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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


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


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?...


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”)


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