collect the thirteen important characters such as days to booting, days to heading,
days to anthesis, leaf area index, flag leaf area duration, days to physiological maturity, plant height, no.
of effective tillers plant-1, spikelets spike-1, grains spike-1, 100 grain weight, yield plant-1 and harvest index,
Analysis of variance was performed using the plant breeding statistical program (PLABSTAT, Version 2N,
Utz, 2007) with the following model:
Yij = gi + rj + εij
Where, Yij was observation of genotype i in replicates j, gi and rj were effects of genotype i and replicates
j, respectively and εij was the residual error of genotype i in replicate j. The replicates were considered
as random factors. Multiple mean comparisons were made with Fisher’s least significant difference (LSD)
procedure using StatGraphics Plus for Windows 3.0 (Statistical Graphics Crop. Rockville, USA).
Calculation of D2 Values: The Mahalanobis' distance (D2) values were calculated from transformed
uncorrelated means of characters according to Rao (1952) and Singh and Chaudhury (1985). For each
combination the mean deviation, i.e Y1
i - Y2
i with i = 1, 2,……......p genotypes were estimated and the D2
was calculated as sum of the squares of these deviations, i.e ( Y1
i - Y2
i)2. The D2 values were estimated
for all possible pairs of combinations between genotypes.
Clustering: The D2 values of genotypes were arranged in order of relative distances from each other by
the method suggested by Tocher (Rao 1952) and Singh and Chaudhary (1985) was used for cluster
formation. Selection indices were constructed using the methods developed by Smith (1936) based on
the discriminate function of Fisher (1936).
Result and Discussion
The genotypes were significantly different from each other for grain yield and different yield contributing
characters. Using Mahalanobis’ D2 statistics and Tocher’s method, the genotypes were grouped into five
clusters (Table 1). Cluster I and II had same no. of genotypes i.e six and they were the largest cluster.
The cluster IV was in second position with 5 genotypes. The cluster III and V contained 4 and 3
genotypes respectively.
Table 1. Clustering pattern of 24 genotypes of wheat based on Mahalanobis’ D2-values and
the member present in each respective cluster
Cluster
number
Number of
genotypes Percent Name of genotypes
І 6 25
Satabdi, Bijoy, Kalayansona, GW-322, CB-51,
Sonalika
ІІ 6 25 Sufi, pavon, CB-53, CB-38 , CB-43, Ananda
ІІІ 4 16.67 Prodip, Sourav, Protiva, Gourav
IV 5 20.83 CB-50, PBW-373, Kheri, Rawal, Anza
V 3 12.5 Raj-3765, HD-1553, Sawgat
Dendrogram indicated grouping of 24 genotypes of wheat into five clusters (Fig. 1). Satabdi, Bijoy,
Kalayansona, GW-322, CB-51 and Sonalika grouped in cluster I with high genetic (7.54) distance; Sufi,
pavon, CB-53, CB-38, CB-43 and Ananda in cluster II which had highest genetic distance (8.17; while
Prodip, Sourab, Protiva and Gourab on cluster III with 6.68 and CB-50, PBW-373, Kheri, Rawal, Anza on
cluster IV with 6.35 which was lowest genetic distance. Finaly Raj-3765, HD-1553, Sawgat on cluster V.
Ferdous et al. 3
1 = Satabdi
2 = Sufi
3=Bijoy
4 = Prodip
5 = Kalayansona
6 = Pavon
7 = CB-53
8 = CB-38
9 = CB-50
10 = Raj 3765
11 = HD 1553
12 = GW 322
13 = CB-51
14 = CB -43
15 = PBW 373
16 = Sonalika
17 = Sawght
18 = Kheri
19 = Ananda
20 = Rawal
21 = Sourab
22 = Protiva
23 = Anza
24 = Gourab
Fig. 1. Dendrogram based on genetic distance, summarizing the data on differentiation
between 24 wheat genotypes according to Mahalanobis’D2 method
Elias and Shamsuddin (2000) carried out an experiment with 16 genotypes of bread wheat for study the
genetic divergence with the help of Mahalanobis D2 -statistics. They constructed six distinct clusters form
those genotypes and reported that grain yield per square meter contributed maximum to the total
divergence. This was followed by 1000-grain weight, number of grains per spike and grain filling period,
whereas vegetative period contributed the least. Ribadia et al., (2007) also studied the genetic divergence
among 50 exotic genotypes of wheat by employing Mahalanobis's D2 analysis based on 10 characters.
The genotypes were grouped into six clusters. Cluster I was the largest with 38 genotypes followed by
clusters II and III containing 7 and 2 genotypes, respectively. The highest inter-cluster distance was
observed between cluster II and V followed by that between cluster I and V, suggesting more variability in
genetic makeup of the genotypes included in these clusters. Cluster II had the highest mean values for
grain yield plot-1.
Selection index was constructed to identify suitable genotypes among 24 varieties of wheat considering
eight characters viz. days to heading, days to anthesis, days to physiological maturity, no. of effective
tillers plant-1, grain spike-1, 100 grain weight, harvest index and grain yield (Table 2). Genotype Anza
achieved the highest selection score (331.81) and ranked as the best followed by the genotypes Rawal,
PBW-373 and Kheri with 323.27, 322.54 and 320.10 respectively. The genotype Raj-3765 was worst
having the lowest selection score of 241.38 followed by HD-1553 (254.39) and CB-53 (264.92). The
expected genetic gain (G) was 49.77 at 5% selection intensity i.e 2-3 highest scoring genotypes from
these 24 wheat genotypes might be recommended for farmers’ cultivation for better yield.
Uddin el al., (1997) stated a selection index revealed that three variable indices including grain yield
plant-1, spikes plant-1, and 1000-grain weight gave the highest relative efficiency over straight selection for
grain yield plant-1 in spring wheat. Siahoosh et al., (2001) conducted an experiment during 1997-98 in two
locations in Iran to evaluate selection indices for increasing grain yield in 25 wheat cultivars and they
showed that grain yield, number of grains spike-1 and number of spikelets spike-1 were the best indices for
increasing grain yield in wheat.
4 Genetic divergence and genetic gain in bread wheat
It could be concluded from the present study that a crossing programme could be made among the
genotypes belonging in cluster I and cluster V for getting maximum heterotic combinations, especially for
yield of bread wheat. Alternatively, among the studied genotypes Anza could be cultivated for better
performance and also it could be suggested that there is possibility to achieve nearly 50% genetic
advance through selection practices among the cultivated varieties.
Table 2. Selection score, rank and expected genetic gain of 24 genotypes of Wheat
considering eight characters
SL No Genotypes Selection score Rank Expected genetic gain
1 Satabdi 305.28 8
2 Sufi 279.70 19
3 Bijoy 280.50 17
4 Prodip 292.09 13
5 Kalayansona 310.72 6
6 Pavon 287.96 15
7 CB-53 264.92 22
8 CB-38 279.37 20
9 CB-50 313.01 5
10 Raj-3765 241.38 24
11 HD-1553 254.39 23
12 GW-322 309.97 7
13 CB-51 304.01 9
14 CB-43 280.15 18
15 PBW-373 322.54 3
16 Sonalika 293.09 11
17 Sawgat 275.40 21
28 Kheri 320.10 4
19 Ananda 300.50 10
20 Rawal 323.27 2
21 Sourab 288.45 14
22 Protiva 292.78 12
23 Anza 331.81 1
24 Gourab 280.75 16
49.77
References
Banglapedia. 2004. National Encyclopedia of Bangladesh. Asiatic Society of Bangladesh. 3:141-142.
Elias, M.A. and Shamsuddin, A.K.M. 2000. Genetic divergence in bread wheat (Triticum aestivum L.) for source sink characters.
Bangladesh Journal of Genetics and Plant Breeding. 13(2):19-24.
Fisher, R.A. 1936. The use of multiple measurements in taxonomic problems. Ann. Euge. 7: 179-188.
Joshi, A.B. and Dhawan, N.L. 1966. Genetic improvement of yield with special reference to self fertilizing crop. Indian Journal of
Genetics and Plant Breeding. 26A:101-113.
Mahalanobis, P.C. 1936. On the generalized distance in Statistics. Proc. Nat. Inst. Sci. Ind. 2:49-55.
Mukhtar, M.S., Rahman, M. and Zafar, Y. 2002. Assessment of genetic diversity among wheat (Triticum aestivum L.) cultivars from
a range of localities across Pakistan using random amplified polymorphic DNA (RAPD) analysis. Euphytica. 128: 417-425.
Rao, C.R. 1952. Advanced statistical method in biometrical research. John Willey and Sons, New York.
Ribadia, K.H., Dobariya, K.L., Ponkia, H.P. and Jivani, L.L. 2007. Genetic diversity in macaroni wheat (Triticum durum Desf.).
J. Maharashtra Agril. Univ. 32(1):32-34.
Siahoosh, M.R., Asad, M.T., Emam, Y., Saidi, A. and Kherradnam, M. 2001. Implication of four selection indices in wheat cultivars
(Triticum aestvum L.) for increasing the grain yield. Iranian J. Agril. Sci. 32(1):219-236.
Singh, R.K. and Chaudhary, B.D. 1985. Biometrical methods in quantitative genetic analysis. Kalyani Publishers, New Delhi.
225-252.
Smith, H.F. 1936. A discriminant function for plant selection. Ann. Eugn. 7:240-250.
Uddin, M.J., Biswanath, M. and Chowdhury, M.A.Z. 1997. Genetic paraments, correlation, path coefficient analysis and selection
indices in wheat, Bangladesh J. Sci. Ind. Res. 32(4):523-528.
Utz, H.F. 2007. PLABSTAT (Version 2N). A computer program for the computation of variances and covariances. Institute of Plant
Breeding, Seed Science and Population Genetics, University of Hohenheim, Stuttgart, Germany.
... This genetic variability of the wheat genotypes may be due to their adaptation to specific environmental conditions. These results are agreed with that of Ferdous et al. (2011). They carried out an experiment with 24 genotypes of bread wheat and got similar results with five clusters. ...
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Genetic diversity among 20 wheat genotypes/cultivars from diverse locations of Pakistan was studied using random amplified polymorphic DNA (RAPD) analysis. A total of 445 DNA fragments were amplified with50 random decamer primers 64.38% of which were polymorphic. Genetic similarity matrix based on Nei & Li's (1979) index detected coefficients ranging from 75.60% to 92.74%. These coefficients were used to construct a dendrogram using unweighted pair group of arithmetic means (UPGMA). The wheat genotypes were clustered into one major group (A) and two small groups (B and C). The most distant genotype in the dendrogram was PARC-1 that was 75.60% to 84.94% genetically similar with the other genotypes and clustered with PARC-3 which formed a group distantly related with the other clusters. Moreover, most of the wheat genotypes developed from the same breeding centre clustered in one group. It has been clearly shown that most of the cultivars except PARC-1possessed narrow genetic background. The information would be helpful for future genome mapping programs as well as for the application of intellectual breeder rights in the country. The study will also work as indicator for wheat breeders to evolve varieties with diverse genetic background to achieve sustainability in wheat production in the country.
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The articles published by the Annals of Eugenics (1925–1954) have been made available online as an historical archive intended for scholarly use. The work of eugenicists was often pervaded by prejudice against racial, ethnic and disabled groups. The online publication of this material for scholarly research purposes is not an endorsement of those views nor a promotion of eugenics in any way.