Estimation of genetic drift and selection from next generation sequencing time-sampled data

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https://hal.inrae.fr/hal-02738798 Submitted on 2 Jun 2020

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Estimation of genetic drift and selection from next generation sequencing time-sampled data

Elsa Rousseau, Frédéric Fabre, Jerome Coville, Ludovic Mailleret, Alain Palloix, Rachid Senoussi, Frédéric Grognard, Benoit Moury

To cite this version:

Elsa Rousseau, Frédéric Fabre, Jerome Coville, Ludovic Mailleret, Alain Palloix, et al.. Estimation of genetic drift and selection from next generation sequencing time-sampled data. Modelling biological evolution 2015: linking mathematical theories with empirical realities, Apr 2015, Leicester, United Kingdom. 2015. �hal-02738798�

Estimation of Genetic Drift and Selection from

Next Generation Sequencing Time-sampled Data

Elsa Rousseau

1,2,3

, Frédéric Fabre

4*

, Jérôme Coville

5

, Ludovic Mailleret

1,2

, Alain Palloix

6

, Rachid Senoussi

5

,

Frédéric Grognard

1

& Benoît Moury

3

1 _{INRIA, Biocore Team, Sophia Antipolis, France,} 2 _{INRA, UMR ISA, Sophia Antipolis, France,} 3 _{INRA, UR Pathologie Végétale, Montfavet, France,} 4 _{INRA, UMR Santé et Agroécologie du Vignoble,}

Villenave d'Ornon cedex, France, 5 _{INRA, UR BioSp, Avignon, France,} 6 _{INRA, UR Génétique et Amélioration des Fruits et Légumes, Montfavet, France,} *_{[email protected]}

❶

Introduction & objectives

 Uncovering how natural selection and genetic drift shape the evolutionary dynamics of pathogen populations within their hosts is an important issue

(e.g. virus emergence). Methods exist to estimate the intensity of drift when neutral genetic markers are available. However few methods allow to jointly estimate the intensities of genetic drift and selection without neutral markers (Foll et al., 2014, Plos Genet.).

❷

Experiments: Demo-genetic dynamics of viruses in their host plant

 Inoculation of 15 pepper genotypes (carrying

the same resistance gene pvr23 _{but distinct}

Quantitative Trait Loci combinations) with a

mixture of 5 Potato virus Y variants .

❸

A mechanistic-statistical model to jointly estimate Ne

and s

 A model for the dynamics of the mean density of variant i V_i(t)

with r_i ,intrinsic rate of increase of variant i,



_i,j ,mutation rate from variant j to variant i (fixed) and K the carrying capacity (fixed)

 ….coupled to a model for the variance of frequencies between hosts:

with X(p,d) the observed frequencies in plant p at time d,

f(d)=(f₁(d),…,f₅(d)) the mean frequencies of virus variants at time d

and α(d) a scale parameter at time d.





=(r₁,… ,r₅, α(6),… α(34)) can be estimated by maximum likelihood

given that mean(r_i)=1 (Fabre et al., 2012, Plos Path.).

❹

Results:

Ne

is varying from 35 to 1200 depending on host genotype

 At 6 dates (6, 10, 14, 20, 27 and 34 dpi), 8 plants

per genotype were sampled and the frequency of each variant in each plant (n=6*8*16) determined with Miseq sequencing of the VPg cistron.

Virus variant Adaptation to pvr23

Letters indicate the amino acid present

at positions 101 (G), 119 (K) and 121 (N) of the VPg cistron G Weak N Weak K Strong GK Strong KN Strong Pepper line 2430 Pepper line 2321 Pepper line 219 Inoculation G N K GK KN

Day 6 Day 14 Day 20 Day 34

1 2 3 4 5 6 7 8 0 0.2 0.4 0.6 0.8 1 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 0 0.2 0.4 0.6 0.8 1 1 2 3 4 5 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 7 8 0 0.2 0.4 0.6 0.8 1 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 Plant sample number

Virus v arian ts fr equ encies 5 1 ) ( ) 1 1 ( i i i i ij j i j j i i i j r dV t V r r V V dt K _{ }  V V  _{ }  _  _  _    _{ } 









1( ),..., 5



( , ) ~p d Dir ( ) V d V (d ) , (d) K d       f   X

 Simulation of datasets with varying s and Ne using a Wright-Fisher model

with selection, mutation and genetic drift in a haploid context (FFPopSim ; Zanin & Neher, 2012, Bioinformatics)

 For each simualted dataset, the vector



of model parameters is estimated.

► _{Validation of a method to jointly estimate both s and Ne without neutral}

loci using NGS method and a time-sampling scheme of independent hosts.

► _{Contrasted patterns of genetic drift and selection between hosts}

Modelling Biological Evolution 2015, Leicester (UK), April-28 – May 1, 2015

Selection coefficient (s) _{Effective population size (Ne)}

True value True value

Es tima ted value ( r i ) Es tima ted value ( α (d ) ) 0.7 0.8 0.9 1 1.1 1.2 1.3 0.7 0.8 0.9 1 1.1 1.2 1.3 20 30 100 300 1000 3000 104 104 3000 1000 300 100 30 20 Ne at day 6 20 50 500 2000 200 Ne at day 6 20 50 500 2000 200

► Aim 1: Developing a method to jointly estimate effective

population sizes (Ne), a measure of genetic drift, and selection coefficients (s) on time-sampled data representing temporal

changes in allele frequencies.

► Aim 2: Identifying genetic background of host plants

increasing genetic drift acting on virus populations…in order to delay the emergence of resistance-breaking virus.

0. 85 0. 90 0. 95 1. 00 1. 05 Virus variants In tri nsic ra tes of incr ease ( r i ) G N K GK KN Pepper genotype 219 2400 2256 2321 2349 2344 2264 2173 2328 2123 2367 2426 221 2430 240 5 10 15 20 25 30 35 0 200 400 600 800 1000

Days after inoculation

Eff ectiv e popula tion siz e (Ne =f( α (d ) ))

Selection intensity Genetic drift intensity

► Mean trajectories of virus variants contain the evidence of selection

and between-hosts variances contain the evidence of genetic drift.

► The fitness landscapes of

the virus population have roughly the same shape

whatever the pepper genotype is.

► Notably, no situation

where “a variant selected in one host is counter-selected

in another host

environment” is reported.

► _{Large variability of Ne}

between pepper genotypes mainly determined by

infection process within the inoculated leaf (days 1 to 6).

► _{Some Quantitative Trait}

Loci are likely to control the Ne of virus populations in