ClimAHealth

This project is maintained by dtortosa

Summary of the project

After migrating out of Africa, human populations were exposed to a wide range of environmental conditions and, particularly, temperatures. Over millennia, exposure to varying temperatures could have modified the response to external temperature in these populations. In other words, this exposure could act as a selective pressure, shaping the metabolism of human populations and leading them to adapt to different temperature regimes. This process leaves detectable signals in the genome, known as signatures of positive selection or adaptation. We can identify these signals using various statistical and machine learning methods.

figure_1 Possible migration waves out of Africa along with locations of major ancient human remains and archeological sites. Saioa López, Lucy van Dorp and Garrett Hellenthal - López, S., van Dorp, L., & Hellenthal, G. (2015). Human Dispersal Out of Africa: A Lasting Debate. Evolutionary Bioinformatics Online, 11(Suppl 2), 57–68. Link doi, link ncbi, CC BY 3.0.

Considering that the mechanisms for coping with temperature changes involve energy production and the use of different energy substrates like glucose or fatty acids, they are closely related to energy balance and cardiometabolic traits. Consequently, past adaptive events that altered the ability of human populations to expend energy in heat production might contribute to differential risk of metabolic disorders between current populations.

ClimAHealth was dedicated to the search of signals of adaptation caused by climate across human populations. We combined statistics sensitive to adaptation with a diverse range of machine learning approaches. This allowed us to test whether signals of adaptation are more frequent in genes implicated in thermogenesis and hence relevant for coping with temperature changes. An excess of adaptation signals within these genes would suggest the existence of past adaptation to temperature conditions. Given the connection of these genes with energy balance, ClimAHealth will also assess whether they tend to associate with cardiometabolic traits.

Work performed and main results achieved so far

ClimAHealth has focused on the detection of recent signatures of adaptation (left in the last 30,000 years). We calculated the probability of adaptation across human genes using a well-known statistic for recent adaptation (the integrated haplotype score; iHS). We also quantified genomic characteristics around these genes that should associate with adaptation if the latter was common enough in the first place. In other words, if adaptation was relatively frequent during recent human evolution, signals of adaptation should occur more frequently in genomic regions that are more likely to influence human phenotypes. In contrast, if signals of adaptation are just false positives, then they should be randomly distributed across the human genome.

We have developed and applied a modeling framework able to predict the probability of adaptation (iHS) using multiple predictors as input. In other words, we have been able to assess the influence of multiple genomic factors on adaptation at the same time. This framework considered a wide range of algorithms from simple linear models to deep neural networks. It applied a rigorous training/evaluation scheme in order to, first, select the best combination of parameters within a given algorithm category and then make a final selection between the best candidates across all categories. The models were evaluated and tested against data not previously seen in order to obtain a rigorous estimate of model performance. Given the high computational burden of this model exploration, we took advantage of initial results of ClimAHealth showing that adaptation signals were more visible in the African Yoruba population compared to non-African populations. Therefore, Yoruba was a good population to battle-test our framework and find the best algorithm and architecture to detect true iHS selection signals. This was a deep neural network with 5 layers and 1000 nodes (among other parameters) that then was used to model iHS in completely different and new populations (25 populations spread across 4 continents). This approach achieved a high predictive power explaining ~80% of the variability of iHS when predicting on unseen data. Notably, we identified multiple genomic factors associated with adaptation across all populations. This suggests that signals of recent adaptation were not randomly distributed across the genome, reducing the likelihood of them being false positives. In other words, according to these results, adaptation should have been relatively frequent in human populations in the past 30 Kya. Importantly, we included as predictor in the models the distance to genes related to thermoregulation in general and thermogenesis in brown adipose tissue in particular. We found a higher probability of adaptation around thermogenic genes compared to the rest of the genome across most of the studied populations. Given the link between thermogenesis and environmental temperature, these adaptation signals provide evidence for climate adaptation in humans.

Finally, we analysed genotype-phenotype associations in a cohort of European adolescents (the HELENA study). Given the role of lipid metabolism in cold-induced thermogenesis, genes implicated in thermogenesis and showing signals of past adaptation could influence the metabolic response of humans to the novel obesogenic environment they are currently exposed to. Several thermogenic genes analysed in the previous steps were significantly associated with cardiometabolic traits like adiposity and blood lipids, as expected given the connection between thermogenesis, lipid usage and energy balance. Therefore, we have defined a set of genes related to thermoregulation that exhibited an accumulation of adaptation signals and, at the same time, some of these genes were significantly associated with cardiometabolic traits. This suggests the existence of past events of climate-driven adaptation that shaped the physiology of human populations and, in turn, this could influence the variability of health-related traits.

The main scripts used to perform these analyses can be found in the following links:

Progress beyond the state of the art

ClimAHealth is advancing the analysis of adaptation signals beyond the state of the art. Previous work in this field has relied on classical correlation and regression approaches, which do not align well with the distribution of adaptation statistics. Leveraging a novel modeling framework, we analyze complex adaptation statistics, considering the impact of multiple factors on the probability of adaptation.

The heterogeneity of the human genome has greatly complicated the search for recent adaptation signals, resulting in a controversial topic. However, our innovative modeling approaches have provided robust evidence indicating relatively frequent adaptation in recent evolutionary times. As a result, ClimAHealth is contributing to answering critical questions in human evolutionary biology. It is also opening new avenues of research by applying novel modeling techniques to model positive selection.

Last, but not least, we defined a list of genes related to brown adipose tissue that was initially validated, confirming its cohesion and relationship with BAT. The accumulation of adaptive signals within this group of genes further supports their functional relevance, making it a promising list of candidate genes for energy balance and obesity-related traits.

Conclusions

In this project, we have developed and applied a novel machine learning framework to model signals of adaptation in the human genome. In general, we found an accumulation of adaptation signals in genes related to thermoregulation and, in particular, in genes related to thermogenesis in brown adipose tissue (BAT). This accumulation was present in multiple human populations across 4 continents, suggesting the existence of multiple events of adaptation to climate in the last 30,000 years. Additionally, several of these thermogenic genes showed associations with cardiometabolic traits in an independent cohort of European adolescents. In sum, these findings provide evidence about widespread events of adaptation to climate across human populations along with potential implications of these events for cardiometabolic health.

Funding

This project is funded by the European Union’s Horizon 2020 research and innovation programme (Marie Skłodowska-Curie Actions; grant agreement number 101030971). This projects only reflects the author’s view and the Agency and the Commission are not responsible for any use that may be made of the information it contains.