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

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?

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

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

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

GWAS for QPM S7_11335555 GRMZM2G015534: Opaque2 chr.7: 10,793,452-10,796,233 S7_10550478

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

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

Fodder Quality Traits

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

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

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.

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_195674953 T 0.44 2.40E-02 0.03 5.65E-04 -0.99 4.71E-02 0.13 4.00E-02 0.79 S1_107446262 A 0.06 1.32E-02 0.09 1.59E-03 -2.05 1.82E-02 0.24 8.84E-03 1.69 S1_237487798 G 0.21 1.20E-02 0.07 2.55E-03 -1.05 6.41E-03 0.27 2.27E-03 1.81 S6_141797403 C 1.49E-02 0.25 1.63E-02 -3.19 1.26E-02 2.90E-03 2.15 S2_28404997 0.04 5.30E-03 2.46E-02 -2.21 3.39E-02 0.12 1.10E-02 1.42 S10_40110690 0.46 5.90E-03 0.05 3.12E-02 -0.72 8.88E-03 S3_28829266 0.42 7.12E-03 3.89E-02 -0.38 3.53E-03 0.15 1.53E-03 1.02 S1_19948579 9.21E-08 0.08 4.23E-02 -0.68 9.65E-03 8.99E-04 1.57 S1_121399240 4.17E-02 0.11 4.41E-02 -1.68 5.26E-03 0.32 1.63E-03 2.32 S8_115757962 0.37 2.90E-02 4.84E-02 -0.54 2.01E-04 0.23 5.95E-05 1.54

Candidate genes around the most significant genomic associations for various forage quality traits SNP Trait Candidate gene Putative function S1_25378160 IVOMD Low phytic acid-1 (GRMZM2G155242) Chelator of minerals such as Fe and Zn; low phytic acid genotypes are considered nutritionally superior. S1_208737192 cinnamoyl CoA reductase1 (GRMZM2G131205) This enzyme participates in phenylpropanoid biosynthesis and may play a role in enhancing digestibility of forage S4_138167741 ME beta-ketoacyl reductase GL8B (GRMZM2G389110) This enzyme participates in fatty acid biosynthesis and polyunsaturated fatty acid biosynthesis. S2_2073247 ADF Lipoxygenase6 (GRMZM2G040095) Catalyses dioxygenation of polyunsaturated fatty acids in lipids

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

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. 2001.

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?

High density (GBS) genotypes of DH Lines 1 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-F46-3-1-1-2-3-2-2-B*4)/(CL02450) 4 (DTPYC9-F69-3-1-1-2-2-1-1-B)/(CL02450) 5 (LaPostaSeqC7-F96-1-2-2-1-B*7)/(CL02450) 6 (POOL16BNSEQC3F22x1-3-3-2-3-BBB)/(CL02450) 7 (POOL16BNSEQC3F32x37-4-1-2-1-BBB)/(CL02450) 8 CA14514-8-3-2-BB/CML470-BB 9 (POOL16BNSEQC3F28x15-3-1-2-1-BB/G18SeqC5F19-1-2-1-2-4-BB)-B-11-BB/(Pop61C1QPMTEYF-54-2-1-1-2-B-1-B/Pop61C1QPMTEYF-40-1-1-1-2-B-1)-B-1-BBB 10 (Pop61C1QPMTEYF-40-1-1-1-2-B-1/(CML161xCML451)-B-23-1-B*4-1)-B-5-BB/G18SeqC5F19-1-2-1-2-4-B*5 CML451 CL02450

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

Predicting performance of DH lines for fodder quality Sample ID IVOMD - Predicted Ivomd - Observed DH_9_157 High IVOMD and ME 57.13344 DH_3_33 56.67567 DH_3_63 55.77989 DH_9_15 55.69189 DH_8_4 55.55677 DH_3_149 55.53976 DH_3_24 55.42179 DH_6_1 Low IVOMD and ME 55.38113 DH_3_10 55.0006 DH_3_21 54.92149 DH_3_138 54.61451 DH_3_35 54.52269 DH_3_61 High ME 54.4248 DH_3_83 54.12171 DH_9_165 High IVOMD 53.62016 DH_9_134 53.60407 DH_9_153 53.47323 DH_3_47 53.40307 DH_3_62 53.3995 DH_3_87 53.35345 DH_3_82 53.30766 HTMA - GS Pred. Accuracy IVOMD 0.44 ME 0.45

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…

Thanks

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