Publishing research findings in scientific journals is one of the most important jobs of a research scientist. The number and quality of the papers you’ve published is one of the ways scientists are evaluated when applying for grants and jobs, primarily because it’s the way you communicate your work and your findings to your colleagues. So as a young scientist, I’m very excited to announce that a paper of mine has just been published!
Its title is “Identifying signatures of sexual selection using genomewide selection components analysis.” We (me and my PhD advisor) published it in an open-access journal, which means anyone can access it and read it. Unfortunately, a large component of the paper is fairly technical, because we had to go into a lot of detail about how we did every little calculation in the model. So I thought I would write a post to explain without much jargon what the paper is about.
First, you need to know a little bit about selection (e.g. natural selection, sexual selection). Selection is a force that acts on phenotypes, or traits and behaviors. Selection comes in many forms, depending on the context. Natural selection favors traits that allow individuals to survive longer whereas sexual selection leads to differential reproductive success (i.e. number of mates or offspring). In order for selection to have an evolutionary impact, selection must leave a signature on the genetics of the population. Those signatures are often in the form of changes in the frequency of the version of the genes (a.k.a. alleles) present in the population. In the field of population genetics (or population genomics), researchers typically sample multiple populations and look for differences in the allele frequencies between populations to determine whether selection has cause the populations to differentiate and how it has affected the genes (is there more variety in the versions of the genes in a population, or has all of the variety been eliminated?). The method used in population genetics can be very effective at determining differences between populations, but it does not tell us anything about whether the selection affecting the populations was due to differential reproductive success (sexual selection) or due to differential survival (natural selection).
So in my latest paper we describe a simulation model I wrote. The model tested a novel application of comparing allele frequencies (like they do in population genetics): comparing allele frequencies between individuals within a population as opposed to between populations. The idea behind this approach is that some forms of selection (e.g. sexual selection) occur at a specific time in the life cycle of an individual. So comparing the frequency of gene varieties between adults and offspring will target the specific time when sexual selection occurred and can narrow down the results to identify only genes affected by sexual selection. The results from our model showed that this approach can be very good at identifying signatures of sexual selection, with some constraints (such as large sample sizes, very strong sexual selection, and essentially no other forms of selection affecting the population). The next step is to test out this genome-wide selection components analysis approach in a natural population, which I’m planning on doing with a population of pipefish.
In the meantime, I’m excited about this paper coming out. It was a lot of work to write the simulation model and to put the whole paper together, and I’m quite proud of it. You can read it online here, if you are so inclined.