Natural selection is the main driver of evolution, but in small populations, where there are weak selective forces, genetic drift takes charge. However, there are many other factors that exert influence under neutrality including mutation, recombination and slight changes affecting binding of transcription factors. The EVOGREN (Evolution of gene regulatory networks by means of natural selection and genetic drift) project has evaluated the evolution of gene regulatory networks (GRNs). The team built on analysis of millions of site changes by developing new algorithms. Increasing the speed of the OmegaPlus algorithm, researchers detected and analysed millions of segregations to find instances of recent positive selection. They also used 2-dimensional Site Frequency Spectrum for this purpose. Using the latest coalescent theory, they simulated the interaction between speciation and population variation, looking at the gene flow. EVOGREN looked at the evolutionary forces at work on human structural variants. Results from the analysis of more than 400 polymorphic human deletions shared with archaic hominids indicate that the genomic landscapes were primarily shaped by purifying selection. Salivary adaptation studies showed that the MUC7 gene has evolved rapidly by episodic positive selection, mediated by pathogens that can interact with protein domains. Control of gene expression is becoming very high profile in areas of health and disease and is the basis of cell differentiation and development. EVOGREN has developed a significant knowledge platform to strengthen computational evolutionary biology. Application to gene expression regulation has significance in many areas – from disease to biotech.
Evolution, gene regulatory networks, natural selection, genetic drift, EVOGREN, algorithms