1 / 23

Genetic Architecture of Kernel Composition in the Nested Association Mapping (NAM) Population

Genetic Architecture of Kernel Composition in the Nested Association Mapping (NAM) Population. Sherry Flint-Garcia USDA-ARS Columbia, MO. Outline. Development of NAM Population Kernel Composition Joint Linkage Mapping Genome-Wide Association Mapping. Genotype Phenotype Composite

colin-gates
Download Presentation

Genetic Architecture of Kernel Composition in the Nested Association Mapping (NAM) Population

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

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

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

  3. Genotype Phenotype Composite Interval Mapping b1520 b2077 u1622 u1552 b2277 m231 b1225 b2248 Linkage-Based QTL Mapping • “Genome Scan” • Identify genomic regions that contribute to variation and estimate QTL effects Position (cM) Parent 1 Parent 2 100 110 120 130 140 50 60 80 90 30 40 70 10 20 0 9 8 7 6 5 LOD Score F1 4 3 F2 population 2 1 0

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

  5. NAM Founders CM37 R4 K148 Mo46 Ky228 Hi27 Oh7B Mo47 NC344 K4 DE-3 Yu796-NS NC360 Mo45 Mo17 B97 CMV3 CO106 A682 Mt42 W401 CI91B NC362 NC262 NC222 A556 DE811 NC258 B103 Tzi25 B105 NC342 NC364 CI187-2 CI3A B77 W117HT Tzi16 MS153 DE1 SD40 A641 NC290A A214N NC250 STIFF STALK B164 NC236 CM7 N7A N28HT H100 DE-2 B57 H84 I205 B64 C123 H105W A635 CO109 ND246 A632 C103 B68 CO125 B79 H91 A634 B84 B14A Hy B76 Ky21 CM174 B104 A661 WD CM105 A554 B75 CI21E 38-11 B37 MS71 Os420 NC260 NC328 R229 Mo44 A679 Mo1W A680 R168 B73 B73Htrhm NC294 NC326 B109 NC368 N192 NC324 NC292 NC314 NC322 NC330 W64A Pa875 NC308 NC372 NC306 NC312 CH9 H49 NC268 NC310 A619 B10 WF9 B46 SD44 OH43 A239 Pa880 T8 A188 Pa762 C49A C49 VA26 Va102 Ky226 Oh43E A654 W153R Va35 Va14 Va59 A659 CI-7 Oh40B Va17 R177 Va22 W22 H95 W182B Va99 PA91 H99 NON STIFF STALK M14 CI90C 33-16 Va85 CH701-30 NC33 VaW6 4226 NC232 L317 B115 R109B MoG I137TN K55 CI66 CI44 NC230 81-1 CI31A MEF 156-55-2 M162W CI64 IL677A K64 Ia5125 E2558W N6 SWEET IA2132 P39 IL14H CML52 T234 SC357 L578 IL101 CML69 CML14 CML38 B52 CML287 EP1 Tzi11 F2 NC366 CML103 CML108 F7 SC213R CO255 CML9 GT112 CML61 NC238 CML254 CML5 T232 GA209 CML314 CML264 Mp339 CI28A CML258 Q6199 CML10 B2 U267Y CML341 CML332 CML11 CML45 MS1334 CML261 CML331 Mo24W D940Y Sg1533 SG18 IDS28 F2834T M37W HP301 IDS69 SA24 IDS91 CML277 CML238 CML322 CML321 A6 F44 Ki14 CML247 Ki11 4722 Ki2021 F6 I-29 TROPICAL-SUBTROPICAL POPCORN CML157Q Ki44 Oh603 Ki43 CML328 NC340 Ki21 CML323 Ki2007 CML228 NC300 CML92 Tx303 A272 CML218 NC320 NC356 NC302 CML77 NC318 NC332 SC55 A441-5 Ki3 NC338 NC358 NC334 CML154Q NC354 TZI18 NC370 TZI10 NC264 Ab28A CML220 Mo18W Tzi9 TX601 CML349 0.1 NC350 CML333 CML158Q NC304 MIXED CML91 Tzi8 CML311 Based on 89 SSR loci CML281 NC346 NC296A parvi-03 NC336 NC352 NC296 ssp. parviglumis Flint-Garcia, et al. (2005) Plant J. NC348 NC298 parvi-14 parvi-30 parvi-49 parvi-36

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

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

  8. The Phenotypic Data • 7 locations of NAM – • 2006: MO, NY, NC, PR, FL 2007: 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) H2 Starch 0.85 Protein 0.83 Oil 0.86 rProtein Oil Starch -0.65 -0.40 Protein 0.32

  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 • Trait N R2(family) R2(QTL)R2(QTL+family) • Starch 21 28.7 58.1 59.1 • Protein 26 25.8 59.9 61.0 • Oil 22 44.5 69.0 69.7 Starch Protein Oil No Epistasis Observed at the NAM Level

  12. Additive Allelic Effects Starch % Sig. AllelesN Min Max (P = 0.05) (%) (%) Starch 180 -0.62 0.65 Protein 206 -0.38 0.34 Oil 174 -0.12 0.21 ^ ^ B73 Protein Oil B73 B73 % %

  13. Validation Efforts • Near Isogenic Lines (NILs) • Genome Scan Association Analysis • Candidate Genes Association Analysis • Fine Mapping Jason Cook

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

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

  16. Chr. 6 Oil Candidate: DGAT1-2 • Encodes acyl-CoA:diacylglycerolacyltransferase • 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) M1 • M2:Phe Insertion M3 M5 M4 NAM Population: 24 Total HapMap.v1 SNPs in DGAT Association Panel: 2 Total 55K SNPs in DGAT

  18. DGAT 1-2 (Chr6: 105,013,351-105,020,258) M1 • M2:Phe Insertion M3 M4 M5 = 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 Syngenta Joe Byrum & Kirk Noel NSF Maize Diversity Project www.panzea.org

  23. GEM Allelic Diversity Project • Genome Wide Association Analysis • “mini-NAM” • Allele Mining

More Related