An epigenetic association analysis of childhood trauma in psychosis reveals possible overlap with methylation changes associated with PTSD

Patients with a severe mental disorder report significantly higher levels of childhood trauma (CT) than healthy individuals. Studies have suggested that CT may affect brain plasticity through epigenetic mechanisms and contribute to developing various psychiatric disorders. We performed a blood-based epigenome-wide association study using the Childhood Trauma Questionnaire-short form in 602 patients with a current severe mental illness, investigating DNA methylation association separately for five trauma subtypes and the total trauma score. The median trauma score was set as the predefined cutoff for determining whether the trauma was present or not. Additionally, we compared our genome-wide results with methylation probes annotated to candidate genes previously associated with CT. Of the patients, 83.2% reported CT above the cutoff in one or more trauma subtypes, and emotional neglect was the trauma subtype most frequently reported. We identified one significant differently methylated position associated with the gene TANGO6 for physical neglect. Seventeen differentially methylated regions (DMRs) were associated with different trauma categories. Several of these DMRs were annotated to genes previously associated with neuropsychiatric disorders such as post-traumatic stress disorder and cognitive impairments. Our results support a biomolecular association between CT and severe mental disorders. Genes that were previously identified as differentially methylated in CT-exposed subjects with and without psychosis did not show methylation differences in our analysis. We discuss this inconsistency, the relevance of our findings, and the limitations of our study.publishedVersio

Change-point detection in binary Markov DNA sequences by the Cross-Entropy method

A deoxyribonucleic acid (DNA) sequence can be represented as a sequence with 4 characters. If a particular property of the DNA is studied, for example, GC content, then it is possible to consider a binary sequence. In many cases, if the probabilistic properties of a segment differ from the neighbouring ones, this means that the segment can play a structural role. Therefore, DNA segmentation is given a special attention, and it is one of the most significant applications of change-point detection. Problems of this type also arise in a wide variety of areas, for example, seismology, industry (e.g., fault detection), biomedical signal processing, financial mathematics, speech and image processing. In this study, we have developed a Cross-Entropy algorithm for identifying change-points in binary sequences with first-order Markov dependence. We propose a statistical model for this problem and show effectiveness of our algorithm for synthetic and real datasets.8 page(s

Change-point detection in biological sequences via genetic algorithm

Genome research is one of the most interesting and important areas of the science nowadays. It is well-known that the genomes of complex organisms are highly organized. Many studies show that DNA sequence can be divided into a few segments, which have various properties of interest. Detection of this segments is extremely significant from the point of view of practical applications, as well as for understanding evolutional processes. We model genome sequences as a multiple change-point process, that is, a process in which sequential data are divided into segments by an unknown number of change-points, with each segment supposed to have been generated by a process with different parameters. Multiple change-point models are important in many biological applications and, specifically, in analysis of biomolecular sequences. In this paper, we propose to use genetic algorithm to identify change-points. Numerical experiments illustrate the effectiveness of our approach to the problem. We obtain estimates for the positions of change-points in artificially generated sequences and compare the accuracy of these estimates to those obtained via Markov chain Monte Carlo and the Cross-Entropy method. We also provide examples with real data sets to illustrate the usefulness of our method.6 page(s

A Cross-entropy method for change-point detection in four-letter DNA sequences

It is well-known that many genomes are highly structured. So determining domains of similar pattern is a very important area of bioinformatics research. This paper describes a Cross-Entropy algorithm for identifying change-points in fourletter DNA sequences. We propose a multiple change-point model for this problem and show effectiveness of our algorithm for simulated and real biomolecular sequences.6 page(s

A hybrid genetic algorithm for change-point detection in binary biomolecular sequences

Genomes of eukaryotic organisms vary in GC ratio, that is, share of DNA bases such that C or G as contrary to T or A. Statistical identification of segments that are internally homogenous with respect to GC ratio is essential for understanding of evolutionary processes and the different functional characteristics of the genome. It appears that DNA segmentation concerns one of the most important applications involving change-point detection. Problems of this type arise in various areas, such as speech and image processing, biomedical applications, econometrics, industry and seismology. In this study, we develop a hybrid genetic algorithm for detecting change-points in binary sequences. We apply our algorithm to both synthetic and real data sets, and demonstrate that it is more effective than other well-known methods such as Markov chain Monte Carlo, Cross-Entropy and Genetic algorithms.8 page(s

Evaluating optimal stopping rules in the multiple best choice problem using the cross-entropy method

Best choice problems can be considered one of the most interesting problems of sequential decision analysis. Problems of this type can arise in a wide variety of fields, including psychological, economical, and ecological applications. In this study, we consider a generalization of the best choice problem when it is possible to make more than one choice. We use the Cross-Entropy method to determine the optimal stopping rules and the value of a game. We include results of numerical experiments illustrating the effectiveness of the approach. We obtain estimates of the thresholds in the optimal stopping rules and compare the accuracy of these estimates with those obtained via asymptotic approximation.8 page(s

Sequential change-point detection via the cross-entropy method

Change-point problems (or break point problems, disorder problems) can be considered one of the central issues of statistics, connecting asymptotic statistical theory and Monte Carlo methods, frequentist and Bayesian approaches, fixed and sequential procedures. In many real applications, observations are taken sequentially over time, or can be ordered with respect to some other criterion. The basic question, therefore, is whether the data obtained are generated by one or by many different probabilistic mechanisms. Change-point problems arise in a wide variety of fields, including biomedical signal processing, speech and image processing, climatology, industry (e.g. fault detection) and financial mathematics. In this paper, we apply the Cross-Entropy method to a sequential change-point problem. We obtain estimates for thresholds in the Shiryaev-Roberts procedure and the CUSUM procedure. We provide examples with generated sequences to illustrate the effectiveness of our approach to the problem.4 page(s

A Problem of sequential choice of multiple objects with given ranks

4 page(s

Analysis of differentially methylated regions in great apes and extinct hominids provides support for the evolutionary hypothesis of schizophrenia

Introduction: The persistence of schizophrenia in human populations separated by geography and time led to the evolutionary hypothesis that proposes schizophrenia as a by-product of the higher cognitive abilities of modern humans. To explore this hypothesis, we used here an evolutionary epigenetics approach building on differentially methylated regions (DMRs) of the genome. Methods: We implemented a polygenic enrichment testing pipeline using the summary statistics of genome-wide association studies (GWAS) of schizophrenia and 12 other phenotypes. We investigated the enrichment of association of these traits across genomic regions with variable methylation between modern humans and great apes (orangutans, chimpanzees and gorillas; great ape DMRs) and between modern humans and recently extinct hominids (Neanderthals and Denisovans; hominid DMRs). Results: Regions that are hypo-methylated in humans compared to great apes show enrichment of association with schizophrenia only if the major histocompatibility complex (MHC) region is included. With the MHC region removed from the analysis, only a modest enrichment for SNPs of low effect persists. The INRICH pipeline confirms this finding after rigorous permutation and bootstrapping procedures. Conclusion: The analyses of regions with differential methylation changes in humans and great apes do not provide compelling evidence of enrichment of association with schizophrenia, in contrast to our previous findings on more recent methylation differences between modern humans, Neanderthals and Denisovans. Our results further support the evolutionary hypothesis of schizophrenia and indicate that the origin of some of the genetic susceptibility factors of schizophrenia may lie in recent human evolution

Analysis of differentially methylated regions in great apes and extinct hominids provides support for the evolutionary hypothesis of schizophrenia

Introduction: The persistence of schizophrenia in human populations separated by geography and time led to the evolutionary hypothesis that proposes schizophrenia as a by-product of the higher cognitive abilities of modern humans. To explore this hypothesis, we used here an evolutionary epigenetics approach building on differentially methylated regions (DMRs) of the genome. Methods: We implemented a polygenic enrichment testing pipeline using the summary statistics of genome-wide association studies (GWAS) of schizophrenia and 12 other phenotypes. We investigated the enrichment of association of these traits across genomic regions with variable methylation between modern humans and great apes (orangutans, chimpanzees and gorillas; great ape DMRs) and between modern humans and recently extinct hominids (Neanderthals and Denisovans; hominid DMRs). Results: Regions that are hypo-methylated in humans compared to great apes show enrichment of association with schizophrenia only if the major histocompatibility complex (MHC) region is included. With the MHC region removed from the analysis, only a modest enrichment for SNPs of low effect persists. The INRICH pipeline confirms this finding after rigorous permutation and bootstrapping procedures. Conclusion: The analyses of regions with differential methylation changes in humans and great apes do not provide compelling evidence of enrichment of association with schizophrenia, in contrast to our previous findings on more recent methylation differences between modern humans, Neanderthals and Denisovans. Our results further support the evolutionary hypothesis of schizophrenia and indicate that the origin of some of the genetic susceptibility factors of schizophrenia may lie in recent human evolution