This year’s Biology of Genomes (BoG) meeting maintained its high standard with another display of excellent and exciting science. One of my favourite presentations was given by Matthew Stephens from the University of Chicago on the topic of False Discovery Rates (FDRs).

The FDR is a basic concept in statistical testing that we all come across in our research. By controlling the FDR, we aim to limit the expected proportion of false positives among significant loci identified by association studies. Slide1 The idea is that under the null hypothesis (H0, that the locus is not associated with the trait), the observed p-values are expected to be distributed uniformly (Fig.1(a)); and under an alternative hypothesis (H1), more of the p-values should be close to zero (Fig.1(b)). In other words, the observed distributions of p-values in a genome-wide scan should be a mixture of these two distributions. The existing FDR methods find a maximum cutoff value (Fig. 1(c)) such that the results with smaller p-values are likely to be true positives from H1.

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