Large-Scale Analysis of Age-Related Genetic Variation For Identification of Potential Therapeutics
With the advent of next-generation sequencing, large-scale genetic association studies are increasingly more viable and hold great potential for improving treatments for complex diseases such as Alzheimer’s or Parkinson’s by unveiling their root causes at the genomic level. However, treatments for age-related disorders are often plagued by a lack of a clear understanding of the genetic basis for aging. We hypothesize that a novel biocomputational approach utilizing Multivariate Distance Matrix Regression (MDMR) will help fill this gap in knowledge. This statistical-analysis tool was employed using the GTEx dataset, an unparalleled NIH database of almost 1000 individuals assembled to study tissue-specific gene expression. Using a distance-based regression methodology and holistic multi-locus perspective, the tool presents a robust measure of the relationship between aging and the human genome, and can also be easily adapted in the future for various complex settings. Upon completion of the GTEx analysis, it was found that age has an extremely significant correlation with genetic variation across tissues, even after correcting for factors such as tissue-dependent differences and manner of death. Furthermore, the principally impacted genes appear to be involved primarily in signal transduction pathways, cellular transportation, or proto-oncogene networks. A set of genes whose variation was strongly related to aging across the majority of tissues was identified, and used to propose drugs or pharmaceutical compounds that may be of use in slowing the detrimental effects of aging. In the future, a similar analysis with proteomics data would likely further elucidate the aspects of aging rooted in the human genome and how they can be better treated.