Molecular Plant Breeding 2016, Vol.7, No.19, 1
-
9
1
Research Article Open Access
Genetic Association Analysis and Selection Indices for Yield Attributing Traits in
Available Chilli (
Capsicum annuum
L.) Genotypes
Rokib Hasan
1
, Matin Akand
2
, Nazmul Alam
1
, Abul Bashar
1
, A K M Mahmudul Huque
3,
1 Plant Breeding and Crop Improvement Laboratory, Department of Botany, Jahangirnagar University, Savar, Dhaka-1342, Bangladesh
2 Regional Spices Research Centre, BARI, Gazipur-1701, Bangladesh
3 Department of Molecular Biology, Division of Life Sciences, Hana Science Hall, Korea University, Seoul 02841, Republic of Korea (South)
Corresponding Author Email
Molecular Plant Breeding, 2016, Vol.7, No.19 doi
Received: 03 Mar., 2016
Accepted: 14 Apr., 2016
Published: 16 May, 2016
Copyright © 2016
Rokib et al., This is an open access article published under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Preferred citation for this article
:
Rokib H., Matin A., Nazmul A., Abul B., and A K M Mahmudul H., 2016, Genetic Association Analysis and Selection Indices for Yield Attributing Traits in
Available Chilli (
Capsicum annuum
L.) Genotypes, Molecular Plant Breeding, 7(19): 1-9 (doi
Abstract
The present investigation was conducted with 30 chilli genotypes at the experimental field of Regional Spices Research
Centre, BARI, Gazipur to assess the genetic association and selection indices among yield and important yield attributing traits. Fruit
length, fruit weight, 100 seed weight and fruits/plant showed significant and positive correlation with yield/plant both at genotypic
and phenotypic level. Path coefficient analysis revealed that fruits/plant had maximum positive direct effect on yield. Besides
fruits/plant; fruit weight, fruit length and number of primary branches/plant also contributed positive direct effect to yield. Selection
indices were constructed through the discriminate functions using five characters. Highest relative efficiency was found for fruit
weight +fruits/plant +yield/plant comparable to other combinations of characters. This research indicated that the selection of high
yielding chilli genotypes based on these three characters might be more efficient. Biplot analysis was also performed to find out
superior genotypes.
Keywords
Chilli; Genotypic correlation; Path analysis; Selection index; Biplot
Introduction
Chilli (
Capsicum annuum
L
.
) (2n = 24) belongs to Solanaceae family, a dicotyledonous flowering plant grown
worldwide, with different names in English, such as hot pepper, chilli pepper and bell pepper etc., (Knapp et al.,
2004; Hunziker, 2001). It is one of the most popular vegetables originated from South and Central America
(Bahurupe et al., 2013). It is a self-pollinated crop but 2 to 96% out-crossing was observed under open pollination
(AVRDC, 2000).
Chilli is an important commercial crop all over the world. Green fruits of chilli are used as vegetable whereas ripe
dried fruits as spice because of its pungency and pleasant flavors (Hasan
et al., 2014). It is inseparably involved
with almost every Bangladeshi cuisine and its demand is increasing day by day owing to its pungency, appealing
color and flavor (Hasan et al., 2015). A rich diversity of chilli exists due to diverse geo climatic regions of
Bangladesh (Hasanuzzaman and Golam, 2011).
Despite having a rich diversity, the production of chili is decreasing day by day. In Bangladesh, the total
production of chilli in was 1.45 mt/ha during 2010-11, but this production was reduced to 1.3 and 1.09 mt/ha
during 2011-12 and 2011-13 respectively (BBS, 2013). The lack of improved genotypes is the prime cause of low
production. Assurance of production of chilli in a large scale is feasible only by means of breeding programmes.
Character association and cause effect analysis are pre-requisites for improvement of any trait of a crop through
selection (Krishnaveni et al., 2006). The correlation coefficient analysis measures the interactive relationship
between different traits and it resolves the component traits on which selection can be relied upon the effect of
improvement (Ajjapplavara, 2005). Assessing the direct and indirect effects of each component towards yield
through path coefficient analysis would help in identifying the reliable characters contributing to yield. It is too
laborious to consider all the yield contributing characters at a time, hence, the breeder requires adequate
