DACOApproved

The page contains all the projects that are currently approved by the HostSeq DACO and have access to the HostSeq databank.

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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: CanCOVery, an explainable AI platform for the personalization of COVID-19 vaccine and treatment usage

Principal Investigator: Ramatoulaye Bah

Affiliation: My Intelligent Machines

Project Summary: My Intelligent Machines (MIMs), based in Montreal, is a leader in artificial intelligence applied to life sciences. We provide Biopharma companies with Artificial Intelligence-powered software enabling the modelling of biological systems, for population stratification, target and biomarker discovery to help our clients develop a more efficient and precision medicine. The COVID-19 pandemic is progressing with the emergence of new strains of viruses. As the genetic material of the virus is changing, the profile of patients who are most at risk of developing deadly forms of the disease is also changing. Biopharmas require computational tools to monitor molecular and clinical aspects of the pandemic evolution, to observe trends and patterns, as well as predict future unmet needs to prioritize the development of certain types of vaccines and treatments. MIMs capitalizes on genomic and clinical data from Canadian Covid-19 patients and associated viruses and will use its expertise in artificial intelligence to provide Medicago, IMV and Immune Biosolutions with the needed monitoring solution. They will be more informed on the evolution of the pandemic in Canada, which will enable them to develop vaccines and treatments that answers the current needs of patients.

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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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*More project details will be added as they get approved.