Abiotic and biotic constraints have widespread yield reducing effects on maize and should receive high priority for maize breeding research. Molecular Breeding offers opportunities for plant breeders to develop cultivars with resilience to such stresses with precision and in less time duration. Recent advances in maize breeding research have made it possible to identify and map precisely many genes associated with DNA markers which include genes governing resistance to biotic stresses and genes responsible for tolerance to abiotic stresses.

1. ANAND AGRICULTURAL UNIVERSITY
ANAND
GUJARAT, INDIA
Speaker Pitambara Course No. GP 692
Major advisor Dr. Y.M. Shukla Reg.No. 04-2648-2015
Minor advisor Dr. S.M. Khanorkar Time 16:00 Hrs
Degree Ph.D. Date 29-09-2016
MOLECULAR BREEDING FOR CLIMATE
RESILIENCE IN MAIZE
1 1

2. CONTENTS
INTRODUCTION OF MAIZE
CLIMATE CHANGE : TRENDS & ISSUES
CLIMATE CHANGE & AGRICULTURE
STRESS IMPOSED BY CLIMATE CHANGE ON MAIZE
STRATEGIES TO MITIGATE CLIMATE RELATED EFFECTS (CLIMATE RESILIENCE).
PLANT BREEDING: MOLECULAR BREEDING (TYPES)
CASE STUDIES
PROJECTS OF CIMMYT
CONCLUSION
FUTURE THRUSTS
2

3. MAIZE (Zea mays L.)
 3rd most important cereal crop after, rice and
wheat. (Gaut and Doebly,1997)
 Also known as the “Queen of Cereals” due to
its high yield potential as compared to other
cereals.
 In India, about 35 per cent of the maize
produced is used for human consumption.
25 per cent each in poultry and cattle feed
15 per cent in food processing industries like
corn flakes, pop corn etc.(FAO 2010)
Kingdom: Plantae
Subkingdom: Tracheobionta
(Vascular plant)
Superdivision: Spermatophyta (seed
plant)
Division: Magnoliophyta
(flowering plant)
Class: Liliopsida (monocot)
Subclass: Commelinidae
Order: Cyperales
Family: Poaceae (grass
family)
Genus: Zea -- corn
Species : Zea mays -- corn
Chromosome number (2n)=20
Genome size=2500 Mbp
Taxonomy of Maize
Area
(Million hectare)
Production
(Million tons)
Productivity
(kg/ha)
World 177.7 970 5470
India 9.09 24.7 2720
Gujarat 0.46 0.69 1500 3 3

4. Morphology of Maize Plant
4 4

5. GROWTH FACTOR
• Maize is a tropical grass that is well adapted to many climates and hence has wide-
ranging maturities from 70 days to 210 days.
• Temperature requirements– The optimum temperature for maize growth and
development is 18 to 32 °C, with temperatures of 35 °C and above considered
inhibitory. The optimum soil temperatures for germination and early seedling
growth are 12 °C or greater, and at tasselling 21 to 30 °C is ideal.
• Rainfall requirements– Maize can grow and yield with as little as 300 mm rainfall
(40% to 60% yield decline compared to optimal conditions), but prefers 500 to
1200 mm as the optimal range.
• Photoperiod : Maize is grown globally from 50°N to 40°S, and from sea level up to
4000 m altitude. Maize is a short-day plant with 12.5 hours/day being suggested
as the critical photoperiod.
• Soils: The preference of most field crops is for fertile, well-drained loamy soils with
pH 5.0 to 8.0.
5 5

6. Climate Change : Trends and Issues
• Climate has been changing in the last three decades and
will continue changing regardless of any mitigation
strategy. (IPCC ;2001, 2007)
6 6

7.  Climate change affects
agriculture adversely.
 Impact varies across regions.
 Climate change has
significant adverse effect on
the average crop yield.(Mall
et al., 2006)
 In the world economies ,
agriculture is amongst the
most vulnerable sectors to
these changes in climate.
(Thorton, 2011)
 Developing countries are more vulnerable to potential damage from climate changes
and South Asia will be particularly hard hit. (IFPRI, 2009)
7 7
Cont.

8. It is expected to cause drastic changes in agroclimatic conditions including
temperature, rainfall, soil nutrients (Abiotic stress) and incidence of pathogens
and pests (Biotic stress) due to climate change.
8
Cont.

9. EFFECT OF CLIMATE CHANGE ON MAIZE
• Heisey and Edmeades (1999) estimated that one quarter of
the global maize area is affected by drought in any given year.
•Additional constraints causing significant yield and economic
losses annually include low soil fertility, pests, and disease.
9 9

10. Abiotic stresses
10 10

11. Ecological
environment
Highland/
transitional
Mid-altitude/
subtropical
Tropical lowland
East and South
East Asia
Branded leaf and
sheath blight
Downey mildew
Borers (Chilo spp.) Borer (Chilo, Sesamia spp.)
South Asia Turcicum blight Turcicum blight Downy mildew
Borers (Chilo, Sesamia spp.) Borers (Chilo, Sesamia spp.)
Sub-Saharan Africa Turcicum blight Gray leaf spot Striga
Common rust Streak virus Streak virus
Ear rots Ear rots Borers
Weevils
Borers (Chilo, Sesamia spp.)
Latin America
and Caribbean
Ear rots Turcicum blight Fall armyworm
Rust Borer (S. W. corn
borer)
Corn stunt complex
Turcicum blight Tar spot complex Ear rots
Ear rots Gray leaf spot
Biotic stresses
11 11

12. Strategies for mitigating climate-related effects
on maize yields
Climate resilience can be generally defined as the capacity to: (1) absorb
stresses and maintain function in the face of external stresses imposed upon
it by climate change and (2) adapt, reorganize, and evolve into more desirable
configurations that improve the sustainability of the system, leaving it better
prepared for future climate change impacts.
Plant breeding and improved management options have made remarkable
progress in increasing crop yields during the past century.
12 12

13. Molecular breeding
Molecular breeding is a general term used to describe modern breeding
strategies where DNA markers are used as a substitute for phenotypic
selection to accelerate the release of improved germplasm.
Strategy Description
Marker-assisted
selection (MAS)
Based on selection of individuals carrying genomic regions involved in the
expression of the trait of interest
Marker-assisted
backcrossing (MABC)
Transfer of a limited number of loci from one genetic background to
another
Marker assisted gene
pyramiding (MAGP)
Desirable alleles of different major QTL is brought together and the true
breeding lines associating alleles of similar effect can be selected to create
a superior genotype
Marker-assisted
recurrent
selection (MARS)
Markers associated with trait of interest are first identified and selection is
based on several genomic regions involved in the expression of complex
traits to assemble the most superior genotype within a population
Genome-wide selection
(GWS)
Based on the prediction of performance. Selection is made on markers
without significance testing and does not require the prior identification of
markers associated with the trait of interest
Current molecular breeding strategies ( Ribaut et al., 2010)
1313

14. Marker Assisted Selection
MAS refers to the use of
DNA markers that are
tightly-linked to target loci
as a substitute for or to
assist phenotypic
screening. By determining
the allele of a DNA marker,
plants that possess
particular genes or
quantitative trait loci
(QTLs) may be identified
based on their genotype
rather than their
phenotype
1414

15. Parents:
1. Elite sensitive line: CML311-2-1-3 (2)
2. Tolerant for waterlogging: CAWL-46-3-1 (0)
RIL developed by SSD method
RIL(S6) were test crossed with CML 451 (late maturing yellow line) and
F1 was evaluated. (RIL-TC)
Water logging treatment was applied through flooding at knee height stage for 7
days continuously
Zaidi et al.,2015New Delhi (CIMMYT)
Linkage and QTL mapping: Parental lines genotyped with 1250 SNP markers for
which KASP assay were designed.
RIL were genotyped by 331 polymorphic SNP markers.
Linkage map was constructed using QTL IciMapping ver3.2 software.
Phenotypic observation and analysis of phenotypic data (Proc Mixed in SAS)
15 15
Case
study 1

16. Table 1 : Mean, variance and heritability estimates for parental lines and RIL families
based on evaluation under waterlogging conditions.
Trait Mean P1(WLT) P2(WST) Range in RIL H
Grain yield (t/ha) 0.80 1.8 0.4 0.06-2.22 0.57
ASI(days) 6.62 1.0 7.8 -3.28-29.79 0.27
Ears/plant(no.) 1.66 2.6 1.2 0.00-4.29 0.47
Pl. ht.(cm) 103.69 138.0 126.3 58.61-160.44 0.46
Ear ht(cm) 32.20 52.4 48.6 13.84-67.86 0.45
Brace roots(no.) 1.66 3.5 0.6 0.00-4.29 0.78
Ear position(ratio) 0.31 0.32 0.28 0.16-0.54 0.44
Chlorophyll(ratio) 16.99 22.32 17.64 7.50-29.60 0.67
Root lodging(%) 14.6 4.2 47.8 0.00-76.26 0.91
Stem lodging(%) 3.9 1.5 35.6 0.00-51.20 0.87
WLT—Waterlogging tolerant parent (CML311-2-1-3); WST—Waterlogging sensitive parent (CAWL-46-3-1); H—Heritability (Broad
sense) estimate
F test(d.o.s <=0.01)
1616
Cont.

17. TraitChro
mosome
Flanking Markers and their physical
positions (Mb)
Confidence Interval
(Mb)
LOD R2
(%)
Additi
ve
Effect
Grain yield
(t/ha)
(5QTL on chr.1,3,5,7,10: RIL)
(1QTL on chr. 5:RIL-TC)
1 PZA03301.2 (240.57)—PZA01921.20
(261.31)
219.62–265.11 6.4 5 -0.60
3 PZA02212.1 (174.55)—PZA02654.3
(178.77)
157.97–198.52 6.1 4.2 -0.64
5 PZA02164.16 (112.18)—PZA01796.1
(160.32)
103.79–178.62 4.5 8.0 -0.52
7 PHM4353.31 (36.39)—PZA02612.1
(48.61)
6.26–61.41 11.6 6.1 0.49
10 PZA01677.1 (70.80)—PZA02941.7
(71.12)
40.93–83.74 5.4 3.6 0.11
Table 2 . QTL identified for waterlogging tolerance using RIL phenotypes
All 10 maize chromosomes were represented in linkage map and constructed 10
linkage group (Total length=2008.2cM)
Out of 331 SNPs ,68 marker deviated from expected ratio(1:1)
Allele frequency of CML311(59.4%) was higher than CAWL46-3-5(41.6%).
Additive effect( contaributed by WLT parent):520-640kg/ha
Contributed by susceptible parent(500kg/ha)
17
Cont.

18. TraitChro
mosome
Flanking Markers and their
physical positions (Mb)
Confidence
Interval(Mb)
LOD R2
(%)
Additive
Effect
TraitChro
mosome
Flanking Markers and their
physical positions (Mb)
Confidence
Interval (Mb)
LOD R2
(%)
Additive
Effect
Table 4 . QTL identified for waterlogging tolerance using RIL test cross phenotypes.
Table 3 . QTL identified for waterlogging tolerance using RIL phenotypes
Additive effect
Additive effect
Additive effect
18 18
Cont.

19. Linkage groups along with QTL identified for traits associated with waterlogging tolerance using RIL and TC phenotypes
(R_: Identified using RIL dataset, T_: Identified using TC data set, Traits: GY—Grain yield, RL—Root lodging, SL—Stem
lodging, BR—Brace roots, M—Plant Mortality %, CC—Chlorophyll content, ASI—Anthesis-Silking interval).
Chr. 1: GY, RL(%) Chr. 2: chlrp. Chr. 3:GY, RL,SL,ASI
1919
Cont.

20. Chr. 4: SL
Chr. 5: GY, PM(%)
Chr. 7: GY, SL, BR,
Chr. 8: SL, BR,
Chr. 10: RL,GY
Physical location of BR (QTL) was overlapped with GY(QTL) on chr-7
2020
Cont.

21. chr Interval
(Mb)
Putative candidate genes Gene Id Functions
1 240-261 Cytochrome P-450- 8(cyp8) GRMZM2G167986 Biosynthesis of endogenous
lipophilic compounds
upon hypoxia
TATA-binding protein GRMZM2G149238 Anaerobic gene expression
3 174-178 phosphoinositide dependent protein
kinase 1
GRMZM2G097821 Anaerobic signal transduction
3 203-213 MADS domain transcription factor
(zmm16)
GRMZM2G110153 Reproductive organ
development
5 8-21.5 Cytochrome b6 GRMZM2G463640 Selective activation under
hypoxic conditions
Single myb histone 6 GRMZM2G095239 Regulation of alcohol
dehydrogenase under low
oxygen conditions
5 112-160 Cysteine Protease (ccp1) GRMZM2G098298 Anoxia-induced root-tip death
7 137-155 Glutathione S transferase16 GRMZM5G895383 Metabolic processes relating to
early development of
brace roots
Table : Putative candidate genes identified in the physical intervals delimited by the flanking markers of
the QTL influencing GY and secondary traits under waterlogged conditions( 22 candidate genes with
known function identified within physical interval responsible for water logging tolerance out of which 6
are associated with anaerobic response
2121
Cont.

22. Marker Assisted Backcross Selection
2222
Transfer of a limited number of loci from one genetic background to another

23. Recurrent parents: 11 inbred lines widely used as testers for hybrid
development in chinese maize breeding.
Donor parents : >200 local elite inbred lines from different
ecological zones
Two generation of back crossing and one generation of selfing were used to produce
>500 BC2F2 (IL sets).
These maize IL sets were subjected to draught screening.
34 IL were selected for having draught tolerance (derived from crossing,
backcrossing and selfing three recurrent parents and 30 donor lines)
Hao et al., 2009
China
23
Case
study 2

24. Name Germination
rate(%)
Average
survival
seedling sper IL
Plant survival
rate (%)
Recurrent
parents
Ye478 89.6 6.1 3.4
Chang7-2 83.5 19.5 11.6
Qi319 90.3 11.1 6.2
Donor parents Shen118 84.5 22 13
DHuang212 93 32 17.2
Zhongi01 90.5 14 7.6
V9 90.5 29 16
K14 81.5 30 18.4
CN962 90.5 22 12.2
Hua 160 78 32 20.5
178 91 21 11.5
B73 93 22 11.8
Table 1.: Germination and plant survival rates among different recurrent and donor
inbreds
(r=o.17 : germ. rate not covalently corelated with plant surviving rate)
2424
Cont.

25. 32 surviving ILs were genotyped with 93 polymorphic SSR markers evenly distributed on maize genome.
(R=A, D=B, Het=H)
By comparing with threshold , total of 7 molecular marker intervals/ marker were
identified deviating from normal segregation indicating their association with draught
tolerance.
Genotyping of ILs for draught tolerance (ILs: Chang7-2/DHuang212 highest surviving plants
after draught treatment) by SSR markers(93 SSR polymorphic loci between two parental lines.
These markers were located on bins 3.04, 4.08, 4.11, 0.04, 7.05, 8.08 and 9.04
QTL identification : genotypic graphs for introgression segments of each IL were constructed using
GGT32 software.
Observed and expected
allelic and genotypic
frequencies at genetic
marker : a significant
deviation of donor allele
frequency at single locus
in ILs from expected
implies positive selection
favoring donor allele(in
excess) or negative
selection against donor
allele (in deficiency)
2525
Cont.

26. Fine mapping of draught tolerant QTL region in bin 3.04:adding more SSR to bin 3.04 (63SSR )
Table 2 : Chi square values for SSR markers in the bin 3.04 for five IL sets with significant deviation in bold
SSR marker Marker position
on IBM2
Chang7-
2/K12
Chang7-
2/DHuang212
Chang7-2/P141 Chang7-2/V9 Qi319/B73
umc1717 191 27.6 34.8 6.6 6.7
bngl1113 191 26.8 0.9 18.9 5.1
bngl 1638 193 23.6 0.8 3.0 6.7
bngl2047 203 43.7 34.8 3.0 6.7
umc1810 223 41.1 28.6
umc1773 280 27.6 7.4 23.6 0.6
umc1087 365 37.2 53.8 1.4 6.7
Table 3 : Chi square values for SSR markers in the bin 3.04 for five IL sets with significant deviation in bold
SSR marker Marker position
on IBM2
Chang7-
2/K12
Chang7-2/DHuang212 Chang7-2/P141 Chang7-2/V9 Qi319/B73
bngl1904 127 17.1 35.0 15.8 6.7
phi099 159 23.6 35.0 8.3 6.7 72.0
phi036 159 41.5 35.0 29.1 6.7 14.2
umc1223 234 23.6 78.5 23.6 6.7
Sharedby2ILsetsSharedby3ILsets
SSR 30 30 29 2927
1-8 Marker intervals were identified for each IL
2626
Cont.

27. 27
Cont.

28. Marker Assisted Gene Pyramiding
2828
Desirable alleles of different major QTL is brought together and the true
breeding lines associating alleles of similar effect can be selected to create a
superior genotype

29. Turcicum leaf blight(TLB) is caused by Exserohilum turcicum
Polysora rust(PR) is caused by Puccinia polysora
Large number maize inbreds were phenotyped for disease resistance and based on
disease reaction responses seven different backcross populations were generated using 5
susceptible inbreds as recurrent parents and 4 donors.
Recurrent: CM137, CM138, CM139, CM140, CM212
Donor: NAI147, SKV21, NAI112, SKV18
Prasanna et al.,2008IARI, New Delhi
MAGP of specific genes /QTL for resistance to TLB and PR into 5 elite but susceptible
maize inbred lines
Temp.-23-28 °C
Humidity : high
29
Case
study 3

30. SSR polymorphic survey was undertaken on selected recipient and donor
parents.
Recipient Donor Target bin locations for
foreground selection
CM137 NAI147 1.01,9.07,10.00
CM138 SKV21 7.05,8.04
CM138 NAI112 3.00,9.03
CM139 NAI112 3.00,9.03
CM139 NAI147 1.01.5.04,10.00
CM140 SKV21 7.05,8.04
CM212 SKV18 1.01,5.04
Table 1 : Recipients and donors of backcross populations and target genomic regions
for foreground selection using SSR markers
Some donor like NAI1447 carry resistance to both TLB and PR.
At bin 10.00 a major gene RppQ for PR resistance has been validated
using SSR marker
3030
Cont.

31. Foreground selection for different resistance gene combinations using polymorphic SSR
markers tagging specific gene/QTL was carried out on BC1F1 and BC2F2 progenies.
Background selection for high recovery of recurrent parent genome was undertaken on
BC1F1 and BC2F1 progenies using polymorphic SSR markers covering maize genome
3131
Cont.

32. Phenotypic screening of BC1F1 and BC2F1 progenies against TLB was carried out at
Naganhalli(N) and Hawalbagh(H) and for PR was carried out at N.
Disease scoring was done by visual estimation of disease severity on 1-5 scale (0-<2.5:
resistant , 2.5-<3: moderately resistant, 3-<4: moderately susceptible while 4-5:susceptible)
146 heterozygotes were selected among 840 BC1F1 progenies , and 16 BC2F1
populations were further developed which were advanced up to BC2F3.
3232
Cont.

33. BC2F3 lines so generated were evaluated in trials under artificial inoculations at N and
H against TLB and at N against PR.
12 different lines have been identified to show resistance to TLB at both locations in
various recurrent parent baackground (except for CM140).
Several other combination of (R-MR, R-MS, MR-MR) have also been derived
2 CML137 based lines recorded resistance to both TLB (at N and H) and PR at N.
There was moderate
correspondance
(R2=0.57) of BC2F3 TLB
scores between N and
H.
Genetic background of recurrent parent had a significant effect on TLB/PR
resistance even when the same donor parent involved.
Selected BC2F3 lines offering resistance to TLB/PR were selfed for deriving BC2F4 lines : from
these a subset of 100 BC2F4 lines were selected showing resistance to both TLB and PR and
are also morphologically similar to their respective recurrent parents
3333
Cont.

34. Marker Assisted Recurrent Selection
MARS refers to the improvement of an F2 population by one cycle of marker-assisted
selection (based on phenotypic data and marker scores) followed commonly by two
or three cycles of marker-based selection (based on marker scores only).
Marker-assisted recurrent selection (MARS) uses markers at each generation to target all traits
of importance and for which genetic information can be obtained. 34

35. Genetic materials 10 tropical biparental populations were used to complete three cycles
of MARS .
In each population, the top eight families from C0 were also advanced using a pedigree
selection scheme.
Testcrosses were generated by crossing the F2:3 families (C0) from each population
with a single-cross tester .
Each C0 population was genotyped with 190–225 SNPs and QTL analysis was performed
for each population.
Three selection cycles were conducted using a subset of 55–87 SNPs that were
significantly associated with grain yield and anthesis silking interval.
Beyene et al.,2016Africa
Test cross populations were evaluated under 2–3 managed drought stresses and 3–4 well-watered condition.
35
Case
study 4

36. From each population, 47–
74 C1S2 lines developed
through MARS Five S5 lines
developed via phenotypic
pedigree selection, The two
founder parents (P1 and P2)
were crossed to a single-
cross tester
(CML395/CML444) (hybrid
formation ).
 Experimental lines were used as female parents, and the single-cross tester was used as the
male parent.
 Seeds were harvested and bulked within each female row plot for use in the testcross
evaluation.
 Testcrosses of each population together with five commercial checks (CZH0616, H513,
WH505, DK8053, and Pioneer 3253) were evaluated in 3–5 WW and 1–3 DS locations.
 An alpha-lattice design with two replications per location was utilized for the trials. 36
Cont.

37. Data collection
Data on grain yield (GY), plant height (PH) and anthesis date (AD) were collected.
Statistical analysis
Analysis of variance for grain yield, anthesis date and plant height within and across DS and
WW locations was performed using the PROC MIXED procedure of SAS.
3737
Cont.

38. Genomic Selection
Genome wide selection is the
simultaneous selection of many
markers which cover the entire
genome in the dense manner so that
all the genes are expected to be in
linkage disequilibrium with atleast
some of the markers.
38
Selection of desirable individual is based on
Genomic Estimated Breeding Value (calculated
by genome wide dense DNA marker
38

39. Test cross of each population along with five commercial checks (WH504, WH505,H513,
CZH0616, DK8033) were planted.
The test cross were phenotyped in 2-4 managed drought stress and 3-4 well watered
locations.
5 WEMA C0 population were selected for genotyping with 191 to 218 SNPs and 3 DTMA
C0 population were genotyped with 197 to 286 SNPs using KASP assay.
Beyene et al.,2015USA
8 breeding populations were selected from set of 34 bi parental breeding population
which were developed from DTMA and WEMA projects.
Initial Test cross derived by crossing 148-300 F2:3(C0) with single cross tester for
phenotypic evolution.
39
39
Case
study 5

40. Breeding scheme is illustrated
Selection of C0 to form C1
Marker-based Selection in C1 and C2
Development
of Lines via
Pedigree
Selection
The selected families were
planted ear-to-row and
intermated to form C1
Genomic estimated breeding
values were calculated for all C1
individuals and the top 10% of the
C1 individuals were selected and
intermated to form C2 as
described above.
Top 10% of
the selected
C0 families for
GS were also
subjected to
inbreeding
under well-
watered
environments
with visual
selection to
develop F5:6
lines
40
Cont.

41. Table 10. Phenotypic Evaluation for Assessing Genetic Gain
41
Cont.

42. The DTMA and WEMA populations showed similar trends with little or no response to
genomic selection from C1 to C2, although gain was observed from C2 to C3.
However, there is also some level of discrepancies in the response to selection from C0
to C1.
The DTMA populations showed a 3.8% reduction in grain yield between C0 and C1,
while the WEMA populations showed the highest gain in grain yield (12.2%) between
C0 and C1.
4242
Cont.

43. Most of the populations had higher grain yield at C3 than at C0.
The response to selection of population JMPop2 was unique in that the best cycle was C0;
however, C3 produced 6.7% more GY than C1 and 2.3% more than C2, indicating an
increase in genetic gains from GS.
4343
Cont.

44. The International Maize and Wheat Improvement Center (CIMMYT), in partnership with
several public and private institutions, is working to develop and deploy improved maize
(Zea mays L.) germplasm that is drought tolerant, nitrogen use efficient (NUE), and disease
resistant for sub-Saharan Africa (SSA), using conventional pedigree selection and molecular
breeding.
Semagn et al.,2014
44

45. Scientist Methods Results Findings
Cairns et al., 2013 GWAS, test cross
performance of DTMA
Association mapping
pannel under
optimum managed
drought and heat
stress
Several new donor
lines La Posta Sequia
F64-2-6-2-2 and
DTPYC9-F46-1-2-1-2
for heat and drought
stress.
GWAS and GBS
identified 8 genomic
region associated with
GY under drought stress
Semagn et al., 2013
In Kenya
Performed QTL
analysis on individual
populations and
combined across 11 to
18 biparental maize
populations
18 QTL for GY in 6
population and for ASI
33 QTL were identified
in 11 population under
drought stress
Meta QTL analysis was
done , 4 mQTL were
associated with GY on
chromosomes 2,6,7,9
Almeida et al.,2014
In Mexico
Evaluated three
biparental populations
under managed
drought and optimum
environment to
identify genomic
region responsible for
Grain yield.
83 QTL for Grain yield.
8 Mb region in bin
3.06 harboring QTL for
different secondary
/morphological
physiological traits.
This region contain two
important candidate
genes namely zmm16
(MADS- domain TFs)
and psb1(PSII unit) that
are responsible for
reproductive organ
development and
photosynthesis.
Drought
45

46. Scientist Methods Results Findings
Bishwanath Das,
2014 (unpublished)
Improved Maize for
African Soils(IMAS) ,
CIMMYT,
Agricultural
Research
Council(ARC)
421 elite inbred line
from IMAS
association
mapping pannel
were test crossed
with African
adapted line
tester(CML539) and
evaluated across 13
Nitrogen stressed
and well fertilized
locations
Identification of 10
best NUE donor
lines
GWAS in IMAS
pannel identified 47
genomic region
highly associated
with GY and low N
Nitrogen use efficiency
46

47. Scientist Methods Results Findings
Mange Gowda,
2014
235 (DTMA) and 380
(IMAS) tropical lines
were phenotyped for
MLN response . GWAS
using 260,000 SNPs
uncover several
genomic regions highly
associated with MLN
disease resistance
2 major QTL
detected by linkage
mapping in bin
3.04/3.05 on chr 3
harbor resistance
genes for multiple
viruses.
SCMV resistance
locus scmv2 was
identified
Semagn et al.,
2014
3 biparental population
were evaluated under
artificial inoculation of
Maize Chlorotic Mottle
Virus (MCMV) and
Sugarcane Mosaic Virus
(SCMV) and genotyped
using 156-289
polymorphic SNPs
3 major QTL on chr.
3 and 6 and few
minor QTL across all
chromosomes with
exception of chr. 8
1 QTL on bin 3.05
explained up to
30% of total
phenotypic
variance for MLN
disease resistance
Maize Lethal Necrosis
4747

48. Scientist/ place Methods Results Findings
CIMMYT-KALRO
(ongoing)
5 biparental populations using F2
enrichment procedure
76 DH populations
which consist of 10,000
DH lines for msv1
during DH1 cycle
Best MSV resistant
lines with good
agronomic traits will
be used as parents
Maize Streak Virus
48

49. International & National Institutes working on
Climate Resilient Agriculture (CRA)
1. International Center for Agricultural Research in the Dry Areas (ICARDA),
Syria
2. International Crop Research Institute for the Semi-Arid Tropics (ICRISAT),
Hyderabad
3. Centro Internacional de Mejoramiento de Maiz y Trigo (CIMMYT), Mexico
4. Central Research Institute for Dryland Agriculture (CRIDA), Hyderabad
5. Indian Agricultural Research Institute (IARI), New Delhi
6. Indian Institute of Horticultural Research (IIHR), Bangalore
7. National Dairy Research Institute (NDRI), Karnal
8. Central Marine Fisheries Research Institute (CMFRI), Cochin
9. Central Institute of Agricultural Engineering (CIAE), Bhopal
10. ICAR-Research Complex for NEH Region, Barapani, Shillong
11. National Institute for Abiotic Stress Management (NIASM), Baramati, Pune
12. Indian Institute of Maize Research (IIMR), New Delhi
13. Central Rice Reasearch Institute (CRRI), Cuttak
14. Directorate of Rice research (DRR), Hyderabad
NICRA-National
InitiativeonClimate
ResilientAgriculture
49

50. 5050
WATERLOGGING STRESS
DROUGHT STRESS
All India Co-ordinated Maize Improvement
Project (AICMIP)
National Agricultural Research Project
AICRP on Maize (NICRA)
Strengthening Research in Maize
Main Maize Research Station, Anand
Agricultural University, Godhra (Panchmahals)

51. Conclusion
Varieties with increased resilience abiotic and biotic stresses will play an important role in
autonomous adaptation to climate change
Molecular Breeding offers opportunities for plant breeders to develop cultivars with
resilience to stresses with precision and in less time duration
Molecular Breeding is an efficient approach to increase genetic gain per crop cycle
.. Hence, efforts of plant breeders, molecular biologists and scientists in meeting the food
requirements on a sustainable basis for ever increasing population are not hampered.
Marker assisted selection (MAS) allows monitoring the presence, absence of the genes in
breeding populations.
Marker assisted backcross breeding effectively integrates major genes or quantitative trait
loci (QTL) with large effect into widely grown adapted varieties.
For complex traits where multiple QTLs control the expression, marker assisted recurrent
selection (MARS) and genomic selection (GS) are employed to increase precision and to
reduce cost of phenotyping and time duration.
51

52. Future Thrust
Optimize MARS and GWS procedures: involvement of multiple disciplines (breeding, biotechnology,
biometrics and bioinformatics)
High cost, non availability and complexity of molecular platforms, reliability of marker profiling and
scoring, limited markers and degree of polymorphism.
Developing high-density SNP platforms to further reduce the cost of SNP profiling
Transgenic crops carrying different stress related regulatory genes can be used.
Use of Genome-Editing Tool like The CRISPR/Cas, TALEN, ZFNs etc.
TILLING by sequencing can be used to identify induced mutations in stress resistance
genes .
QTL x E effects, lack of equipment, resources, technical expertise and lack of application
gap.
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