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
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