Population size and phenotypic measurement are two key factors determining the detection power of quantitative trait loci (QTL) mapping. We evaluated how these two controllable factors quantitatively affect the detection of QTL and their localization using a large F2 murine mapping population and found that three main points emerged from this study. One finding was that the sensitivity of QTL detection significantly decreased as the population size decreased. The decrease in the percentage logarithm of the odd score (LOD score, which is a statistical measure of the likelihood of two loci being lied near each other on a chromosome) can be estimated using the formula 1 - n/N, where n is the smaller and N the larger population size. This empirical formula has several practical implications in QTL mapping. We also found that a population size of 300 seems to be a threshold for the detection of QTL and their localization, which challenges the small population sizes commonly-used in published studies, in excess of 60% of which cite population sizes <300. In addition, it seems that the precision of phenotypic measurement has a limited capacity to affect detection power, which means that quantitative traits that cannot be measured precisely can also be used in QTL mapping for the detection of major QTL.

The use of a constant fold-change to determine significant changes in gene expression has been widely accepted for its intuition and ease of use in microarray data analysis, but this concept has been increasingly criticized because it does not reflect signal intensity and can result in a substantial number of false positives and false negatives. To resolve this dilemma, we have analyzed 65 replicate Affymetrix chip-chip comparisons and determined a series of user adjustable signal-dependent thresholds which do not require replicates and offer a 95% confidence interval. Quantitative RT-PCR shows that such thresholds significantly improve the power to discriminate biological changes in mRNA from noise and reduce false calls compared to the traditional two-fold threshold. The user-friendly nature of this approach means that it can be easily applied by any user of microarray analysis, even those without any specialized knowledge of computational techniques or statistics. Noise is a function of signal intensity not only for Affymetrix data but also for cDNA array data, analysis of which may also be benefited by our methodology.