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: The ROC curves of the four indicators are shown in Figure 5 based on 1000 replicates. For the varying thresholds, the average rates of correctly classifying the true influential cases (detection rates), and the average rates of misclassifying the normal cases as influential cases (false positive rates) are plotted. As expected, outliers with larger σ2 have larger absolute EIFs, and are thus more easily detected. In all panels, the EIFC has the largest area under its ROC curve and hence the best average performance. As the second-best method, QEIF is shown useful when the target shape cannot be fully specified.
Affiliation: The Institute of Statistical Mathematics, Research Organization of Information and Systems, 10-3 Midori-cho, Tachikawa, Tokyo, 190-8562, Japan.