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Adaptive Landscape Genetic

Adaptive Landscape Genetic. Background Case studies Data Statistics Analyses - practicals Fst outliers Arlequin – Hierarchical clustering BayeScan – Bayesian Association gene-landscape ScytheSAM – Logistic regression (model tests).

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Adaptive Landscape Genetic

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  1. Adaptive Landscape Genetic

  2. Background • Case studies • Data • Statistics • Analyses - practicals • Fst outliers • Arlequin – Hierarchical clustering • BayeScan – Bayesian • Association gene-landscape • ScytheSAM – Logistic regression (model tests)

  3. Adaptive genetic variation is defined as the variation found between the genomes of individuals and resulting from natural selection (Holderegger et al. 2006, Lowry 2010)

  4. Under what circumstances and how frequently does genetic adaptation occur in natural populations? • How important are spatial scale and habitat heterogeneity in maintaining adaptive genetic variation? • How has recent global change affected patterns of neutral and adaptive genetic variation? • Are species likely to adapt to ongoing global change on an ecological timescale?

  5. STATISTICAL METHODS • Schoville et al. 2012: • Outlier-Detection Methods (FDIST, LOSITAN, BayesFST, BayeScan) • Detecting Correlations Between Allele Distributions and Environmental Variation (ScytheSAM – log regression, generalized estimating equations (GEE) , BAYENV)

  6. Arabisalpina(Brassicaeae)

  7. Piceaglauca • Prunier et al. (2011) focused their work on a set of 656 expressed genes selected from an expressed sequence tag database developed using white spruce (Piceaglauca). • 26 SNPs from 25 genes were detected as outliers; nearly half of the outliers were located in protein-coding genes, and half of those were located at nonsynonymous nucleotide positions. The loci identified using outlier detection were corroborated by applying the regression method of Joost et al. (2007), with the result that 16 of the 26 outlier SNPs (62%) displayed significant regressions between allele frequency and climatic variation.

  8. human populations • Hancock et al. (2011b) examined environmental correlations with climatic variables for a data set of >620,000 SNPs in 61 human populations from around the world. • Using the BAYENV model, they tested the association of each SNP against nine separate climatic variables. • Some of the strongest associations were with latitude, solar radiation, and temperature and included loci involved in cold tolerance and disease resistance.

  9. Arlequin Simulate Expected values Hierarchical clustering

  10. BayeScan Common gene pool Subpopulation specific FST Selection is introduced: population-specific component (beta; shared by all loci), locus-specific component (alpha; shared by all populations). MCMC

  11. ScytheSAM • ScytheSAM for Windows (alpha release) • logistic regression: test for association between allelic frequencies at marker loci and environmental variables • Includes spatial info (but how? – Joost et al. 2007) • identify loci possibly under natural selection • univariate(all molecular markers vs all environmental variables) • multivariate models (many tests e.g. with 14 environmental variables and 114 loci: 1596 simultaneous logistic regressions for univariate models only).

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