Case-deletion diagnostics for maximum likelihood multipoint quantitative trait locus linkage analysis.
OBJECTIVE
Case-deletion diagnostic methods are tools that allow identification of influential observations that may affect parameter estimates and model fitting conclusions. The goal of this paper was to develop two case-deletion diagnostics, the exact case deletion (ECD) and the empirical influence function (EIF), for detecting outliers that can affect results of sib-pair maximum likelihood quantitative trait locus (QTL) linkage analysis.


METHODS
Subroutines to compute the ECD and EIF were incorporated into the maximum likelihood QTL variance estimation components of the linkage analysis program MAPMAKER/SIBS. Performance of the diagnostics was compared in simulation studies that evaluated the proportion of outliers correctly identified (sensitivity), and the proportion of non-outliers correctly identified (specificity).


RESULTS
Simulations involving nuclear family data sets with one outlier showed EIF sensitivities approximated ECD sensitivities well for outlier-affected parameters. Sensitivities were high, indicating the outlier was identified a high proportion of the time. Simulations also showed the enormous computational time advantage of the EIF. Diagnostics applied to body mass index in nuclear families detected observations influential on the lod score and model parameter estimates.


CONCLUSIONS
The EIF is a practical diagnostic tool that has the advantages of high sensitivity and quick computation.
Mendoza
Maria C B
MC
Department of Biostatistics, The University of Iowa, Iowa City, Iowa, USA. maria.mendoza@fda.hhs.gov
Burns
Trudy L
TL
Jones
Michael P
MP
D003198:Computer Simulation; D008040:Genetic Linkage / Q000235:genetics*; D006801:Humans; D016013:Likelihood Functions; D015233:Models, Statistical; D010375:Pedigree; D040641:Quantitative Trait Loci