Effect of Type-I and Type-II Errors in Mapping QTLs for Crop Improvement

Abstract

One of the challenges for QTL mapping is the difficulty of determining appropriate significance thresholds (critical values) for the two types of errors: (a) that there is a segregating QTL whereas in reality there is not (false positive or Type-I error), and (b) that there is no QTL although it actually is present (false negative or Type-II error). Effective mapping strategies for quantitative traits must allow for the detection of the more important quantitative trait loci (QTLs) while minimizing false positives. Type I (false-positive) and Type II (false-negative) error rates were estimated from a computer simulation of QTL mapping. The problem of determining appropriate threshold values appeared to be difficult because there are many factors that can vary from experiment to experiment and can influence the distribution of the test statistics. These include, but are not limited to, the sample size, the genome size of the organism etc., are under study.