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

Abstract

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.