Noninvasive testing for Down's syndrome (trisomy 21) would greatly reduce the risks associated with the more invasive techniques used currently. Earlier identification of differentially methylated regions between fetal DNA and maternal peripheral blood has now enabled Elisavet Papageorgiou and her colleagues to develop a strategy involving methylated DNA immunoprecipitation in combination with real-time quantitative PCR that discriminates normal from trisomy 21 cases in maternal peripheral blood with high sensitivity. The trials performed worldwide toward noninvasive prenatal diagnosis (NIPD) of Down's syndrome (or trisomy 21) have shown the commercial and medical potential of NIPD compared to the currently used invasive prenatal diagnostic procedures. Extensive investigation of methylation differences between the mother and the fetus has led to the identification of differentially methylated regions (DMRs). In this study, we present a strategy using the methylated DNA immunoprecipitation (MeDiP) methodology in combination with real-time quantitative PCR (qPCR) to achieve fetal chromosome dosage assessment, which can be performed noninvasively through the analysis of fetal-specific DMRs. We achieved noninvasive prenatal detection of trisomy 21 by determining the methylation ratio of normal and trisomy 21 cases for each tested fetal-specific DMR present in maternal peripheral blood, followed by further statistical analysis. The application of this fetal-specific methylation ratio approach provided correct diagnosis of 14 trisomy 21 and 26 normal cases.

Measures of divergence or discrepancy are used either to measure mutual information concerning two variables or to construct model selection criteria. In this paper we focus on divergence measures that are based on a class of measures known as Csiszár's divergence measures. In particular, we propose a measure of divergence between residual lives of two items that have both survived up to some timetas well as a measure of divergence between past lives, both based on Csiszár's class of measures. Furthermore, we derive properties of these measures and provide examples based on the Cox model and frailty or transformation model.