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François Pompanon and Aurélie Bonin (eds.), Data Production and Analysis in Population Genomics: Methods and Protocols,
Methods in Molecular Biology, vol. 888, DOI 10.1007/978-1-61779-870-2_17, © Springer Science+Business Media New York 2012
Chapter 17
Use of Qualitative Environmental and Phenotypic
Variables in the Context of Allele Distribution Models:
Detecting Signatures of Selection in the Genome
of Lake Victoria Cichlids
Stéphane Joost, Michael Kalbermatten, Etienne Bezault,
and Ole Seehausen
Abstract
When searching for loci possibly under selection in the genome, an alternative to population genetics theo-
retical models is to establish allele distribution models (ADM) for each locus to directly correlate allelic
frequencies and environmental variables such as precipitation, temperature, or sun radiation. Such an
approach implementing multiple logistic regression models in parallel was implemented within a comput-
ing program named MATSAM. Recently, this application was improved in order to support qualitative envi-
ronmental predictors as well as to permit the identification of associations between genomic variation and
individual phenotypes, allowing the detection of loci involved in the genetic architecture of polymorphic
characters. Here, we present the corresponding methodological developments and compare the results
produced by software implementing population genetics theoretical models ( DFDIST and BAYESCAN) and
ADM ( MATSAM) in an empirical context to detect signatures of genomic divergence associated with specia-
tion in Lake Victoria cichlid fishes.
Key words: Genome scans, Signature of selection, Genotype × phenotype association, Environmental
variables, Logistic regression, Cichlid fishes, Seascape genetics
On the basis of data produced by genome scans, the main approach
to identify loci under directional selection – or likely to be linked to
genomic regions under directional selection – is to use population
genetics theoretical models to detect outlier molecular markers
showing a larger genetic differentiation than expected under the
neutral hypothesis (1–3).
1. Introduction