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THEME – 4 Detecting Selection Along Environmental Gradients: Analysis of Eight Methods and Their Effectiveness for Outbreeding and Selfing Populations
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THEME – 4 Detecting Selection Along Environmental Gradients: Analysis of Eight Methods and Their Effectiveness for Outbreeding and Selfing Populations

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  • 1. Detecting Selection Along Environmental Gradients: Analysis of Eight Methods and Their Effectiveness for Outbreeding and Selfing Populations Yves Vigouroux Institut de Recherche pour le Développement Montpellier, France International Workshop on “Applied Mathematics and Omics Technologies for Discovering Biodiversity and Genetic Resources for Climate Change Mitigation and Adaptation to Sustain Agriculture in Drylands” Rabat - Morocco, 24-27 June 2014
  • 2. Climate change Rainfall evolution: 1990 - 2090 IPCC 2007 august Challinor et al. Rainy season evolution by 2090
  • 3. Control crosses or genealogy Experimental population Natural population Analysis of given traits Association mapping Association (genealogy, QTL, NAM, MAGIC) Selection: traits? Genome selection scan Segregation biais Methodological approaches
  • 4. Principle of differentiation selection scan Environment – spatial variation Neutral allele: variation due to demographic/gene flow/history effects Selected gene: variation due to demographic/gene flow/history effects and selectionDifferentiation FST
  • 5. • Methods: use or not environmental data? • Sampling design? • Impact of the reproduction system (selfing)? Exemple of environmental gradient in Niger, Africa Environmental Gradients
  • 6. Software QuantiNEMO [Neuenschwander et al. 2008 Bioinformatics] Simulations = - time-forward - individual - modèle flexible  100 populations - 2N = 200  100 unlinked neutral locus  1 linked selected locus  Selfing: {0.0, 0.95}  Different sampling strategy Model si
  • 7. Migration model
  • 8. Modélisation sélection sélection
  • 9. Simulation of selection • Two allele A and a • AA fitness 1, Aa fitness 1-s, aa fitness 1- 2s
  • 10. Simulation of selection: example
  • 11. Modélisation: exemple
  • 12. Sampling : from 1 individual per population to 48.
  • 13. Methods studied Name Reference Data FDIST Beaumont et al. 2006 Population DETSEL Vitalis et al. 2001 Pairs of population FLK Bonhomme et al. 2010 Population BAYSCAN Foll et al. 2008 Population BAYENV Coop et al. 2010 Population SAM Joost et al. 2006 Individual GEE Poncet et al. 2010 Individual Differentiation- based methods Correlation based methods Use of environ. data
  • 14. Evaluation of the methods • Simulation of neutral and selected locus • Use of the method to calculate the proportion of loci detected Simulated % of loci detected selected Neutral Percentage of false positive Expected 5% Selected Percentage of true positive Expected close to 100%
  • 15. ResultsFalsepositiveTruepositive Correlation to environmental data based methods Differentiation (FST) based method
  • 16. Falsepositive Correlation to environmental data based methods Differentiation (FST) based method Results
  • 17. Reproductive system FalseposiitveTruepositive Outbreeding Selfing
  • 18. Selection strenght Method using environmental data are more powerful evenif selection is weaker
  • 19. Sampling A small number of individual is
  • 20. Conclusion - Methods based on differentiation are conservative - Methods based on correlation more powerfull / efficient - Requiere to have good environmental data... - New development: non parametric correlation - Sampling more population is the most efficient  De Mita et al., 2013, Molecular Ecology  De Mita et Siol, 2012, BMC Genetics
  • 21. Current application Genomic analysis A set of 89 000 SNP Detection of selection approach Association mapping
  • 22. Work going on using this approach Medicago truncatulaRice (Oriza sp) Natural populationTraditionnal varieties in Guinea and Madagascar Selfing selfing
  • 23. Acknowledgement IRD, Montpellier On ongoing project: S De Mita UAM Niamey AC Thuillet Y Bakasso JL Pham IS Ousseini C Berthouly CIRAD, Montpellier ISRA Sénégal N Ahmadi N Kané INRA Montpellier L Gay Université de Provence S Manel ARCAD Project Agropolis Researcher Center for Crop Diversity and Adapation

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