Vamathevan, J.J.; (2009) Evolutionary analysis of mammalian genomes and associations to human disease. Doctoral thesis , UCL (University College London).

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Abstract

Statistical models of DNA sequence evolution for analysing protein-coding genes can be used to estimate rates of molecular evolution and to detect signals of natural selection. Genes that have undergone positive selection during evolution are indicative of functional adaptations that drive species differences. Genes that underwent positive selection during the evolution of humans and four mammals used to model human diseases (mouse, rat, chimpanzee and dog) were identified, using maximum likelihood methods. I show that genes under positive selection during human evolution are implicated in diseases such as epithelial cancers, schizophrenia, autoimmune diseases and Alzheimer’s disease. Comparisons of humans with great apes have shown such diseases to display biomedical disease differences, such as varying degrees of pathology, differing symptomatology or rates of incidence. The chimpanzee lineage was found to have more adaptive genes than any of the other lineages. In addition, evidence was found to support the hypothesis that positively selected genes tend to interact with each other. This is the first such evidence to be detected among mammalian genes and may be important in identifying molecular pathways causative of species differences. The genome scan analysis spurred an in*depth evolutionary analysis of the nuclear receptors, a family of transcription factors. 12 of the 48 nuclear receptors were found to be under positive selection in mammalia. The androgen receptor was found to have undergone positive selection along the human lineage. Positively selected sites were found to be present in the major activation domain, which has implications for ligand recognition and binding. Studying the evolution of genes which are associated with biomedical disease differences between species is an important way to gain insight into the molecular causes of diseases and may provide a method to predict when animal models do not mirror human biology.

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