1. Genomic Systems underlying the genetics of adaptation in Arabidopsis thaliana Justin Borevitz Ecology & Evolution University of Chicago http://naturalvariation.org/

2. Light Affects the Entire Plant Life Cycle de-etiolation hypocotyl }

4. Talk Outline Predictable to Seasonal Variation –Local Adaptation in the Lab Population Genetics –Population structure –Extant diversity and new mutation Arrays –Genetic, epigenetic, expression, splicing, and allelic variation Ecological context –Arabidopsis and Aquilegia Predictable to Seasonal Variation –Local Adaptation in the Lab Population Genetics –Population structure –Extant diversity and new mutation Arrays –Genetic, epigenetic, expression, splicing, and allelic variation Ecological context –Arabidopsis and Aquilegia

5. Begin with regions spanning the native geographic range Nordborg et al PLoS Biology 2005

6. Regional/Seasonal Variation What is Local Adaptation? Predictable Seasonal changes unique to each location. Tossa Del Mar Spain Lund Sweden

7. Seasons in the Growth Chamber Changing Day length Cycle Light Intensity Cycle Light Colors Cycle Temperature Sweden Spain Seasons in the Growth Chamber Changing Day length Cycle Light Intensity Cycle Light Colors Cycle Temperature Geneva Scientific/ Percival Developmental Plasticity == Behavior

8. Kurt Spokas Version 2.0a June 2006 USDA-ARS Website Midwest Area (Morris,MN) http://www.ars.usda.gov/mwa/ncscrl

9. Flowering time QTL, Kas/Col RILs Sweden 1 Col-gl1 Kas1 Sweden 2 Col-gl1 Kas1 Spain 1 Col-gl1 Kas1 Spain 2 Col-gl1 Kas1 Number of RILs Flowering time QTL, Kas/Col RILs FRI FLM

10. Kas/Col flowering time QTL GxE Chr4 FRI Chr1 FLM Chr4 FRI

11. Environment and Epistasis

12. Globally Distributed http://www.inra.fr/qtlat/NaturalVar/NewCollection.htm Olivier Loudet

13. Current collections 807 Lines from 25 Midwest Populations –(Diane Byers IL state) – pics published! 1101 Lines from UK, 51 populations –(Eric Holub Warwick, UK) – growing! > 500 lines N and S Sweden (Nordborg) > 400 Lines France and Midwest (Bergelson) 400 lines Midwest (Borevitz) 857 Accessions stock center (Randy Scholl) –pics published Others welcome… Genotyped with Sequenom 149 SNPs $0.03 per

14. 134 Non singleton SNPs of 1234 accessions Global, Midwest, and UK common haplotypes Local Population Structure Megan Dunning, Yan Li

15. 17 Major Haplotypes 80 Major Haplotypes Diversity within and between populations

19. Variation within a field http://naturalvariation.org/hapmap

20. RNA DNA Universal Whole Genome Array Transcriptome Atlas Expression levels Tissues specificity Transcriptome Atlas Expression levels Tissues specificity Gene/Exon Discovery Gene model correction Non-coding/ micro-RNA Gene/Exon Discovery Gene model correction Non-coding/ micro-RNA Alternative Splicing Comparative Genome Hybridization (CGH) Insertion/Deletions Copy Number Polymorphisms Comparative Genome Hybridization (CGH) Insertion/Deletions Copy Number Polymorphisms Methylation Chromatin Immunoprecipitation ChIP chip Chromatin Immunoprecipitation ChIP chip Polymorphism SFPs Discovery/Genotyping Polymorphism SFPs Discovery/Genotyping Control for hybridization/genetic polymorphisms to understand TRUE expression variation RNA Immunoprecipitation RIP chip RNA Immunoprecipitation RIP chip Antisense transcription Allele Specific Expression

21. SNP SFP MMMMMM MMMMMM Chromosome (bp) conservation SNP ORFa start AAAAA Transcriptome Atlas ORFb deletion Improved Genome Annotation

22. Which arrays should be used? cDNA array Long oligo array BAC array

23. Which arrays should be used? Gene array Exon array Tiling array 35bp tile, 25mers 10bp gaps

24. Which arrays should be used? Tiling/SNP array 2007 250k SNPs, 1.6M tiling probes SNP array Ressequencing array How about multiple species? Microbial communities? Pst,Psm,Psy,Psx, Agro, Xanthomonas, H parasitica, 15 virus,

25. Global Allele Specific Expression 65,000 SNPs Transcribed Accession Pairs 12,000 genes >= 1 SNP 6,000 >= 2 SNPs

26. Potential Deletions

27. SFPs and CC*GG Methylome Extract genomic 100ng DNA (single leaf) Digest with either msp1 or hpa2 CC*GG Label with biotin Random primers Hybridize to array HpaII digestion Random labeling MspI digestion * * * A) B) Hpa msp Intensity * * Col Hpa msp Col Hpa msp Van mSFP Hpa msp Van SFP

28. Col♀ x Col♂ Van ♀ x Van ♂ Col ♀ x Van ♂ Van ♀ x Col ♂ Experimental design Four genotypes, each with four biological replicates 4 day old seedlings, white light

29. Fit model Intensity ~ additive + dominant + maternal + additive:enzyme + dominant:enzyme + maternal:enzyme Methylation Polymorphisms, mSFPs

30. Deltap0FALSECalledFDR 1.000.951886516014511.2% 1.250.95104771323907.5% 1.500.9565451150425.4% 1.750.9544841023854.2% 2.000.953298920273.4% SFP detection on tiling arrays

31. methylated features and mSFPs >10,000 of 100,000 at 5% FDR Enzyme effect, on CCGG featuresGxE 276 at 15% FDR mQTL?

32. q-valueenzymeaddenzdomenzmatenz 0.00126713100 0.00540001410481 0.0143872510504 0.0559507250507 0.16733126343507 Genomic Distribution of nonPolymorphic methylation sites

33. AT4G19020 tu19 AT1G27320 tu9 AT2G37080 tu1 AT4G27910 tu9 Col Van F1v F1c Col Van F1v F1c Col Van F1v F1c Control HpaII MspI AT1G51790 intron9 AT1G53240 intron4 AT2G01220 tu5 AT5G14600 intron3 AT1G49730 tu10 AT1G79990 intron9 AT1G20780 tu4 AT2G35350 tu2 AT2G45270 intron9 AT3G11460 tu1 AT4G04340 tu15 AT5G56370 tu3 AT2G18100 tu9 AT3G07740 tu2 AT3G48730 tu3 AT5G63190 tu6 AT5G13960 intron8 AT1G27900 intron7 AT2G45620 intron5 AT4G31140 tu2 Verification of additive x enzyme by genomic PCR

34. AT5G67130 tu7 AT1G19450 tu10 Col Van F1v F1c Col Van F1v F1c Col Van F1v F1c Control HpaII MspI AT2G32730 tu3 AT2G45670 tu11 AT3G04820 tu1 AT3G04610 intron5 AT3G53100 tu2 AT3G23590 intron4 AT1G10910 intron9 AT3G28880 tu8 AT4G23570 tu9 AT4G02500 tu1 AT4G17140 tu8 AT5G14950 tu1 AT5G04710 tu1 AT1G73730 tu2 AT1G19715 intron2 Verification of additive x enzyme by genomic PCR

35. Col Van Col♂ x Van♀ Van♂ x Col♀ CC*GG chromomethylase 2 (CMT2) exon19 CC*GG Verification of additive x enzyme by epiTyper

36. Next Questions What is the genetic architecture of methylation variation? How does it change with the environment and through development? Regional patterns, eg chromatin remodeling When does methylation effect expression?

37. Transcription subUnits (TUs) Intensity(gene/tu) ~ add + dom+mat + error Exon1Exon2 Intron1 Tu1Tu2Tu3 ? cDNA1 cDNA2 cDNA3 X Expression Analysis with Annotation

38. D E Additive, Dominant, Maternal, Genotype Variation

39. v v v c c c v c RT-PCR gDNA PCR Alternative spliced introns Col Van FDR for selection3.5%5% Total introns tested62,051 Total introns43228 Tested introns3272 Confirmed2036 percentage71%56%

40. FDR for selection3% Total exons tested 86,349 Total exons69 Tested exons5 confirmed5 Alternative spliced exons - verification Col Van v v v c c c v c RT-PCR gDNA PCR

41. Ecological and Evolutionary context Abiotic conditions –Light, temperature, humidity –Soil, water Biotic conditions –Pathogens and polinators –Conspecifics, grasses trees

42. Local Population Variation

43. Local adaptation under strong selection

44. Seasonal Variation Matt Horton Megan Dunning

45. Aquilegia (Columbines) Recent adaptive radiation, 350Mb genome

46. Genetics of Speciation along a Hybrid Zone

47. NSF Genome Complexity Microarray development –QTL candidates Physical Map (BAC tiling path) –Physical assignment of ESTs QTL for pollinator preference –~400 RILs, map abiotic stress –QTL fine mapping/ LD mapping Develop transformation techniques –VIGS Whole Genome Sequencing (JGI?) Scott Hodges (UCSB) Elena Kramer (Harvard) Magnus Nordborg (USC) Justin Borevitz (U Chicago) Jeff Tompkins (Clemson)

48. http://www.plosone.org/

49. NaturalVariation.org USC Magnus Nordborg Paul Marjoram Max Planck Detlef Weigel Scripps Sam Hazen University of Michigan Sebastian Zoellner USC Magnus Nordborg Paul Marjoram Max Planck Detlef Weigel Scripps Sam Hazen University of Michigan Sebastian Zoellner University of Chicago Xu Zhang Yan Li Peter Roycewicz Evadne Smith Megan Dunning Joy Bergelson Michigan State Shinhan Shiu Purdue Ivan Baxter University of Chicago Xu Zhang Yan Li Peter Roycewicz Evadne Smith Megan Dunning Joy Bergelson Michigan State Shinhan Shiu Purdue Ivan Baxter