ApplauSim : A Simulation of Synchronous Applause Andi Horni, Lara Montini, IVT, ETH Zürich
Eth meeting switzerland _2015_carlos lara romero
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Transcript of Eth meeting switzerland _2015_carlos lara romero
AdAptA projectLocal adaptation in marginal
alpine populations: an integrated perspective
Carlos Lara-Romero
ETH. April 2015.
• Alpine environments are highly vulnerable to global warming
•Main response of alpine plants Upward range shifts trancking their current climatic niche
Theoretical background
Paulí et al 2012 Science, Marris 2007 Nature, Dullinger et al 2012 Glob. Ecol Biogeogr, Lara-Romero et al 2014 Plos One
• Alpine environments are highly vulnerable to global warming
•Main response of alpine plants Upward range shifts trancking their current climatic niche
•Mediterranean alpine plants Upward migration is not an option (The scalator effect)
Theoretical background
Paulí et al 2012 Science, Marris 2007 Nature, Dullinger et al 2012 Glob. Ecol Biogeogr, Lara-Romero et al 2014 Plos One
• Alpine environments are highly vulnerable to global warming
•Main response of alpine plants Upward range shifts trancking their current climatic niche
•Mediterranean alpine plants Upward migration is not an option (The scalator effect)
• Adaptation and phenotypic plasticity are the main response against new environmental conditions
Theoretical background
Paulí et al 2012 Science, Marris 2007 Nature, Dullinger et al 2012 Glob. Ecol Biogeogr, Lara-Romero et al 2014 Plos One
Objectives & Study species
OBJETIVES
[1] To assess the main limitations on reproductive performance of Mediterranean alpineplants and to test whether local adaptation at small spatial scales has a significant effect on theirfitness.
Silene ciliata Pourret (A Mediterranean alpine specialist)
Objectives & Study species
Silene ciliata Pourret (A Mediterranean alpine specialist)
OBJETIVES
[1] To assess the main limitations on reproductive performance of Mediterranean alpineplants and to test whether local adaptation at small spatial scales has a significant effect on theirsuccess.
Silene ciliata Pourret (A Mediterranean alpine specialist)
Results
• Significant variation in vegetative and reproductive traits
between low and high elevations
Giménez-Benavides et al 2007 Anals of Botany, García-Fernández et al 2012 OIKOS, Lara-Romero et al 2014 Plos One
Silene ciliata Pourret (A Mediterranean alpine specialist)
Results
• Significant variation in vegetative and reproductive traits
between low and high elevations
• Summer drought Selective pressure at low elevations
P (mm)
T (ºC)
Elevation
Giménez-Benavides et al 2007 Anals of Botany, García-Fernández et al 2012 OIKOS, Lara-Romero et al 2014 Plos One
Silene ciliata Pourret (A Mediterranean alpine specialist)
Results
• Significant variation in vegetative and reproductive traits
between low and high elevations
• Summer drought Selective pressure at low elevations
• Seedling establishment Demographic bottleneck
Giménez-Benavides et al 2007 Anals of Botany, García-Fernández et al 2012 OIKOS, Lara-Romero et al 2014 Plos One
P (mm)
T (ºC)
Elevation
Silene ciliata Pourret (A Mediterranean alpine specialist)
Results
• Significant variation in vegetative and reproductive traits
between low and high elevations
• Summer drought Selective pressure at low elevations
• Seedling establishment Demographic bottleneck
• Local adaptation at seedling stage Drought tolerance
Giménez-Benavides et al 2007 Anals of Botany, García-Fernández et al 2012 OIKOS, Lara-Romero et al 2014 Plos One
Objectives
Prof. Alex Widmer Dr. Niklaus Zemp
OBJETIVES
[1] To assess the main limitations on reproductive performance of Mediterranean alpineplants and to test whether local adaptation at small spatial scales has a significant effect on theirfitness.
[2] To identify genes expressed during the development of S. ciliata seedlings and selectcandidate genes that may be involved in adaptation processes.
Mountain 3Mountain 2Mountain 1
Transcriptome comparisons between high and low populations during the seedling stage
Genomic data
6 seedlings
3 High vs 3 Low
1 seedling per population (n = 6)
RNA extraction and Illumina sequencing
Seed collection &Greenhouse sowing
Work flow. Genomic data
Reference-based transcriptome assembly
BWA
Silene latifolia Reference Genome
T G T C G G T C TT G T C G G T C T
T G T C A G T C TT G T C A G T C T
SNP calling – Reads2SNP
High
Low
Differential expression
Candidate Genes
Candidate Genes
High
Low
Functional annotation&
Enrichment analysis
RNA extraction and Illumina sequencing
Seed collection &Greenhouse sowing
Work flow. Genomic data
Reference-based transcriptome assembly
BWA
Silene latifolia Reference Genome
T G T C G G T C TT G T C G G T C T
T G T C A G T C TT G T C A G T C T
SNP calling – Reads2SNP
High
Low
Differential expression
Candidate Genes
Candidate Genes
Optimal
Marginal
Functional annotation&
Enrichment analysis
The novo transcriptome
assembly
RNA extraction and Illumina sequencing
Seed collection &Greenhouse sowing
Work flow. Genomic data
Reference-based transcriptome assembly
BWA
Silene latifolia Reference Genome
T G T C G G T C TT G T C G G T C T
T G T C A G T C TT G T C A G T C T
SNP calling – Reads2SNP
High
Low
Differential expression
Candidate Genes
Candidate Genes
High
Low
Functional annotation&
Enrichment analysis
Genomic data
Pilot study
Study design (n=6) limits detection of outlier SNPs
Impossibility of implementing classical approaches (e.g., pairwise Fst)
How can candidate genes be detected based on single individual per population?
Differential expression analysis
Comparison of expression levels (RPKM) between high and low elevations
RPKM (Reads per kilobase per million mapped reads)
Differential expression analysis
129 contigs differentially expressed
GO term & Enrichment analysis
• 114 contigs annotated
• Response to extracellular stimulus (n=9) & external stimulus (n=19) overrepresented
Comparison of expression levels (RPKM) between high and low elevations
RPKM (Reads per kilobase per million mapped reads)
SNP calling & outlier detection
Reads2SNP
• 7 reads needed to infer genotype• Deletion of paralogous SNPs• Biallelic SNPs with no missing data
• Depth of coverage and posterior probability did not affect outlier detection.
147 118 SNPs & 12 688 contigs(mean =13.7)
SNP calling & outlier detection
Reads2SNP
• 7 reads needed to infer genotype• Deletion of paralogous SNPs• Biallelic SNPs with no missing data
• Depth of coverage and posterior probability did not affect outlier detection.
147 118 SNPs & 12 688 contigs(mean =13.7)
Strategies for selection of candidate genes
[1] Contingency table and Pearson’s Chi-square test (X2)
[2] Dispersal parameter (m, Muller et al 2010 Evolutionary Applications)
[3] Allelic frequency differentials (AFDs)
SNP calling & outlier detection
High Low Expected
A1 14 3 9
A2 4 15 9
Contingency table and Pearson’s Chi-square test (X2)
A1 A1 A1 A1 A1 A1 Plant #1 2 400 mA1 A1 A2 A1 A1 A1 Plant #2 2 370 mA1 A2 A1 A1 A1 A2 Plant #3 2 450 m
A2 A2 A2 A2 A2 A2 Plant #4 1 750 mA2 A2 A2 A1 A1 A2 Plant #5 1 650 mA1 A2 A2 A2 A2 A2 Plant #6
Gene i with 3 SNPsSNP #1 SNP #2 SNP #3 Environmental variable
High
Low
SNP calling & outlier detection
Selection Candidate genes
• Outlier: p value < 0.05 after FDR correction
• 646 genes (contigs) selected
• Enrichment analysis (GO-Term - Biolog. processes)
• Single-organism metabolic processes (n = 155)
Contingency table and Pearson’s Chi-square test (X2)
A1 A1 A1 A1 A1 A1 Plant #1 2 400 mA1 A1 A2 A1 A1 A1 Plant #2 2 370 mA1 A2 A1 A1 A1 A2 Plant #3 2 450 m
A2 A2 A2 A2 A2 A2 Plant #4 1 750 mA2 A2 A2 A1 A1 A2 Plant #5 1 650 mA1 A2 A2 A2 A2 A2 Plant #6
Gene i with 3 SNPsSNP #1 SNP #2 SNP #3 Environmental variable
High
Low
High Low Expected
A1 14 3 9
A2 4 15 9
A1 A1 A1 A1 A1 A1 Plant #1 2 400 mA1 A1 A2 A1 A1 A1 Plant #2 2 370 mA1 A2 A1 A1 A1 A2 Plant #3 2 450 m
A2 A2 A2 A2 A2 A2 Plant #4 1 750 mA2 A2 A2 A1 A1 A2 Plant #5 1 650 mA1 A2 A2 A2 A2 A2 Plant #6 1 900 m
Gene i with 3 SNPsSNP #1 SNP #2 SNP #3 Environmental variable
SNP calling & outlier detection
Dispersal parameter (mx)
Muller et al 2010 Evolutionary Applications
High
Low
A1 A1 A1 A1 A1 A1 Plant #1 2 400 mA1 A1 A2 A1 A1 A1 Plant #2 2 370 mA1 A2 A1 A1 A1 A2 Plant #3 2 450 m
A2 A2 A2 A2 A2 A2 Plant #4 1 750 mA2 A2 A2 A1 A1 A2 Plant #5 1 650 mA1 A2 A2 A2 A2 A2 Plant #6 1 900 m
Gene i with 3 SNPsSNP #1 SNP #2 SNP #3 Environmental variable
SNP calling & outlier detection
Dispersal parameter (mx)
Muller et al 2010 Evolutionary Applications
High
Low
SNP calling & outlier detection
A2
A2 A2 A2
High
Low
ββ = 1937.5 m
Muller et al 2010 Evolutionary Applications
Dispersal parameter (mx)
A1 A1 A1 A1 A1 A1 Plant #1 2 400 mA1 A1 A2 A1 A1 A1 Plant #2 2 370 mA1 A2 A1 A1 A1 A2 Plant #3 2 450 m
A2 A2 A2 A2 A2 A2 Plant #4 1 750 mA2 A2 A2 A1 A1 A2 Plant #5 1 650 mA1 A2 A2 A2 A2 A2 Plant #6 1 900 m
Gene i with 3 SNPsSNP #1 SNP #2 SNP #3 Environmental variable
High
Low
SNP calling & outlier detection
A2
A2 A2 A2
β
mi1
mi2
mi3
mi4
Selection Candidate genes
• Dispersion of each allele ( mx ) Average distance of the allele to β
Muller et al 2010 Evolutionary Applications
Dispersal parameter (mx)
A1 A1 A1 A1 A1 A1 Plant #1 2 400 mA1 A1 A2 A1 A1 A1 Plant #2 2 370 mA1 A2 A1 A1 A1 A2 Plant #3 2 450 m
A2 A2 A2 A2 A2 A2 Plant #4 1 750 mA2 A2 A2 A1 A1 A2 Plant #5 1 650 mA1 A2 A2 A2 A2 A2 Plant #6 1 900 m
Gene i with 3 SNPsSNP #1 SNP #2 SNP #3 Environmental variable
High
Low
High
Low
SNP calling & outlier detection
A2 A2
A2 A2
β mi1
mi2
mi3
mi4
Selection Candidate genes
• Dispersion of each allele ( mx ) Average distance of the allele to β
• Outlier: permutations to detect alleles more geographically clustered
than expected at random
Muller et al 2010 Evolutionary Applications
Dispersal parameter (mx)
A1 A1 A1 A1 A1 A1 Plant #1 2 400 mA1 A1 A2 A1 A1 A1 Plant #2 2 370 mA1 A2 A1 A1 A1 A2 Plant #3 2 450 m
A2 A2 A2 A2 A2 A2 Plant #4 1 750 mA2 A2 A2 A1 A1 A2 Plant #5 1 650 mA1 A2 A2 A2 A2 A2 Plant #6 1 900 m
Gene i with 3 SNPsSNP #1 SNP #2 SNP #3 Environmental variable
High
Low
High
Low
SNP calling & outlier detection
A2 A2
A2 A2
β mi1
mi2
mi3
mi4
Selection Candidate genes
• Dispersion of each allele ( mx ) Average distance of the allele to β
• Outlier: permutations to detect alleles more geographically clustered
than expected at random
• 486 candidate genes
• Enrichment analysis (Biolog. process)
• Lipid metabolic process (n = 53)• Single-organism metabolic processes (n = 59)• Generation of precursor metabolites and energy (n = 31)
Muller et al 2010 Evolutionary Applications
Dispersal parameter (mx)
A1 A1 A1 A1 A1 A1 Plant #1 2 400 mA1 A1 A2 A1 A1 A1 Plant #2 2 370 mA1 A2 A1 A1 A1 A2 Plant #3 2 450 m
A2 A2 A2 A2 A2 A2 Plant #4 1 750 mA2 A2 A2 A1 A1 A2 Plant #5 1 650 mA1 A2 A2 A2 A2 A2 Plant #6 1 900 m
Gene i with 3 SNPsSNP #1 SNP #2 SNP #3 Environmental variable
High
Low
High
Low
SNP calling & outlier detection
Minor allele frequency differentials (AFDs) between high and low elevations
AFD
1 0.5 0 0.5 1
Freq
uen
cy
Turner et al 2010 Nature; Stölting et al 2015 New Phytologist
SNP calling & outlier detection
AFD
-3 -2 -1 0 +1 +2 +3
Freq
uen
cy
Selection Candidate genes
• Outlier: AFDs > 3 SDs the genome-wide average (p-value < 0.001)
• 1222 SNPS & 419 candidate genes
• Enrichment analysis (Biolog. process)
• Carbohydrate metabolic process
Turner et al 2010 Nature; Stölting et al 2015, New Phytologist
Minor allele frequency differentials (AFDs) between high and low elevations
SNP calling & outlier detection
336
20
606
124
6
13
275
Dispersal param. Allele freq.
AFD
SNP overlap among different selection approaches
Venn diagrams showing the extent of overlap among selection approaches based on allele frequencies
6 genes overlapped among three approaches
GO TERM: response to stress & metabolic process
163 genes overlapped among two approaches
• 143 annotated genes
• Enrichment analysis (before FDR correction)
- Response to abiotic stimulus (n = 53)- Response to stress (n = 59)- Several additional terms related to metabolic processes and response to stimulus
Thanks for your attention
Prof. Jose M. IriondoGroup leader
Javier Morente-LópezPh.D student
Luisa RubioPh.D student
Dr. Alfredo García-Fernández