Using Alpha Wisely: Improving Power to Detect Multiple QTL

The increase in the number of available markers for many experimental populations has led to QTL studies with ever increasing marker numbers and densities. The resulting conundrum is that as marker density increases, so does the multiple testing problem. It is important to re-examine the detection of multiple QTL in light of increasing marker density. We explore through simulation whether existing methods have achieved the maximum possible power for detecting multiple QTL and whether increasing the marker density is an effective strategy for locating multiple QTL. In addition to existing methods, such as the maximum, the CET, and the Benjamini-Hochberg and Benjamini-Yekutieli procedures, we propose and evaluate the complete set of order statistics with their corresponding empirical joint distribution. We examine these statistics in conjunction with a novel application of the alpha-spending approach, providing a less conservative solution to the problem of controlling the false discovery rate (FDR) in multiple tests. We conducted a simulation study to assess the relative power of these approaches as well as their ability to control FDR. We find that several of the new approaches have a reasonable FDR, and can substantially improve the experimenter's ability to detect multiple QTL compared to existing approaches in many cases; however, the Benjamini-Hochberg procedure remains a very reasonable choice. The methods are applied to a nine-trait Oat vernalization dataset.