Investigating the evolutionary rate of human disease genes is crucial for understanding the genetic basis of diseases and their evolution over time. This approach can provide insights into the fundamental questions in evolutionary medicine, such as the evolutionary mechanisms and biological properties of genes and gene regulatory networks that contribute to the persistence of disease variants within human populations. This is particularly important for polygenic and complex diseases, which result from the contribution of multiple genes. The genetic basis of such diseases is more complex than monogenic diseases, and we often lack a systematic understanding of the evolutionary rate of the genes associated with such diseases. By investigating 4,576 complex traits in the
GWAS ATLAS database, we explored the relationship between genetic correlation, gene expression level, evolutionary rate, and estimates of selection pressure in human genes across a broader spectrum of polygenic traits. Our results demonstrate the presence of a spectrum among complex traits, shaped by natural selection. Notably, at opposite ends of this spectrum, we find metabolic traits being more likely influenced by purifying selection, and immunological traits that are more likely shaped by positive selection. Our paper is
available here.