Why associations with measures of aridity? - PowerPoint PPT Presentation

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Why associations with measures of aridity?

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  1. Landscape genomics in sugar pines (Pinuslambertiana)Exploring patterns of adaptive genetic variation along environmental gradients. Carl Vangestel

  2. Spatial Genomics Why associations with measures of aridity? • Drought stress common cause mortality and annual yield loss • Shortage of water is one of the strongest environmental constraints and abiotic selective forces in trees • Geography directly affect water availability → clinalvariation in adaptive traits

  3. Spatial Genomics Why associations with measures of aridity? • Future climate change → affect local abiotic conditions and distribution of trees → higher temperatures and increased variability in precipitation SW US → increase in frequency and intensity of drought

  4. Spatial Genomics • Why sugar pine? • Sugar pines are less tolerant to drought stress than other conifer species • → expected to show strong clinal patterns in adaptive genetic variation along aridity gradient • → very sensitive to future climate changes: alterations in current distribution range • One of the most diverse genomes among conifers • → average heterozygosity of specific genes was 26 percent (upper range of pines studied so far)

  5. Spatial Genomics Climate Change current 2030 2060 2090 (Source: USDA Forest Service, RMRS, Moscow Forestry Science Labaratory) • Different scenarios • Hadley Climate Scenario

  6. Spatial Genomics • Detailed knowledge on adaptive variation may become crucial to mitigate impact global climate change • How adaptive variation is distributed over the range of environments is largely unknown • Goal of this study: • identify adaptive SNP’s associated with variation in temperature, precipitation, aridity index (precipitation/potential evapo-transpiration), elevation • functionally annotate these genes • explore both neutral and adaptive variation across the sugar pine’s range

  7. Spatial Genomics N= 338 individuals

  8. Spatial Genomics • Transcriptome assembly: Sanger, 454 (pool) and Illumina (3 ind) • Candidate SNPs selection • Literature • SNP Quality • MYB proteins (stomatal closure, etc ...) • heat shock proteins (prevention of protein denaturation during cellular dehydration) • Trehalose-6-phosphate synthase (osmotic protection cell membranes during dehydration) • LEA proteins (membrane and protein stabilisers, etc ...) • ... • First screening: 67 genes selected • Second screening: 109 under review

  9. Spatial Genomics Multi-analytical approach Bayesian Environmental analyses Generalized linear models Fst Outlier Analysis

  10. Spatial Genomics Neutral SNP

  11. Spatial Genomics Neutral SNP Gene Flow (IBD) Genetic Drift

  12. Spatial Genomics • “Separate” neutral patterns from selective ones • Explore adaptive patterns while accounting for neutral population structure ‘Neutral SNP’ ‘Adaptive SNP’

  13. Spatial Genomics Generalized linear models For each SNP j: ENVi = Environmental value for tree i q1i .. q12n: first n principal components of Q-matrix for tree i

  14. Spatial Genomics Fst Outlier Analysis Arlequin

  15. Spatial Genomics Fst Outlier Analysis BayeScan FDR=0.001 FDR=0.2 FDR=0.05 HPDI SNP10 [0.68,2.35] SNP11: [0.92,2.52] • SNP66: [0.00,2.20]

  16. Spatial Genomics Bayesian Environmental Analysis fancestral Drift: fpopulation deviate Gene flow: deviations covary Transformed variable )

  17. Spatial Genomics Bayesian Environmental Analysis Heat map of var-cov matrix Structure ← pop4 ← pop5 ← pop1 ← pop2 ← pop3 ← pop1 ← pop2 Ω = ← pop3 ← pop4 ← pop5 (Coop et al., 2010)

  18. Spatial Genomics Bayesian Environmental Analysis • Selected 1 SNP per gene for var-cov matrix (excluded putative selective genes) Correlation matrix BayEnv Pairwise Fst matrix

  19. Spatial Genomics Bayesian Environmental Analysis • Formulate null model: drift/gene flow • Alternative model: drift/gene flow + selection Null model: P(θl|Ω, εl) ~ N(εl, εl(1- εl)Ω) Alternative model: P(θl|Ω, εl, β) ~ N(εl + βY, εl(1- εl) Ω) • Bayes Factor: ratio of posterior probability under alternative to the one under null • High BF indicative for SELECTION

  20. Spatial Genomics Bayesian Environmental Analysis