Principal Investigator: Dr Celia Greenwood
Jewish General Hospital, Lady Davis Research Institute
Oncology, 3755 Côte Ste-Catherine Road, H-414 Montreal H3T 1E2
CanadaTags: 27505, causal inference, Mendelian randomization, methodology, pleiotropy
Lead Collaborators: 1) Professor Karim Oualkacha
Collaborating Institutions and Addresses: 1)
Univérsite du Québec À Montréal
201 Ave Président-Kennedy
Montreal H2X 3Y7
Lead Collaborators: 2) Professor Lajmi Lakhal-Chaieb
Collaborating Institutions and Addresses: 2)
Mathematics and Statistics
1045 de la medecine
Office 1056-C Alexandre-Vachon Building
Quebec City G1V 0A6
1a: We are interested in evaluating existing statistical analysis methods for investigating when genetic variants have effects on more than one trait or phenotype, and in developing improved statistical methods of analysis. In this context, we would like to look at the performance of methods using both common and rare genetic variants.
1b: Improved methods for analyzing pleiotropy and causal relations will benefit those who are trying to understand the causal relationships between genetics and phenotypes. The methods will be applicable to many researchers’ analyses.
1c: Since this is methodological work, we will use the data to evaluate the use of the methods in situations that have been well characterized in the literature. For example, we propose to examine SNP associations on lipid traits and relationships with cardiovascular disease. For assessing performance of causal inference methods including bias and power, we propose to divide the data set randomly into partitions to look at stochasticity of the results.
1d: We would be interested in two different subsets. The full data on a restricted set of genotypes for a specific set of lipid-related phenotypes, and a larger set of genotypes on individuals where have been measured.