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
