Genomic regions associated with heat stress tolerance in tropical maize. Kaliyamoorthy Seetharam

1. 13th Asian Maize Conference and Expert Consultation on
“Maize for Food, Feed, Nutrition and Environmental Security
On
9th October, 2018, Ludhiana, Punjab, India
Genomic regions associated with heat
stress tolerance in tropical maize
Kaliyamoorthy Seetharam, Prakash H Kuchanur, Ramesh Chaurashia,
Kamal Pandey, Ayyangouda Patil; Keshab Babu Koirala, Viswanadh
Sudharsanam, Reshmi Rani Das, Madhumal Thayil Vinayan, Sudha K Nair,
Raman Babu and P. H. Zaidi

2. .Major constraints to Maize production
worldwide
Drought
Water logging
Heat stress
Annual mean maximum temp may
increase by 1.4–1.8 °C in 2030 and
2.1–2.6 °C in 2050
Heat stressed areas in SA could
increase by up to 12% in 2030 and
21% in 2050 (Tesfaye etal., 2016)
Rationale

3. Enhanced
through
Molecular
Tools
Obtained
through
breeding
approach
Heat
tolerant
hybrids
GWAS
Need of the hour

4. 10
Heat tolerant maize association mapping
panel (HTAM)

5. HTAM panel
Test crosses evaluated in 9
locations under heat stress
Phenotyped for grain yield
under heat stress
HTAM panel TC with
CML451
HTAM panel DNA isolation
Genotyping by sequencing
Filtering of SNPs based on Call
rate and Minor allele frequency
GWAS by MLM model

6. Hyderabad-HY
17.51N; 8.27E
Bejanki-BJ
18.25N; 9.02E
Bgudi-BG-1, BG2
18.25N; 9.02E
Raichur-RA
16.22N; 7.38E
Ludhiana-LU
30.99N; 5.74E
Jalandhara-JL-1,JL-2
31.32N; 5.57E
Nepalgunj-NG
28.05N; 1.61E
Phenotypic sites

7. 2.77
3.61
2.25
2.48 2.56
3.19
3.65
4.63
6.81
0
1
2
3
4
5
6
7
8
BG-1 LU NG RA BJ BG-2 HY JA-1 JA-2
Location
No of
Entries
H2 Min Max
BG-1 290 0.62 1.26 4.36
LU 290 0.50 1.99 5.26
NG 290 0.36 1.54 3.22
RA 335 0.34 1.71 3.60
BJ 420 0.53 1.15 4.87
BG-2 285 0.69 1.27 5.49
HY 479 0.61 2.20 5.24
JA-1 471 0.39 3.23 5.51
JA-2 435 0.41 5.42 8.06
Phenotypic performance of HTAM panel
TC for grain yield under heat stress

8. Location
Total No. of
SNPs used
for GWAS
Total No.
of SNPs
used for
PCA
BG-1 289,060 124,496
LU 289,060 124,496
NG 289,060 124,496
RA 288,826 124,832
BJ 281,901 115,936
BG-2 289,061 124,496
HY 281,268 113,263
JA-1 282,186 114,654
JA-2 286,786 121,597
Total No of SNPs form GBS-
955,690
SNPs for PCA Analysis
CR-0.9, MAF-0.1
SNPs for GWA Analysis
CR-0.7, MAF-0.05
GWAS for grain yield under heat stress

9. Population structure based on the first two
Eigen values of PCA

10. Linkage disequilibrium (LD) plot representing the
average genome-wide LD decay

11. Manhattan plot for grain yield under heat stress
BG-1
LU
NG
RA
Chromosome Number
-log10Pvalue

12. BJ
BG-2
HY
JA-1
JA-2
Chromosome Number
-log10Pvalue
Manhattan plot for grain yield under heat stress

13. Location
No. of SNP
significant at
P≤10-5
Significant MTAs
at calculated P
value cut off*
BG-1 44 19
LU 29 13
NG 28 9
RA 20 8
BJ 23 9
BG-2 25 12
HY 38 24
JA-1 40 28
JA-2 23 14
• None of the SNPs were common between the location
*Cut off calculated as per Cui, et al 2016,

14. Haplotype
block/
SNPs
Occurrence
in No of
locations
BG-1 LU NG RA BJ BG-2 HY JA-1 JA-2
SNP-1 2  
SNP-2 2  
Hap_4 2  
SNP-3 5     
SNP-4 2  
Hap_6 2  
SNP-5 2  
SNP-6 2  
Hap_9.1 3   
Hap_9.2 2  
Hap_9.3 2  
SNP-7 2 
SNP-8 2  
Haplotype frequency & Trend regression

15. Haplotype
block/SNPs
Chr Markers Used
Haplotypes
No
P-Value R2
Bonferroni
P
Favorable
Allele
SNP-1 1 S1_177613739 2 1.14E-04 3.68E-02 2.77E-02 G
SNP-2 1 S1_202550281 2 6.06E-07 5.42E-02 1.47E-04 T
Hap_4 4
S4_179804506,
S4_179804514
2 1.47E-04 5.42E-02 3.57E-02 AT
SNP-3 5 S5_24337729 2 9.32E-08 7.33E-02 2.26E-05 G
SNP-4 5 S5_5626614 2 2.24E-06 6.17E-02 5.45E-04 A
Hap_6 6
S6_164097936,
S6_164097938
2 6.15E-07 1.10E-01 1.50E-04 TT
SNP-5 7 S7_30875396 2 3.82E-06 4.88E-02 9.27E-04 G
SNP-6 9 S9_152043636 2 2.84E-05 4.32E-02 6.90E-03 C
Hap_9.1 9 S9_26578085, S9_26618809 3 1.09E-09 9.16E-02 2.66E-07 TT
Hap_9.2 9 S9_26821739, S9_26821740 2 5.24E-05 6.30E-02 1.27E-02 AG
Hap_9.3 9 S9_41360950, S9_41517742 2 5.68E-05 7.62E-02 1.38E-02 TC
SNP-7 10 S10_127255564 2 6.28E-06 7.37E-02 1.53E-03 T
SNP-8 10 S10_143169509 2 4.17E-05 3.95E-02 1.01E-02 T

16. Conclusion
• The major genomic regions with favorable alleles for
grain yield under heat stress can be introgressed
into elite and locally adapted genetic background
• Findings from the present study could serve as a
strong base for future functional studies to dissect
heat stress tolerance
• GWAS with environmental factors as covariate
analysis will help to address the GEI

17. Acknowledgement
• Team HTAM and Private and public sector
partners

18. Thank you
for your
interest!
Photo Credits (top left to bottom right): Julia Cumes/CIMMYT, Awais
Yaqub/CIMMYT, CIMMYT archives, Marcelo Ortiz/CIMMYT, David
Hansen/University of Minnesota, CIMMYT archives, CIMMYT archives
(maize), Ranak Martin/CIMMYT, CIMMYT archives.