SIB Young Bioinformatician Award
Joshua Payne carried out his research at the University of Zurich, in SIB group Evolutionary Systems Biology led by Andreas Wagner. He is awarded for his work on "The robustness and evolvability of transcription factor binding sites".
Mutations that affect the level, timing, or location of gene expression are important drivers of adaptation. Such mutations often occur within non-coding, regulatory regions of the genome, including the binding sites of sequence-specific proteins known as transcription factors. To determine how mutations in transcription factor binding sites may contribute to adaptation, we took a network-based approach to study the binding preferences of nearly 200 transcription factors in yeast and mouse. In these networks, vertices represent binding sites, where two sites are connected by an edge if they differ in a single nucleotide. We found that the sites bound by any one factor typically form a connected network, are often robust to mutation, and are adjacent to many sequences that bind different transcription factors. These findings help clarify the seemingly contradictory roles regulatory regions play in buffering gene expression patterns against mutation, while simultaneously providing a rich source of evolutionary innovations. More generally, these observations constitute the first empirical evidence that robustness and evolvability – the ability to bring forth novel adaptations via mutation – need not be conflicting properties.
SIB Best Graduate Paper Award
Josephine Daub carried out her research at the University of Bern, in SIB group Computational Population Genetics led by Laurent Excoffier. She is awarded for her work on “Evidence for Polygenic Adaptation to Pathogens in the Human Genome”.
With the rise of modern humans in Africa and their migration throughout the rest of the world, human populations had to cope with new environments, as well as adapt to new climates, diets and pathogens. Most approaches that aim to find genes involved in these adaptive events have focused on the detection of outlier loci, which resulted in the discovery of individually ´significant´ genes with strong effects. However, in the same way that several small effect mutations can cause a complex disease, one could imagine that a collection of small effect mutations on several genes could have a large positive effect on a given biological pathway. Surprisingly, such a polygenic mode of adaptation has not been systematically investigated in humans, and this was the focus of our paper. We proposed to evidence polygenic selection by detecting signals of adaptation at the pathway or gene set level, instead of analysing single independent genes. Using a gene-set enrichment test to identify genome-wide signals of adaptation among human populations, we found that most pathways globally enriched for signals of positive selection are either directly or indirectly involved in immune response.