Influence analysis in quantitative trait loci detection.
Bottom Line: We derive general formulas of influence functions for profile likelihoods and introduce them into two standard quantitative trait locus detection methods-the interval mapping method and single marker analysis.These methods are shown useful in the influence analysis of a real dataset of an experimental population from an F2 mouse cross.By receiver operating characteristic analysis, we confirm that the proposed methods show better performance than existing diagnostics.
Affiliation: The Institute of Statistical Mathematics, Research Organization of Information and Systems, 10-3 Midori-cho, Tachikawa, Tokyo, 190-8562, Japan.Show MeSH
Related in: MedlinePlus
Mentions: Figure 4 shows the simulation results for the four models. The empirical distribution functions of Tk and (k=0,1) are depicted as black and gray lines, respectively. We find that the distribution of and are close to each other, which means that the distribution of ( or ) is stable for the existence of outliers. We also find that and are close to T0 and distinct from T1. This indicates that when no outlier exists the false positive rate is appropriately controlled (i.e., unbiased) and when outliers exist it has statistical power in detecting influential individuals. We also tried simulations with two outliers. The results are similar and omitted.
Affiliation: The Institute of Statistical Mathematics, Research Organization of Information and Systems, 10-3 Midori-cho, Tachikawa, Tokyo, 190-8562, Japan.