Using burden testing to understand more about the pathophysiology and general risk associated with an individual’s genes
Prediction of risk-related disease is an emerging focus in preventative medicine. It has the potential to enable doctors to explore novel prevention strategies, health maintenance, and/or treatment plans. Predicted risk encompasses a multitude of factors, including an individual’s age, sex, family and personal history, overall lifestyle habits, and genomic information. With the introduction of new in silico methods, scientists are able to delve deeper into the role(s) rare protein-altering genetic variants play in an individual’s susceptibility to disease. Genetic variants affecting the risk of disease may, individually, be too rare to study reliably. On the other hand, genetic burden testing may be performed to assess whether there is an excess of rare gene-damaging variants found within a gene or set of genes/pathway. The purpose of this project is to establish a method to assess the genetic burden of disease through analysis of rare-protein variants. Whole genome sequencing data from 10 control individuals was filtered and analyzed using an analytical tool to locate loss-of-function variants and variants predicted to be either damaging or deleterious. Using a series of custom R scripts and a pathway prediction tool, these variants were then mapped to genes and the genes used to elucidate exactly which biological pathways may be affected. Results showed that each individual has approximately 2,000 predicted damaging variants affecting his or her genes. Pathway analysis revealed numerous pathways having an excess genetic burden of these damaging variants. The next step is to test whether genetic burden scores can predict which diabetic individuals will go on to develop diabetic retinopathy (DR) by comparing a group of diabetic individuals who developed DR to a control group of diabetic individuals who did not develop DR. Genetic burden testing has been shown to be able to assess the risk of rare genetic variants and identify specific biological pathways bearing excessive genetic burdens. With additional research, genetic burden tests could lead to advanced methods of disease prevention and/or early detection.