Génétique des populations dans
l’espace et dans le temps:
reconstruire l’histoire démographique
des populations
Laboratoire Evolution et Diversité Biologique, CNRS, Toulouse
Population and Conservation Genetics Group, IGC, Portugal
Biodiversité et Informatique, Grenoble, 28 Juin 2011
Photos: E. Quéméré, F. Jan, B. Goossens
POPULATION GENETIC DATA
CONSERVATION GENETICSHabitat Fragmentation
Population Decline
Admixture in Domesticates
HUMAN PAST DEMOGRAPHYNeolithic Transition
Admixture in Human Populations
NEW METHODS / SOFTWARE
POPULATION AND CONSERVATION GENETICS
---CAGTCAGTCAGT---
mitochondrial DNAY chromosome
G G T T GG G G G
G T
G G T T GG G G GG G T T GG G G G
G TG T
• Impact de la fragmentation de l’habitat sur la diversité intra-spécifique :– Diversité génétique et tailles des fragments
– Distance géographique entre fragments sur la différenciation génétique
– Barrières au flux géniques (routes, rivières, fleuves, villages, etc.) ?
– Déterminer, quantifier et dater d’éventuels événements démographiques (goulots d’étranglements, expansions, mélanges) ayant influencé la diversité actuelle
– Importance relative de la fragmentation d’origine anthropique et des phénomènes naturels
– Importance de la structure spatiale, des expansions et contractions spatiales, de la structure sociale
Problématiques
1 cm = 5
km
Agricultural lands
(mostly oil palm
plantations)Lower Kinabatangan
Wildlife Sanctuary
Kinabatangan River
Main road (Sandakan
– Lahad Datu)
Villages
Sulu Sea
Virgin Jungle Reserves
The Kinabatangan
Floodplain
The Data
– 279 samples collected with 32 twice (hair + faeces)
– 247 individuals (176 nests and 71 faeces) extracted and amplified
– 200 individuals genotyped for 14 microsatellite loci divided into 9 samples (S1 to S9)
– 14*200 = 2800 genotypes (7 missing = 0.25%)
– 2 di and 12 tetra loci.
Time
Size
PresentPast
N1 > N0 ?
N0
ExponentialLinear
ta (in generations)
N1 < N0 ?
Sampling
Parameters:
N0 : current size
N1 : ancestral size
ta: nb of generations
since the pop started
to increase or
decrease
Reparameterize:
r = N0/N1
tf = ta/ N0
= 2 N0
Model of Beaumont 1999 and Storz et Beaumont, 2002
Microsatellite
Data
ExponentialLinear
r = N0/N1
tf = ta/ N0
= 2 N0
Exponential
N0, N1, ta,
Microsatellite
Data
Storz and Beaumont, 2002
Model of Beaumont 1999
Population size change
FE: Forest exploitation
F: Farmers
HG: Hunter-gatherers
thin line: S1
thick line : S2
Time since the population size change
Conclusions (at the time)
1. Strong signal for a bottleneck
2. The signal is robust to the mutation model
3. The signal is robust to a linear or exponential decrease
(assuming a specific mutation model)
4. The population decrease is very important
5. The population decrease is very recent: recent anthropogenic
changes
Habitat Fragmentation (and loss) in Daraina
44 000 ha of fragmented forest
Indian OceanLoky River
Manambato River
Golden-crowned sifaka
Propithecus tattersalliErwan Quéméré
Habitat Fragmentation (and loss ?) in Daraina
44 000 ha of fragmented forest
Océan Indien
Pictures: E. Quéméré
Habitat Fragmentation (and loss) in Daraina
Faeces from 230 individuals
(105 social groups)
13 microsatellites
Questions:
1. Role of the road as a barrier to
gene flow
2. Role of savanna / grasslands
3. Role of the Manankolana river
Habitat Fragmentation (and loss) in Daraina
Rôle de la rivière Manakolana:
* structure la diversité
* ancienne barrière ?
* lieu de peuplements humains ?
* etc.
Habitat fragmentation and loss
time
time
GENETIC DIVERSITY
POPULATION SIZE (FINITE)
Present
Past
TIME
GENETIC DIVERSITY
MUTATIONS
GENETIC DRIFT
NEWMUTATIONS
LOSS OF MUTATIONS
Different demographic histories can produce
similar or counter-intuitive results
Ne = 500
Present
Past
Ne = 100
Ne = 1000
Ne = 1000
Ne = 100
GENETIC DIVERSITY
GENETIC DIVERSITY
Present
Present
DIFFERENT TEMPORAL DYNAMICS OF THE TWO MEASURES
TIME
NUMBER OF ALLELESHETEROZYGOSITY: function(nA, freq.)
EQUILIBRIUM (N1 pre-bottleneck)
EQUILIBRIUM (N0 post-bottleneck)
GENETIC DIVERSITY
GENETIC DIVERSITY
Present
Past
TIME A B ... N
Population size change :
recognizable signature
PROBLEM : STRUCTURED POPULATIONS GENERATE A SIMILAR
SIGNATURE
TRUE AND FALSE SIGNATURES: WHO SHOULD YOU BELIEVE?...
Habitat fragmentation and loss
time
time
• Models of population structure (100 demes in all simulations) – n-island model (100 islands)
– Stepping-stone (10 x 10) (toroidal)
• Parameters used– Stepwise mutation model assumed to simulate data
– FST
values used { 0.01 ; 0.05 ; 0.1 ; 0.25 }
– θ values used { 1; 10 }
– Number of loci { 5 ; 20 }
– 50 individuals sampled (100 genes)
• Sampling schemes :– n-islands model: samples from 1, 2, 10 and 50 demes
– Stepping stone model: samples from 1, 2 neighbouring and 2 distant demes
• 10 independent data sets for each parameter set (except 20 loci and 10 demes)
Effect of population structure on bottleneck signals
Differentiation
Diversity
(mutation and pop size)
Stepping-stone
n-island
Effect of population structure on bottleneck signals
Bottleneck signals
Can we separate population structure from population crash?
Conclusions
1. Population structure can mimic bottleneck signals
2. The signal is particularly strong when
1. Genetic differentiation is high (gene flow is limited)
2. Genetic diversity is high
3. The number of loci used is large
3. The effect is less important when more than one
population is sampled
Need to
develop methods that can separate these two kinds of
scenarios (structure versus bottleneck)
ad hoc ways to minimize the genetic structure effect is to
spread sampling (one individual per “population”)
Habitat fragmentation and loss
time
time
or population structure
time
or both ?
Vers de nouveaux outils de simulation (1)
P-A Bouttier
V. Sousa
R. Rasteiro
SPLATCHE-like: L. Excoffier et Cie
ce
ll
Layer 2
Layer 1
K: carrying capacity
F: friction
m: migration rate
r: growth rate
γ: admixtureGenetic parameters: mutation rates,
sequence length, etc
popstructure.txt
• SG1 is connected to pops 2, 4, 5•SG3 is connected to pops 2, 4, 5•SG2, SG4, SG5 are connected to all other SG’s
MATRIX (ngroups*ngroups)
S1 S2 S3 S4 S5
S1 0 1 0 1 1
S2 1 0 1 1 1
S3 0 1 0 1 1
S4 1 1 1 0 1
S4 1 1 1 1 0
SG1
SG3
SG5 SG4
SG2
Vers de nouveaux outils de simulation (2)
E. Quéméré – C. Vanpé – B. Parreira
1 MALE, 1 FEMALEnm=1; nf=1; ndm=1; ndf=1
1 DOMINANT M/Fn NON-DOMINANT F/Mnm=1; nf=n; ndm=1; ndf=0nm=n; nf=1; ndm=0; ndf=1
n MALES,
m FEMALESnm=n; nf=m; ndm=0; ndf=0
Dominance = priority in reproduction
CONCLUSIONS
• La génétique du paysage a tendance à ignorer le temps
• Les méthodes d’inférence en génétique des populations
ont tendance à ignorer l’espace
• Comment intégrer ces deux notions ?
• Madagascar est un lieu privilégié pour cela: colonisation
humaine récente
Anna Rozzi
JE VOUS REMERCIE POUR VOTRE ATTENTION
Brigitte Crouau-Roy Univ. Paul Sabatier, Toulouse, France
Lounes Chikhi CNRS and Univ. Paul Sabatier, Toulouse, France
Instituto Gulbenkian de Ciência, Oeiras, Portugal
Bárbara Parreira, Rita Rasteiro, Vitor Sousa Inst. Gulbenkian de Ciência, Oeiras,
Portugal
Pierre Luisi, Pierre-Antoine Bouttier INSA, Toulouse, France
Benoît Goossens Cardiff Univ., UK – Sabah Wildlife Dept, Malaysia
Mark Beaumont: Reading Univ., UK
Erwan Quéméré: Univ. Paul Sabatier,Toulouse, France
Pedro Fernandes: Bioinformatics Unit, IGC