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Tracking the genetic legacy of past human populations through the grid

Tracking the genetic legacy of past human populations through the grid. Nicolas Ray University of Geneva & UNEP/GRID-Europe. Swiss Grid Day, Bern, November 26 th 2009 . Adapted from Cavalli -Sforza & Feldman, 2003. Human migrations. [ 12,000 ]. [55,000]. Homo sapiens sapiens.

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Tracking the genetic legacy of past human populations through the grid

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  1. Tracking the genetic legacy of past human populationsthrough the grid Nicolas Ray University of Geneva & UNEP/GRID-Europe SwissGrid Day, Bern, November 26th 2009

  2. Adapted from Cavalli-Sforza & Feldman, 2003 Human migrations [12,000] [55,000] Homo sapiens sapiens

  3. Why aiming at a good demographic model • 1. Better understand human evolution • Origin of modern human (when, where, how many?) • Relationship with other members of the Homo genus • 2. Distinguish between the effect of demography and those of selection (biomedical applications)

  4. Observed patterns of genetic diversity in contemporary populations A complex past demography fluctuation in effective pop. size substructure migrations Gene-specific factors mutations recombination selection

  5. Adapted from Cavalli-Sforza & Feldman, 2003 A complex demography [10,000] demographic and spatial expansions [55,000] population bottlenecks secondary contacts population isolation fast migration events

  6. SPLATCHE SPatiaLAnd Temporal Coalescences in Heterogeneous Environment (http://cmpg.unibe.ch/software/splatche)

  7. Carrying capacity low high From environment to demography Spatial resolution: 100 km

  8. From environment to demography Friction low high

  9. Cell or deme Pop. size time Demographic simulations stepping-stone model (cellular automata)

  10. Demography and spatial expansion Population density

  11. Genetic simulations

  12. Summary statistics • Within population: • S, p • Between populations • PairwiseFST • Global FST • Globally • S, p ACCTAGTACAATCGGTAATGCCATTGGT Modèle de mutation Mutation Simulated genealogy TCCTTGTA…ATTGGT ACCGAGTA…GTTGGT

  13. ApproximateBayesian Computations (ABC) Computational issues Computer clusters 1-10 mio. UBELIX (>500 nodes) Zooblythii (~40 nodes)

  14. Computational issues A fully spatially-explicit model using 500 loci in 800 individuals:  10 CPU-years Adding long-distance dispersal:  20 CPU-years

  15. SPLATCHE on the grid • early 2005: joined the Biomed VO of the EGEE project • mid 2005: tested on GILDA test bed, and deployed on the Grid • since mid 2006: production mode and optimization

  16. GRID Statistical tools Posterior distribution of demographic/genetic parameters of interest Use of SPLATCHE on the grid N simulations • Independent simulations: • the more CPUs, the better • job failures are not that bad

  17. Submission time multi-threaded application using up to 30 RBs (used for the WISDOM project) Fetching time of job outputs in-house multi-threaded solution for checking status and getting outputs GRID Optimizations Reduction of the number of simulations (Daniel Wegmann) By MCMC. Promising results (~50 times less sims) 5 mio. simulations

  18. Geographic origin of human dispersal Ray et al. (2005) GenomeResearch

  19. Interactions among populations Interaction between modern humans and Neanderthals in Europe Currat & Excoffier(2004), PLoS Biol.

  20. Cane toad invasion in Australia • Estoup, A., Baird, S. J. E., Ray, N., Currat, M., Cornuet, J.-M., Santos, F., Beaumont, M. A. and L. Excoffier. Combining genetic, historical and geographic data to reconstruct the dynamics of the bioinvasion of cane toad Bufomarinus. Submitted

  21. Take-home message

  22. Thankyou!

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