1. ILRI-CIMMYT perspective on How can Genomics help improve fodder quality in maize? Raman Babu, Vinayan MT, Zaidi PH and M Blummel

2. Outline GWAS & GS – two powerful tools
Genetics of fodder traits – DTMA & CIMMYT-Asia
How to leverage genomics for dual purpose maize?
How to mine the DH lines for fodder quality without extensive field and lab testing?

3. How is Genomics redefining the maize breeding space?
High density genotypes
High precision phenotypes +
Powerful statistics
High efficiency computing
GWAS GS
Robust “marker-trait associations” for diverse traits
Reduced ‘environmental dependence’ (eg. Breeding for MSV resistance possible from Mexico!)
Susceptible entries discarded even before planting (thanks to seed-DNA genotyping!)
Robust predictions enable resource efficient breeding! (less no. of yield evaluation plots)
Enables speedier delivery of improved source populations
“Open source” GS facilitates exchange of crucial information

4. Marker statistics - GBS
Total no. of markers
681,257
markers with two alleles
545,741
markers with one allele
135,516
MAF > 0.05
259,789
MAF > 0.01
417,289
Call Rate > 0.9
92,307
Call Rate > 0.7
392,325
Call Rate > 0.5
642,903
Markers for GWAS
Call Rate (0.9) & MAF (0.05)
39,392
Call Rate (0.5) & MAF (0.01)
388,342

5. GWAS for Grain Color GRMZM2G300348-PSY1 – chr6 -82,017,148-82,021,007

6. GWAS for QPM
S7_
GRMZM2G015534: Opaque2 chr.7: 10,793,452-10,796,233
S7_

7. GWAS for IR phenotypes
IR (1) – 12
IR (0) - 527
ALS1

8. GWAS for fodder quality in maize
Models
Corrected for Structure (GLM)
Corrected for Structure & Kinship (MLM)
Multi-locus MLM
Panels
DTMA-AM TCs
CIMMYT-Asia TCs

9. Fodder Quality Traits

10. In vitro digestibility
GWAS for fodder traits in DTMA-AM
Nitrogen %

11. Acid Detergent Fibre (ADF)
xyloglucan endo-transglycosylase/hydrolase
xyloglucan endo-transglycosylase/hydrolase is associated with fiber elongation in cotton (Shao et al. 2011)

13. GWAS for IVOMD in CIMMYT-Asia AM Panel
Association analysis based on high density GBS (~400K SNPs) data in CIMMYT-Asia association panel for ‘IVOMD’ reveals tentative genomic candidate regions on chr1, chr3, chr4 and chr10. The most significant SNP on chr1 was co-located with cinnamyl coA reductase.

14. Genomic regions that are significantly associated (MLM-P <0
Genomic regions that are significantly associated (MLM-P <0.05) with all the four forage quality traits (ndm, adf, me and ivomd) with minor alleles having positive effects.
Marker
Minor Allele
MAF
P-ndm
AE-ndm
P-adfdm
AE-adfdm
P-me
AE-me
P-ivomd
AE-ivomd
S5_ T
0.44
2.40E-02
0.03
5.65E-04
-0.99
4.71E-02
0.13
4.00E-02
0.79
S1_ A
0.06
1.32E-02
0.09
1.59E-03
-2.05
1.82E-02
0.24
8.84E-03
1.69
S1_ G
0.21
1.20E-02
0.07
2.55E-03
-1.05
6.41E-03
0.27
2.27E-03
1.81
S6_ C
1.49E-02
0.25
1.63E-02
-3.19
1.26E-02
2.90E-03
2.15
S2_
0.04
5.30E-03
2.46E-02
-2.21
3.39E-02
0.12
1.10E-02
1.42
S10_
0.46
5.90E-03
0.05
3.12E-02
-0.72
8.88E-03
S3_
0.42
7.12E-03
3.89E-02
-0.38
3.53E-03
0.15
1.53E-03
1.02
S1_
9.21E-08
0.08
4.23E-02
-0.68
9.65E-03
8.99E-04
1.57
S1_
4.17E-02
0.11
4.41E-02
-1.68
5.26E-03
0.32
1.63E-03
2.32
S8_
0.37
2.90E-02
4.84E-02
-0.54
2.01E-04
0.23
5.95E-05
1.54

15. Candidate genes around the most significant genomic associations for various forage quality traits
SNP
Trait
Candidate gene
Putative function
S1_
IVOMD
Low phytic acid-1 (GRMZM2G155242)
Chelator of minerals such as Fe and Zn; low phytic acid genotypes are considered nutritionally superior.
S1_
cinnamoyl CoA reductase1 (GRMZM2G131205)
This enzyme participates in phenylpropanoid biosynthesis and may play a role in enhancing digestibility of forage
S4_ ME
beta-ketoacyl reductase GL8B (GRMZM2G389110)
This enzyme participates in fatty acid biosynthesis and polyunsaturated fatty acid biosynthesis.
S2_
ADF
Lipoxygenase6 (GRMZM2G040095)
Catalyses dioxygenation of polyunsaturated fatty acids in lipids

16. Two Possible Applications
1. Marker Assisted Introgressions – will require extensive validation and dedicated breeding efforts
2. Marker-enabled Prediction (Genomic Selection) – low cost option and enables identifying likely genotypes with superior fodder quality traits among the proven GY genotypes

17. Genomic Selection DTMA & CAAM New DH Lines Meuwissen et al. 2001.
The simplicity of the work-flow projected here masks the magnitude of shift it represents, when implemented successfully. I believe the GS approach and the specific genomic region based approaches can go hand in hand, complementing each other.
Meuwissen et al

18. Evaluation of DH-TCs for fodder quality
Predicting performance of DH lines for fodder quality
~1200 DH lines generated from 10 F1s (Pioneer)
Per se evaluation
Formation of
DH-TCs
Evaluation of DH-TCs for fodder quality
Seed Increase
Season-1
Season-2
Season-3
Season-4/5/6
Can markers help augment the pace of DH utilization in the breeding program?

19. High density (GBS) genotypes of DH Lines1 4 7 5 3 6 2 9 10 8
Pops
Pedigree 1
((CML165xKI45)-B-14-1-B*4-1)/(CL02450) 2
(CA03139-BBB-2-BB)/(CML451) 3
(DTPYC9-F B*4)/(CL02450) 4
(DTPYC9-F B)/(CL02450) 5
(LaPostaSeqC7-F B*7)/(CL02450) 6
(POOL16BNSEQC3F22x BBB)/(CL02450) 7
(POOL16BNSEQC3F32x BBB)/(CL02450) 8
CA BB/CML470-BB 9
(POOL16BNSEQC3F28x BB/G18SeqC5F BB)-B-11-BB/(Pop61C1QPMTEYF B-1-B/Pop61C1QPMTEYF B-1)-B-1-BBB 10
(Pop61C1QPMTEYF B-1/(CML161xCML451)-B-23-1-B*4-1)-B-5-BB/G18SeqC5F B*5
CML451
CL02450

20. Predicting performance of DH lines for fodder quality
CAAM and DTMA as Training
DH lines as Test set
Predicted Best and Worst DH lines based on ME…
DH_8_4
High_HYD_ME
DH_3_43
DH_3_21
DH_3_149
DH_3_63
DH_3_10
DH_3_121
DH_3_24
DH_2_71
Low_HYD_ME
DH_2_24
DH_6_41
DH_2_86
DH_6_110
DH_6_84
DH_6_30
DH_2_92
DH_2_49
DH_6_111
DH_2_66
DH_6_45

21. Predicting performance of DH lines for fodder quality
Sample ID
IVOMD - Predicted
Ivomd - Observed
DH_9_157
High IVOMD and ME
DH_3_33
DH_3_63
DH_9_15
DH_8_4
DH_3_149
DH_3_24
DH_6_1
Low IVOMD and ME
DH_3_10
DH_3_21
DH_3_138
DH_3_35
DH_3_61
High ME
DH_3_83
DH_9_165
High IVOMD
DH_9_134
DH_9_153
DH_3_47
DH_3_62
DH_3_87
DH_3_82
HTMA - GS
Pred. Accuracy
IVOMD
0.44 ME
0.45

22. Excellent opportunity to mine thousands of DH lines for fodder quality…
Marker-assisted screening of DH lines generated in Mexico/Africa for specific genomic regions and GS model SNPs
Trait-associated SNPs and GS models for fodder quality traits…
Thousands of DH lines in CIMMYT seed stores in Africa and Mexico…

23. Thanks

25. GWAS for Fodder Quality Traits
GWAS conducted for in vitro digestibility, lignin and fiber content; 10 significant regions identified for each trait
A GS model is being developed to identify genotypes likely to be superior for fodder quality traits from advanced generation breeding lines/DHs