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Published bySabrina Merritt Modified over 9 years ago
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ILRI-CIMMYT perspective on How can Genomics help improve fodder quality in maize? Raman Babu, Vinayan MT, Zaidi PH and M Blummel
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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?
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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
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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
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GWAS for Grain Color GRMZM2G300348-PSY1 – chr6 -82,017,148-82,021,007
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GWAS for QPM S7_ GRMZM2G015534: Opaque2 chr.7: 10,793,452-10,796,233 S7_
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GWAS for IR phenotypes IR (1) – 12 IR (0) - 527 ALS1
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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
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Fodder Quality Traits
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In vitro digestibility
GWAS for fodder traits in DTMA-AM Nitrogen %
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Acid Detergent Fibre (ADF)
xyloglucan endo-transglycosylase/hydrolase xyloglucan endo-transglycosylase/hydrolase is associated with fiber elongation in cotton (Shao et al. 2011)
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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.
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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
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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
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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
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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
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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?
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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-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
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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
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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
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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…
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Thanks
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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
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