Project 458607

Introduction: COVID-19 has had a disastrous impact on Canada and the world. I will focus my research on investigating genetic features that could contribute to disease severity: human leukocyte antigens (HLA) and copy number variants (CNV). These are related to immunity and diseases that impact immune function like Down syndrome. In smaller studies, HLA have been shown to influence COVID-19 severity. CNV require greater investigation. Objectives: First, I will validate my proposed methods. Then they will be employed to classify the genetic traits and their clinical impact, re-validating other groups' work when available. Dataset: We will investigate these features using whole genome sequence (WGS) data from the Quebec COVID-19 Biobank, the largest in Canada and one of the largest in the world. HLA Investigation: CookHLA will be used to predict HLA-determining genes. While every patient's genome was sequenced, typing is challenging because variants are similar and numerous. CookHLA uses state-of-the-art statistics to predict HLA using WGS data. We will validate this method and then analyze our data with several statistical models. CNV Investigation: First, several algorithms that detect these genetic features will be compared to determine the most robust one. The chosen script will then be used with a validated tool to investigate CNV's clinically relevant features. Impact: This research will determine the clinical relevance of key genetic traits. If valid, they could identify means of improving clinical outcomes, saving lives and healthcare dollars, and allocate preventative care. It will establish a framework to investigate the traits in other diseases since many organ systems are influenced by HLA and CNV. Finally, the efficient computational model will enable others to incorporate it in their analyses, yielding higher-impact science.