Identifying multiple change points in a linear mixed effects model

Although change‐point analysis methods for longitudinal data have been developed, it is often of interest to detect multiple change points in longitudinal data. In this paper, we propose a linear mixed effects modeling framework for identifying multiple change points in longitudinal Gaussian data. Specifically, we develop a novel statistical and computational framework that integrates the expectation–maximization and the dynamic programming algorithms. We conduct a comprehensive simulation study to demonstrate the performance of our method. We illustrate our method with an analysis of data from a trial evaluating a behavioral intervention for the control of type I diabetes in adolescents with HbA1c as the longitudinal response variable. Copyright © 2013 John Wiley & Sons, Ltd.     

Estimating haplotype relative risks in complex disease from unphased SNPs data in families using a likelihood adjusted for ascertainment

The understanding of complex diseases and insights to improve their medical management may be achieved through the deduction of how specific haplotypes may play a joint effect to change relative risk information. In this paper we describe an ascertainment adjusted likelihood-based method to estimate haplotype relative risks using pooled family data coming from association and/or linkage studies that were used to identify specific haplotypes. Haplotype-based analysis tends to require a large amount of parameters to capture all the information that leads to efficiency problems. An adaptation of the Stochastic Expectation Maximization algorithm is used for haplotypes inference from genotypic data and to reduce the number of nuisance parameters for risk estimation. Using different simulations, we show that this method provides unbiased relative risk estimates even in case of departure from Hardy-Weinberg equilibrium.     

Detection of gene–environment interactions in a family‐based population using SCAD

Gene–environment interaction (GxE) is emphasized as one potential source of missing genetic variation on disease traits, and the ultimate goal of GxE research is prediction of individual risk and prevention of complex diseases. However, there are various challenges in statistical analysis of GxE. In this paper, we focus on the three methodological challenges: (i) the high dimensions of genes; (ii) the hierarchical structure between interaction effects and their corresponding main effects; and (iii) the correlation among subjects from family‐based population studies. In this paper, we propose an algorithm that approaches all three challenges simultaneously. This is the first penalized method focusing on an interaction search based on a linear mixed effect model. For verification, we compare the empirical performance of our new method with other existing methods in simulation study. The results demonstrate the superiority of our method under overall simulation setup. In particular, the outperformance obviously becomes greater as the correlation among subjects increases. In addition, the new method provides a robust estimate for the correlation among subjects. We also apply the new method on Genetics of Lipid Lowering Drugs and Diet Network study data. Copyright © 2017 John Wiley & Sons, Ltd.