The genomics of local adaptation in Blue Tits

Understanding the evolutionary history of the populations helps to pinpoint locally adapted genes.

“It is not the strongest of the species that survives, nor the most intelligent that survives. It is the one that is most adaptable to change.” This quote is often attributed to Charles Darwin, but it turns this sentence does not appear in any of his writings. Instead these words can be traced back to a 1963 speech by Louisiana State University business professor Leon C. Megginson who presented his interpretation of Darwin’s work at the convention of the Southwestern Social Science Association (see here for the whole story). Whoever said it, this quote captures an important aspect of the evolutionary process, namely adaptation to local environments. We already have a good idea of how this works on an individual level, but can we also identify signatures of local adaption in the genome? A recent paper in the journal Evolutionary Applications tried to do this by studying several Blue Tit (Cyanistes caeruleus) populations in France.

 

Genome Scans

Before we travel to southern France (not easy in these Covid-times), we first have to understand how we can detect local adaption in the genome. A common method concerns Fst genome scans, where you calculate the population genetic statistic Fst for different genomic regions (usually referred to as windows). Fst is a measure of genetic differentiation and ranges from 0 to 1. Populations with an Fst = 0 are genetically indistinguishable, while an Fst = 1 points to completely differentiated populations. By calculating this statistic for different genomic regions, you can pinpoint sections of the genome that are significantly differentiated and might contain genes involved in adaptation to particular environments.

Finding locally adapted genes thus boils down to finding peaks in Fst across the genome. Sounds simple, but there is a catch. Other evolutionary processes can also give rise to an increase in Fst. Genetic drift, for example, can lead to genetic differentiation between populations through random fluctuations in the frequency of certain gene variants (or alleles). Recombination – the shuffling of chromosomal segments during meiosis – can also affect Fst. In regions of low recombination, several genes will be linked together (i.e. they reside on the same segment) and selection for one deleterious gene variant will thus affect the frequency of all linked genes. If different deleterious alleles unrelated to local conditions are selected against in different populations, the genomic regions in which they reside will diverge genetically, resulting in higher Fst-values. This process – background selection – can complicate the search for locally adapted genes.

A final process to keep in mind is gene flow: the exchange of genetic material between populations. Similar to mixing two paint cans, gene flow results in homogenization of the genome and can erase the genetic signatures of local adaptation. Several studies – both empirical work and simulations – have shown that a few immigrants can strongly decrease the genetic distance between two populations.

The relationship between genetic differentiation (Fst) and recombination rate in Ficedula Flycatchers. Notice the increase in Fst (top) coincides with a decrease in recombination rate. Adapted from Burri et al. (2015) Genome Research

 

Countless Comparisons

After this quick course in population genetics, we can finally turn to the Blue Tits. Charles Perrier and his colleagues focused on two particular pairs of populations. The first comparison concerns birds living in deciduous and evergreen forests. Previous work revealed differences in morphology and behavior between these populations. Specifically, birds breeding in deciduous forest habitats are taller, more aggressive, and lay larger and earlier broods than birds in evergreen forests. The second comparison comprised mainland versus island populations of Blue Tits. The island birds are smaller and more colorful than their mainland relatives and have been classified as a different subspecies (caeruleus on the mainland and ogliastrae on Corsica and Sardinia).

Let’s start with the deciduous vs. evergreen populations. The researchers sampled and compared four population pairs. Genome scans revealed a few Fst-outliers, but these genomic regions were unique for each comparison. This lack of repeatability is probably due to high levels of gene flow between these populations. And indeed, demographic analyses indicated ongoing gene flow between the deciduous and evergreen populations. As explained above, the mixing of a few migrations can erase genetic differences, including signatures of local adaptation. In addition, these results suggest that the traits involved in adaptation might be encoded by many alleles of small effect (i.e. polygenic architecture). These small effects are extremely difficult to pick up with genome scans and require a different approach, such as quantitative genomics.

Several comparisons of deciduous vs. evergreen forest populations revealed several Fst-outliers that were unique to each comparison. From: Perrier et al. (2020) Evolutionary Applications

 

Recombination Rate

The situation for mainland vs. island birds was slightly different. The demographic analyses showed that gene flow between these populations stopped about 10,000 years ago, most likely due to the isolation of Corsica after a rise in sea level. This abrupt termination in gene flow could have set the scene for local adaption to build-up in the genome. And indeed, the researchers found several outlier regions that could be involved in adaptation to the local conditions. But before we open the champagne (we are in France after all) to celebrate the discovery of several locally adapted genes, we should have a look at recombination rate. The researchers report a correlation between genetic differentiation and recombination rate: high levels of Fst coincide with low recombination rates. Hence, they have to admit that “increased differentiation in regions with low recombination is not necessarily due to positive selection, or at least not alone, and [can be] largely influenced by the effect of recombination in interaction with background selection.” More analyses are needed to disentangle these effects. The search continues.

The correlation between genetic differentiation (Fst) and recombination rate shows that regions of high Fst tend to exhibit low recombination rates. From: Perrier et al. (2020) Evolutionary Applications

 

An Extra Oddity

Apart from the search for locally adapted genes, the researchers uncovered an inversion on chromosome 3. Inversions are essentially regions in the DNA that has been flipped around (see this blog post for more on these peculiar segments) and they can link several genes together that consequently evolve as a single “super-gene”. If the linked genes affect the same traits, they can significantly speed up adaptation. The origin and evolutionary dynamics of this inversion – which is unique to the mainland population – remain to be investigated but the researchers can already speculate:

While the putative genomic inversion on chromosome 3 was absent from Corsica and detected in mainland individuals, its level of divergence from the noninverted sequence indicated that it was likely twice older than the beginning of divergence between blue tit populations from mainland and Corsica. This could first suggest that this polymorphism emerged in mainland blue tit populations and then did not introgress the Corsican populations, maybe due to a local disadvantage and/or genetic incompatibilities or simply due to drift coupled to little gene flow. A second hypothesis could be that this inversion was present in Corsican populations but had been purged out due to a local disadvantage.

More exciting questions to explore in these Blue Tit populations.

 

References

Perrier, C., Rougemont, Q., & Charmantier, A. (2020). Demographic history and genomics of local adaptation in blue tit populations. Evolutionary Applications, 13(6), 1145.

Feature image © Pierre Dalous | Wikimedia Commons

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