Empirical threshold values for quantitative trait mapping.

TLDR

The detection of genes controlling quantitative traits is a key interest in genetics, and QTL mapping methods using genetic marker maps are widely employed. The study aims to determine appropriate threshold values for declaring significant QTL effects in QTL mapping. The authors present an empirical permutation‑test method for estimating data‑specific QTL thresholds and demonstrate it on two real plant population datasets. Simulation of a backcross design shows that increasing marker density lowers the estimated threshold values.

Abstract

The detection of genes that control quantitative characters is a problem of great interest to the genetic mapping community. Methods for locating these quantitative trait loci (QTL) relative to maps of genetic markers are now widely used. This paper addresses an issue common to all QTL mapping methods, that of determining an appropriate threshold value for declaring significant QTL effects. An empirical method is described, based on the concept of a permutation test, for estimating threshold values that are tailored to the experimental data at hand. The method is demonstrated using two real data sets derived from F(2) and recombinant inbred plant populations. An example using simulated data from a backcross design illustrates the effect of marker density on threshold values.