Guilt-by association approach to identify novel human aging-related genes using protein domains

Elucidating the genetic reasons associated with aging and longevity could greatly help in designing strategies to extend years of healthy life in humans. Extensive studies have been carried out in model organisms to find the effect of genes on aging. Understandably, human aging is difficult to research due to the complexity of the processes involved in aging, along with the time and ethical constraints associated with the human life. In spite of these constraints, the Human Ageing Genomic Resources (HAGR) has compiled the GenAge database, a curated list of aging related genes in humans and model organisms using information from published literature. We hypothesized that biological feature-based data mining approaches can overcome the existing limitations associated with human aging research. In this study we develop a computational method to identify aging related human genes that may play a potential role in aging and life span related processes. We employed protein domain information and guilt-by association approach to predict potential aging related genes, which resulted into the identification of twenty-seven novel human aging related genes.