Folien

Advances in high-throughput genotyping technology lead to the realization of genome wide association (GWA) studies which helped identify new susceptibility loci of complex traits. Such studies usually start with genotyping fixed arrays of single nucleotide polymorphisms (SNPs) in an initial sample. Out of these lists of markers are compiled which will be further genotyped in independent samples. Due to the very low a priori probability of a true positive association, the fast majority of marker signals will turn out to be false positive.

As shown by Frayling (2007) for type II diabetes mellitus, some of the true associations will not be among those with the smallest p-values. Consequently alternative methods to sort single SNP data have been proposed such as the “q-value” (Storey and Tibshirani, 2003; Storey et al. 2004), the “false positive report probability” (FPRP; Wacholder et al., 2004), and the “Bayesian false-discovery probability” (BFDP; Wakefield, 2007, 2008).

In this talk I will briefly review these methods and will compare them using data from a GWA scan for the phenotype “early onset extreme obesity” (Hinney et al., 2007).