Historical Genomics of US Maize: Domestication and Modern Breeding
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Historical Genomics of US Maize: Domestication and Modern Breeding
- 1. Genome-Wide View of Breeding History and Selection in North American Maize Dr. Jeffrey Ross-Ibarra University of California at Davis Patterns of diversity across the genome are reflective of the historical processes of selection, drift, and mutation that have acted on a species during its evolution. Here, we apply population genetic analysis of two genomic datasets to assess the spatial and temporal signatures of evolution in the maize genome. We first describe genome-wide patterns of evolution during two epochs of selection, domestication and modern breeding, using full-genome resequencing of a number of maize and teosinte lines. Then, using a chronologically-stratified panel of corn belt lines, we dissect temporal patterns of ancestry and selection in greater detail across the 80+ years of North American corn breeding. We find distinct impacts of selection during domestication and modern breeding on diversity and gene expression, identify candidate regions targeted by selection, and describe changes in genetic structure and ancestry during the evolution of modern corn belt lines.
- 2. HISTORICAL GENOMICS OF MAIZE Jeffrey Ross-Ibarra www.rilab.org
- 3. Acknowledgements Ed Buckler (Cornell, USDA) Jeff Glaubitz (Cornell) Major Goodman (NC State) Mike McMullen (Missouri, USDA) Chi Song (BGI) Nathan Springer (Minnesota) Doreen Ware (CSHL, USDA) Xun Xu (BGI) Matthew Hufford Joost van Heerwaarden Tanja Pyhäjärvi Jer-Ming Chia
- 4. HISTORICAL GENOMICS OF MAIZE 3 % 3 3 % 02 % 22222222222222222222222222222222222222222222222222222222222222200000000000000000000000000000000222222000022022222022200000000000000000000000000000000000000000000000000000000000000000022222200000000000000000000000020000000 % 2+ Spatial resolution -- genome resequencing Temporal resolution -- SNP genotyping
- 5. HISTORICAL GENOMICS OF MAIZE 3 % 3 3 % % 2222222222222222222222222222222222222222222222222000000000000000000022022020020222200200000000000000000000000000000000000000222220000000000000000020000000 % 2+ Spatial resolution -- genome resequencing Temporal resolution -- SNP genotyping
- 6. ρ 0.0020.0060.01 00.010.020.030.04 ρρ ππ lower 1% cent10 cM/Mb ρ Chr 10 Maize Hapmap I: low-copy fraction of genome Gore et al. 2009 Science • Re-sequenced low-copy portion of genome of 27 inbreds • High diversity genome, but large regions of low recombination • Identified low diversity putative targets of selection • No comparison to older material • Cannot distinguish domestication from modern improvement
- 7. Chia et al. Submitted Maize Hapmap II: full genome resequencing 75 resequenced genomes 21M SNPs75 resequenced genomes 21M SNPs Sequenced Genomes Sequence Depth Locality Improved 35 5.04 X 13 Temperate, 13 Tropical, 3 Mixed, 6 Chinese Landraces 23 5.34 X 9 South American, 14 North American parviglumis 14 3.81 X 4 Jalisco/Nayarit, 9 Balsas, 1 Oaxaca 1 1Oaxaca mexicana 2 6.83 X 1 Jalisco, 1 Morelos Tripsacum 1 12.62 X Colombia parviglumis Landraces Improved Lines Domestication Improvement Hufford et al. Submitted
- 8. Maize Parviglumis Maize Parviglumis Genic Tajima's D Tajima's D Density -4 -2 0 2 4 0.00.10.20.30.40.5 Hufford et al. Submitted
- 9. Maize Parviglumis Maize Parviglumis Genic Tajima's D Tajima's D Density -4 -2 0 2 4 0.00.10.20.30.40.5 Density 0.00.10.20.30.40.5 Genome-wide Tajima's D Tajima's D Density -4 -2 0 2 4 0.00.10.20.30.4 Hufford et al. Submitted
- 10. Chen et al. 2010 Genome Research COG3-like FMP25-like frq PAC10-like SEC14-like NSL1-like NCU02261 B Carib LA Out Ellison et al. 2011 PNAS • Cross-Population Composite Likelihood Ratio • Identifies extended regions of differentiation • Compares to simulated model • Define selected regions, estimate s
- 11. Artificial selection in the maize genome • Stronger selection during domestication (s~0.015 vs. s~0.003) • Selection strength consistent with archaeology natural selection • Continued selection on domestication genes (18% overlap) • ~3,000 genes in selected regions. Identified ~1100 candidates • 5-10% of selected regions had no genes Hufford et al. Submitted he maize genome log s Frequency -6 -5 -4 -3 -2 050001500025000 domestication improvement
- 12. Gore et al. Science 2009 Mb chr 8 proportion FST Mb chr 8 proportion 0.00.20.40.60.81.0 fixed shared unique temperate unique tropicalemfixed shared unique temperate unique tropical 0 20 40 60 80 100 120 140 160 180 00.050.150.250.35 Population-specific selection
- 13. Population-specific selection Hufford et al. Submitted • Many population-specific loci • Stronger overall estimate of selection • Still weaker than domestication • Lower power to rule out drift
- 14. Candidate genes for maize improvement Hufford et al. Submitted
- 15. Selection on the transcriptome • Expression at 18K genes of 27 maize and teosinte using Nimblegen array • Domestication directly selected on candidate gene expression • Breeding has worked with highly expressed genes • Modern breeding selected for heterosis in expression Candidates: DOM IMP Change in expression * -- Decreased variance * * Tissue specificity# -- overall higher expression Dominance in crosses# -- inter intra # Data from Stupar et al. 2008 BMC Plant Bio; Sekhon et al. 2011 Plant J. Hufford et al. Submitted
- 16. Regions with no genes likely regulatory teosintemaize Swanson-Wagner et al. Submitted Expression
- 17. Genetic load in modern maize • ~1% of SNPs disrupts predicted protein • 10,117 premature stop-codons • 2557 (or 12%) high-confidence genes has a stop-codon in ≥1line • 870 genes have a segregating stop- codon in ≥ 2 of improved lines Chia et al. Submitted
- 20. Artificial selection across the maize genome
- 21. HISTORICAL GENOMICS OF MAIZE 3 % 3 3 % 02 % 22222222222222222222222222222222222222222222222222222222222222200000000000000000000000000000000222222000022022222022200000000000000000000000000000000000000000000000000000000000000000022222200000000000000000000000020000000 % 2+ Spatial resolution -- genome resequencing Temporal resolution -- SNP genotyping
- 22. Detailed historical look at selection during modern breeding • Larger sample size • Increased chronological resolution • Take into account heterotic group structure • Focus on corn belt varieties Resolve caveats of resequencing approach
- 23. Genotyping and Sampling • 400 N.American corn belt lines • Genotype on public Illumina 55K • Tracked population structure, ancestry, and selection over time 99 94 70 137 land races 1950 inbreds 1960-1970 inbreds ex-PVP 0 1 2 3 van Heerwaarden et al. Submitted (Oh43,W22, B14) (B73, 207, Mo17)
- 24. Population structure through time van Heerwaarden et al. Submitted
- 25. Population structure through time van Heerwaarden et al. Submitted C
- 26. Population structure through time van Heerwaarden et al. Submitted C C
- 27. Population structure through time van Heerwaarden et al. Submitted C C
- 28. Population structure through time van Heerwaarden et al. Submitted C C
- 29. Population structure through time van Heerwaarden et al. Submitted Iodent NSS SS
- 30. Number and diversity of ancestors decreases through time ancestry van Heerwaarden et al. Submitted c. Iodent NSS SS effective#ancestorsdiversityofancestors
- 31. SS 0.0 0.1 0.2 0.3 0.4 undefined OH43 CI31A HY B8 B10 B14 B96 B37 I205 OH07 CI3A CI187-2 CI540 I224 WD456 Oh3167B Tr9-1-1-6 FE2 LE773 ND203 B73 A634 A635 N28 H100 B64 B14A B68 CM105 B84 A632 N196 LH195 LH205 LH196 LH194 991 LH74 PHG39 LH132 G80 78004 78010 4676A 78002A 794 LP5 PHT55 H8431 1 2 3 Ancestral contributions of individual lines
- 32. Ancestral contributions of individual lines IDT 1 2 3 0.0 0.1 0.2 0.3 0.4 undefined OH43 WF9 A375 A509 A556 I198 B2 H5 HY B164 G ND203 B10 B14 L317 I205 CI187-2 C49A I224 AH83 Tr9-1-1-6 C11 IDT A638 M14 207 PHN34 PHP76 PHW86 PHG50 PHG35 PHG71 IB014 PHG83 LH150 PHG29 PHG72 PHG84 PHZ51 PHV78 PHK42 PHN11 PHH93 PHJ33 PHN73 PHR62
- 33. Weak landrace structure still evident in modern inbreds ancestryproportion Iodent NSS SS
- 34. Selection and frequency over time Duvick et al. 2004 Plant Breeding Reviews advantageous alleles show continual increase in frequency over time
- 35. •Identify genetic structure across all time periods (PCA,Tracy-Widom) •Treat time period as a phenotype for association mapping •For each SNP, compare two models: •M0: Frequency determined by drift and population structure (covariance matrix of all SNPs) •M1: Frequency determined by drift, population structure AND environment (time) •Bayes Factor for each SNP Time as a phenotype to identify selection 0 1 2 3 Coop et al. 2010 Genetics
- 36. Temporal association identifies candidates of selection • 236 Candidate regions (1021 genes) with consistent BF across runs • Include stress response, lignin biosynthesis, auxin response • Two of the top candidates ZmErecta (US7847158) and ZmArgos (US7834240) patented for plant growth, yield ssociation identifies candidates te regions (1021 genes) with consistent BF 0 1 2 3 0.00.20.40.60.81.0 era p
- 37. Temporal association identifies candidates of selection • 236 Candidate regions (1021 genes) with consistent BF across runs • Include stress response, lignin biosynthesis, auxin response • Two of the top candidates ZmErecta (US7847158) and ZmArgos (US7834240) patented for plant growth, yield
- 39. No abnormal haplotypes, ancestry at selected sites 0 1 2 3 01234 p p p p p p p p era ratioobserved/randomized
- 40. No abnormal haplotypes, ancestry at selected sites
- 41. Genome-wide ancestry not determined by selection
- 42. Weak correlation between good alleles and contribution to modern lines 1950’s inbreds 1980’s inbreds ex-PVP lines selected regions %ancestry
- 43. Weak correlation between good alleles and contribution to modern lines
- 44. Structure and selection in corn belt maize
- 45. Some final thoughts on adaptation...
- 46. modern corn phenotypic space FISHER-ORR MODEL OF ADAPTATION
- 47. modern corn teosinte phenotypic space FISHER-ORR MODEL OF ADAPTATION
- 48. modern corn teosinte phenotypic space FISHER-ORR MODEL OF ADAPTATION
- 49. landraces phenotypic space FISHER-ORR MODEL OF ADAPTATION
- 50. landraces phenotypic space FISHER-ORR MODEL OF ADAPTATION
- 51. Brown et al. 2011 PLoS Genetics • Effect sizes of ear vs. other consistent with Fisher-Orr model • Larger effects → larger fitness difference → stronger selection
- 52. ThankYou!