theme – 4 detecting selection along environmental gradients: analysis of eight methods and their...

Detecting Selection Along Environmental

Gradients: Analysis of Eight Methods and Their Effectiveness for

Outbreeding and Selfing Populations

Yves Vigouroux Institut de Recherche pour le Développement

Montpellier, France

International Workshop on “Applied Mathematics and Omics Technologies for Discovering Biodiversity and Genetic Resources for Climate Change Mitigation and Adaptation to Sustain Agriculture in Drylands”

Rabat - Morocco, 24-27 June 2014

Climate change

Rainfall evolution: 1990 - 2090

IPCC 2007 august

Challinor et al.

Rainy season evolution by 2090

Control

crosses or

genealogy

Experimental

population

Natural

population

Analysis of

given traits Association mapping Association (genealogy,

QTL, NAM, MAGIC)

Selection:

traits? Genome selection

scan

Segregation

biais

Methodological approaches

Principle of differentiation selection

scan

Environment – spatial variation

Neutral allele: variation due to demographic/gene flow/history effects

Selected gene: variation due to demographic/gene flow/history effects and selection Differentiation

FST

• Methods: use or not environmental data?

• Sampling design?

• Impact of the reproduction system (selfing)?

Exemple of environmental gradient in Niger, Africa

Environmental Gradients

Software QuantiNEMO [Neuenschwander et al. 2008 Bioinformatics]

Simulations =

- time-forward

- individual

- modèle flexible

100 populations - 2N = 200

100 unlinked neutral locus

1 linked selected locus

Selfing: {0.0, 0.95}

Different sampling strategy

Model

si

Migration

model

Modélisation

séle

cti

on

sélection

Simulation of selection

• Two allele A and a

• AA fitness 1, Aa fitness 1-s, aa fitness 1-

2s

Simulation of selection: example

Modélisation: exemple

Sampling : from 1 individual per population to 48.

Methods studied

Name Reference Data

FDIST Beaumont et al. 2006 Population

DETSEL Vitalis et al. 2001 Pairs of population

FLK Bonhomme et al. 2010 Population

BAYSCAN Foll et al. 2008 Population

BAYENV Coop et al. 2010 Population

SAM Joost et al. 2006 Individual

GEE Poncet et al. 2010 Individual

Differentiation-based methods

Correlation based

methods

Use of environ. data

Evaluation of the methods

• Simulation of neutral and selected locus

• Use of the method to calculate the

proportion of loci detected

Simulated % of loci detected

selected

Neutral Percentage of false

positive

Expected 5%

Selected Percentage of true

positive

Expected close to 100%

Results Fals

e p

ositiv

e

Tru

e p

ositiv

e

Correlation to environmental data based methods Differentiation (FST) based method

Fals

e p

ositiv

e

Correlation to environmental data based methods Differentiation (FST) based method

Results

Reproductive system

Fals

e p

osiitv

e

Tru

e p

ositiv

e

Outbreeding Selfing

Selection strenght

Method using environmental data are more powerful evenif selection is weaker

Sampling

A small number of individual is

Conclusion

- Methods based on differentiation are conservative

- Methods based on correlation more powerfull / efficient

- Requiere to have good environmental data...

- New development: non parametric correlation

- Sampling more population is the most efficient

De Mita et al., 2013, Molecular Ecology De Mita et Siol, 2012, BMC Genetics

Current application

Genomic analysis A set of 89 000 SNP Detection of selection approach Association mapping

Work going on using this approach

Medicago truncatula

Rice (Oriza sp)

Natural population Traditionnal varieties in Guinea

and Madagascar

Selfing

selfing

Acknowledgement

IRD, Montpellier On ongoing project:

S De Mita UAM Niamey

AC Thuillet Y Bakasso

JL Pham IS Ousseini

C Berthouly

CIRAD, Montpellier ISRA Sénégal

N Ahmadi N Kané

INRA Montpellier

L Gay

Université de Provence

S Manel

ARCAD Project Agropolis Researcher Center for Crop Diversity and Adapation