Wheat Genotyping by Karnal
Bunt Resistance Associated SSR Markers Depict Connotation with their Phenotypic
Response against Tilletia indica
Aasma1*,
Shahzad Asad1, Muhammad Fayyaz1, Sania Begum2, Muhammad Aqeel2, Muhammad Nadeem
Hassan3 and Usman Waheed4*
1Crop Diseases Research
Institute, National Agricultural Research Centre, Islamabad, Pakistan
2National Institute for
Genomics and Advanced Biotechnology, National Agricultural Research Centre,
Islamabad, Pakistan
3Department of Biosciences,
COMSATS University Islamabad, Islamabad Campus
4Department of Pathobiology,
Univerisity of Veterinary and Animal Sciences, Lahore, Sub campus Jhang,
Pakistan
*For Correspondence: aasmafahad@outlook.com; dr.usman.waheed@gmail.com
Received 18 March 2020;
Accepted 11 October 2020; Published 10 December 2020
Abstract
Genetic characterization of wheat genotypes and their response to the
pathogen is necessary for devising suitable disease management strategies. In this
study, 42 wheat genotypes were screened during Rabi season of 2015–2016 and
2016–2017, to explore the existence of Karnal bunt (KB) resistance using
seventeen SSR markers followed by their phenotypic response against the
virulent isolate (PB-25) of Tilletia
indica reported earlier from Faisalabad, Pakistan. Among 42 wheat
genotypes, 14 exhibited highly resistant (HR), six showed resistant (R) while
22 showed moderately susceptible (MS) reaction during Rabi season of 2015–2016.
Second year (Rabi 2016–2017) results showed the same reactions with few
exceptions that showed depletion in the resistance. Out of seventeen SSR
markers, thirteen were able to amplify bands linked with KB resistance in
different wheat genotypes. A total of 40 alleles were detected for the 42
genotypes with an average of 3.1 alleles per SSR. The maximum number of
polymorphic bands (8 amplicons) were generated by the SSR primer Xgwm174
followed by Xgwm340 (7 amplicons). The present study showed a strong
association between the genotypic and phenotypic response of different wheat
genotypes. However, these genotypes (99172, DN-102, NRL-1130, V-11160, V-11005-2013, 12266, 11098, 11C023,
PR-111, PR-112, PR-113, NR-423,
NR-429, NR-436, 9459-1, KT-338, FSD-08,
TW1150, ESW-9525, NIA-CIM-04-10) were found consistently resistant in both
years and can be utilized in the breeding program for the incorporation of high
yields. © 2021 Friends Science Publishers
Keywords: SSR Markers; Karnal bunt; Resistance; Wheat;
Genotyping; Coefficient
infection
Introduction
Wheat (Triticum
aestivum L.) is a staple diet of many countries and extensively grown in
the world (Huang and Roder 2004). It is an important source of fiber, fat,
sugar, minerals, protein, and carbohydrates that can partially meet human
energy needs (Topping 2007). Various biotic and abiotic factors affect the
wheat production quantitatively and qualitatively (Carris et al. 2006; Farooq et al. 2011, 2014; Kumar et al. 2016). Fungal diseases are one of
the major biotic factors which affect the wheat yield by causing diseases such
as rust, powdery mildew, loose smut and karnal bunt (Bonde et al. 1997). Karnal bunt (KB), caused by a fungus Tilletia indica (T. indica), is a floret infecting fungus belonging
to order Ustilaginales. It is also known as partial bunt as it typically
infects the part of kernels and replaced it by powdery black teliospores masses
(Carris et al. 2006). The infected
kernels emit fishy odor due to the production of trimethylamine and affects the
quality (Kumar et al. 2016). It is a
concern because 1 to 4% infection in kernels makes wheat grain unpalatable
whereas 5% infection in grain seed lot caused chemical changes in flour quality
and gluten content (Rush et al.
2005).
The T. indica
differs from other pathogens as it infects plant during anthesis and sporulates
on the same generation of that infected host (Carris et al. 2006). Infected sori of KB infected seeds are broken easily
during harvesting which further contaminates the healthy seeds, soil and
machinery and are the major source of inoculum. These spores can blow by the wind
to the long distances contaminating the healthy fields (Bonde et al. 2004). The thick-walled, dormant T. indica spores can survive in warm,
dry and harsh summer conditions during post-harvest period in the soil up to 5
years and germinate at the optimum temperature of 20 to 25°C (Gill et al. 1993).
The KB is a
highly alarming issue to wheat grain industry not only in terms of yield losses
but also limited international trade due to quarantine regulations (Brar et al. 2018). The loss of export market
due to KB in any country would lead to a reduction in production, price and
ultimately farmer’s income (Gupta et al.
2019). Total loss due to KB was estimated in North West Mexico to be 7.02
million dollar/year and was 27 million dollars reported in USA (Brennan et al. 1990; Vocke et al. 2002). In Pakistan, KB prevalence has been increased over
time in Punjab and Khyber Pakhtunkhwa (Aasma et al. 2012) and has posed a threat to wheat production even in the
dry and hot region of Punjab (Khan et al.
2010). Raees et al. (2013) reported
39% yield losses from Rawalpindi and Chakwal of Punjab. Maximum disease
prevalence was observed in Chakwal (50%) as compared to Rawalpindi (35%).
Sajjad et al. (2018) observed 91.7%
KB prevalence in district Lodhran while lowest in Rahimyar Khan (68.2%).
Aasma et al.
(2019a) surveyed different districts of Punjab and KP and reported the
prevalence of KB in Jehlum (18.5%), Chakwal (15.9%), Kohat (40.0%), Charsadda
(33.3%) and Mansehra (13.3%) whereas samples from Haripur, Malakand, and
Mingora showed no visual infection during 2013. While in another survey
conducted during 2014, recorded the prevalence from the samples collected from
Gujranwala (70.4%), Muzfarghar (46.7%), Jhelum (35.3%), Peshawar (43.2%),
Charsadda (33.3%), Nowshera (25.0%) and Buner (11.57%) which shows an
increasing trend of the disease.
Effective
management approaches are necessary to reduce crop damage. Being the seed, soil
and air-borne sporidial disease, its control is limited through the fungicidal
application (Gupta et al. 2019);
therefore, host plant resistance is considered to be the paramount choice for
KB pathogen management. The use of resistant varieties is the most practical
and economical method (Sharma et al.
2008).
Jafari et al. (2000) worked for the exploration
of the sources of resistance against KB by inoculating the spikes using the
injection method on 26 wheat advanced cultivars/ genotypes at the booting
stage. Coefficient of infection and the percentage of infected grains for each
genotype were estimated. They found most of the cultivars showed a susceptible
reaction; however, the two cultivars exhibited partial resistance to KB. A
significant correlation was found between the coefficient of infection and the
percentage of infected grain for each genotype. Kumar et al. (2014) screened
150 genotypes of wheat for KB resistance by artificial inoculations
under field conditions during the consecutive 2009–2010 seasons. The results
showed variability in the incidence of disease among the genotypes. The disease
incidence ranged from 0.2–63.1 and 12 genotypes exhibited highly resistant (HR)
while 6 showed resistant, 6 as moderately resistant reaction, whereas the rest
of genotypes were found moderately susceptible to highly susceptible. Several
genotypes (HD29, W485, KB 2012–03 (in PBW343 background), ALDAN
"S"/IAS 58, and H 567.71/3∗PAR) were
screened and it was identified that these genotypes carry stable resistance in
multiple environments in India (Sharma et
al. 2005). Intensive breeding has been carried out for KB resistance.
However, breeding is often hindered by the lack of a simple, rapid and
environment independent methods of screening host genotypes against pathogens.
Moreover, transfer of genes through conventional breeding is laborious and
time-consuming. Alternately molecular tools provide a better understanding of
the existing genetic variability needed to be considered for further progress
in breeding programmes (Fischer and Edmeodes 2010). Simple identification of
PCR-based markers associated with KB resistance provides the possibility of
using a marker-assisted selection scheme in the development of resistant wheat
cultivars (Kumar et al. 2007). Hence,
detection of disease resistance genes coupled with screening could be helpful
in different molecular breeding programs.
Kumar et al. (2015) discovered SSR markers linked
with KB resistance during the year 2010 and 2011. They tested different inbred
genotypes by crossing of resistant and susceptible parent and found a lot of
variation among these genotypes based on the coefficient of infection. Out of
70 markers, 42 were polymorphic. They detected, with an average of 2.09 alleles
per locus. In addition to the already identified KB resistant primers, they
found a new marker Xgwm 6 that was linked with KB resistance.
The purpose of this study was to analyze different
Pakistani wheat genotypes to detect KB resistance genes, identify the
reproducible SSR markers associated with KB resistance and infer the
association of wheat genotypes with their phenotypic response against the
virulent isolate of T. indica. Thus,
it could be effectively utilized in the breeding program to combat the disease.
Materials and
Methods
Plant material
Forty-two wheat genotypes were used in this study (Table
1). The seeds were collected from the National Coordinated Wheat Program,
National Agriculture Research Center, Islamabad, Pakistan.
Phenotypic screening of wheat
Forty-two wheat genotypes (Table 1) were sown in the pots during November 2015 and the same set
of genotypes were repeated for screening during November 2016 to confirm the
disease resistance sustainability in the glasshouse at NARC using CRD design.
Each genotype was replicated thrice in different pots. These pots
were kept under controlled condition i.e. temperature 18–20ºC and misting
system of automatic sprinkling water 5 times per day to maintain humidity (approx.
80%); (Brar et al. 2018). Recommended agronomic practices of fertilizers and
irrigation were followed as and when required (Tandon and Sethi 1991).
Inoculum
preparation
Fresh
sporidial suspension derived from teliospores of an aggressive isolate PB-25 found to be aggressive in a previous study
conducted by Aasma et al. (2019b) was used as a reference
isolate, and inoculum was prepared according to the method of (Brar et al. 2018). The concentration of
inoculum was adjusted to 4–5 × 104 spores mL-1 with a
haemocytometer (Tandon et al. 1994).
Boot
inoculation
The plants were inoculated by boot inoculation method at
booting stage following the procedure of Singh and Krishna (1982) and Aujla et
al. (1983). Three spikes per genotype were inoculated just before the
emergence of awns. 1 mL of T. indica
inoculum (Prepared above) was injected into the boot with the aid of a
hypodermic syringe. The inoculated spikes were covered with glycine bags to
maintain the maximum moisture content for fungus proliferation and later were
tagged and labelled (Aujla et al.
1989).
Harvesting and scoring
The harvesting of the individually inoculated spikes was
done at maturity (58–60 days) after inoculation. Each spike was collected in
paper bags and then threshed manually. The infected seeds were divided into
different degrees of infection depending on the extent of the damage on the
severity/scoring scale 0–5 (Aujla et al. 1989; Aasma et al. 2019b); (Table 2; Fig. 1). These infection grades have numerical
values (Table 2) therefore to obtain a *gross total; the numeric value was
multiplied with the number of grains present in each grade (Warham 1986; Aujla et
al. 1989; Riccioni et al. 2008; Aasma et al. 2019b). The CI was calculated as
follows:
Percent infection of each
genotype was calculated by using the following formula (Jafari et al. 2000).
Molecular
genotyping/ screening for KB resistance
Seeds of 42 wheat genotypes (Table 1) were surface sterilized with 1% Clorox (Giri et al. 2001) and sown in test tubes
containing moist cotton swab at the bottom. The tubes were incubated at 22oC
until the seedlings established with 2–3 leaves (Giri et al. 2001). These leaves were harvested for DNA extraction.
Extraction of
DNA
Genomic DNA
was extracted from fresh leaves of wheat using the CTAB method
as described by Doyle and Doyle (1987). Fresh leaves (200 mg) of each genotype
were
Fig.
1: Disease severity rating scale of Karnal Bunt (Warham
1986; Aujla et al. 1989)
ground with a
mortar and pestle having 2–3 mL of CTAB 2% solution. Then, 750 μL
of the leaf tissue was placed in a 1.5 mL Eppendorf tube and incubated in a
water bath for 30 min at 65°C. Subsequently, 750 μL of a chloroform
mixture: isoamyl alcohol was added in 24:1 ratio. The supernatant was placed in
a new Eppendorf tube after it was centrifuged at 12000 rpm, 4°C for 10 min. For
DNA precipitation, 600 μL of ice-cold isopropanol was added and
incubated at 4°C for 20 min. To discard the supernatant, the tubes were
centrifuged at 12,000 rpm for 10 min. The DNA pellet was washed with ethanol
(70%). After adding ethanol (70%), the tube was centrifuged at 12,000 rpm for
10 min at room temperature. The DNA pellet was then dried overnight and then
dissolved in 50–100 μL ddH2O. Next, 1 μL of RNase (10
mg/mL) was added to remove RNA and stored at -20°C for further use. The DNA was
electrophoresed on agarose gel (1%) by applying 100 volts for 40–50 min and
observed under UV light using a gel documentation system (Biometra). Seventeen
SSR markers (Table 5) linked with KB used to amplify the resistance genes.
A PCR reaction consisted of buffer (1X), primers (2 µM),
dNTPs (0.4 µM) Taq polymerase (0.2 U), MgCl2 (2.4 mM),
DNA (20–25ng) was amplified on automated Thermal Cycler (Applied Biosystems)
with the following cycling conditions; initial Denaturation at 94°C for 3 min followed
by 40 cycles of 94°C for 40 sec, 50–55°C for 40 sec, extension at 72°C for 1
min, and a final extension at 72°C for 10 min. The amplified products were
electrophoresed on 1.8% Agarose gel at a constant voltage of 100V for 1hr. The
gel was visualized under UV light to observe the presence and absence of bands
(Kumar et al. 2015).
Statistical Analysis
Table
1: List of wheat genotypes and their pedigrees used in this
study
Code |
Genotypes |
Pedigree |
V-1 |
99172 |
KAUZ/PASTOR//V.3009 |
V-2 |
99346 |
MH-97/FAREED-06 |
V-3 |
112802 |
NOT AVAILABLE |
V-4 |
DN-102 |
CHAM6/ATTILA//PASTOR |
V-5 |
CT 09137 |
SERI.1B*2/3/KAUZ-2/BOW//KAUZ/4/ |
V-6 |
SRN 09111 |
PRL/2*PASTOR//PBW343*2/KUKUNA/3/ROLF07 |
V-7 |
NRL-1123 |
PSN/BOW//MILAN/3/2*BERKUT |
V-8 |
NRL-1130 |
SOKOLL/EXCLAIBUR |
V-9 |
V-12001 |
WAXWING/4/SNI/TRAP#1/3/KAUZ/*/TRAP//KAUZ |
V-10 |
V-10110 |
KAUZ/CMH77A-308////BAU/3/INQ-91
|
V-11 |
V-11160 |
KAUZ//ALTAR 84/AOS/3/MILAN/KAUZ/4/HUITES/5/ T.DICOCCON
P194624/AE.SQUARROSA(409)// BCN /6 /2*KAUZ// ALTAR84 /
AOS/3/MILAN/KAUZ/4/HUITE |
V-12 |
12266 |
ATTILA/3*BCN//BAV92/3/TILHI/5/BAV92/3/PRL/SARA//TSI/VEE#5/5/CROC_1/AE.SQUARROSA(224)//2*OPATA
|
V-13 |
11098 |
BABAX/LR43//BABAX/6/MOR/VEE#//DUCULA/3/DUCCULA/4/MILAN/5/BAU/MILAN/7/SKAUZ/BAV92 |
V-14 |
11138 |
WHEAR/KRONSTAD F2004 |
V-15 |
12304 |
WAXWING/4/SNI/TRAP#1/3/KAUZ*2/TRAP//KAUZ/5/TECUE#1 |
V-16 |
11C022 |
SOKOLL//SUNCO/2*PASTOR |
V-17 |
11C023 |
SOKOLL/EXCALIBER |
V-18 |
AUR-08010 |
WBLL1*2/4/YACO/PBW65/3/KAUZ*2/TRAP/KAUZ |
V-19 |
TW11510 |
93T347/V94105 |
V-20 |
HD-29 |
NOT AVAILABLE |
V-21 |
ESW-9525 |
KAUZ/Gen |
V-22 |
DANI-1313 |
Kiran/Khirman/Bhittai |
V-23 |
NIA-CIM-04-10 |
PBW343*2/KONK |
V-24 |
PR-106 |
MTRWA92.161/PRINIA/5/SERI*3//RL06010/4*YR/3/PASTOR/4/ |
V-25 |
PR-110 |
KAUZ//ALTAR0B84/AOS/3/MILA/KAUZ/4/HUITIES/7/CAL/NH/H567.71/3/SERI/4/CAL/NH//H567.71/5/5*KAUZ/6/PASTOR
|
V-26 |
PR-111 |
TOB/ERA//TOB/CN067/3/PLO/4/VEE#5/5/KAUZ/6/ |
V-27 |
PR-112 |
KAUZ//ALTAR 84/AOS/3/MILAN/KAUZ/4/HUITES/7/
CAL/NH//H657.71/3/SERI/4/CAL/NH//H567.71/5/5*KAUZ/6/PASTOR |
V-28 |
PR-113 |
WHEAR//INQLAB91*2/TUKURU |
V-29 |
NR-423 |
MTRWA92.161/PRINIA/5/SERI*3//RL6010/4*YR/3/PASTOR/4/BAV92 |
V-30 |
NR-429 |
CNO79//PF70354/MUS/3/PASTOR/4/BAV92/5/FRET2/KUKUNA//FRET2/6/MILAN/KAUZ//PRINIA/3/BAV92
|
V-31 |
NR-436 |
NAC/TH.AC//3*PVN/3/MIRLO/BUC/4/2*PASTOR/5/T.DICOCCON
PI94624 / AE.SQUARROSA (409)// BCN/6/ WBLL4// BABAX.1B.1B*2
/PRL/3/PASTOR |
V-32 |
NR-449 |
SOKOLL//FRTL/2*PIFED
|
V-33 |
9459-1 |
LU26S x 8120 |
V-34 |
KT-338 |
SOKOLL/WBLLI
|
V-35 |
09-FJ-34 |
ERAF 2000/4/ FONCHAN#3/ TRT" S"// VEE#
9/3/COOK/VEES// DOVE"S/ SERI
|
V-36 |
SKD-11 |
Pb96/V87094//MH-97 |
V-37 |
V-11005-2013 |
NOT AVAILABLE |
V-38 |
FSD-08 |
PBW-65/2*PASTOR |
V-39 |
WL-711 |
S308/CHRIS//KAL |
V-40 |
Aas-11 |
KHP/D31708//CM74A370/3/CIAN079/4/RL6043/*4NAC |
V-41 |
TW96018 |
BK2002 X LU26S/AE. cylindricaD/PAK-81 |
V-42 |
Pak-13 |
MEX94.27.1.20/3/SOKOLL//ATTILA/3*BCN |
V= variety, KT= Kohat, Pak-Pakistan, FSD= Faisalabad,
NR= NARC, PR= Pir Sabak, SKD= Sarkand, AUR= University of Arid agriculture, FJ=
Fateh Jang, TW= Thal wheat, DN= Dera Ismail Khan, Aas = Released variety,
WL-711= Susceptible variety, HD-29= Resistant variety, NRL= NIFA Rain fed
lines, NIA= Nuclear institute of Agriculture, ESW= Elite Spring Wheat
Table 2:
Rating scale used to assess wheat genotypes for Karnal bunt (T. indica)
Infection Category/Grade |
Symptoms |
The assigned numerical value for the
calculation (CI) |
0 |
Healthy |
0 |
1* |
Inconspicuous point infection (trace), (5% seed bunted). |
0.25* |
2* |
Well-developed point infection (25% seed bunted) |
0.25* |
3 |
Infection spreading along the groove (50%
seed bunted) |
0.5 |
4 |
Three-quarters of seed converted to sorus
(75% seed bunted) |
0.75 |
5 |
Seed completely converted to sorus (100%
seed bunted) |
1.0 |
*Categories combined
to calculate Coefficient of Infections for comparison
The incidence
of disease and the coefficient of infection (CI) were calculated to verify the
level of resistance and susceptibility of the genotypes (Bonde et al.
1996). The data for both years was calculated and all the genotypes were categorized as highly resistant, resistant,
moderately susceptible, susceptible and highly susceptible based on their
coefficient of infection means (Aujla et
al. 1989; Aasma et al. 2019b).
Analysis of variance for CI and PI were calculated. LSD was applied on all
pairwise comparisons of CI and PI and was calculated by using Statistix
software. Design of the experiments was completely randomized with three
replications. Band frequency was calculated based on the presence (1) and
absence (0) (Ghosh et al. 1997).
Polymorphic information content (PIC) was calculated as follows PIC= 1-(p/q) 2
where p is total alleles detected at a given marker locus, q is a collection of
genotypes studied. Cluster analysis was done to estimate the genetic similarity
between wheat genotypes using R software. Minitab 16 software was used for
cluster analysis of combined data of percent infection and marker data. The
association between percent infection and wheat genotypic marker data was
estimated using R software.
Results
Screening of wheat germplasms for resistance against T. indica
A total of 42 genotypes were screened during the years
2015 and 2016 (Table 1). The results during the year 2015 revealed, thirteen
genotypes (NRL-1130, V-11160, 12266, 11C023, TW1150,
ESW-9525, NIA-CIM-04-10, PR-111,
Fig.
2: PCR amplification of Pakistani wheat genotypes
associated with Karnal bunt resistance in Wheat using Xgwm-149 marker: lane 20-
HD-29 (resistant): lane 39- Wl-711 (susceptible); Lane M-100 bp ladder
Fig.
3: PCR amplification of Pakistani wheat genotypes
associated with Karnal bunt resistance in Wheat using Xgwm-174 marker: lane 20-
HD-29 (resistant): lane 39- Wl-711 (susceptible); Lane M-100 b ladder
PR-112, NR-423, NR-429, KT-338 and FSD-08) exhibited HR
reaction. Seven genotypes (99172, DN-102, 11098, PR-113, 9459-1 NR-436 and
V-11005-2013) showed R reaction while, twenty two genotypes (99346, 112802, CT
09137, SRN 09111, NRL-1123, V-12001, V-10110, 11138, 12304, 11C022, AUR-08010,
TW96018, DANI-1313, PR-106, PR-110, NR-449, 09-09-FJ-34and SKD-11, Pak-13,
Aas-11, WL-711 and HD-29) showed MS reaction. The screening of the same set of
genotypes was repeated during 2016, the results revealed almost the similar
reactions except for few genotypes that showed the trend of depletion in the
resistance. These genotypes included, SRN09111, NRL-1130, V-12001, V10110,
12266, 11C023, TW1150 PR-106, PR-111 and FSD-08, WL-711 (Table 3).
Amplification of Karnal bunt resistant genes in wheat genotypes
13 out of 17 SSR primers were selected based on their
reproducibility, level of polymorphism and numbers of the amplicons (Table 5).
A total of 40 alleles were detected for the 42 genotypes with an average of
3.07 alleles per primer. PIC value ranged from 0.14–0.99. Results of this study
revealed that the size of the amplified DNA product ranged from 100–1000 base
pairs (bp). The maximum number of polymorphic bands was generated by primers
Xgwm174 resulting in 8 amplicons followed by Xgwm340 that gave 7 amplicons
(Table 6). Banding patterns of 42 wheat genotypes generated by primers Xgwm149,
Xgwm174 are presented in Fig. 2 and 3.
Cluster analysis of wheat genotypes based on SSR markers
The Euclidean similarity coefficient was calculated using
data from 13 SSR primers. The value of the similarity coefficient (Sm) ranged
from 1.0–4.5. A phylogenetic tree generated using UPGMA cluster analysis showed
all the 42 wheat genotypes were separated. Wheat genotypes were clustered into
two major groups (I & II). These groups were further classified into
sub-groups, (the genotypes showing further minor similarities within the
group). Group, I was further divided into two groups (IA & IB). The group,
IA comprised of 6 genotypes whereas Group I B has 12 genotypes. Group II was
subdivided into three groups (IIA, IIB and IIC). IIA comprised of 9 genotypes
whereas IIB was comprised of 4 genotypes. Group IIC carried 11 genotypes that
were split into many small groups and showed varying degrees of similarity
(Fig. 4).
Infection of wheat genotypes against T. indica PB-25
Wheat genotypes were screened with an aggressive isolate
PB-25 (Aasma et
al. 2019b) during the
years 2015 and 2016. The results revealed that in 2015, Pak-13 showed maximum
PI (78.9%) while NRL-1130
(V-8), V-11160 (V-11), 12266 (V-12), 11C023 (V-17), TW11510 (V-19), ESW-9525
(V-21), NIA-CIM-04-10 (V-23), PR-112 (V-27), NR-423(V-29), NR-429 (V-30),
KT-338 (V-34), FSD-08 showed least percent infection (10%). During the year
2016, WL-711 showed maximum PI (90.4%)
followed by AUR-08010 (86.6%) whereas TW11510 (V-19), ESW-9525 (V-21), NIA-CIM-04-10 (V-23),
PR-111 (V-26), PR-112 (V-27), NR-423(V-29), NR-429 (V-30), KT-338 (V-34) showed
minimum PI (10%); (Table 4). Analysis of variance of the means of PI of these
42 genotypes was found statistically significant.
Cluster analysis based on infection and marker data
Based on PI during 2015 and
2016, marker data analysis of UPGMA clustering tree of 42 wheat genotypes using
Minitab-16 was performed (Fig. 5). PI ranged from 80.6–100% in 2015 and 85.9–100%
in 2016. While the coefficient ranged from 77.4–100% respectively. Wheat
genotypes were clustered into 2 groups I & II. Group, I consisted of 21
genotypes while Group II was further divided into three major groups II A, II B
and II C. Group II A comprised of 8, while Group II B comprised of 5 and II C
represented 8 genotypes respectively (Fig. 5).
Association of genetic data with
percent infection of 42 wheat genotypes
PI of wheat genotypes was found significantly associated
with genetic bands data. Out of 40 bands, 21 bands showed association with the
PI of wheat genotypes calculated in 2015. The total association was found at
52.5%. Eighteen bands were highly significant with percent infection at p<0.001, while two were significant
at P < 0.01 and one at p<0.05. In the present study, the
statistical t-value is relatively far from zero and is relatively large to the
standard error, which indicated a relationship between molecular results and
percent infection of 42 wheat genotypes (Table 7).
Significant
association of band data with a PI of wheat calculated in 2016 was also
observed. Out of 40 bands, 20 bands were found to be associated with the PI.
Fifty percent association was found and the coefficients were not equal to
zero. Seventeen bands showed high significance at P < 0.001, one was at P <
0.01 while two were significant at P <
0.05. It was further observed that t-values were also high in the
association of bands and PI (2016) (Table 8). In the present study, we found
two models 33~ aa and 1~ bb (Table 8
and 9) with statistically significant model P < 0.05 and relatively high
R-square (0.10, 0.099) and adjusted R-square (0.0796, 0.077) value
respectively. While other models were not fit model as p values of model were
higher than the predetermined value.
Discussion
Table
4: Mean value of aggressiveness analysis of 42 wheat
genotypes based on Seed infestation (PI) during 2015 and 2016
LSD and PI of 42 wheat genotypes (2015-16) |
LSD and PI of 42 wheat genotypes (2016-17) |
||
Wheat genotypes |
Mean (PI*) |
Wheat genotypes |
Mean (PI*) |
PR-106 |
78.55A |
WL-711 |
90.42 A |
WL-711 |
76.87AB |
AUR-08010 |
86.58 AB |
Aas-11 |
76.06AB |
PR-110 |
81.61 ABC |
Pak-13 |
70.88ABC |
11138 |
78.81 A-C |
V-12001 |
69.95 A-C |
V-10110 |
76.67 A-C |
09-FJ-34 |
69.85 A-C |
09-FJ-34 |
76.08 A-C |
V-10110 |
68.98 A-C |
TW96018 |
75.20 A-C |
11C022 |
68.24 A-C |
11C022 |
74.82 A-C |
TW96018 |
66.59 A-D |
CT 09137 |
74.17 A-C |
HD-29 |
66.14 A-D |
Pak-13 |
74.05 A-C |
DN-102 |
64.47 A-D |
V-12001 |
72.10 A-D |
99346 |
64.22 A-E |
SRN-09111 |
70.65 A-D |
DANI-1313 |
64.17 A-E |
DANI-1313 |
69.35 A-E |
NR-449 |
63.89 A-E |
NRL-1123 |
66.55 A-F |
112802 |
60.95 A-F |
PR-106 |
65.98 A-F |
12304 |
60.85 A-F |
99346 |
65.56 A-F |
NRL-1123 |
59.90 A-F |
SKD-11 |
65.47 A-F |
PR-110 |
57.41 A-G |
12304 |
64.06 A-G |
AUR-08010 |
57.14 A-G |
NR-449 |
63.19 A-H |
SKD-11 |
56.72 A-G |
112802 |
61.03 B-H |
SRN-09111 |
54.42 A-G |
Aas-11 |
58.41 C-I |
11138 |
53.14 B-G |
FSD-08 |
53.98 C-J |
99172 |
49 C-G |
12266 |
45.34 D-K |
CT 09137 |
46.35 C-H |
11098 |
41.94 E-L |
9459-1 |
43.33 D-H |
HD-29 |
41.30 F-L |
11098 |
42.52 D-H |
PR-113 |
37.03 G-M |
NR-436 |
39.52 E-H |
NRL-1130 |
35.83 H-M |
PR-113 |
38.67 F-H |
99172 |
33.33 I-M |
V-11005-2013 |
35.05 GH |
11C023 |
32.99 I-M |
PR-111 |
10.00 I |
V-11160 |
30.22 J-M |
NRL-1130 |
10.00 I |
NR-436 |
24.29 K-M |
V-11160 |
10.00 I |
9459-1 |
23.33 K-M |
12266 |
10.00 I |
DN-102 |
21.11 K-M |
11C023 |
10.00 I |
V-11005-2013 |
14.67 LM |
TW1150 |
10.00 I |
TW1150 |
10.00 M |
ESW-9525 |
10.00 I |
ESW-9525 |
10.00 M |
NIA-CIM-04-10 |
10.00 I |
NIA-CIM-04-10 |
10.00 M |
PR-112 |
10.00 I |
PR-111 |
10.00 M |
NR-423 |
10.00 I |
PR-112 |
10.00 M |
NR-429 |
10.00 I |
NR-423 |
10.00 M |
KT-338 |
10.00 I |
NR-429 |
10.00 M |
FSD-08 |
10.00 I |
KT-338 |
10.00 M |
|
|
|
|
LSD value at 0.05= 24.8 |
LSD value at 0.05= 27.6 |
Column means followed by a common letter are not
statistically different from each other at 5% level of probability
Karnal bunt is a very important disease in Pakistan; however,
relatively very little work is done. The major work in Pakistan is focused on a
few surveys, disease screening for resistance and
chemical control. The current study was designed to identify the resistance
in wheat genotypes and their association with percent infection of T. indica isolate. For this study, an
aggressive isolate PB-25 was used for screening which was previously studied by
Aasma et al. (2019a, b).
Table
5: List of SSR markers linked to Karnal Bunt resistance in
wheat genotype
Primer Name |
Sequence 5´…….3´ |
Ann Temp °C |
Expected Size |
Chromosomal Location |
Reference |
Xgwm469 |
5'CAA CTC AGT GCT CAC ACA ACG 3' 5'CGATAACCACTCATCCACACC 3' |
60 |
168,176 |
6D |
Kumar et al. (2007) |
Xgwm666 |
5'GCA CCC ACA TCT TCG ACC 3' 5' TGCTGCTGGTCTCTGTGC 3' |
58 |
195 |
1A |
|
Xgwm425 |
5'GAG CCC ACA AGC TGG CA 3' 5' TCGTTCTCCCAAGGCTTG 3' |
58 |
120 |
2A |
|
Xgwm99 |
5'AAG ATG GAC GTA TGC ATC ACA 3' 5' GCCATATTTGATGACGCATA 3' |
60 |
112,122, 136 |
1AL |
Sehgal et al.
(2008) |
Xgwm149 |
5'CAT TGT TTT CTG CCT CTA GCC 3' 5' CTAGCATCGAACCTGAACAAG 3' |
58 |
185 |
4BL |
|
Xgwm174 |
5'GGG TTC CTA TCT GGT AAA TCC C 3' 5' GACACACATGTTCCTGCCAC 3' |
58 |
180,370 |
5DL |
|
Xgwm340 |
5'GCA ATC TTT TTT CTG ACC ACG 3' 5' ACGAGGCAAGAACACACATG 3' |
58 |
110 |
3BL |
|
Xgwm6 |
5 CGT ATC ACC TCC TAG CTA AAC TAG3' 5' AGCCTTATCATGACCCTACCTT 3' |
60 |
290 |
4B |
Kumar et al.
(2015) |
Xwmc235 |
5‘-ACTGTTCCTATCCGTGCACTGG-3‘ 5‘-GAGGCAAAGTTCTGGAGGTCTG-3‘ |
58 |
225 bp |
5B |
|
Xgwm604 |
5‘-TATATAGTTCAATATGACCCG-3‘ ; 5‘- ATCTTTTGAACCAAATGTG-3‘ |
50 |
160 bp |
1B/5B |
|
Xbarc140 |
5‘-CGCCAACACCTACCATT -3‘; 5‘-TTCTCCGCACTCACAAAC-3‘ |
52 |
140 bp |
2B/5B/5D |
|
Xbarc 59 |
5‘-GCGTTGGCTAATCATCGTTCCTTC-3‘ 5‘-AGCACCCTACCCAGCGTCAGTCAAT-3‘ |
55 |
180 bp |
2D/5B |
|
Xbarc232 |
5‘-CGCATCCAACCATCCCCACCCAACA-3‘ 5‘
CGCAGTAGATCCACCACCCCGCCAGA-3‘ |
58 |
200 bp |
5A/5B/5D |
|
Xgwm499 |
5‘-ACTTGTATGCTCCATTGATTGG-3‘ 5‘-GGGGAGTGGAAACTGCATAA-3‘) |
60 |
120 bp |
5B |
|
Xgwm637 |
5'AAA GAG GTC TGC CGC TAA CA 3' 5' TATACGGTTTTGTGAGGGGG 3' |
58 |
120 |
4A |
|
Xgwm538 |
5'GCA TTT CGG GTG AAC CC 3' 5' GTTGCATGTATACGTTAAGCGG 3' |
60 |
250 |
4B |
|
Xgwm337 |
5'CCT CTT CCT CCC TCA CTT AGC 3' 5' TGCTAACTGGCCTTTGCC 3' |
60 |
175 |
1D |
Table 6: Number of alleles
generated by using 13 SSR markers
Sr. No. |
Marker |
No. of
alleles generated |
Size range
bp |
PIC |
1 |
Xgwm-637 |
1 |
120 |
0.70 |
2 |
Xgwm-469 |
2 |
100-140 |
0.55 |
3 |
Xgwm-538 |
1 |
120 |
0.14 |
4 |
Xgwm-337 |
4 |
100-600 |
0.78 |
5 |
Xgwm-99 |
2 |
112-120 |
0.89 |
6 |
Xgwm-149 |
1 |
165 |
0.52 |
7 |
Xgwm-174 |
8 |
150-1000 |
0.89 |
8 |
Xgwm-340 |
7 |
100-600 |
0.99 |
9 |
Xgwm-425 |
1 |
120 |
0.18 |
10 |
Xwmc-235 |
3 |
100-350 |
0.59 |
11 |
Xgwm-604 |
2 |
100-140 |
0.77 |
12 |
Xbarc-59 |
2 |
200-500 |
0.53 |
13 |
Xbarc-232 |
6 |
200-500 |
0.84 |
PIC: Polymorphic information content: The highest and
lowest PIC values are indicated by bold, underlining
Among total 42 wheat
genotypes tested, thirteen genotypes showed HR reaction, seven genotypes
showed R reaction while twenty-two showed MS reaction during 2015–2016. Same
pattern of resistance and susceptibility was observed during 2016–2017 with few
exceptions. However, few genotypes lost resistance in 2016 which indicated the
complication in managing this disease even with the resistance sources. This advocated that a continuous screening
program at the national level needed to be initiated as it is being followed in
the case of rusts (Nagarajan et al. 1997). Results on percent seed
infection revealed that Pak-13 showed a high percentage of infection. In
comparison during 2016, WL-711 exhibited high percent of infection (90.42%).
Results of a few genotypes showed inconsistency in the reaction even though the
same isolate was used for inoculation. Aujla et al. (1989) recorded a
similar trend in genotype HD 2967 which showed 1S reaction, was found to be 3S
against a particularly virulent isolate. This may be attributed to these
genotypes do not confer stable resistance against the pathogen and such
genotypes should be discouraged in the breeding program for KB resistance. A
similar trend of resistance of some of the genotypes has been reported by Ullah
et al. (2012). Synthetic genotypes showed resistance and few tend
towards moderate susceptibility similar findings were reported by Villareal et
al. (1995, 1996).
Table
7: Association of wheat genotypes bands with percent
infection of wheat inoculated with T.
indica inoculum during 2015-2016
Sr. No. |
Model |
Multiple R Squared |
Adjusted R square |
Std. Error (Estimate) |
Std. Error |
t-value |
P-value |
1 |
1 ~ aa |
0.080 |
0.06 |
0.81 |
0.15 |
5.29 |
4.38×10-06*** |
2 |
2~ aa |
0.004 |
-0.02 |
0.72 |
0.15 |
4.82 |
1.98×10-05*** |
3 |
3~ aa |
0.007 |
-0.02 |
0.95 |
0.09 |
10.24 |
7.31×10-13*** |
4 |
4~ aa |
0.005 |
-0.02 |
0.60 |
0.16 |
3.74 |
0.000567 *** |
5 |
5~ aa |
0.004 |
-0.02 |
0.82 |
0.11 |
7.40 |
4.59×10-09*** |
6 |
6~ aa |
0.001 |
-0.02 |
0.87 |
0.10 |
8.43 |
1.72×10-10*** |
7 |
7~ aa |
0.024 |
0.00 |
0.96 |
0.11 |
8.70 |
7.37×10-11*** |
8 |
9~ aa |
0.045 |
0.02 |
1.03 |
0.05 |
21.84 |
< 2×10-16*** |
9 |
10~ aa |
0.001 |
-0.02 |
0.86 |
0.12 |
7.22 |
8.06×10-09*** |
10 |
11~ aa |
0.019 |
-0.01 |
0.56 |
0.15 |
3.75 |
0.000551*** |
11 |
13~ aa |
0.004 |
-0.02 |
0.72 |
0.14 |
5.30 |
4.22×10-06*** |
12 |
14~ aa |
0.045 |
0.02 |
1.03 |
0.05 |
21.84 |
< 2×10-16*** |
13 |
15~ aa |
0.010 |
-0.01 |
0.96 |
0.09 |
10.33 |
5.56×10-13*** |
14 |
17~ aa |
0.024 |
0.00 |
0.96 |
0.11 |
8.70 |
7.37×10-11*** |
15 |
19~ aa |
0.000 |
-0.02 |
0.59 |
0.16 |
3.74 |
0.000572*** |
16 |
22~ aa |
0.024 |
0.00 |
0.40 |
0.16 |
2.51 |
0.0163* |
17 |
28~ aa |
0.011 |
-0.01 |
0.72 |
0.13 |
5.50 |
2.21×10-06*** |
18 |
32~ aa |
0.001 |
-0.02 |
0.72 |
0.14 |
5.17 |
6.46×10-06*** |
19 |
33~ aa |
0.102 |
0.08 |
0.34 |
0.11 |
3.20 |
0.00263** |
20 |
35~ aa |
0.005 |
-0.02 |
0.59 |
0.15 |
3.87 |
0.00038*** |
21 |
37~ aa |
0.026 |
0.00 |
0.30 |
0.12 |
2.43 |
0.0197* |
|
|
|
|
|
|
|
|
***= Significant at 0.001, **= Significant at 0.01, *=
Significant at 0.05
Table
8: Association of wheat genotypes bands with Percent
infection of wheat inoculated with T.
indica inoculum during the Year 2016
Sr. no. |
Model |
Multiple R Squared |
Adjusted R square |
Std. Error (Estimate) |
Std. Error |
t-value |
P-value |
1 |
1~ bb |
0.10 |
0.08 |
0.86 |
0.16 |
5.42 |
2.93×10-06*** |
2 |
2~ bb |
0.00 |
-0.02 |
0.74 |
0.16 |
4.69 |
3.03×10-05*** |
3 |
3~ bb |
0.00 |
-0.02 |
0.91 |
0.10 |
9.37 |
9.56×10-12*** |
4 |
4~ bb |
0.01 |
-0.01 |
0.64 |
0.17 |
3.83 |
0.000431*** |
5 |
5~ bb |
0.01 |
-0.02 |
0.81 |
0.12 |
6.95 |
1.91×10-08*** |
6 |
6~ bb |
0.01 |
-0.02 |
0.94 |
0.11 |
8.76 |
6.19×10-11*** |
7 |
7~ bb |
0.02 |
0.00 |
0.96 |
0.11 |
8.39 |
1.95×10-10*** |
8 |
9~ bb |
0.07 |
0.04 |
1.05 |
0.05 |
21.49 |
< 2×10-16*** |
9 |
10~ bb |
0.01 |
-0.02 |
0.78 |
0.12 |
6.35 |
1.39×10-07*** |
10 |
11~ bb |
0.01 |
-0.02 |
0.59 |
0.16 |
3.78 |
0.000502*** |
11 |
13~ bb |
0.02 |
0.00 |
0.66 |
0.14 |
4.68 |
3.16×10-05*** |
12 |
14~ bb |
0.07 |
0.04 |
1.05 |
0.05 |
21.49 |
< 2×10-16*** |
13 |
15~ bb |
0.01 |
-0.02 |
0.95 |
0.10 |
9.78 |
2.79×10-12*** |
14 |
17~ bb |
0.02 |
0.00 |
0.96 |
0.11 |
8.39 |
1.95×10-10*** |
15 |
19~ bb |
0.02 |
-0.01 |
0.73 |
0.16 |
4.49 |
5.74×10-05*** |
16 |
28~ bb |
0.00 |
-0.02 |
0.78 |
0.14 |
5.70 |
1.14×10-06*** |
17 |
32~ bb |
0.01 |
-0.01 |
0.65 |
0.15 |
4.49 |
5.78×10-05*** |
18 |
33~ bb |
0.05 |
0.02 |
0.28 |
0.11 |
2.50 |
0.0165* |
19 |
34~ bb |
0.07 |
0.05 |
0.24 |
0.09 |
2.53 |
0.0153* |
20 |
35~ bb |
0.03 |
0.00 |
0.50 |
0.16 |
3.17 |
0.0029** |
***= Significant at 0.001, **= Significant at 0.01, *=
Significant at 0.05
Fig.
4: Dendrogram of 42 wheat genotypes on the basis of 13 SSR
markers linked to Karnal bunt resistance
Fig. 5: Dendrogram distributions of 42 genotypes in groups on the basis of
combined percent Incidence of both years (2015 & 2016) and marker data of
13 SSR markers linked to Karnal bunt resistance
Molecular markers have been a very powerful tool for characterizing and
assessing the genetic diversity of wheat genotypes (Edwards and
Caskey 1991). It is important to know the usefulness of the markers before they
can be used extensively for crop improvement programs. However, it is not
guaranteed that the markers identified in the population will be suitable in
another group (Kumar et al. 2015). In
this study, 17 SSR markers were tested for the presence of KB resistance genes
on 42 wheat genotypes. Among 17 SSR markers, 13 markers gave the amplified
product in the present study, 9 SSRs (Xgwm 469, Xgwm-337, Xgwm-99, Xgwm174,
Xgwm-340, Xgwm- 235, Xgwm-604, Xbarc 59 and Xbarc-232) were found to be
multi-allelic. Since micro-satellite primers are locus-specific, each pair of
primers must amplify one locus. In previous studies, many scientists reported
relatively higher numbers of alleles per locus in bread wheat (Roder et al. 1998; Stephenson et al. 1998; Vasu et al. 2000). Prasad et al.
(2000) also observed multiple loci for each pair of primers. Ahmed (2002)
identified 156 allelic variations at 43 SSR loci, ranging from 2 to 8 alleles
per locus with an average of 3.6 alleles per locus. Kumar et al. (2007) found 179 alleles at 46 SSR loci with an average of
3.9 alleles per locus with a range of 1 to 8 alleles on each locus. The results
of the present study and other studies clearly showed that SSR markers are very
informative and confirmed the presence of multiple alleles per locus (Roder et al. 1995; Singh et al. 2003; Kumar et al.
2007).
In the
present study, the microsatellite markers Xgwm-637, Xgwm-469, Xgwm-538, Xgwm
337, Xgwm-99, Xgwm-149, Xgwm-174, Xgwm-340, Xgwm- 425, Xgwm-235, Xgwm-604,
Xbarc-59 and Xbarc-232 were found to be associated with resistance. In earlier
studies, similar findings were reported by Sehgal et al. (2008) that these four markers Xgwm99 (1AL), Xgwm149 (4BL),
Xgwm174 (5DL) and Xgwm340 (3BL) are associated with resistance genes in BC5F4
progenies. On the contrary, Kumar et al.
(2007) reported two markers (Xgwm 337-1D and Xgwm 637-4A) had an association
with the resistance of wheat against KB. In another study, Kumar et al. (2015) also confirmed the
association of the primers Xgwm 337, Xgwm 637, Xgwm 538, Xgwm-469 and Xgwm-425
with the resistance of KB in different populations. Vasu et al. (2000) discovered a microsatellite locus associated with the
gene of KB resistance, Xgwm637 that has been assigned to 4AL. Recently, Brar et al. (2018) identified QTL for KB
resistance and some QTL on chromosomes 4A, 7A and 6A are likely to be
associated with SSR markers used in this study.
In this study, DNA fragments ranged in size from about
100 bp to 1000 bp compared to Manifesto et
al. (2001) reported amplified DNA fragments ranging in size from 115 bp to
285 bp, whereas Abbas et al. (2008)
detected amplified DNA fragments ranging in size from 250 bp to 1000 bp while
Kumar et al. (2015) observed range
from 100 bp to 300 bp. It has been previously reported that KB resistance was
controlled by major and minor genes (Singh et
al. 1996) and DNA polymorphism between genotypes has been achieved from
microsatellite marker amplification. It is a useful application for assessing
genetic diversity and polymorphism (Lang et
al. 2001).
Development
of resistant varieties was difficult due to the confusing impact of the
environment and limited sustainable genetic sources to the nature of quarantine
resistance to KB. Therefore, the identification of markers associated with KB
resistance using marker-assisted selection (MAS) could help in complementing
phenotypic selection. Kumar et al.
(2016) reported SSR markers Xbarc59, Xgwm232, Xgwm235, and Xgwm604 were linked
with KB resistance gene. Similar results were found in our study. A dendrogram
was constructed using allelic diversity based on SSR marker data, to determine
the genetic relationship between selected wheat genotypes. The dendrogram
revealed that all genotypes fall into two main categories with a similarity
factor of 4.5. Genetic diversity and population structure information of
breeding genotypes of wheat will benefit breeders to make improved usage of
their genetic resources and management of genetic variation in their breeding
programs. In the present study, the significant genetic divergence of wheat
genotypes was observed with SSR markers.
Association
of genetics data and percent infection of two years of 42 wheat genotypes were
found to be highly significant. An association of 52.5% was recorded in 2015–16
while 50% in 2016–2017. Furthermore, R-square and adjusted R were found to have
low value. R-square reflects the goodness of fit
of the model to the population taking into account the sample size and the
number of predictors used. As we were looking for the association not for prediction,
so R-square was not importantly considerable
in our results. The sample size was small in our study as 42 wheat genotypes
were studied for the molecular and field data. The small sample size was also
affected by the model goodness fit.
Conclusion
Results suggested that molecular markers can explain the
phenotypic variations for KB resistance in wheat. The use of SSR markers in
these populations showed significant results and had the potential for
detecting resistance in wheat. Further work is needed to narrow down the
environmental factors (i.e., humidity
and temperature) along with PI as predictors to estimate the KB status on
wheat.
Acknowledgements
The authors would acknowledge the lab members of Plant
Genomics Laboratory of the National Institute for Genomics and Advanced
Biotechnology (NIGAB), National Agricultural Research Center, Islamabad) for
their support and facilities.
Author Contributions
AA and SA, MF planned the experiments, AA interpreted
the results and did write-up of the manuscript, and MA statistically analyzed
the data. SA, MN, US, SB checked and improved the write-up of manuscript.
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