1 / 20

Why associations with measures of aridity?

Landscape genomics in sugar pines ( Pinus lambertiana ) Exploring patterns of adaptive genetic variation along environmental gradients. Carl Vangestel. Spatial Genomics. Why associations with measures of aridity? Drought stress common cause mortality and annual yield loss

lareina-mccarty
Download Presentation

Why associations with measures of aridity?

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related