Alex Karagrigoriou

Fetal-specific DNA methylation ratio permits noninvasive prenatal diagnosis of trisomy 21

The trials performed worldwide toward noninvasive prenatal diagnosis (NIPD) of Downs 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.

MeDIP real-time qPCR of maternal peripheral blood reliably identifies trisomy 21

To reevaluate the efficiency of the 12 differentially methylated regions (DMRs) used in the methylated DNA immunoprecipitation (MeDIP) real‐time quantitative polymerase chain reaction (real‐time qPCR) based approach, develop an improved version of the diagnostic formula and perform a larger validation study.

A new non-invasive prenatal diagnosis of Down syndrome through epigenetic markers and real-time qPCR

Introduction: Non-invasive prenatal diagnosis (NIPD) of Down syndrome is rapidly evolving. Currently, two applications for NIPD of Down syndrome have been developed with potential and have displayed positive results; the NIPD using next-generation sequencing technologies and the NIPD using the methylated DNA immunoprecipitation (MeDIP) real-time quantitative polymerase chain reaction (qPCR). Areas covered: The MeDIP real-time qPCR approach is based on the identification of differentially methylated regions (DMRs) and their use for discriminating normal from Down syndrome cases. DMRs were identified using high-resolution oligo-arrays. A subgroup of DMRs was selected for further investigation. Through the design of a discriminant equation which combines the results obtained from different DMRs, normal and abnormal cases are correctly classified indicating 100% sensitivity and specificity. Expert opinion: Previous studies have also identified DMRs between non-pregnant female blood and placental DNA. However, these methods have been associated with a number of limitations including the low sensitivity and/or specificity of the assays, the limited number of identified DMRs or methylation sensitive sites and SNPs located on DMRs. These limitations have been overawed by the development of the MeDIP real-time qPCR-based methodology.

Applied Reliability Engineering and Risk Analysis: Probabilistic Models and Statistical Inference

• expert treatment of probabilistic models and statistical inference from leading scientists, researchers and practitioners in their respective reliability fields • detailed coverage of multi-state system reliability, maintenance models, statistical inference in reliability, systemability, physics of failures and reliability demonstration • many examples and engineering case studies to illustrate the theoretical results and their practical applications in industry

Cumulant plots and goodness-of-fit tests for the inverse Gaussian distribution

This paper uses a standardized version of the logarithm of the empirical moment generating function in order to construct plots for assessing the appropriateness of the inverse Gaussian distribution. Variability is added to the plots by utilizing asymptotic and finite-sample results. The plots have linear scales and do not rely on the use of tables or special functions. In addition, they are equivalent to a goodness-of-fit test whose critical values are obtained from fitted equations involving the sample size and the estimated shape parameter of the inverse Gaussian distribution. Three data sets are used to illustrate the plots. A similar test is also proposed whose critical values are found through parametric bootstrap. An extensive simulation study shows that the new tests maintain good stability in level and high power across a wider range of distributions and sample sizes than other tests.

On Distributional Properties and Goodness-of-Fit Tests for Generalized Measures of Divergence

The aim of this work is the investigation of a generalized family of measures of divergence which includes the well-known Csiszárs, Cressie and Reads, and the Basu–Harris–Hjort–Jones (Basu et al., 1998) measures. A weakly consistent estimator of the generalized measure of divergence is proposed and its sampling properties and the corresponding asymptotic distribution are obtained. Finally, tests for multinomial populations are constructed and simulations are performed.

Forecasting ARMA models: a comparative study of information criteria focusing on MDIC

This paper deals with the implementation of model selection criteria to data generated by ARMA processes. The recently introduced modified divergence information criterion is used and compared with traditional selection criteria like the Akaike information criterion (AIC) and the Schwarz information criterion (SIC). The appropriateness of the selected model is tested for one- and five-step ahead predictions with the use of the normalized mean squared forecast errors (NMSFE).

On Semi-Markov Modelling and Inference for Multi-state Systems

In this work we focus on multi state systems that we model by means of semi-Markov processes. The sojourn times are seen to be independent not identically distributed random variables and assumed to belong to a general class of distributions that includes several popular reliability distributions like the exponential, Weibull, and Pareto. We obtain maximum likelihood estimators of the parameters of interest and for various quantities related to the system under study.

Measures of Divergence in Model Selection

In this chapter a number of measures of divergence are presented and the way model selection criteria are constructed via measures of divergence is discussed. The construction of the divergence information criterion based on a new family of measures of divergence is presented and the lower bound of the mean squared error of prediction is established. Some illustrative simulation results are also given.

Vector autoregressive order selection and forecasting via the modified divergence information criterion

This paper examines the problem of order selection in connection to the forecasting performance for vector autoregressive (VAR) processes. For this purpose we present a generalisation of the modified divergence information criterion (MDIC) for VAR models and compare it with traditional information criteria by Monte Carlo methods for different data generating processes for small, medium, and large sample sizes. The VAR modified divergence information criterion (VAR/MDIC) shows remarkable good results by choosing the correct model more frequently than the known traditional information criteria with the smallest mean squared forecast error.