False discoveries and models for gene discovery

Abstract

In the search for genes underlying complex traits, there is a tendency to impose increasingly stringent criteria to avoid false discoveries. These stringent criteria make it hard to find true effects, and we argue that it might be better to optimize our procedures for eliminating and controlling false discoveries. Focusing on achieving an acceptable ratio of true- and false-positives, we show that false discoveries could be eliminated much more efficiently using a stepwise approach. To avoid a relatively high false discovery rate, corrections for 'multiple testing' might also be needed in candidate gene studies. If the appropriate methods are used, detecting the proportion of true effects appears to be a more important determinant of the genotyping burden than the desired false discovery rate. This raises the question of whether current models for gene discovery are shaped excessively by a fear of false discoveries.