Collaborative Research Team Project #12
Statistical Methods for Challenging Problems in Public Health Microbiology
This project explores new techniques for analyzing whole-genome sequencing (WGS) data, in order to solve statistical problems that arise in public health microbiology.
Research Category: Health & Biology
Why Study Public Health Microbiology?
Pathogenic microbial organisms cause a significant burden of disease, especially in hospital settings. Drug resistance, whereby a pathogen no longer responds to a drug treatment, is particularly relevant today.
Pathogen outbreaks require the development of surveillance tools to rapidly track and disrupt the chain of transmissions. Making fast, reliable and affordable WGS methods available to health authorities has the potential for major benefit.
WGS methods are still in their infancy. In order to fully harness their power, novel statistical and algorithmic techniques for microbial WGS data must be developed.
Areas of Exploration
Includes developing a likelihood-based method for calling genomic variants (e.g. SNPs, insertions, deletions or alleles of specific genes) informed by a microbial evolutionary model.
Algorithms for WGS Data Analysis
Includes training a statistical or machine learning algorithm to combine multiple signals into a single call. This is done to predict drug resistance, phylogenetic relatedness or epidemiological relatedness directly from WGS data.
Estimating Sample Size and Power
Includes designing methods for estimating the power of studies for detecting regular genotype-phenotype associations in bacteria, as well as epistatic interactions. These interactions are known to require prohibitively high sample sizes in human genetics.
Solving Global Challenges
Research Team’s Goal
To tackle three unsolved statistical challenges that arise in public health microbiology, and deploy the developed methods in a publicly available computational platform.
People Behind the Project
Alexandre Bouchard-Côté | Department of Statistics, University of British Columbia
Leonid Chindelevitch | School of Computing Science, Simon Fraser University
Luis Barreiro | Centre Hospitalier Universitaire Sainte-Justine, Montréal
Art Poon | Department of Pathology and Laboratory Medicine, University of Western Ontario
Jesse Shapiro | Department of Biological Sciences, Université de Montréal
Liangliang Wang | Department of Statistics and Actuarial Science, Simon Fraser University
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Statistical Methods for Challenging Problems in Public Health Microbiology 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.