Presented during Planery Session 5 ICRAF Science Week 2011

1. Genotype to Phenotype Forest Tree Genomics: Genome Sequencing (de novo and resequencing), Marker-Based Breeding and Landscape Genomics

2. Kathie Jermstad

3. Growth Adaptability Straightness Disease resistance Wood quality Insect resistance Molecular Breeding and Forest Health Diagnostics in Conifers

5. Traits that are Controlled by Single Genes

7. Association MappingPinus taeda (loblolly pine) Pseudotsuga menziesii (Douglas-fir) Populus trichocarpa (black cottonwood)

8. Isozyme Genetic Markers The Three Toms White et al. 2007

9. History of Marker Breeding

10. a b c a b c a b c a b c A B C A B C A B C A B C X X Parent 3 Parent 4 Parent 1 Parent 2 A B C a b c F1 A B c a B C a B C A B c A b c a b C a b C A b c A b c a B C ABc a b C a B c A b c A b c A b c a B C a B c Bb Bb Bb BB BB BB bb bb bb     HEIGHT      BB Bb bb GENOTYPE Quantitative Trait Locus Mapping A B C a b c X F1 B b Knott et al. (1997) TAG 84:810-820

11. RFLPs and Genetic Maps in Pine

12. QTL Mapping in Pine

13. cDNA, DNA Sequencing, PCR Markers

14. Dominant RAPD Markers were useful for mapping resistance genes

15. VERIFICATION VERIFICATION VERIFICATION UNRELATED DETECTION UNRELATED UNRELATED DETECTION DETECTION RELATED RELATED RELATED QTL mapping of wood property traits in loblolly pine emfa ecwc ewsg lmfa lcwc lwsg vol% LG 2 SCALE 0 cM Aco_1 0.0 10 cM PtNCS_CAD-08_b PtIFG_3012_43 12.7 LG 3 15.0 PtIFG_2150_A 19.6 LG 1 19.9 PtIFG_2885_B 20.1 PtIFG_2006_C 0.0 estPtIFG_1934_a 0.3 PtIFG_2145_1 3.4 estPtIFG_8569_a 29.5 PtIFG_2819_12 PtIFG_2538_B 30.2 PtIFG_2068_A 7.8 PtIFG_653_d PtIFG_2897_d 10.4 PtIFG_2086_13 PtIFG_975_3 12.2 PtIFG_1626_c PtIFG_2564_A 40.3 PtIFG_1A7_A 42.6 estPtIFG_8500_a 18.8 estPtIFG_9022_a 43.1 PtIFG_2697_A PtIFG_2536_1 46.5 PtIFG_138_B 24.1 PtIFG_1A7_D 46.8 estPtNCS_22C5_a 30.1 PtIFG_2006_A PtIFG_2588_1 32.5 estPtNCS_C612F_a 33.8 estPtIFG_48_a 58.3 estPtINCS_20G2_a estPaINRA_PAXY13_a 59.5 estPtIFG_9053_a estPtIFG_8843_a estPtIFG_464_a 62.2 PtUME_Ps3_A PtIFG_1633_a 66.0 PtIFG_2718_3 44.8 estPtIFG_8537_a PtIFG_48_1 78.4 PtIFG_2745_1 54.2 estPtIFG_2253_a estPtIFG_8939_a 83.4 PtIFG_1918_3 estPpINR_AS01G01_a 57.4 PtIFG_3006_1 GlyHMT estPtIFG_1576_a PtIFG_1918_h 59.5 83.8 PtIFG_2253_A 86.1 86.3 estPtIFG_8612_a 64.2 PtIFG_2090_2 PtIFG_1623_A C4H-1 90.9 67.6 69.4 estPtIFG_66_a 92.8 70.1 PtIFG_2782_31 PtIFG_1636_3 94.6 PtIFG_1626_a 95.4 Pta14A9 PtIFG_1457_b 78.2 PtIFG_2986_A 102.7 estPtIFG_9198_a PtIFG_1D11_A 104.0 estPtIFG_8496_a PtIFG_2988_21 83.6 PtIFG_2146_31 PtIFG_2718_1 86.8 LAC estPtIFG_2889_a 95.7 PtIFG_1165_a 121.1 PtIFG_2889_21 98.9 estPtIFG_8781_a 104.1 PtIFG_2145_76 107.4 PtIFG_2441_1 PtIFG_2145_5 109.0 estPtIFG_107_a PtIFG_2931_b PtIFG_1D9_2 113.4 estPtNCS_6N3E_a 113.6 PtIFG_2393_1 SAMS-1 116.2 6Pgd_11 140.7 PtIFG_2931_A Knott et al. 1997 TAG 94: 810-820 Sewell et al. 2000 TAG 101:1273-81 Sewell et al. 2002 TAG 104:214-22 Brown et al. 2003 Genetics 164:1537-46 PtIFG_851_1 154.6 estPpaINRA_AS01C10-1_a

16. 2 r2 2n 1n Association Genetics in Conifers Large and Random Mating Population Neale & Savolainen. 2004. Trends in Plant Science. 9:325-330

17. Three approaches to MAS(classified by mapping precision) Modified from Grattapaglia (2007)

18. DNA microarrays to identify genes implicated in the formation of the wood cell wall, through studies of their specific regulation, abundance, or interactions 2000-2003 Expressed genes identification from differentiating xylem (71,377 ESTs in Genbank) QTL mapping Association mapping

19. Phenotype Wood Properties 2001-2004 Water Deficit Resequence SNP Disease Resistance Association Genotype: Illumina- BeadStation 500G-BeadLab Platform, 150,000 data points per week at UCD Genome Center

20. Association Populations Weyerhaeuser – 500 clones University of Florida – 1000 clones NCSU – 500 clones Distribution of NCSU

21. Fluorescence Polarization with Terminator-Dye Incorporation (FP-TDI) - a method for SNP detection

22. High throughput SNP genotyping in forest trees Pre-SNP era, average marker data points per study ~ 5,000 data points Assume 2,000 studies in total X 5,000 data points ~ 10M data points Last 5 years, forest tree projects at UCD-GC ~ 33M data points

23. wood specific gravity Wood Quality traits microfibril angle S3 secondary wall S2 S1 primary wall cell wall chemistry early late lignin hemicellulose cellulose

24. Candidate Genes Functional and/or expressional studies Monolignol biosynthesis (Peter and Neale 2004)

25. GeneticassociationbetweenSNPs and woodpropertytraits in loblollypine Gonzalez-Martinez et al. 2007. Genetics. 175:399-409

26. Water Use Efficiency Stable carbon isotope discrimination in foliage, in two sites (Cuthbert & Palatka). Strong family structure (partial diallel), including 15-24 offspring from 61 families. Cuthbert Palatka

27. FBRC association population in loblolly pine Partial diallel, 15-24 offspring from 61 families. Association with CID (Carbon Isotope Discrimination, related to Water Use Efficiency, in two sites: Cuthbert and Palatka). Analyses using the Quantitative Transmission Disequilibrium Test (QTDT) González-Martínezet al. 2008. Heredity. 101:19-26

28. Disease Resistance

29. Fusiform Rust Allelic Frequency Distributions between Case(Gall+) and Control(Gall-) Groups Ersoz et al. 2010. PLoS ONE. 5:1-12

31. Individual genes can be associated with complex traits

33. Size of effects of individual genes are small %PVE < 0.05

35. Re-sequencing 10K Genes DNA extractions Information on Individuals Sequences

36. ADEPT2 Resequencing Status ~ 23,000 SNPs in 5,772 Amplicons

37. PineSAP – Sequence Alignment and SNP Identification DNASam – DNA Sequence Analysis and Manipulation Wegrzyn et al. 2009 Bioinformatics 25:2609-2610 Eckert et al 2010 Molecular Ecology Resources 10:542-545 http://dendrome.ucdavis.edu

38. Diversity, Divergence and Selection Eckert et al. (In Prep)

40. Metabolome

41. Wood Properties

42. Drought-tolerance

43. Disease resistanceAssociation Population (409 clones) SNP markers Illumina Infinium: 3938 SNPs for 3100 genes Eckert et al. 2010. Genetics 185: 969-982. Eckert et al. 2010. Genetics 185: 969-982.

44. We couldn’t afford one of those cool PCR robots, so we just got 2 graduate students and a cardboard box. The Cartoon Lab by Ed Himelblau 1055 384-well plates!

45. ADEPT2: Gene Expression Phenotypes: Main Results: 81 SNPs (FDR Q < 0.10) associated to expression for 33 xylogenesis genes: 31 SNPs were nonsynonymous 18 SNPs were synonymous 20 SNPs were intronic 12 SNPs were in UTRs Effect sizes for SNPs in range 1.5-4.5% (r2 from GLM) Most effects were non-additive and due to rare alleles Pleiotropy inferred for 8 genes ΔΔCT values from 112 xylogenesis related genes Palleet al. (2011) Tree Genetics and Genomes. 7:193-206.

46. ADEPT2: Metabolome Phenotypes: Main Results: 61 associations (FDR Q < 0.10) involving 56 SNPs and 44 metabolites. Effect sizes moderate for single SNPs (r2: 4-12%) 292 metabolites from GC-TOF-MS including free amino acids, free fatty acids, sugars and a number of organic acids Statistical Models: Regression on ancestry corrected genotypes and phenotypes for each SNP Bayesian linear mixed models with multiple SNPs and terms for kinship and population structure Eckert et al. New Phytologist (Submitted)

47. ADEPT2: Drought-Tolerance Phenotypes: Main Results: Broad sense heritability 0.4-0.5 Moderate genetic correlations among phenotypes (0.3-0.4). 14 associations detected (FDR Q < 0.05): 6 SNPs with foliar nitrogen 7 SNPs with d13C 1 SNP with height SNP effects small to moderate (GLM: r2 4-9%) Effects largely additive Associated SNPs were mostly to SNPs with low minor allele frequencies (MAF < 0.10). Carbon isotope ratio (d13C), foliar nitrogen content and 2nd year height measured in common garden. BLUPs incorporated spatially autocorrelated errors across the common garden. Statistical Models: Linear mixed and general linear models with and without population structure and kinship corrections for each SNP and trait Cumbieet al. (2011) Heredity Online.

48. ADEPT2: Disease-Resistance Phenotypes: Main Results: 10 associations with small effects for a diverse set of genes Lesion length post infection with Fusarium circinatum collected after 4, 8, and 12 weeks Statistical Models: Bayesian linear mixed models with multiple SNPs and terms for kinship and population structure Quesada et al.(2010) Genetics 186:677-686

50. 14 total SNPs identified

51. 7 are of unknown function, predicted proteins, or have no sequence similarity with genes in the databasePeter et al. (unpublished)

52. ADEPT2: Environmental Associations Environmental Gradients: Main Results: 5 associations (FDR Q < 0.10) with small effects mostly with aridity during spring. Eckert et al. 2010. Genetics 185: 969-982. Seasonal aridity gradients across the range of loblolly pine. Statistical Models: Regression on ancestry corrected genotypes and phenotypes for each SNP Ancestry corrections performed via multiple regression and PCA. Eckert et al. 2010. Genetics 185: 969-982.

54. Breeders would welcome genomic information

56. Statistical approaches for estimating molecular breeding values must be further developed

57. Tree breeders must be trained in the application of genomic breeding technologies

59. CTGN built upon previous research

60. Tree Improvement Infrastructure Tree Improvement Cooperatives: Long-term collaborations with public, private, & academic partners Distributed ownership & responsibilities Goal: to support regeneration activities and decision tools

61. Progeny Tests

62. Genotyped Populations

64. http://dendrome.ucdavis.edu/

65. Barcoded tree samples tracked for DNA extraction & associated with phenotypic data and metadata

66. Deliver genotyping data in formats that are accessible for large-scale analysis

67. DiversiTree

68. http://dendrome.ucdavis.edu/DiversiTree

69. Flexible, workspace environment to allow the user to query sequence data, genetic maps (CMAP), annotations, and genotype data

70. Forest Tree Genetic Stock Center (FTGSC)

71. http://dendrome.ucdavis.edu/FTGSC

72. Physical Stock Center for Conifers and other forest trees that integrates with Sample tracking and DiversiTree querieshttp://dendrome.ucdavis.edu

73. Extension and Education Nick Wheeler

74. Newsletters

75. Short-Courses / Workshops

77. Statistical approaches for estimating molecular breeding values must be further developed

78. Tree breeders must be trained in the application of genomic breeding technologies

80. Guiding Principles of the Loblolly Pine Genome Project EMPOWERMENT. Our goal is to develop the technologies, platforms and bioinformatics infrastructures to rapidly and inexpensively sequence large and complex genomes of coniferous forest trees. This will allow the forestry community to begin sequencing the many genomes of economic and ecological importance without a dependence on centralized genome centers. ADAPTIVE. We recognize the sequencing technologies are developing rapidly and that we must have the expertise and flexibility to rapidly adopt new approaches into our overall sequencing strategy. COMPARATIVE. We recognize the power of comparative genomics approaches in assembling and annotating genome sequences and will use this approach throughout the project.

81. The pine genome is characterized by diverse and highly diverged sequences Anna S. Kovach1, Jill L. Wegrzyn2, Genis Parra3, Carson Holt4, George E. Bruening5, Carol Loopstra6, James Hartigan7, Mark Yandell4, Charles H. Langley8, Ian Korf3, David B. Neale2,9 1 Genetics Graduate Group, University of California, Davis, CA 95616, USA. 2 Department of Plant Sciences, University of California, One Shields Avenue, Davis, CA 95616, USA. 3 Genome Center, Division of Biological Sciences, University of California, Davis, CA 95616, USA. 4Eccles Institute of Human Genetics, University of Utah, Salt Lake City, Utah 84112, USA. 5 Department of Plant Pathology, University of California, Davis, CA 95616, USA. 6Dept of Ecological Science and Management, Texas A&M University, College Station, TX 77843, USA. 7Agencourt Bioscience Corporation, Beverly, MA 01915, USA. 8 Section of Evolution and Ecology, University of California at Davis, Davis, CA 95616, USA. 9 Institute of Forest Genetics, USDA Forest Service, Davis, CA 95616, USA. Kovach A.S., Wegrzyn J.L., Parra G., Holt C., Bruening G.E., Loopstra C.A., Hartigan J., Yandell M., Langley C.H., Korf I., Neale D.B. (2010) The Pinustaeda genome is characterized by diverse and highly diverged repetitive sequences. BMC Genomics. 11:1-38.

82. Kovach A.S., Wegrzyn J.L., Parra G., Holt C., Bruening G.E., Loopstra C.A., Hartigan J., Yandell M., Langley C.H., Korf I., Neale D.B. (2010) The Pinustaeda genome is characterized by diverse and highly diverged repetitive sequences. BMC Genomics. 11:1-38.

84. Conifer Comparative Genomics Project ( http://dendrome.ucdavis.edu/ccgp ) loblolly pine/Douglas Fir loblolly pine/slash pine loblolly pine/sugar pine

85. “I like trees because they seem more resigned to the way they have to live than other things do” ~ Willa Cather 1913

86. ADAPTATION IN ALPINE CONIFERS David B Neale – UC Davis Elena Mosca, Erica Di Pierro, Nicola La Porta – FEM Giovanni Vendramin – CNR Firenze Piero Belletti – Torino University

87. Introduction Coniferous forests are potentially quite sensitive to climate change Climate change effects: - shift in species ranges to higher elevations due to increase in T Fagus sylvatica L. Pinus mugo - change in forest stand species richness - effects on the interactions among species within the same habitat Impatto dei cambiamenti climatici sulla biodiversita' in Trentino

88. Picea abies Species studied Larix decidua Abiesalba Pinus mugo Aim -effects of climate on conifer population genetics - evidence of local adaptation along environmental gradient Pinus cembra Impatto dei cambiamenti climatici sulla biodiversita' in Trentino

89. CONIFEROUS FORESTS CLIMATE CHANGE EXTINCTION persistence through MIGRATION persistence through ADAPTATION GENETIC DIVERSITY CONSERVATION Impatto dei cambiamenti climatici sulla biodiversita' in Trentino

90. 1. GENETIC DIVERSITY Definition: measure the degree of polymorphism within a population SNP = Single Nucleotide Polymorphism Impatto dei cambiamenti climatici sulla biodiversita' in Trentino

91. Re-sequencing project: Aim: studying the genetic diversity in forest populations Larix decidua Abiesalba Pinus mugo Pinus cembra Impatto dei cambiamenti climatici sulla biodiversita' in Trentino

92. Results: Estimates of genetic diversity: - Watterson’s θ and θπ Genetic diversity: - count of SNP number P. cembra has low genetic diversity Highly adapted In danger ?! Impatto dei cambiamenti climatici sulla biodiversita' in Trentino

93. 2. ADAPTATION Aim: studying the possible interactions between genetic data and environmental factors Methods: ENVIRONMENT DATA SAMPLING PINE NEEDLES GPS DEVICE MODIS/ECA&D TOOLS GENOTYPING CHIP GPS location, Temperature Precipitation… Single Nucleotide Polymorphism DATA ANALYSIS Impatto dei cambiamenti climatici sulla biodiversita' in Trentino

94. 1.Sampling a. Macroscale level: geographical distribution Environmental factors: Elevation Soil Type Expositions Pure/Mixed stands Picea abies/Abies alba Pinus mugo/Pinus cembra Ecological extremes Impatto dei cambiamenti climatici sulla biodiversita' in Trentino

95. A. alba P. abies P. mugo L. decidua P. cembra b. Local scale: Trentino-Alto Adige Provinces - Altitudinal gradient: 2 aspects: North/South 3 plots: high/medium/low elevation 25 trees per plot - Soil gradient: 2 types: lime /silicate soil 2 sides: West/East Adige 65 trees per site -Ecological extremes 25 trees per site - Pure/Mixed stands Picea abies/Abies alba Pinus mugo/Pinus cembra Impatto dei cambiamenti climatici sulla biodiversita' in Trentino

96. On the field - fresh needles collection for each tree In the lab - make the fresh needles dry - DNA extraction Impatto dei cambiamenti climatici sulla biodiversita' in Trentino

97. 2. SNP Genotyping: definition - is the measurement of genetic variations of SNP between species members. Chip Distribution of reaction AA AB BB Fluorescence Data visualization Impatto dei cambiamenti climatici sulla biodiversita' in Trentino

98. 2. Genotyping chip: design SNPs selection : Genotyping chip design: Impatto dei cambiamenti climatici sulla biodiversita' in Trentino

99. AA AB BB Good 3. Data quality checking and final dataset production Bad Finaldataset Impatto dei cambiamenti climatici sulla biodiversita' in Trentino

100. BZ TN 4a. Population structure analysis: STRUCTURE Pritchard et al. Genetics 2000 Picea abies K=4 DISCRIMINANT ANALYSIS of PRINCIPAL COMPONENTS Jombart et al. BMC Genetics 2010 K=3 Impatto dei cambiamenti climatici sulla biodiversita' in Trentino

101. K=3 Abies alba K=7 K=8 Larix decidua K=3 Impatto dei cambiamenti climatici sulla biodiversita' in Trentino

102. K=4 Pinus cembra K=6 K=4 K=4 Pinus mugo Impatto dei cambiamenti climatici sulla biodiversita' in Trentino

103. 4b. Genetic data and geography: A. alba L. decidua 3 2 1 5 4 PCA between geographic areas P. cembra P. mugo Impatto dei cambiamenti climatici sulla biodiversita' in Trentino

104. 4c. Genetic data and climatic data: Multivariate analysis 3 2 A. alba L. decidua 1 4 PCA on these factors: -Lat & Long -Elevation -Aspect -Slope -seasonal T average -seasonal T max and T min -seasonal cumulate P P. cembra P. mugo Impatto dei cambiamenti climatici sulla biodiversita' in Trentino

105. Bayesian analysis Bayenv Coop et al. Genetics 2010 N of SNPs with BF factor > 3 3 2 1 4 MAF and PC2 score A. alba PC2 seasonal T min, T min coldest month, T mean driest quarter

107. exploring the interactions between species sharing the same habitat;

108. autocorrelation analysis on individual trees;

109. looking at the altitudinal gradient and soil type at local scale (Trentino-Alto Adige).CONSERVATION STRATEGIES

110. Acknowledgments Funding: