Designing a resampling approach to control for population stratification in rare variant association tests
Principal Investigator: Professor Shengying Qin
Approved Research ID: 34716
Approval date: September 14th 2018
Recent technology advances have enabled large-scale analysis of rare variants, but properly testing rare variants associated with complex disorders remains a significant challenge. Most rare variant testing methods assume samples from a single population, which is often not true for large studies such as UK Biobank. We propose a population-informed resampling method for studies of multiple populations for rare variant tests. Preliminary results showed this empirical approach can effectively control for false-positives while maintaining statistical power. We plan to apply this method to various human complex disorders and traits including cancer, heart diseases, stroke, diabetes, arthritis, osteoporosis, eye disorders, autoimmune inflammatory, depression and forms of dementia. The findings will help us further elucidating biological mechanism of threatening illness, eventually lead to new insight into drug research and development. The project duration will be around 2 years.