A framework for controlling false discovery rates and minimizing the amount of genotyping in the search for disease mutations

Objectives: To develop a method for designing studies to find disease mutations that can achieve a set of goals with respect to proportions of false and true discoveries with the minimum amount of genotyping. Methods: Derivation of an analytical framework supplemented with simulation techniques. The approach is illustrated for a fine mapping study and a whole- genome linkage disequilibrium scan. Results: The use of multiple stages where earlier stages are characterized by very high false discovery rates ( FDR) followed by an abrupt change to the required FDR in the final stage results in a 50 - 75% reduction in genotyping. The proportion of true discoveries is a much more important determinant of the genotyping burden than the FDR. Neither sample size nor controlling the false discoveries will present major problems in whole- genome LD scans but the amount of genotyping will be extremely large even if the study is completely designed to minimize genotyping. Conclusions: The proposed statistical framework presents a simple and flexible approach to determine the design parameters e. g. sample size, p values at which tests need to be performed at each stage) that minimize the genotyping burden given a set of goals for the percentage of true and false discoveries. Copyright (C) 2003 S. Karger AG, Basel.