Agricultural

Communication

Biosci. Biotech. Res. Comm. 11(2): 216-223 (2018)

Relationship among combining ability, heterosis and

genetic distance in maize (

Zea maize

L.) inbred lines

under water-de cit conditions using line × tester and

molecular analysis

Sharareh Fareghi

, Aghafakhr Mirlohi

* and Ghodratollah Saeidi

Student of Plant Breeding, Department of Agronomy and Plant Breeding, College of Agriculture, Isfahan

University of Technology, Isfahan, Iran

2,3

Professor of Genetics and Plant Breeding, Department of Agronomy and Plant Breeding, College of Agriculture,

Isfahan University of Technology, Isfahan, Iran

ABSTRACT

This study was conducted to investigate the relationships of combining ability, hybrid performance and genetic dis-

tance using Sequence Related Ampli ed Polymorphism (SRAP) data and other data sets obtained from analysis of

agronomic performance of the CIMMYT maize inbred lines. For this purpose, 13 lines and four testers were crossed

through controlled pollination in a line × tester design scheme to develop 52 hybrids. These hybrids were evaluated

together with two standard checks (KSC704 and KSC705) for grain yield under two soil moisture environments for

two years (2014-2015). Pair-wise genetic distance (GD) was estimated based on Jaccard (J) and simple matching (SM)

coef cients. The variance components of speci c combining ability (SCA) were higher than general combining ability

(GCA), hence non-additive gene effects contributed to hybrid performance. There was no coincidence between the

SRAP data and morphological assessments in this study. Signi cant and positive association of general combining

ability with mid parent heterosis (MPH) under drought stress conditions is an indicator that GCA can be useful to

predict MPH during selections under water stress conditions. However, correlations of genetic distances with heterosis

under both conditions were too low to be predictive of hybrid vigor.

KEY WORDS: WATER STRESS, GENETIC DISTANCE, HYBRID PERFORMANCE, LINE × TESTER, SEQUENCE RELATED AMPLIFIED

POLYMORPHISM

216

ARTICLE INFORMATION:

*Corresponding Author: mirlohi@cc.iut.ac.ir

Received 12

April, 2018

Accepted after revision 17

June, 2018

BBRC Print ISSN: 0974-6455

Online ISSN: 2321-4007 CODEN: USA BBRCBA

Thomson Reuters ISI ESC / Clarivate Analytics USA and

Crossref Indexed Journal

NAAS Journal Score 2018: 4.31 SJIF 2017: 4.196

Online Contents Available at: http//www.bbrc.in/

DOI: 10.21786/bbrc/11.1/4

Sharareh, Aghafakhr and Ghodratollah

INTRODUCTION

Maize is one of the most important crops worldwide,

which serves as food, animal feed and raw materials of

bioenergy. It is stated that, maize is queen of cereal crops

due to high yielding potential and genetic diversity. The

global production of this crop has increased during last

years. However, its yield is reduced due to water de -

cit, which is one of the most important environmental

factors affecting agricultural productivity worldwide,

(Prasanna, 2012, Aminu et al., 2014 Li et al., 2017).

Heterosis, a powerful phenomenon in the evolution

of plants, has been used extensively in crop produc-

tion. However, identi cation and selection of appropri-

ate parental combinations which produce superior F1

hybrids, is one of the most important stages in hetero-

sis utilization (Mohammed et al., 2014). Hybrid breeders

have always been concerned to the selection of appro-

priate parental lines without making all of the possible

crossing among the available lines. Selection of parents

with various genetic backgrounds is hardly substantial

in the development of hybrids having optimal expres-

sion of heterosis (Hallauer et al., 2010 Pheirim et al.,

2017).

During the past decades, several procedures have

been utilized for prediction of heterosis, including per-

formance of parental lines, combining ability, genetic

diversity which determined using multivariate analysis

of morphological and agronomic traits and molecular

markers (Mohammadi et al., 2008). Selection based on

phenotypic traits is extremely in uenced by environ-

mental factors, and the presence of genotype × envi-

ronment interactions can hide the actual genetic value.

Moreover, because of strong dominance effects of genes

controlling maize yield, hybrid performance may not be

predicted from the performance of parental lines, reli-

ably. Furthermore, in breeding programs with a large

numbers of inbred lines, making and evaluation of all

of the possible crosses is not only expensive and bor-

ing, but also practically dif cult and time consuming

(Mohammadi et al., 2008).

In maize, several methods have been expanded for the

prediction of hybrid performance by means of genetic

markers (Frisch et al., 2010; Maenhout et al., 2010). Con-

sidering the cost and time which is required for  eld

evaluation of hybrids, the utilization of genetic mark-

ers for identi cation of best heterotic combination of

parental lines can be a suitable alternative (Mohammed

et al., 2014). Molecular markers have been widely uti-

lized in breeding programs, as a tool for the selection of

the best parental lines of crosses; and as potential tools

for the prediction of the heterosis from a certain cross.

Parental genetic distance has been considered as a

feasible indicator for hybrid performance (Melchinger,

1999). Breeders are strongly interested to the prediction

of hybrid performance from parental genetic distance.

Because the preferable crosses could be identi ed by

means of genetic distance before  eld evaluation of all

hybrid combinations. This can increase the ef ciency

of hybrid breeding programs, (Mohammed et al., 2014).

Estimated genetic distances can be related with hybrid

performance from  eld experiments, and the extension

of molecular marker systems such as sequence related

ampli ed polymorphisms (SRAP) have considerably

amended the power of the genetic distance estimation

between genotypes.

Several researchers have used genetic distance to pre-

dict hybrid performance (Dhliwayo et al., 2009; Devi and

Singh, 2011; George et al., 2011); however, their results

were inconsistent with each other. Some researchers

reported a positive correlation between marker based

genetic distance and hybrid performance (Amorim et

al., 2006; Srdic et al., 2007; George et al., 2011), while

other researchers have reported no correlation between

these two phenomenon (Balestre et al., 2008; Dhliwayo

et al., 2009; Devi and Singh, 2011). Hence, the poten-

tial utilization of molecular markers in predicting the

amount of hybrid performance in maize needs more

research. Though signi cant associations were found

between hybrid performance and genetic diversity in

several investigations, the level of association varied

widely from one study to another. Moreover, the reli-

ability of molecular markers in estimating genetic dis-

tance depends on several factors such as the number of

markers, their mode of inheritance and uniform distribu-

tion across the genome (Hahn et al., 1995; Mohammadi

et al., 2008).

To the best of our knowledge, there is a little infor-

mation about the association of genetic divergence and

hybrid performance in CIMMYT maize inbred lines,

which asks for studies to determine genetic distance as

suitable predictor of hybrid performance in this germ-

plasm. It is also needed to examine combining ability

of parents as predictor of heterosis and F1 performance

comparing with genetic distance measured by molecu-

lar markers. Therefore, this study was conducted to 1)

investigate the possibility of predicting the hybrid per-

formance using SRAP data and other data sets acquired

from analysis of agronomic performance of the CIMMYT

maize inbred lines; 2) determine associations among

genetic distance of molecular markers in parents, het-

erosis, F1 performance, general combining ability (GCA)

and speci c combining ability (SCA) effects of parents

and crosses, and compare the strategies to determine

hybrid performance based on parental genetic distance

(GD), GCA and SCA for heterosis; and 3) evaluation of

coincidence between the SRAP data and morphological

data.

BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS RELATIONSHIP AMONG COMBINING ABILITY, HETEROSIS AND GENETIC DISTANCE 217

Sharareh, Aghafakhr and Ghodratollah

MATERIALS AND METHODS

PLANT MATERIALS AND EXPERIMENTAL SITE

The experiment was conducted during two years (2014-

2015) at the Research Farm of Agriculture and Natural

Resources Research Center, Kermanshah, Iran (longitude

of 47° 26’ E, latitude of 34° 8’ N and altitude of 1346

m) on a silty clay loam soil. The mean annual precipita-

tion and temperature are 538 mm and 12.2 °C for the

region, respectively. In this study, a set of 13 inbred lines

were selected and crossed through controlled pollination

with four temperate maize testers using a line × tester

matting design to produce 52 hybrid combinations. The

origin and pedigree of the lines and testers are given in

Table 1.

FIELD EXPERIMENT

In this experiment, 52 hybrids derived from line × tester

matting scheme along with two standard checks were

planted in the  eld according to a randomized com-

plete block design (RCBD) with three replications, at

two moisture environments (normal and water stress).

Each plot was included 2 rows of 4 m long with an

inter-row spacing of 0.75 m and in-row plant spacing

of 18 cm. Under the normal and stress moisture envi-

ronments, plants were irrigated when 50% and 65% of

the total available soil water was depleted from the root

zone, respectively. Soil moisture was measured based on

standard gravimetric methods (Clarke Topp et al., 2008).

The irrigation was applied by using a basin irrigat ion

system. The amount of water for each irrigation treat-

ment was measured using a volumetric counter. Grain

yield per plot was recorded on  ve randomly selected

plants per replication.

SRAP ANALYSIS OF INBRED LINES

Genetic characterization of all of the inbred lines and

testers was done using a set of 30 SRAP primer pairs.

Genomic DNA was extracted from fresh leaves of each

line or tester according to the method of Murray and

Thompson (1980). PCR reactions were performed in a

10µl reaction mix and ampli ed products were resolved

by using 6% polyacrylamide gel followed by silver

staining.

STATISTICAL ANALYSES

Analysis of variance (ANOVA) for each moisture envi-

ronment was conducted using the PROC GLM of SAS

(SAS Institute 2008). Genotypes were considered as  xed

effects while years, moisture environments and replica-

tions were considered as random. The SAS program was

used for the line × tester analysis to compute the SCA

effects (Singh and Chaudhary, 1977). The GCA effects

of lines and testers, the SCA effect of crosses, and their

interactions with the year were estimated based on the

factorial mating design as follows:

ijk

+ g

+ s

+ e

+ ge

+ se

ijk

Table 1. Information on maize lines and testers used in the study

Parents Name of lines and testers/pedigree Origin

Line 1 4-CHTSEY,2002/1389/9=1390/13=1391/10 CIMMYT germplasm

Line 2 4-CHTSEY,2002/1389/19=1390/21=1991/70 CIMMYT germplasm

Line 3 7-CHTSEY,2002/1389/33=1390/33=1391/61 CIMMYT germplasm

Line 4 7-CHTSEY,2002/1389/35=1390/37=1391/64 CIMMYT germplasm

Line 5 K18 × 2-CHTHIY, 2002/1389/59=1390/73=1391/43 derived from cross k18 × CIMMYT originated line

Line 6 K18 × 2-CHTHIY, 2002/1389/61=1390/77=1391/46 derived from k18 × CIMMYT originated line

Line 7 XT03 Derived from unknown China -source

Line 8 4-CHTSEY, 2002/1390/5=1391/6 CIMMYT germplasm

Line 9 4-CHTSEY, 2002/1390/9=1391/8 CIMMYT germplasm

Line 10 7-CHTSEY, 2002/1390/41=1391/22 CIMMYT germplasm

Line 11 20-CHTSEY,2002/1390/45=1391/25 CIMMYT germplasm

Line 12 20-CHTSEY,2002/1390/53=1391/31 derived from CIMMYT germplasm

Line 13 MO17 × 6-CHTHEY, 2002/1390/69=1391/40 derived from cross MO17 × CIMMYT originated line

Tester 1 MO17 CL. 187–2 × C103

Tester 2 0K18 derived from MO17 changes

Tester 3 A679 A B73 back-cross derived line [(A662 × B73)(3)]

Tester 4 K166B derived from CIMMYT germplasm

218 RELATIONSHIP AMONG COMBINING ABILITY, HETEROSIS AND GENETIC DISTANCE BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS

Sharareh, Aghafakhr and Ghodratollah

Where Y

ijk

; is performance of the hybrid when ith line

is crossed to jth tester, in the kth year,  is the overall

mean, g

is the effect of the ith line, g

is the effect of the

jth tester, s

is the interaction of the ith line with the jth

tester, e

is the effect of the kth year, (ge)

is the interac-

tion of the g

and e

, (ge)

is the interaction of the g

and

, (se)

ijk

is the interaction of s

and e

For each cross combination (P1 × P2) mid parent het-

erosis (MPH) was calculated according to Falconer and

Mackay (1996) as follows:

Mid parent heterosis (MPH) = [(F1- MP) / MP] × 100

where F1 is the mean of the F1 hybrid performance and

MP is mean performance of two parental inbred lines.

Better parent heterosis (BPH) was calculated as:

BPH = [(F1- BP) / BP] × 100

where BP = mean of the better parent.

Genetic distance (GD) between each pair of parents

was estimated from the binary matrix, using Jaccard and

simple matching coef cients through the NTSYSpc ver-

sion 2.0. Cluster analysis was done based on the UPGMA

method. For evaluation of the correlation between two

similarity matrices (molecular and phenotypic data),

Mantel test in NTSYS software was applied. Mean of the

trait in each moisture environment was used to calculate

correlation coef cients between genetic distance, grain

yield, MPH, BPH, GCA and SCA.

RESULTS AND DISCUSSION

Presence of an appropriate value of heterosis for grain

yield and predicting hybrid performance is important in

hybrid breeding programs. The degree of heterosis may

in uence by genetic diversity of the germplasm being

used. The magnitude of heterosis which was observed in

this study indicates that there is an opportunity to use

this germplasm for extending hybrid varieties appropri-

ate for stress and non-stress conditions.

Analysis of variance of grain yield for each of the

two moisture environments showed signi cant geno-

type effects, indicating the existence of genetic varia-

tion among the genotypes. However genotype × year

interaction was non-signi cant in both moisture envi-

ronments, indicating that the genotypes were consistent

over the years (Table 2). Nevertheless, signi cant dif-

ferences (p < 0.01) among parents and F1 hybrids in

both moisture environments were found and indicated

that the data was suitable for genetic analysis of line

× tester design. The mean squares for lines and testers

which determine the GCA effects were also signi cant

and showed the predominance of additive gene action

in controlling grain yield. However, the mean squares of

testers were higher than that of the lines in normal con-

ditions and it was vice versa for water stress conditions.

The signi cance of line × tester interaction revealed that

SCA was also important in the control of grain yield and

indicated that non-additive gene effects also play an

important role in the controlling of this trait (Table 2).

Average grain yield for parents (Table 3) and hybrid

combinations (Table 4) showed a remarkable reduction

under water stress and it ranged from 8.68 ton/ha for

L8 × T1 to 15.16 ton/ha for L9 × T4 under normal con-

ditions. However, under water stress conditions, this

ranged from 4.95 ton/ha for L10 × T1 to 11.99 ton/ha

for L5 × T3. The GCA effect was positive and signi -

cant for two parents of T3 and T4 under both normal

and water stress conditions (Table 3). However, under

normal conditions four parents and under water stress

conditions  ve parents showed signi cant and positive

values of GCA for grain yield (Table 3). Under normal

conditions  ve hybrids and under water stress condi-

tions seven hybrids expressed signi cant and positive

values of SCA for grain yield (Table 4). Moreover, under

normal conditions  ve hybrids and under water stress

conditions eight hybrids showed signi cant and nega-

tive SCA for this trait.

Table 2. Analysis of variance (ANOVA) for

combining ability of total grain yield based on

line × tester matting design under normal and

drought stress conditions.

Source of variation df

Mean squares (MS)

NS S

Year (Y) 1 18.11 17.41

Block (R) 4 37.18** 1.20

Genotype (G) 53 14.99** 19.33**

F1 vs. Check 1 2.65 39.26

Check 1 35.02** 6.02*

F1 51 14.84* 19.20**

Lines (L) 12 21.31** 36.36**

Testers (T) 3 46.94** 35.45**

L×T 36 10.00** 11.89**

G×Y 53 2.56 0.85

(F1 vs. Check) × Y 1 0.65 5.38

Check × Y 1 1.84 0.04

F1 × Y 51 2.61 0.77

L × Y 12 2.85 0.38

T × Y 3 0.71 0.86

L × T × Y 36 2.69 0.95

Error 212 2.32 1.12



A - 1.96 1.91



D - 4.88 7.29

*,**Signi cant at 0.05 and 0.01 probability levels, respectively.

NS: Non-stress, S: Stress

BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS RELATIONSHIP AMONG COMBINING ABILITY, HETEROSIS AND GENETIC DISTANCE 219

Sharareh, Aghafakhr and Ghodratollah

Mid parent heterosis (MPH) and better parent het-

erosis (BPH) values were signi cant and positive for all

hybrids under both normal and water stress conditions.

MPH ranged from 70% for L10 × T1 to 230% for L7 × T3

under normal conditions and from 0.63% for L10 × T3

to 3.15% for L5 × T3 under water stress conditions. The

range of BPH was from 39% for L10 × T1 to 183% for L7

× T3 under normal conditions, and from 0.62% for L10

× T3 to 2.92% for L5 × T3 under water stress conditions

(Table 4).

SRAP data showed low genetic distances among

parental lines. Distances ranged from 0.181 for L12 ×

T4 to 0.423 for L11 × T2 based on Jaccard coef cient,

and from 0.142 for L12 × T4 to 0.358 for L9 × T1 based

on simple matching coef cient (Table 4). The average

genetic distance among inbred lines of this study based

on Jaccard and simple matching coef cients were 0.33

and 0.25, respectively; indicating low levels of poly-

morphism among them. Ndhlela et al. (2015) stated that

low genetic distances can be attributed to the mixing of

germplasm by CIMMYT for population improvement at

the expense of hybrid breeding.

In  eld experiments, the most expensive and time

consuming step of hybrid breeding programs is the

identi cation of inbred lines expressing higher hetero-

sis (Mohammadi et al., 2008). Plant breeders often have

used SCA of hybrids in identifying better parental lines

for extension of hybrid combinations. However, when a

large numbers of inbred lines are available in a breeding

program, more useful tools are needed. In maize, genetic

distance determined by molecular markers is the main

strategy which has been followed for determination of

hybrid performance and its potential for this purpose has

been evaluated in several researches. In these researches,

the extent of correlation differed greatly from one trait

to another and also varied extensively with the germ-

plasm used in different studies.

The correlation coef cients of GD calculated based

on Jaccard and simple matching coef cients were neg-

ligible and non-signi cantly different from zero for

each of TGY, MPH and BPH (Table 5). Therefore, pre-

diction of hybrid performance for grain yield based on

genetic distance estimated by SRAP markers cannot be

a practical approach and this was in agreement with the

results of Dhliwayo et al. (2009), Devi and Singh (2011)

and Ndhlela et al. (2015). However, some studies have

reported powerful correlation between hybrid perfor-

mance and parental genetic distance (Melchinger, 1999;

Singh and Singh, 2004).

Mohammadi et al. (2008) suggested that insuf cient

genome coverage, sample size of the parental lines and

progenies and different levels of dominance effects on

traits are some important reasons for the low correla-

tion between genetic distance and hybrid performance.

Other possible reason for this issue is utilization of

unlinked markers to the trait in estimation of genetic

distance. For solving this problem, Bernardo (1992) sug-

gested identifying of speci c marker loci with close link-

age to chromosomal segments controlling target traits.

Although genetic distance was not a reliable predictor

of hybrid performance, some promising approaches such

as BLUP (Best Linear Unbiased Prediction) along with

molecular marker data have been extended for predict-

ing hybrid performance using genetic distances. How-

ever, in this study signi cant and positive associations

were observed between TGY, BPH and MPH with SCA

effects of crosses. Moreover, a signi cant and positive

correlation was found between GCA and MPH under

water stress conditions (Table 5). This correlation is an

indicator that GCA can be useful to predict MPH during

selections under water stress conditions.

In this study, correlations based on genetic distance

estimates using simple matching coef cient were relatively

higher than correlations based on GD estimated using Jac-

card coef cient. This shows that the extent of correlation

coef cient was not only impressed by the germplasm under

study, but also by the genetic distance measures.

Cluster analysis grouped the 17 lines and testers into

three major groups (Fig. 1). However, the cluster analy-

Table 3. Grain yield (GY) means and general combining

ability (GCA) values for parental lines used in this study

under normal (NS) and water stress (S) conditions.

Parents

Grain yield

(GY) (Ton/ha)

General combining

ability (GCA) (Ton/ha)

NS S NS

L1 5.85 2.77 -1.12

L2 4.14 3.07 0.41

L3 5.49 2.40 0.57

L4 6.39 3.85 0.98**

L5 5.30 2.72 0.37

L6 6.33 3.22 0.54

L7 3.77 2.73 0.07

L8 4.91 2.82 -1.90**

L9 4.17 2.62 0.65

L10 7.15 3.05 -0.83*

L11 6.18 3.21 -0.98**

L12 5.74 2.57 1.33**

L13 5.70 2.88 -0.09

T1 4.56 2.34 -0.71**

T2 4.80 2.71 -0.63**

T3 5.27 3.06 0.75**

T4 5.53 3.04 0.59**

*, ** Signi cant at 0.05 and 0.01 probability levels, respectively.

NS: Non-stress, S: Stress

220 RELATIONSHIP AMONG COMBINING ABILITY, HETEROSIS AND GENETIC DISTANCE BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS

Sharareh, Aghafakhr and Ghodratollah

Table 4. Grain yield (GY) means, speci c combining ability (SCA), mid parent heterosis (MPH) and better

parent heterosis (BPH) estimates for 52 F

hybrids of line × tester under normal (NS) and water stress (S)

conditions, and genetic distance (GD) between respective parental lines using simple matching (SM) and

Jaccard’s (J) coef cients based on SRAP markers.

Hybrids

GY (Ton/ha) SCA (Ton/ha) MPH (%) BPH (%)

GD J GD SM

NS S NS S NS S NS S

L1×T1 10.55 7.35 -0.01 -0.43 103** 188** 80** 166** 0.327 0.245

L1×T2 10.73 7.56 0.08 1.04* 101** 176** 83** 173** 0.359 0.250

L1×T3 12.59 8.24 0.56 -0.81 126** 183** 115** 170** 0.308 0.221

L1×T4 11.23 9.00 -0.63 0.20 97** 210** 92** 196** 0.350 0.275

L2×T1 12.32 6.87 0.23 -0.34 183** 154** 170** 124** 0.353 0.270

L2×T2 12.57 7.79 0.38 0.45 181** 170** 162** 154** 0.292 0.196

L2×T3 11.62 6.79 -1.94** -1.17* 147** 122** 121** 121** 0.290 0.206

L2×T4 14.72 10.92 1.32 1.06* 204** 257** 166** 255** 0.312 0.240

L3×T1 14.21 7.28 1.96** -0.28 183** 207** 159** 203** 0.276 0.211

L3×T2 12.21 7.63 -0.13 0.37 137** 199** 122** 182** 0.349 0.255

L3×T3 11.61 7.71 -2.11** -0.80 116** 183** 112** 152** 0.301 0.225

L3×T4 13.84 7.75 0.28 0.71 151** 185** 150** 155** 0.280 0.221

L4×T1 12.20 6.66 -0.46 -0.26 123** 115** 91** 73** 0.395 0.324

L4×T2 11.42 8.41 -1.33 -0.75 104** 157** 79** 119** 0.388 0.289

L4×T3 14.73 8.93 0.60 -0.39 153** 159** 130** 132** 0.350 0.270

L4×T4 15.15 10.00 1.19 1.40** 154** 191** 137** 160** 0.376 0.314

L5×T1 14.59 6.76 2.54** -0.11 196** 167** 175** 148** 0.388 0.324

L5×T2 10.82 8.36 -1.33 -1.56** 114** 208** 104** 207** 0.370 0.279

L5×T3 14.51 11.99 0.98 0.80 175** 315** 174** 292** 0.333 0.260

L5×T4 11.16 9.57 -2.20** 0.87 106** 232** 102** 215** 0.398 0.343

L6×T1 11.97 7.22 -0.26 -0.48 120** 160** 89** 124** 0.291 0.216

L6×T2 12.21 6.77 -0.10 -0.87 119** 129** 93** 111** 0.297 0.201

L6×T3 12.79 10.58 -0.90 -0.24 121** 237** 102** 229** 0.283 0.201

L6×T4 14.79 10.86 1.26 1.59** 149** 247** 134** 238** 0.306 0.235

L7×T1 11.78 6.47 0.03 0.92* 183** 155** 158** 137** 0.240 0.181

L7×T2 11.70 9.90 -0.14 -0.35 173** 264** 144** 262** 0.347 0.255

L7×T3 14.92 9.98 1.70* 0.40 230** 245** 183** 227** 0.331 0.255

L7×T4 11.46 10.74 -1.59* -0.96* 146** 272** 107** 254** 0.256 0.201

L8×T1 8.68 7.62 -1.10 0.43 83** 195** 77** 170** 0.232 0.176

L8×T2 10.11 5.09 0.24 -0.20 108** 84** 106** 80** 0.327 0.240

L8×T3 11.21 5.21 -0.04 0.91* 120** 77** 113** 71** 0.290 0.221

L8×T4 11.98 5.71 0.90 -1.14* 129** 95** 117** 88** 0.259 0.206

L9×T1 12.38 8.84 0.05 0.71 184** 256** 172** 237** 0.422 0.358

L9×T2 11.61 5.79 -0.81 -1.07* 159** 117** 142** 114** 0.408 0.314

L9×T3 13.02 7.16 -0.78 0.32 176** 152** 147** 134** 0.390 0.314

L9×T4 15.16 8.76 1.53* 0.04 212** 210** 174** 188** 0.412 0.358

L10×T1 9.92 4.95 -0.93 0.00 70** 84** 39** 62** 0.342 0.265

L10×T2 11.92 6.79 0.98 0.02 100** 136** 67** 123** 0.340 0.240

L10×T3 12.75 4.96 0.43 0.89 105** 63** 78** 62** 0.336 0.250

L10×T4 11.67 6.23 -0.48 -0.90* 84** 105** 63** 105** 0.354 0.284

L11×T1 11.07 5.42 0.38 -1.18** 106** 95** 79** 69** 0.373 0.324

L11×T2 11.59 10.48 0.80 1.82** 111** 254** 87** 227** 0.423 0.348

L11×T3 11.60 6.67 -0.56 0.81 103** 113** 88** 108** 0.368 0.309

L11×T4 11.39 5.82 -0.61 -1.45** 94** 86** 84** 81** 0.356 0.314

BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS RELATIONSHIP AMONG COMBINING ABILITY, HETEROSIS AND GENETIC DISTANCE 221

Sharareh, Aghafakhr and Ghodratollah

L12×T1 12.78 7.57 -0.24 0.34 148** 208** 123** 194** 0.220 0.172

L12×T2 13.20 7.82 0.09 0.40 150** 196** 130** 189** 0.385 0.304

L12×T3 14.88 8.88 0.39 0.06 170** 216** 159** 191** 0.298 0.235

L12×T4 14.07 8.62 -0.25 -0.80 150** 207** 145** 184** 0.181 0.142

L13×T1 9.41 5.26 -2.18** 0.69 83** 102** 65** 83** 0.329 0.240

L13×T2 12.93 6.58 1.25 0.72 146** 136** 127** 129** 0.234 0.147

L13×T3 14.72 6.06 1.66* -0.80 168** 104** 158** 98** 0.310 0.216

L13×T4 12.16 5.20 -0.73 -0.62 117** 76** 113** 71** 0.353 0.270

*, **Signi cant at 0.05 and 0.01 probability levels, respectively.

NS: Non-stress, S: Stress

Table 5. Correlation coef cients of grain yield (GY), Mid parent heterosis (MPH) and Better

parent heterosis (BPH) with each of SRAP-based genetic distance estimates using Jaccard

(GDJ) and simple matching (GDSM) coef cients, general and speci c combining ability (GCA

and SCA, respectively) under normal (NS)and water stress (S) conditions.

Environments

GY (Ton/ha) MPH (%) BPH (%)

NS S NS S NS S

GDJ -0.023 0.028 -0.028 -0.03 -0.063 -0.045

GDSM 0.027 0.062 0.006 0.012 -0.02 -0.015

GCA 0.012 -0.243 0.049 0.661** 0.202 -0.073

SCA 0.689** 0.323** 0.565** 0.318** 0.528** 0.309**

*, **Signi cant at 0.05 and 0.01 probability levels, respectively.

NS: Non-stress, S: Stress

GY, grain yield; MPH, Mid parent heterosis; BPH, Better parent heterosis; GDJ, Jaccard genetic distance; GDSM, Simple

matching genetic distance; GCA, General combining ability; SCA, Speci c combining ability.

sis based on phenotypic traits and SRAP markers could

not separate parents based on geographical or ecological

data. Moreover, in this study there was no coincidence

between the SRAP data and morphological estimations,

which indicated poor association and agreement of

molecular marker diversity with that of phenotypic one

(r= 0.15 under normal and r= 0.10 under water stress

conditions). Several reasons are given for the discord-

ance between these two sets of data. Accumulation of

some characteristics having adaptive value in speci c

habitats subjected to similar ecologic conditions (Steiner

and Los Santos, 2001), differences between the evolu-

tionary rates of phenotypic traits with adaptive value

and those originating from selectively neutral DNA (Lin-

hart and Grant, 1996), selection pressure for homogeni-

zation of different traits in parental germplasm and the

different genomic regions evaluated with both markers

(Amini et al., 2011), are some of these probable reasons.

In conclusion, prediction of heterosis is critical and

valuable in hybrid breeding programs. In this regard,

a potentially powerful approach is the application of

genetic distance speci ed by molecular markers. In this

study, associations between genetic distance estimates

(GDJ and GDSM) with heterosis effects were negligible

and non-signi cant. Thus, prediction of heterosis based

on genetic distances estimated by SRAP markers cannot

be a practical approach. Use of unlinked markers to the

target traits, insuf cient genome coverage, sample size

of the parental lines and progenies and different levels

of dominance effects on target traits are some of prob-

able reasons for the low correlations between genetic

distance and hybrid performance. On the other hand,

identifying of speci c marker loci with close linkage

to chromosomal segments controlling target traits and

application of statistical methods such as BLUP along

with molecular marker data are some of the solutions

for this problem. A signi cant and positive association

FIGURE 1. Dendrogram depict-

ing genetic relationships among

parental lines involved in line ×

tester analysis, based on SRAP

data using UPGMA method and

Jaccard’s coef cient.

222 RELATIONSHIP AMONG COMBINING ABILITY, HETEROSIS AND GENETIC DISTANCE BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS

Sharareh, Aghafakhr and Ghodratollah

among GCA and MPH under drought stress conditions

is an indicator that GCA can be useful to predict MPH

during selections under water stress conditions.

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