Quantitative Trait Loci: A Meta-analysis

TLDR

The article introduces a method for integrating QTL findings from independent studies. The method employs a modified Akaike criterion to determine the true number of QTL represented by detected loci, selecting among models with varying QTL counts and is evaluated through simulations across diverse scenarios. Simulations show the approach consistently narrows QTL confidence intervals when few true QTL exist, and it is demonstrated on maize data from an online database.

Abstract

Abstract This article presents a method to combine QTL results from different independent analyses. This method provides a modified Akaike criterion that can be used to decide how many QTL are actually represented by the QTL detected in different experiments. This criterion is computed to choose between models with one, two, three, etc., QTL. Simulations are carried out to investigate the quality of the model obtained with this method in various situations. It appears that the method allows the length of the confidence interval of QTL location to be consistently reduced when there are only very few “actual” QTL locations. An application of the method is given using data from the maize database available online at http://www.agron.missouri.edu/.