Tracking the genetic legacy of past human populationsthrough the grid

NICOLAS RAY UNIVERSITY OF GENEVA & UNEP/GRID-EUROPE

Swiss Grid Day, Bern, November 26th 2009

Human migrations

Adapted from Cavalli-Sforza & Feldman, 2003

[12,000]

[55,000]

Homo sapiens sapiens

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)

Why aiming at a good demographic model

Gene-specific factorsmutationsrecombinationselection

A complex past demographyfluctuation in effective pop. sizesubstructuremigrations

Observed patterns of genetic diversity in contemporary populations

A complex demography

Adapted from Cavalli-Sforza & Feldman, 2003

[10,000]

[55,000]

demographic and spatial expansions

population bottlenecks

fast migration events

population isolation

secondary contacts

SPLATCHESPatiaL And Temporal Coalescences in Heterogeneous Environment

(http://cmpg.unibe.ch/software/splatche)

From environment to demography

Spatial resolution: 100 km

low

high

Carrying capacity

low

high

Friction

From environment to demography

Demographic simulations

stepping-stone model (cellular automata)

Cell or deme

Pop

. siz

e

time

Demography and spatial expansion

Population density

Genetic simulations

Simulated genealogy

MutationModèle de mutation

ACCT

AGTA

CAAT

CGGT

AATG

CCAT

TGGT

TCCTTGTA…ATTGGT

ACCGAGTA…GTTGGT

Summary statistics– Within population:

• S, p– Between populations

• Pairwise FST

• Global FST

– Globally• S, p

Draw paramet

er values from priors

Simulate one

genealogy

Generate

genetic data

Compute

summary

statistics

1-10 mio.

Computer clusters

UBELIX (>500 nodes)Zooblythii (~40 nodes)

APPROXIMATE BAYESIAN COMPUTATIONS (ABC) 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

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

N simulations

Independent simulations: - the more CPUs, the better- job failures are not that bad

GRID

Posterior distribution of demographic/genetic parameters of interest

Statistical tools

Optimizations

5 mio. simulations

GRID

Reduction of the number of simulations (Daniel Wegmann)By MCMC. Promising results (~50 times less sims)

Submission timemulti-threaded application using up to 30 RBs (used for the WISDOM project)

Fetching time of job outputsin-house multi-threaded solution for checking status and getting outputs

Geographic origin of human dispersal

Ray et al. (2005) Genome Research

Interactions among populations

Interaction between modern humans and Neanderthals in Europe

Currat & Excoffier (2004), PLoS Biol.

Plausible introduction site 1LAGOON CREEK (first sight: 1979)

Initial introduction site in AustraliaGORDONVALE (1935)

KDM

NW

B

T

RE

120 0 120 240 360Kilometers

19991982

19881992

1995

1996

19971998

Plausible introduction site 2NORMANTON (first sight: 1964)

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 Bufo marinus. Submitted

Take-home message

A good human demographic model is important

Realistic spatially-explicit approaches are essential

The grid is key for sufficient exploration of parameter spaceUser support and connections outside one’s discipline is crucial

THANK YOU!