Developing statistical methods to discover genetic variants underlying longitudinal decline in lung function

COPD is a common inflammatory lung condition that is characterized by airflow limitation and symptoms of cough and shortness of breath. Globally, it affects 384 million people and is responsible for ~4-7% of all deaths. Longitudinal genome-wide association studies (GWAS) are needed to unravel the molecular determinants of dynamic quantitative traits underlying COPD, such as decline in lung function over time.
Analysis of longitudinal GWAS to find biomarker of lung function decline was unsuccessful in the past. None of the discovered biomarkers were replicable. A novel statistical methods is needed to address challenges faced by current methods, to more powerfully and precisely discover biomarkers from longitudinal GWAS.
We will develop a novel statistical method based on Bayesian hierarchical model improve biomarker discovery from longitudinal GWAS. This novel method will be applied to unique data owned by our collaboration team, and try to find novel biomarkers for dynamic traits underlying COPD.

Intern: 
Yan Xu
Faculty Supervisor: 
Xuekui Zhang
Province: 
British Columbia
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