Adaptively weighted association statistics.
We investigate methods for testing gene-disease outcome associations in situations where the genetic relationship potentially varies among subjects with differing environmental or clinical attributes. We propose a strategy which modestly increases multiple testing by evaluating weighted test statistics which focus (or enrich) association tests within subgroups and use a Monte-Carlo method, based on simulating from the approximate large sample distribution of the statistics, to control type 1 error. We also introduce a stage-wise calculated test statistic which allows more complex weighting on multiple environmental variables. Results from simulation studies confirm improved power of the proposed approaches compared to marginal testing in many situations.
LeBlanc
Michael
M
Fred Hutchinson Cancer Research Center, Seattle, Washington, USA. mleblanc@fhcrc.org
Kooperberg
Charles
C
D000465:Algorithms; D003198:Computer Simulation; D004777:Environment; D015894:Genome, Human; D055106:Genome-Wide Association Study; D005838:Genotype; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D009010:Monte Carlo Method; D009101:Multiple Myeloma / Q000235:genetics; D020641:Polymorphism, Single Nucleotide; D012044:Regression Analysis; D016896:Treatment Outcome