Presentacion de 11th Asian Maize Conference which took place in Beijing, China from November 7 – 11, 2011.

1. Association Mapping, Breeder Ready
markers and Genomic Selection
Raman Babu, Jill Cairns, Gary Atlin, PH Zaidi, Pichet Grudloyma, George
Mahuku, Sudha K Nair, Natalia Palacios, Pixley Kevin, Jose Crossa, BM
Prasanna and all the Breeders of CIMMYT

2. Outline
 Association Mapping for Drought Tolerance – CIMMYT‟s
experience
● Are there large effect genes for GY_stress?
● Should we bother about “rare alleles” that have large
effects?
 Association Mapping for Disease Resistance
 Association Mapping (Candidate-gene based) for
Carotenoids
 „Breeder-ready‟ markers for disease resistance and ProA
 Integrating Genomic Selection in the breeding Pipeline

3. LD and Population structure
in DTMA-AM panel based on
55K SNPs
 Average distance between two
markers is 55kb and Average EM-
R2 is 0.26
 LD in DTMA panel is low and
hence suitable for association
mapping
 Population structure is ‘mild’ and LaPosta Seq
association results were corrected DTP
for structure (through PCA) and
kinship by MLM
CIM-CALI

4. DTMA-AM panel and 55K SNPs can identify large
effect genes – 1. Grain Color
Psy1 92 – Yellow lines (1)
R² = 37%
186 – white lines (0)
SNP with largest significant association with
grain color located within one of the exons of
Phytoene Synthase1 (psy1) on chr.6

5. DTMA-AM panel and 55K SNPs can identify large
effect genes – 2. QPM 10 – QPM lines (1)
268 – Normal lines (0)
Opaque2
Ask2 at 7.01
R² = 8% R² = 16%
Besides opaque-2 and ask-2, several minor QTL regions
influencing kernel modification and tryptophan content
identified that overlap with previously reported regions…

6. Mapping Drought Tolerance
Strategy GWAS
AM-panel ~ 300 inbreds – TCd with CML312
Known DT sources La Posta Sequia C7; DTP C9, MBR etc.
Phenotyping 10 locations – Stress & Optimal
Heritabilities Kiboko-10-Late (0.64), M-10 –Tlalti (0.67), Thailand-
10 (0.49), M-Tlalti-09 (0.54), Zim-10 (0.22) Across
locations: 0.35
Phenotype used in Combined BLUPs of TC_GY under stress, corrected for
GWAS anthesis date
Genotyping Genome-wide, high density markers – 55K SNPs and GBS
markers (500K SNPs)
Statistical Model General Linear Model (PCA correction) and Mixed Linear
Model (PCA + Kinship – Q+K)

7. 12 -15 significant genomic regions identified for DT
7.0%
5.8%
5.7% 7.3%
5.7% 6.2%
5.5%
5.1%
5.1%
4.9%
 Only 147 SNPs (~15 genomic regions) had R2 values more than 5%

8. Significant Genomic Regions associated with
TC_GY_Stress
Average GY of the stress trials – 1.3 t/ha
Heritability across locations – 0.32
Effect
SNP Chr Position P value MAF R2 (%) (kg/ha) Candidate Gene
SYN39332 10 142655119 9.62E-06 0.49 7.6 29.3 Starch Synthase
PZE-107042377 7 72216348 1.49E-05 0.32 7.3 35.6 Myb family transcription factor-related protein
PZE-108046876 8 77237318 2.33E-05 0.35 6.9 -34.4
PZE-110029252 10 50842298 7.77E-05 0.40 6.8 -26.3
PZE-107032355 7 45011599 6.62E-05 0.38 6.2 38.6
SYN37988 2 146399448 3.84E-05 0.26 6.1 49.5 TSA: Zea mays contig27975, mRNA sequence
PZE-101090321 1 80757998 1.67E-04 0.46 4.9 -31.4
PZE-109041733 9 62608362 1.35E-04 0.42 4.9 25.5
PZE-104047052 4 78536398 1.21E-04 0.32 4.7 30.1

10. Rare Alleles with Large Effects
Average for Average for Average for
Marker Chr Position P Minor Allele MAF DD Dd dd Effect (kg/ha) DD Dd dd
PZE-104042524 4 67259441 3.70E-03 A 0.14 1499.5 1414.1 1382.8 116.6 7 59 188
PZE-101066401 1 49827350 1.54E-02 A 0.04 1487.8 1391.5 96.3 10 0 257
SYN36769 4 4914023 7.83E-03 A 0.06 1479.7 1355.9 1391.7 88.0 14 2 249
SYN26515 1 63053588 2.42E-03 A 0.06 1472.1 1253.0 1391.0 81.0 15 1 251
SYN1035 5 5786027 3.24E-02 G 0.07 1463.1 1395.2 1390.3 72.8 16 2 240
PZE-110053356 10 100124247 4.70E-03 A 0.11 1331.7 1381.2 1401.8 -70.1 17 24 224
PZE-104113536 4 194565443 7.31E-04 A 0.13 1334.7 1282.7 1405.3 -70.6 33 3 231
PZE-102096857 2 107898705 3.07E-03 G 0.08 1329.7 1400.4 -70.6 20 0 238
PZE-109074314 9 116545321 2.21E-04 G 0.08 1328.8 1400.3 -71.5 20 0 246
PZE-105127701 5 183968110 2.24E-04 A 0.07 1323.4 1400.7 -77.4 19 0 249
PZE-102121069 2 162773047 8.92E-04 G 0.06 1320.1 1400.2 -80.1 17 0 250
PZE-106064720 6 116886483 1.09E-02 A 0.07 1318.2 1307.6 1401.3 -83.1 17 1 248
SYN14434 2 15813081 1.40E-03 A 0.08 1314.6 1433.4 1398.6 -84.0 19 1 221
PZE-106056703 6 107499158 1.98E-04 G 0.06 1310.2 1307.6 1401.2 -91.0 15 1 251
SYN8914 3 194356323 4.25E-03 G 0.08 1307.9 1381.6 1400.1 -92.2 9 25 226

11. PZE-101066401
1
Rare Alleles with 49827350 SYN36769
4
GY 4914023
Positive Large A
POB.502 c3 F2 10-3-2-1-BBBBBB-B
(kg/ha)
1429.0 A
GY
(kg/ha)
[SYN-USAB2/SYN-ELIB2]-12-1-1-2-
Effects POB.502c3 F2 9-14-1-2-B-B-B-B
CLQ-RCYQ28=(CLQ6502*CLQ6601)-
1482.4 BBB 1497.3
[CML440/[[[K64R/G16SR]-39-1/[K64R/G16SR]-
20-2]-5-1-2-B*4/CML390]-B-39-2-B-4-#-1-
B-34-2-2-B*6-B 1476.1 B//ZM303c1-243-3-B-1-1-B]-9-1
PZE-104042524 DTPWC9-F24-4-3-1-B-B-B 1554.0 [[KILIMA ST94A]-30/MSV-03-1-10-B-
DTPWC9-F115-1-4-1-1-B-B-B 1483.4 1-B-B-1xP84c1 F27-4-1-6-B-5-B] F8-3-
4 2-2-1 x G16SeqC1F47-2-1-2-1-BBBB-
67259441 DTPWC9-F103-2-1-1-1-B-B-B 1469.6 B-xP84c1 F26-2-2-6-B-3-B]-3-1-
DTPYC9-F46-3-4-1-1-B-B-B 1535.9 B/CML395]-1-1 1419.5
GY [Pob.SEW-HG"B"c0F39-1-1-1-1xMBR
DTPYC9-F46-3-9-1-1-B-B 1461.7 C5 Bc F22-2-1-4-B-B-B-B-2-2-B-B-
A (kg/ha)
DTPYC9-F46-1-2-1-2-B-B 1606.1 B/CML442]-1-1 1333.2
90[SPMATC4/P500(SELY)]#-B-4-2-B-B 1483.8 DTPYC9-F13-2-1-1-2-B-B 1379.5
[Cuba/Guad C3 F34-2-1-1-B-B-B x
CML264Q]-1-1 1376.4
DTPYC9-F46-3-9-1-1-B-B 1461.7 CML-322 1428.5
La Posta Seq C7-F125-2-1-1-2-B-B-B 1436.8 SYN26515 DTPWC9-F115-1-4-1-1-B-B-B 1483.4
1 DTPWC9-F31-1-3-1-1-B-B-B 1492.0
La Posta Seq C7-F103-2-2-2-1-B-B-B 1626.9 DTPWC9-F67-1-2-1-2-B-B-B 1506.5
63053588
La Posta Seq C7-F180-3-1-1-1-B-B-B 1593.5 GY
DTPWC9-F104-5-4-1-1-B-B-B 1454.3
DTPYC9-F46-3-4-1-1-B-B-B 1535.9
La Posta Seq C7-F96-1-1-1-B-B 1482.1 A (kg/ha) DTPYC9-F46-3-9-1-1-B-B 1461.7
DTPYC9-F72-1-2-1-1-B-B 1411.4 CML444-B 1501.9 DTPYC9-F46-1-2-1-1-B-B 1552.7
S87P69Q(SIYF) 109-1-1-4-B 1518.4 DTPYC9-F46-1-2-1-2-B-B 1606.1
DTPWC9-F67-2-2-1-B-B-B 1568.7
CLQ-RCYQ40 = (CML165 x CLQ-6203)-B-
9-1-1-B*8 1509.3
CML497=[CL-00331*v]-3-B-3-2-1-B*6 1443.1
DTPWC9-F115-1-4-1-1-B-B-B 1483.4
DTPWC9-F109-2-6-1-1-B-B-B 1467.8
DTPWC9-F67-1-2-1-2-B-B-B 1506.5
DTPWC9-F104-5-4-1-1-B-B-B 1454.3
DTPWC9-F128-1-1-1-1-B-B-B 1390.9
DTPYC9-F143-5-4-1-2-B-B-B 1442.1
DTPYC9-F143-1-6-1-B-B 1414.6
DTPWC9-F67-2-2-1-B-B-B 1568.7

12. PZE-106056703 SYN14434
Rare Alleles with 6
107499158
2
15813081
Negative Large G
[CML444/CML395//DTPWC8F31-4-2-1-
6]-2-1-1-1-B*4 1331.949
A
P501SRc0-F2-47-3-2-1-B-B
[CML444/CML395//DTPWC8F31-1-1-2-2-
1268.038
Effects [(CML395/CML444)-B-4-1-3-1-
B/CML395//DTPWC8F31-1-1-2-2]-5-1-
BB]-4-2-2-2-2-BB-B
[CML444/CML395//DTPWC8F31-1-1-2-2-
1267.39
2-2-BB 1346.993 BB]-4-2-2-2-1-BB-B 1408.142
CML 384xMBR/MDR C3 Bc F58-2-1-3- 02SADVL2B-#-17-1-1-B 1419.196
SYN8914 B-B-B-B-3-1-B-B-BB-B 1344.688 [CML440/[[[K64R/G16SR]-39-1/[K64R/G16SR]-20-2]-
3 MBR C6 Bc F280-2-B-#-1-1-B-B-B-B-B- 5-1-2-B*4/CML390]-B-39-2-B-4-#-1-B//ZM303c1-243-
B 1256.056 3-B-1-1-B]-9-1
194356323
[G16SeqC1F47-2-1-2-1-BBBB-B-xP84c1 [CML144/[CML144/CML395]F2-5sx]-1-3-1-
G F27-4-1-6-B-5-B] F23-2-1-2-3 x P43C9- 3-B*4 1397.445
[CML198/ZSR923S4BULK-2-2-X-X-X-X-1- 1-1-1-1-1-BBBB-1-xP84c1 F26-2-2-6-B- [CML198/ZSR923S4BULK-2-2-X-X-X-X-1-
BB]-3-3-1-1-2-B*7 1196.562 3-B]-2-1-B/CML395]-1-1 1258.137 BB]-3-3-1-1-2-B*7 1196.562
S99TLWQ-B-8-1-B*5 1245.322 [M37W/ZM607#bF37sr-2-3sr-6-2-X]-8- [CML144/[CML144/CML395]F2-8sx]-1-1-1-
2-X-1-BB-B-xP84c1 F27-4-3-3-B-1-B] B*5 1171.759
4001 1292.372 F29-1-2-1-6 x [KILIMA ST94A]-30/MSV- [CML144/[CML144/CML395]F2-8sx]-1-2-3-
CLA41 1389.549 03-2-10-B-1-B-B-xP84c1 F27-4-1-6-B-5- 2-B*5 1203.073
(A.I.Z.T.V.C. 20-3-1-1-2-B-B x B]3-1-2-B/CML442]-1-1 1190.413 CLA222 1337.217
A.I.Z.T.V.C.PR93A-17-1-3-1-1-B-B)-B- [Pob.SEW-HG"B"c0F39-1-1-1-1xMBR [M37W/ZM607#bF37sr-2-3sr-6-2-X]-8-2-X-
C5 Bc F22-2-1-4-B-B-B-B-2-2-B-B- 1-BB-B-xP84c1 F27-4-3-3-B-1-B] F29-1-2-1-
14TL-1-3-B-B 1252.957 B/CML442]-1-1 1333.209 6 x [KILIMA ST94A]-30/MSV-03-2-10-B-1-B-
[G16SeqC1F47-2-1-2-1-BBBB-B-xP84c1 [MBR Et/MBR Bc C1 F4-1-1-3-B-B-B- B-xP84c1 F27-4-1-6-B-5-B]3-1-2-
F27-4-1-6-B-5-B] F23-1-3-1-1 x [KILIMA Bx1760B B1 Bco x Comp.-B-1-1-1-1-B- B/CML442]-1-1 1190.413
ST94A]-30/MSV-03-2-10-B-1-B-B- B-B/CML395]-1-1 1354.8 [Cuba/Guad C3 F34-2-1-1-B-B-B x
xP84c1 F27-4-1-6-B-5-B]-2-1- [CML 329/MBR C3 Am F103-1-1-2-B-B CML264Q]-1-1 1376.38
x CML486]-1-1 1346.293 CA00344 / PAC777F2-6-1-1-BB-B-B-BB 1321.875
B/CML395]-1-1 1270.448
[(87036/87923)-X-800-3-1-X-1-B-B-1-1- P44 C10MH8-30-4-B-4-1-B-B-B-B- 1329.436
POB.501c3 F2 13-8-2-1-BBBB 1383.065 1-B-B-xP84c1 F26-2-2-4-B-2-B] F47-3- P147-#136-5-1-B-1-BBB 1356.154
CL-RCY031=(CL-02410*CML-287)-B-9-1- 1-1-3 x M37W/ZM607#bF37sr-2-3sr-6- CLQ-6211=P62QC6HC13-1-3-BBB-6-B-7-6-
1-2-B*7 1433.411 2-X]-8-2-X-1-BB-B-xP84c1 F27-4-3-3-B- BBBB-7-9-B-B-B-B 1311.726
1-B]-3-2-B x P33c3 F64-1-1-4-BB]-1-1 1295.392 CML269=P25STEC1F13-6-1-1-#-BBB-f-##-
P390amC3/285x287 F73-3-2- B*6-B 1407.819
3xMIRTC5Am F96-1-1-1-3-1)-1-1-B 1399.776 CL-02143 P21C6S1MH247-5-B-1-1-2-BBB-
CL-G1837=G18SeqC2-F141-2-2-1-1-1- 1-##-B*10 1471.196
2-##-2-B*4 1275.469 CML421=P31DMR#1-55-2-3-2-1-B*18-B 1252.385
CML421=P31DMR#1-55-2-3-2-1-B*18- DTPWC9-F66-2-1-1-2-B-B-B 1291.755
B 1252.385
DTPWC9-F73-2-1-1-1-B-B-B 1329.332

13. Rare Alleles – Candidate genes
Candidate genes
Putative function
identified by Rare Alleles
upstream of a DNA biding/membrane
bound receptor Many membrane bound receptors like Rpk1, shown to confer DT in AT.
Less documented helicase domain proteins in AT proved for DT in CK
DEAD box Helicase domain dependent pathways
cross-talk between ethylene signalling and drought response pathways well-
related to ethyline insensitive2 documented
glyco poteins rich in hydroxy proline was first studied in Tracheophytes
Extensin like cell wall protein which can with stand severe stress
Annexin IV domain Role of Annexins in DT well-documented in AT
Peroxidase protein known for involvement in DT in rice, AT etc.
Major Facilitator Superfamily (MFS)
Transporters plays key roles in different stress conditions
over expression of Aspartate aminotransferase along with other
Aminotransferase genes has been patented for DT
CREB domain containing TF Known component in stress related pathways
Ubiquitin subgroup known component in drought tolerance pathways

14. Traits for which AM analysis accomplished in
DTMA-AM panel
GY_Stress_BLUPs
MSV
GLS
NDVI
Senescence
SPAD
Canopy senescence
ASI
Root traits (Shovelomics!)
Anaerobic Emergence
% reduction in shoot weight under waterlogged conditions
% reduction in root weight under water logged conditions

15. Following up the AM results
● BC-NILs for validation of important genomic
regions
● Identify MARS progenies with contrasting
genotypes and check the drought phenotypes
● Genotype the DH lines from DT x Normal
crosses and check the phenotypes
● Introgress validated genomic regions into tester
lines through MAS

16. Artesian – Recent Drought Tolerant
Hybrid from Syngenta
Base Hybrid Artesian Hybrid

17. Artesian – how was it developed?
Strategy Association mapping (candidate gene-based)
BC-MAS of 4-8 QTLs
DT source germplasm CML333, CML322, Cateto SP VII (Brazil), Confite Morocho AYA
38 (Peru), or Tuxpeno VEN 692 (Venezula)
AM-panel 575 inbreds – 47 different testers (mostly S-2 and S-3 TCHs)
Phenotyping 4 locations (Colorado, California and Chile) – Optimal & stress -
Yield reduction under stress was 40-60% from optimal
Genotyping 85 polymorphisms (corresponding to 57 candidate genes) and
149 random polymorphisms across 600 lines – in total only
~250 markers
Effect sizes of identified 60 to 650 kg/ha
genomic regions
Minimum P value of any 0.0001
significant region

18. Significant Conclusions – DT mapping
 LD in DTMA-AM panel is low and hence conducive
for association mapping
 55K genotype data is capable of identifying large
effect genes
 „Reasonably large effect‟ genomic regions (10-15)
do exist for GY_Stress and co-locate with genes,
previously implicated for DT in At, rice and maize
 9 genomic regions that had robust p-values
together explained 35% of phenotypic variance for
GY_Stress_Combined
 Lines with multiple donor segments identified for
validation and introgression

19. Two Key genes in carotenoid biosynthetic pathway identified
Association Mapping
based on candidate
gene sequences
Lycopene epsilon cyclase (Harjes
et al. 2008; Science)
Hydroxylase (CrtRB-1/HydB-1)
(Yan et al. 2010; Nature Genetics)

20. Breeder-ready markers developed and routinely being used in the
H+ breeding program of CIMMYT for CrtRB1 and LcyE
AM leads to identification of
High
Key genes and polymorphisms ProvitA
+ + = maize!
MAS for MAS for
Deep orange LycE HydB
Polymorphisms validated in ears
diverse tropical genetic
backgrounds and breeder-ready
high throughput markers
developed
Routine use of markers and
selection of favorable
genotypes in H+ breeding
program

21. Allele Mining for CrtRB1 (HydB1) across various
Association Mapping Panels
Panel Genotypes with Fav. White(W)/Yellow(Y)
allele/Total
CIMMYT_Syngenta 24*/501 – 16 new sources All Yellow (Y)
CAM Panel
IMAS 16/430 (6 from ARC, SA and 14-W and 2-Y
3 from KARI)
Subtropical Collections 71/1131 – many new sources 24-W and 47-Y
ADP lines of 19/122 – “1” and 23/122 – “H”
SYNGENTA
PS: * out of 24, 8 were previously fixed for fav. allele of CrtRB1 in the H+ breeding
program through MAS

22. Association Mapping for Disease Resistance
MSV – Harare 2010 data (Heritability = 0.79) GLS-combined analysis (Heritability = 0.6)

23. MSV – Harare 2010 data (Heritability = 0.79)
Significant chromosomal regions (P < 1.0E-05) associated with MSV
resistance (Har-2010 data) based on DTMA-AM panel and 55K genotype
data (MLM)
Trait Trait
FDR (False Minor average average
Corr/Trend Corr/Trend discovery R2 Minor Allele Major for Minor for Major
Marker Chr Position P value Chi-square rate) (%) Allele Freq. Allele allele allele
PZE-101093951 1 86065123 4.50E-08 29.92 0.002 11.5 A 0.34 G 1.83 3.08
PZE-101098418 1 92204598 6.47E-07 24.77 0.011 9.5 G 0.36 A 2.15 2.95
SYN36281 1 187128850 1.93E-06 22.67 0.019 8.7 G 0.11 A 2.21 2.72
PZE-101094082 1 86384320 2.45E-06 22.21 0.020 8.5 G 0.39 A 1.99 3.10
PZE-104024779 4 28770811 4.04E-06 21.24 0.022 8.2 A 0.15 G 2.26 2.73
PZE-101098295 1 91837910 5.31E-06 20.72 0.022 8.0 A 0.33 G 2.12 2.92
PZE-108038832 8 59948253 5.57E-06 20.63 0.021 7.9 A 0.47 G 2.63 2.70
PZE-103070254 3 111066077 6.36E-06 20.38 0.022 7.8 G 0.24 A 3.07 2.52
PZE-101094056 1 86365447 6.37E-06 20.37 0.021 7.8 G 0.50 A 2.16 3.16
PZE-108039819 8 62905375 7.00E-06 20.19 0.022 7.8 G 0.46 A 2.62 2.69
PZE-101090488 1 80905706 7.02E-06 20.19 0.020 7.8 A 0.29 G 1.83 3.00
PZE-104016598 4 16339600 7.13E-06 20.16 0.019 7.8 A 0.33 C 2.21 2.87
PZE-102080891 2 64845534 7.21E-06 20.14 0.019 7.7 A 0.28 C 2.19 2.84
PZE-101098960 1 93244458 7.76E-06 20.00 0.019 7.7 A 0.40 G 3.11 2.36

24. Validation of AM regions and Breeder-ready markers for MSV
PZE01132220936
PHM14104_23
PZE0175698629
PZA00529_4
PZA02090_1
PZA03527_1
PZA02614_2
PZA03651_1
Candidate SNPs for MSV
Chr.1 Chr.3 Chr.4 Chr.8
Msv1 R R R PZE0186365075
csu1138_4
PZA00944_1
S S S PZE0195148805
PZE01101110579
PZE01111422982
R PZE0175698629
R S S PZE-101093951
S
R
S R S
S
R
S S R
S

25. Genomic Selection

26. Genomic Selection
Using 55K SNP data across 300 individuals in the AM
panels
Trait RR-BLUP B-LASSO RP
Grain Color (Binary) 0.8 0.82 0.87
QPM (Binary) 0.96 0.96 0.95
ProA - Quant 0.39 0.42 0.6
GLS - Quant 0.52 0.53 0.55
MSV - Quant 0.62 0.61 0.60
GY - Quant 0.34 0.35 0.36

27. Integrating GS in breeding pipeline
(DH + off-season nusery + GS)
Season Activity
Summer • Grow 50-100 F2s/BC1s
• Select 50 plants/cross and cross to haploid inducer
Winter • Chromosome doubling of putative haploids to get DHs
• Seed chip (one kernel/DH) 2500 – 5000 DH kernels
• Discard disease susceptible DHs through specific marker
screening
• Select DHs through GY-GEBVs and seed Increase (top 5-
10%)
Summer • Test cross GEBV-selected DH lines to one/two testers
• Yield trials of DH-TCHs

28. THANKS

29. % phenotypic variance explained by structure
alone…in DTMA-AM panel
% phenotypic variance
Trait/Location explained by 10PCs
GY_Stress_Combined_BLUP 15.8
MSV (Harrae2010+09-1) 38.2
GLS_Combined 25.1
GLS_Har_10 8.8
GLS_Kakamega 11.5
GLS_Columbia_Scatalina 30.2
GLS_San Pedro_Mexico 29.6
GLS_Acatec_Mex 23
GLS_Paraguacito_Columbia 6.7