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Genotype to phenotype forest tree genomics: genome sequencing (de novo and resequencing), marker-based breeding and landscape genomics
 

Genotype to phenotype forest tree genomics: genome sequencing (de novo and resequencing), marker-based breeding and landscape genomics

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Presented during Planery Session 5 ICRAF Science Week 2011

Presented during Planery Session 5 ICRAF Science Week 2011

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  • The plot in the lower left gives the sampled counties and population structure estimates for the NCSU population. Colors designate different genetic clusters. The plot on the right is a generic SNP genotyping plot used to call SNP genotypes.
  • The graph shows the inferred gene network for the targeted genes from Sree’s TGG paper.
  • The colored matrix gives all pairwise correlations among 292 metabolites. The histogram shows the distribution of the values colored in part A. The plot in the lower right lists in order (top to bottom) of the % phenotypic variance explained for SNPs identified in the Bayesian linear mixed models in a general linear model with population structure covariates. These are the bars. The line gives the # of SNPs identified in the Bayesian linear mixed models with significant effects.
  • Photo is of the NCSU common garden. The plot shows the spatial variogram across the garden.
  • Shown in the plot is the distribution of lesion length BLUPs from Quesada et al. 2010. Effect in the table gives the SNP effect/phenotypic standard deviation as a percent. This then gives the effect size scaled to the variation in the phenotype.
  • Table not yet complete.

Genotype to phenotype forest tree genomics: genome sequencing (de novo and resequencing), marker-based breeding and landscape genomics Genotype to phenotype forest tree genomics: genome sequencing (de novo and resequencing), marker-based breeding and landscape genomics Presentation Transcript

  • Genotype to Phenotype
    Forest Tree Genomics: Genome Sequencing (de novo and resequencing), Marker-Based Breeding and Landscape Genomics
  • Kathie Jermstad
  • Growth
    Adaptability
    Straightness
    Disease resistance
    Wood quality
    Insect resistance
    Molecular Breeding and Forest Health Diagnostics in Conifers
  • Traits that are Controlled
    by Single Genes
  • Genomic Approaches to Complex Trait Dissection
    • Quantitative Trait Locus (QTL) Mapping
    • Association Mapping
    Pinus taeda
    (loblolly pine)
    Pseudotsuga menziesii
    (Douglas-fir)
    Populus trichocarpa
    (black cottonwood)
  • Isozyme Genetic Markers
    The Three Toms
    White et al. 2007
  • History of Marker Breeding
  • a
    b
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    X
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    Parent 3
    Parent 4
    Parent 1
    Parent 2
    A
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    C
    a
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    F1
    A
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    ABc
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    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
  • RFLPs and Genetic Maps in Pine
  • QTL Mapping in Pine
  • cDNA, DNA Sequencing, PCR Markers
  • Dominant RAPD Markers were useful for mapping resistance genes
  • 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
  • 2
    r2
    2n
    1n
    Association Genetics in Conifers
    Large and Random Mating Population
    Neale & Savolainen. 2004. Trends in Plant Science. 9:325-330
  • Three approaches to MAS(classified by mapping precision)
    Modified from Grattapaglia (2007)
  • 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
  • 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
  • Association Populations
    Weyerhaeuser – 500 clones
    University of Florida – 1000 clones
    NCSU – 500 clones
    Distribution of NCSU
  • Fluorescence Polarization with Terminator-Dye Incorporation (FP-TDI) - a method for SNP detection
  • 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
  • wood specific gravity
    Wood Quality traits
    microfibril angle
    S3
    secondary
    wall
    S2
    S1
    primary
    wall
    cell wall chemistry
    early
    late
    lignin
    hemicellulose
    cellulose
  • Candidate Genes
    Functional and/or expressional studies
    Monolignol biosynthesis
    (Peter and Neale 2004)
  • GeneticassociationbetweenSNPs and
    woodpropertytraits in loblollypine
    Gonzalez-Martinez et al. 2007. Genetics. 175:399-409
  • 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
  • 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
  • Disease Resistance
  • Fusiform Rust Allelic Frequency Distributions between Case(Gall+) and Control(Gall-) Groups
    Ersoz et al. 2010. PLoS ONE. 5:1-12
  • From the ADEPT project we found:
    • Candidate gene association genetics approach works nicely in forest trees
    • Individual genes can be associated with complex traits
    • Desirable alleles can be discovered for breeding and conservation
  • However…
    • Associations must be validated
    • Size of effects of individual genes are small %PVE < 0.05
    • Number of candidate genes in association screens must be increased
  • 2005-2010
    Resequencing and SNP discovery performed on all
    publicly available Loblolly pine EST contigs (7,424)
    High-throughput computational solutions developed for
    bioinformatic SNP determination and sequence analysis
    Phenotyping:
    Wood Quality
    Disease Resistance
    Drought-Tolerance
    Gene Expression
    Metabolites
    Genotype 1-2 SNPs
    per Candidate Gene on a 7600 Illumina OPA
    Sequencing, Assembly, & Analysis of 10 BAC clones
  • Re-sequencing 10K Genes
    DNA
    extractions
    Information on Individuals
    Sequences
  • ADEPT2 Resequencing Status
    ~ 23,000 SNPs in 5,772 Amplicons
  • 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
  • Diversity, Divergence and Selection
    Eckert et al. (In Prep)
  • Association genetics for loblolly pine
    Phenotypic Trait Categories:
    • Gene Expression
    • Metabolome
    • Wood Properties
    • Drought-tolerance
    • Disease resistance
    Association 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.
  • 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!
  • 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.
  • 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)
  • 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.
  • 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
  • Significant SNPs, 95% CI, for % Lignin
    • Phenotypic values are the % lignin estimated from the sum of lignin assigned peaks normalized to known standards
    • 14 total SNPs identified
    • 7 are of unknown function, predicted proteins, or have no sequence similarity with genes in the database
    Peter et al. (unpublished)
  • 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.
  • From the ADEPT2 project we found:
    • We have a discovery path in place to account for much of the variation in complex traits in forest trees and missing h2 should not be a problem
    • Breeders would welcome genomic information
    • The cost of applying this technology has become affordable in applied programs
  • However…
    • Associations must be validated in real breeding populations
    • Statistical approaches for estimating molecular breeding values must be further developed
    • Tree breeders must be trained in the application of genomic breeding technologies
    • Much of the variation remains unaccounted for
  • Bringing Genomic Assisted Breeding to Application in Tree Improvement
    $5.9M (2007-2011)
  • CTGN built upon previous research
  • 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
  • Progeny Tests
  • Genotyped Populations
  • Informatics to Add Value to Tree Breeders
    • Sample Tracking Interface
    • http://dendrome.ucdavis.edu/
    • Barcoded tree samples tracked for DNA extraction & associated with phenotypic data and metadata
    • Deliver genotyping data in formats that are accessible for large-scale analysis
    • DiversiTree
    • http://dendrome.ucdavis.edu/DiversiTree
    • Flexible, workspace environment to allow the user to query sequence data, genetic maps (CMAP), annotations, and genotype data
    • Forest Tree Genetic Stock Center (FTGSC)
    • http://dendrome.ucdavis.edu/FTGSC
    • Physical Stock Center for Conifers and other forest trees that integrates with Sample tracking and DiversiTree queries
    http://dendrome.ucdavis.edu
  • Extension and Education
    Nick Wheeler
  • Newsletters
  • Short-Courses / Workshops
  • However…
    • Associations must be validated in real breeding populations
    • Statistical approaches for estimating molecular breeding values must be further developed
    • Tree breeders must be trained in the application of genomic breeding technologies
    • Much of the variation remains unaccounted for
  • PineRefSeq
    David B. NealeUniversity of California, Davis
    Pieter J. de JongChildren’s Hospital of Oakland Research Institute
    Charles H. LangleyUniversity of California, Davis
    Carol LoopstraTexas A&M University
    Doreen S. MainWashington State University
    KeithanneMockaitisIndiana University
    Steven L. SalzbergUniversity of Maryland
    Jill L. WegrzynUniversity of California, Davis
    James A. YorkeUniversity of Maryland
  • 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.
  • 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.
  • 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.
  • Conifer Comparative Genomics Project
    ( http://dendrome.ucdavis.edu/ccgp )
    loblolly pine/Douglas Fir loblolly pine/slash pine loblolly pine/sugar pine
  • “I like trees because they seem more resigned to the way they have to live than other things do” ~ Willa Cather 1913
  • 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
  • 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
  • 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
  • CONIFEROUS FORESTS
    CLIMATE CHANGE
    EXTINCTION
    persistence through
    MIGRATION
    persistence through
    ADAPTATION
    GENETIC DIVERSITY
    CONSERVATION
    Impatto dei cambiamenti climatici sulla biodiversita' in Trentino
  • 1. GENETIC DIVERSITY
    Definition:
    measure the degree of polymorphism within a population
    SNP = Single Nucleotide Polymorphism
    Impatto dei cambiamenti climatici sulla biodiversita' in Trentino
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 2. Genotyping chip: design
    SNPs selection :
    Genotyping chip design:
    Impatto dei cambiamenti climatici sulla biodiversita' in Trentino
  • AA
    AB
    BB
    Good
    3. Data quality checking and final dataset production
    Bad
    Finaldataset
    Impatto dei cambiamenti climatici sulla biodiversita' in Trentino
  • 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
  • K=3
    Abies alba
    K=7
    K=8
    Larix decidua
    K=3
    Impatto dei cambiamenti climatici sulla biodiversita' in Trentino
  • K=4
    Pinus cembra
    K=6
    K=4
    K=4
    Pinus mugo
    Impatto dei cambiamenti climatici sulla biodiversita' in Trentino
  • 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
  • 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
  • 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
  • Preliminary conclusion
    1. Population structure analysis:
    In general, each species showed a certain demographic structure mainly correlated with geography.
    2. Genetic data and climatic data:
    - In general, samples sites can be grouped mainly according to seasonal temperature and precipitation.
    - The majority of the SNPs which had BFs greater than three were found in PC1, with the exception of A. alba.
    3. Future developments:
    • completing and integrating the existing analysis on the relation between genetic data and climatic data;
    • exploring the interactions between species sharing the same habitat;
    • autocorrelation analysis on individual trees;
    • looking at the altitudinal gradient and soil type at local scale (Trentino-Alto Adige).
    CONSERVATION STRATEGIES
  • Acknowledgments
    Funding: