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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
Tracking the genetic legacy of past human populationsthrough the grid

Nicolas Ray

University of Geneva & UNEP/GRID-Europe

SwissGrid Day, Bern, November 26th 2009

why aiming at a good demographic model
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)

Observed patterns of genetic diversity in contemporary populations

A complex past demography

fluctuation in effective pop. size



Gene-specific factors




a complex demography

Adapted from Cavalli-Sforza & Feldman, 2003

A complex demography


demographic and spatial expansions


population bottlenecks

secondary contacts

population isolation

fast migration events


SPatiaLAnd Temporal Coalescences in Heterogeneous Environment


from environment to demography





From environment to demography

Spatial resolution: 100 km

demographic simulations

Cell or deme

Pop. size


Demographic simulations

stepping-stone model (cellular automata)

simulated genealogy

Summary statistics

    • Within population:
      • S, p
    • Between populations
      • PairwiseFST
      • Global FST
    • Globally
      • S, p


Modèle de mutation


Simulated genealogy




ApproximateBayesian Computations (ABC) Computational issues

Computer clusters

1-10 mio.

UBELIX (>500 nodes)

Zooblythii (~40 nodes)

computational issues
Computational issues

A fully spatially-explicit model using 500 loci in 800 individuals:

 10 CPU-years

Adding long-distance dispersal:

 20 CPU-years

splatche on the grid
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
use of splatche on the 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

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



Reduction of the number of simulations (Daniel Wegmann)

By MCMC. Promising results (~50 times less sims)

5 mio. simulations

geographic origin of human dispersal
Geographic origin of human dispersal

Ray et al. (2005) GenomeResearch

interactions among populations
Interactions among populations

Interaction between modern humans and Neanderthals in Europe

Currat & Excoffier(2004), PLoS Biol.

cane toad invasion in australia
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