An application and empirical comparison of statistical analysis methods for associating rare variants to a complex phenotype.
The contribution of collections of rare sequence variations (or 'variants') to phenotypic expression has begun to receive considerable attention within the biomedical research community. However, the best way to capture the effects of rare variants in relevant statistical analysis models is an open question. In this paper we describe the application of a number of statistical methods for testing associations between rare variants in two genes to obesity. We consider the relative merits of the different methods as well as important implementation details, such as the leveraging of genomic annotations and determining p-values.
The Scripps Translational Science Institute, 3344 North Torrey Pines Court, Suite 300, La Jolla, CA 92037, USA. vbansal@scripps.edu
Vikas
V
Bansal
Ondrej
O
Libiger
Ali
A
Torkamani
Nicholas J
NJ
Schork
D000581:Amidohydrolases / Q000235:genetics; D016022:Case-Control Studies; D019295:Computational Biology; D056726:Genetic Association Studies / Q000706:statistics & numerical data*; D014644:Genetic Variation; D055106:Genome-Wide Association Study / Q000706:statistics & numerical data; D006801:Humans; D008994:Monoacylglycerol Lipases / Q000235:genetics; D009765:Obesity / Q000235:genetics; D012044:Regression Analysis; D000099252:Fatty Acid Amide Hydrolases
D000581:Amidohydrolases; D008994:Monoacylglycerol Lipases; D000099252:Fatty Acid Amide Hydrolases