DACOApproved

The page contains all the projects that are currently approved by the HostSeq DACO.

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Project Title: CGEn HostSeq – Canadian COVID-19 Human Host Genome Sequencing Databank

Principal Investigator: Lisa Strug & Lloyd Elliot

Affiliation: The Hospital for Sick Children

Project Summary: We hope to identify genetic differences that may explain why some people who get COVID-19 get really sick or have poor health outcomes and others do not. Using HostSeq datasets, we will analyze the genomes of patients to examine how variations in their DNA might be associated with their likelihood of becoming infected as well as with how their immune system reacted to the virus. We will use statistical and computational methods to analyze the requested datasets. We also plan to make our results available to other researchers to help them answer their important questions.

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Project Title: Cardiac Genome Clinic: a collaborative model for identifying the genetic basis of heart failure in the whole-genome era

Principal Investigator: Raymond Kim

Affiliation: The Hospital for Sick Children

Project Summary: Research on COVID-19 has established that the virus can cause a variety of cardiovascular disorders and myocardial injury in infected individuals. The Cardiac Genome Clinic (CGC) aims to utilize the HostSeq Database to identify genomic risk factors for individuals’ susceptibility to develop clinical cardiovascular manifestations of COVID-19 infection. The CGC will perform genome analysis on the HostSeq genomic data of individuals who were infected with COVID-19 and who have documented phenotypic data of having developed a clinical cardiovascular manifestation as a result. We will correlate genomic and phenotypic data to identify candidate risk factors for COVID-19 related cardiovascular disease. Finally, we will assess whether the candidate risk factors identified are present in our study’s internal database of individuals with non-COVID related cardiovascular disease, to assess whether the genomic risk factors may also be applicable to non-COVID populations. We will use the HostSeq genomic and phenotypic datasets to complete our study.

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Project Title: DeepHostSeverity: Deep learning and genomic driven COVID-19 severity assessment tool

Principal Investigator: Pingzhao Hu

Affiliation: University of Manitoba

Project Summary: Patients with COVID-19 show great differences in disease severity and clinical outcomes. Hence, a COVID-19 severity (i.e., vital status, ICU admission, ventilation utility) assessment tool is urgently needed. Recently, advanced genetic prediction tool, such as polygenic risk score (PRS), has been shown to be a significant promising way for predicting the risk of complex traits, but limited studies have been performed to use this strategy for COVID-19 severity. Here we will explore to develop artificial intelligence-based methods to calculate polygenic risk scores of complex traits. We will apply the approaches to analyze the whole genome sequencing data from CGEn HostSeq Databank to predict COVID-19 severity. We will also compare the performance of the model with that based on the phenotype data of the individuals from CGEn HostSeq Databank.

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Project Title: Population based evaluation of polygenic risk scores for common diseases

Principal Investigator: Jordan Lerner-Ellis

Affiliation: Mount Sinai Hospital

Project Summary: Common diseases such as diabetes, cardiovascular disease and cancer affect a substantial proportion of the population. These conditions are caused by a combination of genetic and environmental factors. A proportion of the population is at elevated risk for diseases due to the combined effects of multiple common genetic variants that each confer modest risk increases. The combined effect of many genetic risk variants can be used to construct polygenic risk scores (PRS). In particular, PRS for certain cancers, cardiometabolic disease, and other common diseases have demonstrated promise for identifying a proportion of the population at elevated genetic risk. In this study, we will calculate PRS using genome data from HostSeq databank and assess how well the PRS predict disease status.

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Project Title: Association studies exploring the relationships between specific HLA alleles, serology, and viral lineages on SARS CoV-2 symptom severity

Principal Investigator: Jordan Lerner-Ellis

Affiliation: Mount Sinai Hospital

Project Summary: HLA (Human Leukocyte Antigens) are proteins in the body that play a role in disease defense and specialize in detecting and eliminating foreign bodies. The genes involved in the HLA complex have a highly variable genetic code, meaning individuals in a population will commonly have slightly different sequences of these genes (called alleles). The HLA immune response is crucial against a viral infection such as SARS Cov2 that causes COVID-19, and different HLA alleles may result in a stronger or weaker immune response against the virus. The goal of this project is to discover any associations between specific HLA alleles, immune response and severity of COVID-19 symptoms. Identifying which alleles confer a stronger or weaker response may lead to more accurate targeted treatments, prevention and prophylaxis. HostSeq Data will be used in conjunction with GENCOV data to develop a large sample size thereby increasing the power to identify associations, and HostSeq Data will also be used for validation purposes for GENCOV specific study markers that may not be available through other participating HostSeq projects.

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Project Title: Clinically Relevant DNA Variation in the HostSeq Cohort

Principal Investigator: Jordan Lerner-Ellis

Affiliation: Mount Sinai Hospital

Project Summary: DNA biobanks developed for COVID-19 research have the potential to discover medically actionable findings that may be relevant not only for identifying individuals at high risk for COVID-19 related disease, but that also predict genetic conditions for which prevention, treatment or management strategies may be available. This study will identify the number and frequency of disease-causing and uncertain genetic variants identified in clinically relevant genes in an ostensibly healthy population. As genome sequencing becomes common place for population-based screening or diagnostic programs, this work will provide valuable insight into what types of clinically actionable genetic results may be identified and what implications they may have for individuals and families. This will help inform health programs on the utility of genome technology for personalized medicine. 

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Project Title: Identification of repeat expansion in patients with severe COVID-19 using ExpansionHunter

Principal Investigator: Jordan Lerner-Ellis

Affiliation: Mount Sinai Hospital

Project Summary: Human DNA contains regions where patterns of nucleotides are repeated many times. The exact number of repeats varies from person to person, and when these repeats get too long, they can cause a class of diseases known as repeat expansion disorders. Almost 50 different repeat expansion disorders have been identified to date, and an important consideration of these disorders is clinical anticipation, which is when the severity of disease increases, and age of onset decreases through subsequent generations. Using ExpansionHunter, an algorithm that identifies repeats, and the GENCOV and HostSeq datasets, we will develop an algorithm that will determine if the repeat is disease causing, and return information about the diseases in which it has been implicated. This may shed light on a possible correlation between COVID-19 severity and repeat expansions.

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Project Title: Association study with genetic loci for COVID-19 and symptoms, severity, serology

Principal Investigator: Jordan Lerner-Ellis

Affiliation: Mount Sinai Hospital

Project Summary: During the past two years, COVID-19 has led to a worldwide crisis. One of the key challenges in controlling this pandemic is to determine why some COVID-19 patients are asymptomatic while others can be fatally ill. We aim to identify genetic factors that are associated with COVID-19 symptoms, severity, and immune response. Identifying such genetic markers could inform clinical care and targeted treatment development.

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Project Title: The host genetic determinants of COVID-19

Principal Investigator: Brent Richards

Affiliation: Lady Davis Institute (McGill University)

Project Summary: While the COVID-19 pandemic has caused millions of premature deaths worldwide, most infected individuals only suffered a mild and uncomplicated illness. This range of clinical presentation is a hallmark of COVID-19 and is likely partially caused by underlying genetic susceptibilities to the virus. Understanding which genetic variants are associated with more severe disease may provide insights into the pathophysiology of COVID-19, which can be used to accelerate COVID-19 therapeutics development. Using common genetic variants, this approach has been used successfully to support the use of therapies currently in use for COVID-19 (e.g. interleukin-6 inhibitors). As part of the COVID-19 Host Genetics Initiative working group on whole-genome sequencing, we here propose to use modern genetic sequencing technologies to study rare genetic determinants of COVID-19. These should provide additional insights into how COVID-19 causes severe disease, and how we can better prevent and treat it.

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Project Title: In silico evaluation of the impact of pandemic-inherited study design biases with application to a thromboembolism-informed HD-GWAS of COVID-19 severe outcomes

Principal Investigator: France Gagnon

Affiliation: Dalla Lana School of Public Health (University of Toronto)

Project Summary: Considerable variability in symptoms and outcomes are observed among individuals infected by the virus SARS-CoV-2. One of the factors that partially explains this variability is the genetic make-up of an individual. Through analyzing the sequencing data of human genome of thousands of Canadians, our study will help understand why some people manifest hospitalization COVID-19 while others develop mild non-hospitalization illness. We will focus on genomic regions related with thromboembolisms, which is the condition where a blood clot (thrombus) formed in a vessel travel through the blood causing an obstruction. The frequency of thromboembolism in COVID-19 individuals are high and it is associated with increased risk of death. However, defining the causal risk factors of a new disease, as COVID-19, is challenging because the participants are recruited from different studies across the country with its own diagnosis methods, disease severity definitions, periods of time, etc. The variability criteria between the studies can impair the precise association of the exposure (genetic factor) with the disease. Thus, we will also evaluate how differences among studies designs can injure finding true genetic risks with COVID-19.

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Project Title: Post COVID hyperinflammation: A syndrome beyond the name (STOP MIS-C)

Principal Investigator: Rae Yeung

Affiliation: The Hospital for Sick Children

Project Summary: COVID-19 can cause a life-threatening Multisystem Inflammatory Syndrome in Children (MIS-C) typically weeks after a mild or even asymptomatic infection. Each wave of COVID-19 cases is closely followed by a wave of previously healthy children presenting to hospital in shock and heart failure due to uncontrolled inflammation. The new syndrome closely resembles Kawasaki Disease (KD). Similar to KD, the heart is one of the key organs affected with 1 in 3 children needing intensive care management for heart failure, and 1 in 4 children developing coronary artery inflammation and damage. It is critical to enable health care teams to rapidly recognize MIS-C, identify high risk children and control the life-threatening inflammation before it damages the child’s heart. Our team has studied and dissected the reasons responsible for inflammation leading to shock and hyperinflammation in KD and have identified key biomarkers and targets for treatment. These have been rapidly translated to the bedside resulting in new medications and improved outcomes. These lessons have been transferred to MIS-C, providing the evidence to guide development of effective therapeutic approaches. Our team includes doctors, scientists, and families working together to tackle this serious disease. We will use machine learning and artificial intelligence to analyze biologic/genomic and clinical data to rapidly diagnose MIS-C and predict which child will develop severe disease. We’ve done this before in other diseases and already have a strong and deeply committed Canada-wide team, expertise and infrastructure in place from our other projects and networks. We have partners in Europe and the USA, where we can learn from each other and be most efficient and not have to reinvent the wheel. We will seamlessly share information to rapidly improve care for affected children around the world.

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Project Title: Covid-19 genome analysis

Principal Investigator: Inanc Birol

Affiliation: BC Cancer

Project Summary: COVID-19 is a highly lethal viral pandemic caused by SARS-CoV-2 coronavirus. Although the virus strains are being rapidly sequenced for tracking and vaccine development, little is known about host susceptibility to SARS-CoV-2 infection. In this project we address this gap. The Human Leukocyte Antigen (HLA) genes play an important role in our immune systems. These genes are key to many aspects of human physiology and medicine, including our body’s response to infections. HLA genes show high variability between individuals and groups. While much attention has been on how existing health conditions of patients impact clinical outcomes, contribution of variability in HLA genes to these outcomes is under-studied. Here, we explore the link between variations in the HLA genes in a large group of COVID-19 patients whose genomes are sequenced as part of the Canadian COVID Genomics Network. We investigate patterns of HLA variants associated with disease development, severity, and mortality.

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Project Title: Dissecting the genetic architecture of COVID-19 comorbidities and identifying biomarkers for high-risk individuals

Principal Investigator: Celia Greenwood

Affiliation: Lady Davis Institute for Medical Research

Project Summary: A significant proportion of the population has been affected by COVID-19 both in Canada and worldwide. Although most COVID-19 infections are not life-threatening, a large number of COVID-19-related mortality has occurred. It is therefore important to understand who are more likely to suffer from severe symptoms and to explore approaches to mitigate illness amongst these people. Based on clinical observations, some pre-existing chronic diseases (comorbidities), such as Alzheimer’s disease or dementia, chronic pulmonary diseases, hypertensive and cardiovascular diseases, and diabetes, might contribute to increased COVID-19 mortality. To better understand the role of comorbidities in disease manifestation and progression, we will characterize genetic factors driving the heterogeneity of COVID-19 comorbidities within the Canadian population and assess how they are associated with COVID-19 clinical outcomes. Using genetics-based causal inference methods, we will also attempt to identify potential predictive biomarkers and drug targets specifically for individuals having adverse comorbidities.

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Project Title: Cluster analysis of patients infected by COVID-19 and a risk forecast model based on clustering

Principal Investigator: Sanjeena Dang

Affiliation: Carleton University

Project Summary: Patients infected by Covid-19 have a diverse range of symptoms and severity, varying from mild cough to fatal complications. To understand variation in the response to COVID-19 infections, it is important to understand and characterize the variations among these patients with respect to the demographic information as well as other comorbidities. Using the dataset from CGEn Host Genome Sequencing Initiative, the proposed research will focus on: Aim 1: Cluster individuals into different groups/classes based on comorbidities, demographic information, and clinical assessments and characterize these clusters. Aim 2: Identify genetic risk variants for developing severe COVID-19 outcomes. Unique characteristic genomic regions will be identified and used to build a predictive model for various levels of severity in COVID-19 response. Identifying individuals who are most at risk and prioritizing appropriate treatments in a timely manner to high-risk individuals could be valuable in developing resource allocation strategies and minimizing adverse health outcomes.

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Project Title: HostSeq as a Canadian population control for comparative studies of autism spectrum and related disorders.

Principal Investigator: Stephen Scherer

Affiliation: The Hospital for Sick Children

Project Summary: Our current studies which aim to determine the role of genetic variation in complex neurodevelopmental disorders require access to a large number of patient samples in order to capture the full spectrum of genetic changes which may contribute to these disorders. Equally important, is having access to a sufficiently large number of unaffected and unrelated population control samples for comparison. Having access to whole genome sequencing data from a large set of clinically unaffected samples for comparison (HostSeq) will help to further identify and characterize the role and impact of genetic variation in ASD and other related neurodevelopmental disorders.

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Project Title: Genetics of Mortality in Critical Care (GenOMICC)

Principal Investigator: David Maslove

Affiliation: Queen’s University

Project Summary: While some patients who get COVID-19 experience only mild symptoms, others develop severe illness, sometimes requiring advanced life support, including ventilator care in an ICU. Prior studies suggest that the propensity to develop critical COVID-19 is at least in part genetically determined. Through this study, we will further investigate the genetic traits associated with critical illness, in a

diverse population of Canadians with COVID-19. Our results will help deepen our understanding of COVID-19, and lead to better treatments.

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Project Title: Investigating human genetic variation in phosphorylation sites in association to SARS-CoV-2 infection

Principal Investigator: Juri Reimand

Affiliation: Ontario Institute for Cancer Research

Project Summary: Human genomes have small differences that determine the many features we have inherited, including disease risks of COVID-19. We hypothesize that some of these differences in individual genomes also determine how proteins in our cells interact with one another in complex interaction networks. Previous studies since the start of the pandemic have mapped the molecular interaction networks involved in virus infection and the human responses to infections as well as the COVID-19 disease. In this project, we will use HostSeq data to study individual genetic differences through the lens of these disease-associated molecular interaction networks. We will use AI tools our lab and others have developed to learn about such genetic variants and their potential involvement in infections and disease. Our research will help us better understand how SARS-CoV-2 infects humans and how our genetic differences may contribute to disease risk and outcomes.

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More project details will be added as they get approved.