Looking into the “genetic future”: simulations of a genetic rescue plan 10.12.2020

The huge amount of information contained in the genomes of the species we study will allow us to reconstruct their past history and, most importantly,  to envisage conservation actions to preserve genetic variability and “genetic health” (for example, avoiding inbreeding depression and genetic load). But can we predict the genetic effects of a conservation strategy? This would be very useful to compare the efficiency of plans with the same general goal but with different approaches and implementation aspects.
A way to fast-forward the lives of these endangered animals is through genetic simulations. Thanks to the increasing computing power of the modern clusters, more and more accurate and realistic simulations can be carried out, to the point that entire genomes of entire populations of individuals can be simulated. These genomes can include any type of neutral, deleterious and beneficial mutations, whose evolution can be subject to drift, selection, migration, recombination, sexual selection and so on.
One of the aims of our project is to set up simulations to forecast the effect of genetic rescues, i.e the translocation of animals to reduce the extinction risk of an endangered population. As a model species for these simulations we will use the Marsican brown bear. The program we chose is called SLiM (Selection on linked Mutations). This relatively new software is a so called forward simulator, meaning that the simulation doesn’t start from a given situation and go back in time to sequentially reach the ancestors until the last common ancestor (the so-called backward or coalescent simulations), but they start from a baseline scenario and simulate the events in chronological order.

An example screenshot of SLiM’s graphic user interface. In the upper section the “population” information are displayed: each of the six rectangles represent a subpopulation and each square in it is an individual (different colors represent different fitness values). In the middle section the simulated genome is represented (the small barely visible yellow bars represent mutations segregating in the genome) and in the lower section is reported the code to be written for the simulated scenario.

In our simulations, we will let a large (“European”) and a small (“Marsican”) populations diverge and evolve, allowing drift and selection to shape their genomes (i.e. the frequency/amount of neutral, beneficial and deleterious mutations). At the end of this divergence process (whose simulated time will resemble the real divergence time estimated from genetic data), we’ll check that the modelled populations we obtained are genetically similar to the real populations, comparing some summary statistics computed in real and simulated populations (e.g., genetic variability and differentiation between populations). At that point we will be at “present time”, ready to simulate different hypothetical conservation plans and predicting their genetic consequences.
For example, we can simulate the introduction of some “European” individuals into the “Marsican” population and predict what would happen to the average fitness of the Marsican individuals, to the number of deleterious mutations in the population and to the unique genetic characteristic of the Marsican bears (likely reduced by introducing “foreign” genomes). The number of introduced animals can be selected, as well as their sex, age, genetic background (variability, fitness…), which is very useful to identify the introduction protocol with the largest genetic/fitness benefit for the Marsican population .
Doesn’t it sound interesting? If you want to know more about specific aspects of this part of the project, write us in the comments and we’ll give more information in one of the next posts!

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