Collaborative Research Team Project #13

Statistical Methods for the Analysis of Genetic Data with Survival Outcomes

This project explores statistical methods for the analysis of genetic data with correlated survival outcomes. It sets out to enhance collaborations among various Canadian teams and researchers in this field.

Research Category: Health & Biology

Why Study Survival Outcomes?

With recent advancements in high-throughput technologies, substantial research is being done to investigate the role of genetic variants in hereditary diseases. 

Understanding the role that particular genes play in the time to disease onset can have important implications for medical interventions. For example, in recommending prophylactic surgery to remove a disease-free organ, in an attempt to prevent the development of disease in that area.

Through ​​modeling and estimating the effect of medical interventions on survival outcomes, we can gain a better understanding of individual risk factors and possible treatments for genetic diseases.

Areas of Exploration

Time-to-Event Outcomes

Includes modelling the dependence between time-to-event outcomes occurring among related individuals in the context of genetic studies. Also deriving estimation and hypothesis testing procedures for the model parameters.

Test Development

Includes deriving tests to detect association between high-dimensional genome-wide variant data and survival outcomes.

Genetic Association Studies

Includes developing causal models and inferring direct and indirect genetic effects on survival outcomes in genetic association studies.

Medical Interventions

Includes modelling and estimating the effects of medical interventions such as screening and prophylactic surgery (e.g. mastectomy, oophorectomy) on survival outcomes in the context of genetic studies.

Solving Global Challenges

Research Team’s Goal

To enhance collaborations among Canadian researchers in the field of statistical genetic data analysis, with a focus on correlated survival outcomes. 

This is achieved through the joint supervision of graduate students and postdoctoral fellows, and the organization of annual scientific team meetings and conferences. 

People Behind the Project

Project Team

Lajmi Lakhal-Chaieb | Université Laval

Richard Cook | University of Waterloo

Laurent Briollais | Lunenfeld Tanenbaum Research Institute of Mount Sinai Hospital, Toronto


Shelley Bull | Lunenfeld-Tanenbaum Research Institute

Yun-Hee Choi | Schulich School of Medicine and Dentistry, Western University

Yildiz Yilmaz | Memorial University


Statistical Methods for the Analysis of Genetic Data with Survival Outcomes is a Collaborative Research Team project. This program tackles complex problems through a three-year research and training agenda.

CANSSI offers approximately $200,000 for this type of project, which requires a team of faculty, postdocs, and students.