Joint inference of adaptive and demographic history from temporal population genomic data

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Joint inference of adaptive and demographic history

from temporal population genomic data

Miguel Navascués, Vitor Antonio Pavinato, S. de Mita, J.-M. Marin

To cite this version:

Miguel Navascués, Vitor Antonio Pavinato, S. de Mita, J.-M. Marin. Joint inference of adaptive

and demographic history from temporal population genomic data. Joint inference of adaptive and

demographic history from temporal population genomic data, Nov 2019, Toulouse, France.

�hal-02790079�

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Joint inference of adaptive and demographic history from temporal

population genomic data

Miguel Navascu´

es, Vitor A.C. Pavinato, St´

ephane De Mita, Jean-Michel Marin

INRA, Universit´

e de Montpellier, Uppsala universitet

miguel.navascues@ebc.uu.se

7/10/2019

(3)

Joint inference of adaptive and demographic history from temporal

population genomic data

Miguel Navascu´

es, Vitor A.C. Pavinato, St´

ephane De Mita, Jean-Michel Marin

INRA, Universit´

e de Montpellier, Uppsala universitet

miguel.navascues@ebc.uu.se

7/10/2019

(4)

Temporal genetic data for the study of evolution

(5)

Temporal genetic data for the study of evolution

(6)

Joint Inference of Demography and Selection

Demographic inference assumes that effect of selection in

genome-wide patterns of diversity can be ignored

Selection inference focuses on detecting loci under selection (outliers)

(7)

Joint Inference of Demography and Selection

Demographic inference assumes that effect of selection in

genome-wide patterns of diversity can be ignored

Selection inference focuses on detecting loci under selection (outliers)

(8)

Joint Inference of Demography and Selection

Demographic inference assumes that effect of selection in

genome-wide patterns of diversity can be ignored

Selection inference focuses on detecting loci under selection (outliers)

(9)

Model and approach

forward simulation + approximate Bayesian computation via random forest

equilibrium mutation-drift-selection

Haller & Messer (2019)doi:10.1093/molbev/msy228(Software SLiM )

(10)

Model and approach

forward simulation + approximate Bayesian computation via random forest

(11)

Model and approach

forward simulation + approximate Bayesian computation via random forest

?

(12)

Model and approach

forward simulation + approximate Bayesian computation via random forest

?

(13)

Model and approach

forward simulation + approximate Bayesian computation via random forest

?

N_e

proportion of loci under selection genetic Load

…

(14)

Demography and Selection

Does joint inference work?

(15)

Demography and Selection

0 1 2 3 0 1 2 3 true log10Ne lo g10 N ^ (OOB prediction)e −15−15 −10 −5 0 −10 −5 0 true logit(P ) P ^ (OOB prediction)

(16)

Genetic Drift (“demography”)

Does inference of “demography” account for the action of selection?

(17)

Genetic Drift (“demography”)

0 1 2 3 0 1 2 3 true log10Ne lo g10 N ^ (OOB prediction)e 0 1 2 3 0 1 2 3 true log10Ne lo g10 N ^ (from e F ^ST )

(18)

Genetic Drift (“demography”)

0 1 2 3 0 1 2 3 true log10Ne lo g10 N ^ (OOB prediction)e 0 1 2 3 0 1 2 3 true log10N lo g10 N ^ (OOB prediction)

(19)

Is there adaptive diversity? (Genetic Load)

(20)

Is there adaptive diversity? (Genetic Load)

This is NOT Mutation Load

The presence of beneficial mutations creates Substitution Load, it

shows that there is adaptive diversity

L = 0: there is not genetic diversity to adapt to environmental

pressures

L ≈ 1: there is a lot of selection (risk of depletion of diversity?)

(21)

Is there adaptive diversity? (Genetic Load)

This is NOT Mutation Load

The presence of beneficial mutations creates Substitution Load, it

shows that there is adaptive diversity

L = 0: there is not genetic diversity to adapt to environmental

pressures

L ≈ 1: there is a lot of selection (risk of depletion of diversity?)

(22)

Is there adaptive diversity? (Genetic Load)

This is NOT Mutation Load

The presence of beneficial mutations creates Substitution Load, it

shows that there is adaptive diversity

L = 0: there is not genetic diversity to adapt to environmental

pressures

L ≈ 1: there is a lot of selection (risk of depletion of diversity?)

(23)

Is there adaptive diversity? (Genetic Load)

This is NOT Mutation Load

The presence of beneficial mutations creates Substitution Load, it

shows that there is adaptive diversity

L = 0: there is not genetic diversity to adapt to environmental

pressures

L ≈ 1: there is a lot of selection (risk of depletion of diversity?)

(24)

Is there adaptive diversity? (Genetic Load)

−8 −6 −4 −2 0 2 4 6 −8 −6 −4 −2 0 2 4 6 true logit(L ) logit( L ^) (OOB prediction)

(25)

Is evolutionary rescue likely? (θP

S

= 4N

e

µP

S

)

(26)

Is evolutionary rescue likely? (θP

S

= 4N

e

µP

S

)

θP

S

> 1: every generation new beneficial mutations arrive

θP

S

< 1: several generation of waiting time for an new beneficial

mutation to arrive

(27)

Is evolutionary rescue likely? (θP

S

= 4N

e

µP

S

)

θP

S

> 1: every generation new beneficial mutations arrive

θP

S

< 1: several generation of waiting time for an new beneficial

mutation to arrive

(28)

Is evolutionary rescue likely? (θP

S

= 4N

e

µP

S

)

−8 −6 −4 −2 0 2 4 6 −8 −6 −4 −2 0 2 4 6 true log10θS lo g10 θ ^ (OOB prediction)S

(29)

Feral honey bee in California

Photo Andreas Trepte,photo-natur.net, CC-BY-SA

(30)

Feral honey bee in California

Cridland et al. (2018) GBE

doi:10.1093/gbe/evy007

Avalon, Catalina Island (Los Angeles)

Samples from 1910 (2) & 2014 (5)

Genome resequencing (236Mbp)

1 year = 1 generation

(31)

Results bees

ˆ

N

e

= 90

95%CI(23–887)

log10(Ne) probability density −1 0 1 2 3 4 5 0.0 0.5 1.0 1.5 2.0

ˆ

P = 4 × 10

−5

95%CI(10

−6

–10

−2

)

logit(P) probability density −15 −10 −5 0 0.0 0.1 0.2 0.3 0.4 0.5

(32)

Conclusions & Perspectives

Estimation of demography in the presence of pervasive selection

Estimation of adaptive potential of populations

Practical limitations: improve computational efficiency

Model limitations:

I

Background selection

I

Adaptation from standing variation

I

Complex demography (migration/structure)

I

Hard selection

(33)

Conclusions & Perspectives

Estimation of demography in the presence of pervasive selection

Estimation of adaptive potential of populations

Practical limitations: improve computational efficiency

Model limitations:

I

Background selection

I

Adaptation from standing variation

I

Complex demography (migration/structure)

I

Hard selection

(34)

Conclusions & Perspectives

Estimation of demography in the presence of pervasive selection

Estimation of adaptive potential of populations

Practical limitations: improve computational efficiency

Model limitations:

I

Background selection

I

Adaptation from standing variation

I

Complex demography (migration/structure)

I

Hard selection

(35)

Conclusions & Perspectives

Estimation of demography in the presence of pervasive selection

Estimation of adaptive potential of populations

Practical limitations: improve computational efficiency

Model limitations:

I

Background selection

I

Adaptation from standing variation

I

Complex demography (migration/structure)

I

Hard selection

(36)

Conclusions & Perspectives

Estimation of demography in the presence of pervasive selection

Estimation of adaptive potential of populations

Practical limitations: improve computational efficiency

Model limitations:

I

Background selection

I

Adaptation from standing variation

I

Complex demography (migration/structure)

I

Hard selection

(37)

Conclusions & Perspectives

Estimation of demography in the presence of pervasive selection

Estimation of adaptive potential of populations

Practical limitations: improve computational efficiency

Model limitations:

I

Background selection

I

Adaptation from standing variation

I

Complex demography (migration/structure)

I

Hard selection

(38)

Conclusions & Perspectives

Estimation of demography in the presence of pervasive selection

Estimation of adaptive potential of populations

Practical limitations: improve computational efficiency

Model limitations:

I

Background selection

I

Adaptation from standing variation

I

Complex demography (migration/structure)

I

Hard selection

(39)

Conclusions & Perspectives

Estimation of demography in the presence of pervasive selection

Estimation of adaptive potential of populations

Practical limitations: improve computational efficiency

Model limitations:

I

Background selection

I

Adaptation from standing variation

I

Complex demography (migration/structure)

I

Hard selection

(40)