Evaluating Strategies for Marker Ranking in Genome-wide Association Studies of Complex Traits
Background Genome wide association studies (GWAS) were highly successful in identifying new susceptibility loci of complex traits Such studies usually start with genotyping fixed arrays of genetic markers in an initial sample Out of these markers, some are selected which will be further genotyped in independent samples Due to the very low a priori probability of a true positive association, the vast majority of all marker signals will turn out to be false positive Thus several methods to sort marker data have been pro posed which will be evaluated here Objectives We compared statistical proper ties of ranking by p values q values the False Positive Report Probability (FPRP) and the Bayesian False Discovery Probability (BFDP) Methods We performed simulation studies for a genomic region derived from GWAS data sets and calculated descriptive statistics as well as mean square errors with regard to the true marker ranking Additionally we applied all measures to a GWAS for early onset extreme obesity superimposing a priori information on candidate genes Results Despite the known more extreme probability results for traditional p values we observed that both p values and the BFDP were more precise in reconstructing the, true' order of the markers in a region In addition, the BFDP was useful to attenuate unexpected effects at a genome wide scale Conclusions For the purpose of selecting, markers from an initial GWAS and within the limits of this study, we recommend either ranking by p values or the application of a full, Bayesian approach for which the BFDP is a first approximation
genome wide association study p value q value fprp bfdp