Presentation is loading. Please wait.

Presentation is loading. Please wait.

Genetic Architecture of Kernel Composition in the Nested Association Mapping (NAM) Population Sherry Flint-Garcia USDA-ARS Columbia, MO.

Similar presentations

Presentation on theme: "Genetic Architecture of Kernel Composition in the Nested Association Mapping (NAM) Population Sherry Flint-Garcia USDA-ARS Columbia, MO."— Presentation transcript:

1 Genetic Architecture of Kernel Composition in the Nested Association Mapping (NAM) Population Sherry Flint-Garcia USDA-ARS Columbia, MO

2 Outline Development of NAM Population Kernel Composition Joint Linkage Mapping Genome-Wide Association Mapping

3 Linkage-Based QTL Mapping “Genome Scan” Identify genomic regions that contribute to variation and estimate QTL effects Genotype Phenotype Composite Interval Mapping F 2 population Parent 1 F1F1 Parent 2

4 Linkage (QTL) Mapping Genome scan Structured population High power Low resolution Analysis of 2 alleles Association Mapping Candidate gene testing Unstructured population Low power High resolution Analysis of many alleles Nested Association Mapping Structured families nested within an unstructured population High Power High Resolution Analysis of many alleles

5 NAM Founders P39 M37W CML277 B97 CML103 CML69 CML52 CML228 CML247 CML332 IL14H Ky21 Ki11 Ki3 MS71 Mo18W Oh7B M162W Tx303 Tzi8 CML333 NC358 NC300 HP301 OH43

6 NAM Development Current genetic map consists of: 4699 RILs 1106 SNP loci Average marker density - one marker every 1.3 cM Yu, et al. (2008) Genetics; McMullen, et al. (2009) Science Association Linkage

7 Kernel Composition in NAM Starch Amylose Amylopectin Fiber Oil Fatty Acid Profiles Protein Zeins Amino Acid Profiles

8 The Phenotypic Data 7 locations of NAM – 2006: MO, NY, NC, PR, FL2007: MO, NY Self pollinated seed samples NIR analysis for starch, protein, and oil content (% kernel - dry matter basis) Two sweet corn families excluded >6000 rows per location

9 Phenotypic Data Statistics Heritability Trait Correlations (23 Families) rProteinOil Starch -0.65-0.40 Protein 0.32 H 2 Starch0.85 Protein0.83 Oil0.86

10 NAM Analysis in SAS Permutations for selection thresholds ~10 -5 Joint stepwise regression; Proc GLMSelect Family main effect & markers within families Final model; Proc GLM Estimate effects (P = 0.05) Genome Scan; Proc Mixed Maximum likelihood with background cofactors Epistasis; all (611,065) pair-wise combinations

11 NAM Kernel Quality Architecture TraitNR 2 (family) R 2 (QTL) R 2 (QTL+family) Starch 2128.758.159.1 Protein 2625.859.961.0 Oil 2244.569.069.7 Starch Protein Oil No Epistasis Observed at the NAM Level

12 B73 Additive Allelic Effects Starch Oil B73 Protein Sig. Alleles NMinMax (P = 0.05)(%)(%) Starch180-0.620.65 Protein206-0.380.34 Oil174-0.120.21 B73 % % % ^^

13 Validation Efforts Near Isogenic Lines (NILs) Genome Scan Association Analysis Candidate Genes Association Analysis Fine Mapping CandidateMarkerChr.Dist.Trait Floury1m2212266.6Oil Opaque2 Modifier/Mucronatem2612297.3Protein Brittle Endosperm1m6195724.7Starch DGAT1-2m7086841.5Oil Waxy1m96891224.4Starch Jason Cook Estimated TraitAlleleMarkerChr.Dist. (cM)Effect (%) OilTx303m7076841.5 0.21 OilCML322m4013462.1 0.11 OilCML228m941116.5 0.11 OilTx303m5655692.6- 0.12 ProteinCML103m6575756.6 0.21 StarchTzi8m3533420.5 0.43

14 Genetic vs. Physical Distance Joint Linkage Mapping - Oil Physical Distance (bp) Genetic Distance (cM) Joint Linkage Mapping - Oil

15 Genome Wide Association (GWAS) 1.6 Million HapMap v1 SNPs projected onto NAM Bootstrap (80%) sampling to test robustness Physical Distance (bp) GWAS - Oil BPP Joint Linkage Mapping - Oil

16 Chr. 6 Oil Candidate: DGAT1-2 Encodes acyl-CoA:diacylglycerol acyltransferase Fine mapped by Pioneer-Dupont Zheng, et al. (2008) Nature Genetics High parent = 19% oil High allele = 0.29% additive effect DGAT is the largest effect kernel quality QTL in NAM 4.4% 5.3% 3.6% 3.9% Phenylalanine insertion in the C-terminus of the protein

17 DGAT 1-2 (Chr6: 105,013,351-105,020,258) MarkerTraitPopulationAnalysis MethodBPPP-ValueEffect M1Oil282 Assn.MLM (Q+K)-1.2E-040.18 M2Oil282 Assn.MLM (Q+K)-9.9E-040.16 M3OilNAMGWAS - Bootstrap31-0.18 M4Oil282 Assn.MLM (Q+K)-4.3E-050.19 M4StarchNAMGWAS - Bootstrap51--0.38 M5OilNAMGWAS - Bootstrap67-0.13 M5StarchNAMGWAS - Bootstrap11--0.31 NAM Population: 24 Total HapMap.v1 SNPs in DGAT Association Panel: 2 Total 55K SNPs in DGAT M1 M3 M5M4 M2: Phe Insertion

18 DGAT 1-2 (Chr6: 105,013,351-105,020,258) M1 M3 M5M4 M2: Phe Insertion ? = B73 Allele = Non-B73 Allele

19 What’s Next for NAM? NextGen sequencing of the 5000 NAM RILs Potentially 30-50 Million SNPs Identify very precisely where recombination events are in the mapping population. This will VASTLY improve the mapping resolution of NAM and GWAS.

20 Conclusions Genetic Architecture of Kernel Quality Traits Governed by many QTL (N = 21-26) Many QTL in common with prior studies Effect sizes are small to moderate Allele series are common Genome Wide Association Studies (GWAS) Results confirm many QTL and candidate genes Resolution will improve with more markers on NAM RILs (define recombination events)

21 What Does This Mean To You? Identifying Functional Markers for MAS (Distantly) Linked markers not accurate Parent Selection = Allele Mining Valuable alleles are often masked. Selection for specific alleles is more accurate than selecting based on parental phenotype.

22 Acknowledgements NSF Maize Diversity Project Syngenta Joe Byrum & Kirk Noel

23 250 Races B47 (SS) PHZ51 (NSS) Allele Library 2500 lines GEM Allelic Diversity Project Genome Wide Association Analysis “mini-NAM” Allele Mining

Download ppt "Genetic Architecture of Kernel Composition in the Nested Association Mapping (NAM) Population Sherry Flint-Garcia USDA-ARS Columbia, MO."

Similar presentations

Ads by Google