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Eth meeting switzerland _2015_carlos lara romero

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Eth meeting switzerland _2015_carlos lara romero

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Eth meeting switzerland _2015_carlos lara romero

  1. 1. AdAptA project Local adaptation in marginal alpine populations: an integrated perspective Carlos Lara-Romero ETH. April 2015.
  2. 2. • 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
  3. 3. • 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
  4. 4. • 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
  5. 5. Objectives & Study species OBJETIVES [1] To assess the main limitations on reproductive performance of Mediterranean alpine plants and to test whether local adaptation at small spatial scales has a significant effect on their fitness. Silene ciliata Pourret (A Mediterranean alpine specialist)
  6. 6. Objectives & Study species Silene ciliata Pourret (A Mediterranean alpine specialist) OBJETIVES [1] To assess the main limitations on reproductive performance of Mediterranean alpine plants and to test whether local adaptation at small spatial scales has a significant effect on their success.
  7. 7. 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
  8. 8. 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
  9. 9. 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
  10. 10. 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
  11. 11. Objectives Prof. Alex Widmer Dr. Niklaus Zemp OBJETIVES [1] To assess the main limitations on reproductive performance of Mediterranean alpine plants and to test whether local adaptation at small spatial scales has a significant effect on their fitness. [2] To identify genes expressed during the development of S. ciliata seedlings and select candidate genes that may be involved in adaptation processes.
  12. 12. Mountain 3 Mountain 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)
  13. 13. 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 T T G T C G G T C T T G T C A G T C T T 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
  14. 14. 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 T T G T C G G T C T T G T C A G T C T T 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
  15. 15. 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 T T G T C G G T C T T G T C A G T C T T 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
  16. 16. 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?
  17. 17. Differential expression analysis Comparison of expression levels (RPKM) between high and low elevations RPKM (Reads per kilobase per million mapped reads)
  18. 18. 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)
  19. 19. 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)
  20. 20. 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)
  21. 21. 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 m A1 A1 A2 A1 A1 A1 Plant #2 2 370 m A1 A2 A1 A1 A1 A2 Plant #3 2 450 m A2 A2 A2 A2 A2 A2 Plant #4 1 750 m A2 A2 A2 A1 A1 A2 Plant #5 1 650 m A1 A2 A2 A2 A2 A2 Plant #6 Gene i with 3 SNPs SNP #1 SNP #2 SNP #3 Environmental variable High Low
  22. 22. 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 m A1 A1 A2 A1 A1 A1 Plant #2 2 370 m A1 A2 A1 A1 A1 A2 Plant #3 2 450 m A2 A2 A2 A2 A2 A2 Plant #4 1 750 m A2 A2 A2 A1 A1 A2 Plant #5 1 650 m A1 A2 A2 A2 A2 A2 Plant #6 Gene i with 3 SNPs SNP #1 SNP #2 SNP #3 Environmental variable High Low High Low Expected A1 14 3 9 A2 4 15 9
  23. 23. A1 A1 A1 A1 A1 A1 Plant #1 2 400 m A1 A1 A2 A1 A1 A1 Plant #2 2 370 m A1 A2 A1 A1 A1 A2 Plant #3 2 450 m A2 A2 A2 A2 A2 A2 Plant #4 1 750 m A2 A2 A2 A1 A1 A2 Plant #5 1 650 m A1 A2 A2 A2 A2 A2 Plant #6 1 900 m Gene i with 3 SNPs SNP #1 SNP #2 SNP #3 Environmental variable SNP calling & outlier detection Dispersal parameter (mx) Muller et al 2010 Evolutionary Applications High Low
  24. 24. A1 A1 A1 A1 A1 A1 Plant #1 2 400 m A1 A1 A2 A1 A1 A1 Plant #2 2 370 m A1 A2 A1 A1 A1 A2 Plant #3 2 450 m A2 A2 A2 A2 A2 A2 Plant #4 1 750 m A2 A2 A2 A1 A1 A2 Plant #5 1 650 m A1 A2 A2 A2 A2 A2 Plant #6 1 900 m Gene i with 3 SNPs SNP #1 SNP #2 SNP #3 Environmental variable SNP calling & outlier detection Dispersal parameter (mx) Muller et al 2010 Evolutionary Applications High Low
  25. 25. SNP calling & outlier detection A2 A2 A2 A2 High Low β β = 1937.5 m Muller et al 2010 Evolutionary Applications Dispersal parameter (mx) A1 A1 A1 A1 A1 A1 Plant #1 2 400 m A1 A1 A2 A1 A1 A1 Plant #2 2 370 m A1 A2 A1 A1 A1 A2 Plant #3 2 450 m A2 A2 A2 A2 A2 A2 Plant #4 1 750 m A2 A2 A2 A1 A1 A2 Plant #5 1 650 m A1 A2 A2 A2 A2 A2 Plant #6 1 900 m Gene i with 3 SNPs SNP #1 SNP #2 SNP #3 Environmental variable High Low
  26. 26. 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 m A1 A1 A2 A1 A1 A1 Plant #2 2 370 m A1 A2 A1 A1 A1 A2 Plant #3 2 450 m A2 A2 A2 A2 A2 A2 Plant #4 1 750 m A2 A2 A2 A1 A1 A2 Plant #5 1 650 m A1 A2 A2 A2 A2 A2 Plant #6 1 900 m Gene i with 3 SNPs SNP #1 SNP #2 SNP #3 Environmental variable High Low High Low
  27. 27. 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 m A1 A1 A2 A1 A1 A1 Plant #2 2 370 m A1 A2 A1 A1 A1 A2 Plant #3 2 450 m A2 A2 A2 A2 A2 A2 Plant #4 1 750 m A2 A2 A2 A1 A1 A2 Plant #5 1 650 m A1 A2 A2 A2 A2 A2 Plant #6 1 900 m Gene i with 3 SNPs SNP #1 SNP #2 SNP #3 Environmental variable High Low High Low
  28. 28. 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 m A1 A1 A2 A1 A1 A1 Plant #2 2 370 m A1 A2 A1 A1 A1 A2 Plant #3 2 450 m A2 A2 A2 A2 A2 A2 Plant #4 1 750 m A2 A2 A2 A1 A1 A2 Plant #5 1 650 m A1 A2 A2 A2 A2 A2 Plant #6 1 900 m Gene i with 3 SNPs SNP #1 SNP #2 SNP #3 Environmental variable High Low High Low
  29. 29. SNP calling & outlier detection Minor allele frequency differentials (AFDs) between high and low elevations AFD 1 0.5 0 0.5 1 Frequency Turner et al 2010 Nature; Stölting et al 2015 New Phytologist
  30. 30. SNP calling & outlier detection AFD -3 -2 -1 0 +1 +2 +3 Frequency 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
  31. 31. 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
  32. 32. Thanks for your attention Prof. Jose M. Iriondo Group leader Javier Morente-López Ph.D student Luisa Rubio Ph.D student Dr. Alfredo García-Fernández

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