Interview with Dr Pascale Gerbault

Posted Wed, Jun, 15,2016

This author interview is by Dr Pascale Gerbault, of University College London. Dr Gerbault's full paper, Forward-in-Time, Spatially Explicit Modeling Software to Simulate Genetic Lineages Under Selection, is available for download in Evolutionary Bioinformatics.

Please summarize for readers the content of your article.
This article introduces a software (SELECTOR, available here that helps the understanding of genetic diversity. Genetic diversity at the population level is mainly shaped by demographic history (population expansion, migrations, population bottlenecks and founder effects), genetic drift (the random sampling of alleles from one generation to the rest) and selection (e.g. positive, negative, and directional.) It can be very challenging to disentangle the specific combination of factors that led to an observed genetic pattern, since they are all at play and distinct combinations can lead to similar patterns (equifinality). SELECTOR allows us to test various combinations and identify those that allow explanations of observed patterns of genetic diversity.

How did you come to be involved in your area of study?
Distribution of genetic diversity for a specific set of populations can differ depending on genetic locus considered. I am a population geneticist and I wanted to explain why this was the case. To do so, there is no better way than simulating genetic population history. I had read the work of Dr Mathias Currat, who is experienced in using such simulation models to address population genetics questions, and I asked if I could work with him and Prof. Alicia Sanchez-Mazas ( in the University of Geneva.

What was previously known about the topic of your article?
Various simulation modeling software exist that simulate the evolution of genetic diversity through space and time. Some use backward in time approach (i.e. the coalescent), some consider a limited number of populations, very few include some selective processes, and when they do they are limited to positive or negative selection.

How has your work in this area advanced understanding of the topic?
The main advances with SELECTOR is that it is very flexible (various types of selection and sophisticated population relationships can be modeled, and this is all set via text files) and it is highly computationally efficient.

What do you regard as being the most important aspect of the results reported in the article?
I find the most important aspect of the results reported is that SELECTOR allows to assess how balancing selection (that maintain multiple variant of a gene in a population) can strongly reduce the effect of genetic differentiation due to genetic isolation (populations that do not mix). While this may seem intuitive, it could not be explicitly assessed in a statistically and reproducible manner in a two-dimension realistic framework. Not only does SELECTOR allow us to formally test such hypotheses, but it also provides an invaluable framework to explore other unexplained situations.

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