Identifying Differential Methylation in Cancer Epigenetics via a Bayesian Functional Regression Model

2021 ◽  
Author(s):  
Farhad Shokoohi ◽  
David A. Stephens ◽  
Celia M.T. Greenwood
Keyword(s):  
Missing Values ◽  
Disease Risk ◽  
Read Depth ◽  
Differential Methylation ◽  
Functional Regression ◽  
Cancer Epigenetics ◽  
Multiple Group ◽  
Sequencing Technologies ◽  
Multiple Covariates ◽  
Complex Features

AbstractDNA methylation plays an essential role in regulating gene activity, modulating disease risk, and determining treatment response. Researchers can obtain insight into methylation patterns at a single nucleotide level utilizing next-generation sequencing technologies. However, complex features inherent in the data obtained via these technologies pose challenges beyond the typical big data problems. Identifying differentially methylated cytosines (dmc) or regions is one of such challenges. Current methodologies for identifying dmcs fall short in handling low read-depth data and missing values, capturing functional data patterns, granting multiple covariates (categorical, continuous, or combination), and multiple group comparisons. We have developed an efficient method to identify dmcs based on a Bayesian functional regression approach, termed DMCFB, that tackles these shortcomings. Through simulation studies, we establish that DMCFB outperforms current methods and results in better smoothing, and efficient imputation. We apply the proposed method to analyze a dataset containing patients with acute promyelocytic leukemia and control samples. With DMCFB, we discovered many new dmcs, and more importantly, exhibited enhanced consistency of differential methylation within islands and at their adjacent shores. Furthermore, we detected differential methylation at more of the binding sites of the fused gene involved in this cancer.

scholarly journals Multi-omics approach to precision medicine for immune-mediated diseases

2021 ◽  
Vol 41 (1) ◽  
Author(s):  
Mineto Ota ◽  
Keishi Fujio
Keyword(s):  
Treatment Response ◽  
High Throughput Sequencing ◽  
Disease Risk ◽  
Clinical Information ◽  
Clinical Settings ◽  
Social Significance ◽  
Sequencing Technologies ◽  
Immune Mediated ◽  
Recent Innovation ◽  
Future Direction

AbstractRecent innovation in high-throughput sequencing technologies has drastically empowered the scientific research. Consequently, now, it is possible to capture comprehensive profiles of samples at multiple levels including genome, epigenome, and transcriptome at a time. Applying these kinds of rich information to clinical settings is of great social significance. For some traits such as cardiovascular diseases, attempts to apply omics datasets in clinical practice for the prediction of the disease risk have already shown promising results, although still under way for immune-mediated diseases. Multiple studies have tried to predict treatment response in immune-mediated diseases using genomic, transcriptomic, or clinical information, showing various possible indicators. For better prediction of treatment response or disease outcome in immune-mediated diseases, combining multi-layer information together may increase the power. In addition, in order to efficiently pick up meaningful information from the massive data, high-quality annotation of genomic functions is also crucial. In this review, we discuss the achievement so far and the future direction of multi-omics approach to immune-mediated diseases.

scholarly journals Long Noncoding RNAs as New Architects in Cancer Epigenetics, Prognostic Biomarkers, and Potential Therapeutic Targets

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Didier Meseure ◽  
Kinan Drak Alsibai ◽  
Andre Nicolas ◽  
Ivan Bieche ◽  
Antonin Morillon
Keyword(s):  
Protein Function ◽  
Massively Parallel Sequencing ◽  
Malignant Tumors ◽  
Biological Fluids ◽  
Expression Profiles ◽  
Noncoding Rnas ◽  
Therapeutic Targets ◽  
Cancer Epigenetics ◽  
Protein Coding ◽  
Sequencing Technologies

Recent advances in genome-wide analysis have revealed that 66% of the genome is actively transcribed into noncoding RNAs (ncRNAs) while less than 2% of the sequences encode proteins. Among ncRNAs, high-resolution microarray and massively parallel sequencing technologies have identified long ncRNAs (>200 nucleotides) that lack coding protein function. LncRNAs abundance, nuclear location, and diversity allow them to create in association with protein interactome, a complex regulatory network orchestrating cellular phenotypic plasticity via modulation of all levels of protein-coding gene expression. Whereas lncRNAs biological functions and mechanisms of action are still not fully understood, accumulating data suggest that lncRNAs deregulation is pivotal in cancer initiation and progression and metastatic spread through various mechanisms, including epigenetic effectors, alternative splicing, and microRNA-like molecules. Mounting data suggest that several lncRNAs expression profiles in malignant tumors are associated with prognosis and they can be detected in biological fluids. In this review, we will briefly discuss characteristics and functions of lncRNAs, their role in carcinogenesis, and their potential usefulness as diagnosis and prognosis biomarkers and novel therapeutic targets.

scholarly journals Self-reported health behaviors and longitudinal cognitive performance: Results from the Wisconsin Registry for Alzheimer’s Prevention

2019 ◽  
Author(s):  
Kimberly D. Mueller ◽  
Rebecca L. Koscik ◽  
Derek Norton ◽  
Martha C. Morris ◽  
Erin M. Jonaitis ◽  
...  
Keyword(s):  
Executive Function ◽  
Health Behaviors ◽  
Cognitive Decline ◽  
Disease Risk ◽  
Cognitive Activity ◽  
Model Averaging ◽  
Multiple Covariates ◽  
Reported Health ◽  
Secondary Outcomes ◽  
Age Range

AbstractBackgroundStudies have suggested associations between self-reported engagement in health behaviors and reduced risk of cognitive decline. Most studies explore these relationships using one health behavior, often cross-sectionally or with dementia as the outcome. In this study, we explored whether several individual self-reported health behaviors were associated with cognitive decline when considered simultaneously, using data from the Wisconsin Registry for Alzheimer’s Prevention (WRAP), an Alzheimer’s disease risk-enriched cohort who were non-demented and in late midlife at baseline.MethodWe analyzed longitudinal cognitive data from 828 participants in WRAP, with a mean age at baseline cognitive assessment of 57 (range = 36-78, sd = 6.8) and an average of 6.3 years (standard deviation = 1.9, range = 2-10) of follow-up. The primary outcome was a multi-domain cognitive composite, and secondary outcomes were immediate/delayed memory and executive function composites. Predictors of interest were self-reported measures of physical activity, cognitive activity, adherence to a Mediterranean-style diet (MIND), and interactions with each other and age. We conducted linear mixed effects analyses within an Information-theoretic (IT) model averaging (MA) approach on a set of models including covariates and combinations of these 2- and 3-way interactions. The IT approach was selected due to the large number of interactions of interest and to avoid pitfalls of traditional model selection approaches.ResultsModel-averaged results identified no significant modifiable behavior*age interactions in relationship to the primary composite outcome. In secondary outcomes, higher MIND diet scores associated with slower decline in executive function. Men showed faster decline than women on delayed memory, independent of health behaviors. There were no other significant interactions among any other health behaviors and cognitive trajectories.ConclusionsWhen multiple covariates and health behaviors were considered simultaneously, there were limited weak associations with cognitive decline in this age range. These results may be explained alone or in combination by three alternative explanations: 1) the range of cognitive decline is in middle age is too small to observe relationships with health behaviors, 2) the putative associations of these health behaviors on cognition may not be robust in this age range, or 3) the self-reported measures of the health behaviors may not be optimal for predicting cognitive decline. More study may be needed that incorporates sensitive measures of health behaviors, AD biomarker profiles, and/or other disease comorbidities.

Genomics Overview

2010 ◽  
Author(s):  
W Gregory Feero
Keyword(s):  
Genetic Testing ◽  
Family History ◽  
Disease Risk ◽  
Association Studies ◽  
Clinical Care ◽  
Family Health ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Sequencing Technologies ◽  
Genome Wide

New genomic applications are affecting internal medicine subspecialties and will soon affect the practices of all physicians. This chapter discusses the fields of genetics versus genomics and details the fundamentals of a genomic approach to health care. It includes special considerations such as the intersection between genomics and evidence-based medicine, genetic discrimination, the regulation of genetic testing, and the marketing of genetic testing directly to consumers. The chapter looks at genome-wide association studies and clinical care, as well as sequencing technologies. Tables offer examples of patterns of inheritance, clinical recommendations and red flags raised by family history, and intended uses for genetic tests. One figure shows an example pedigree obtained by using the US surgeon general's My Family Health Portrait family history tool, while the other shows the chromosomal locations of genetic markers associated with disease risk discovered in genome-wide association studies between 2005 and 2009. This chapter contains 41 references.

Genomics Overview

2010 ◽  
Author(s):  
W Gregory Feero
Keyword(s):  
Genetic Testing ◽  
Family History ◽  
Disease Risk ◽  
Association Studies ◽  
Clinical Care ◽  
Family Health ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Sequencing Technologies ◽  
Genome Wide

New genomic applications are affecting internal medicine subspecialties and will soon affect the practices of all physicians. This chapter discusses the fields of genetics versus genomics and details the fundamentals of a genomic approach to health care. It includes special considerations such as the intersection between genomics and evidence-based medicine, genetic discrimination, the regulation of genetic testing, and the marketing of genetic testing directly to consumers. The chapter looks at genome-wide association studies and clinical care, as well as sequencing technologies. Tables offer examples of patterns of inheritance, clinical recommendations and red flags raised by family history, and intended uses for genetic tests. One figure shows an example pedigree obtained by using the US surgeon general's My Family Health Portrait family history tool, while the other shows the chromosomal locations of genetic markers associated with disease risk discovered in genome-wide association studies between 2005 and 2009. This chapter contains 41 references.

Identifying the tissues-of-origin of circulating cell-free DNAs is a promising way in noninvasive diagnostics

2020 ◽  
Author(s):  
Xiaoqing Peng ◽  
Hong-Dong Li ◽  
Fang-Xiang Wu ◽  
Jianxin Wang
Keyword(s):  
Disease Risk ◽  
Parasitic Infection ◽  
Prenatal Testing ◽  
Methylation Pattern ◽  
Fragmentation Pattern ◽  
Dna Fragments ◽  
Noninvasive Diagnostics ◽  
Sequencing Technologies ◽  
Comprehensive Picture ◽  
Fragmentation Patterns

Abstract Advances in sequencing technologies facilitate personalized disease-risk profiling and clinical diagnosis. In recent years, some great progress has been made in noninvasive diagnoses based on cell-free DNAs (cfDNAs). It exploits the fact that dead cells release DNA fragments into the circulation, and some DNA fragments carry information that indicates their tissues-of-origin (TOOs). Based on the signals used for identifying the TOOs of cfDNAs, the existing methods can be classified into three categories: cfDNA mutation-based methods, methylation pattern-based methods and cfDNA fragmentation pattern-based methods. In cfDNA mutation-based methods, the SNP information or the detected mutations in driven genes of certain diseases are employed to identify the TOOs of cfDNAs. Methylation pattern-based methods are developed to identify the TOOs of cfDNAs based on the tissue-specific methylation patterns. In cfDNA fragmentation pattern-based methods, cfDNA fragmentation patterns, such as nucleosome positioning or preferred end coordinates of cfDNAs, are used to predict the TOOs of cfDNAs. In this paper, the strategies and challenges in each category are reviewed. Furthermore, the representative applications based on the TOOs of cfDNAs, including noninvasive prenatal testing, noninvasive cancer screening, transplantation rejection monitoring and parasitic infection detection, are also reviewed. Moreover, the challenges and future work in identifying the TOOs of cfDNAs are discussed. Our research provides a comprehensive picture of the development and challenges in identifying the TOOs of cfDNAs, which may benefit bioinformatics researchers to develop new methods to improve the identification of the TOOs of cfDNAs.

scholarly journals Integrative Deep Models for Alternative Splicing

2017 ◽  
Author(s):  
Anupama Jha ◽  
Matthew R. Gazzara ◽  
Yoseph Barash
Keyword(s):  
Alternative Splicing ◽  
Missing Values ◽  
Probabilistic Models ◽  
Genomic Sequence ◽  
Target Function ◽  
Detailed Comparison ◽  
Modeling Framework ◽  
Sequencing Technologies ◽  
Splicing Regulators

AbstractAdvancements in sequencing technologies have highlighted the role of alternative splicing (AS) in increasing transcriptome complexity. This role of AS, combined with the relation of aberrant splicing to malignant states, motivated two streams of research, experimental and computational. The First involves a myriad of techniques such as RNA-Seq and CLIP-Seq to identify splicing regulators and their putative targets. The second involves probabilistic models, also known as splicing codes, which infer regulatory mechanisms and predict splicing outcome directly from genomic sequence. To date, these models have utilized only expression data. In this work we address two related challenges: Can we improve on previous models for AS outcome prediction and can we integrate additional sources of data to improve predictions for AS regulatory factors. We perform a detailed comparison of two previous modeling approaches, Bayesian and Deep Neural networks, dissecting the confounding effects of datasets and target functions. We then develop a new target function for AS prediction and show that it significantly improves model accuracy. Next, we develop a modeling framework to incorporate CLIP-Seq, knockdown and over-expression experiments, which are inherently noisy and suffer from missing values. Using several datasets involving key splice factors in mouse brain, muscle and heart we demonstrate both the prediction improvements and biological insights offered by our new models. Overall, the framework we propose offers a scalable integrative solution to improve splicing code modeling as vast amounts of relevant genomic data become available.Availability: code and data will be available on Github following publication.

scholarly journals Exploring the relation between socioeconomic position and DNA methylation in a European population

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
D Petrovic ◽  
C Carmeli ◽  
B Bodinier ◽  
M Chadeau-Hyam ◽  
G Ehret ◽  
...  
Keyword(s):  
Life Course ◽  
Socioeconomic Position ◽  
Disease Risk ◽  
Premature Death ◽  
Population Based ◽  
European Population ◽  
Differential Methylation ◽  
Genome Wide ◽  
Cardiometabolic Disorders ◽  
The Life Course

Abstract Background Previous investigations have reported that adverse socioeconomic circumstances across the life-course lead to the alteration of major biological processes, eventually resulting in a higher disease risk and premature death. In particular, a low life-course socioeconomic position (SEP) has been associated with a modified epigenetic signature of loci involved in inflammation, the physiological response to stress, and other regulatory processes. Methods In this study, we investigated the association between nine indicators of SEP across the life-course and the differential methylation of 451'000 genome-wide CpG markers, using data from 690 adults included in a Swiss population-based study. We further examined the interrelations between the SEP-related CpGs, and the biological pathways in which the identified markers are involved. Results Three SEP indicators in adulthood were associated the differential methylation of 161 genome-wide CpG markers, whereby 156 CpGs were less methylated in people with low versus high SEP. Among the identified CpGs, a substantial proportion of markers were no longer associated with SEP upon accounting for health behaviors and cardiometabolic disorders. In addition, the identified CpGs were found to be involved in immune, inflammatory, and cancer-related processes. Conclusions Our results support the hypothesis that adverse socioeconomic circumstances may lead to the dysregulation of inflammatory processes, eventually resulting in the occurrence of serious chronic conditions such as atherosclerosis, diabetes, or cancer. Key messages Socioeconomic position is a major determinant of health-related outcomes. Epigenetic modifications may constitute a biological mechanism through which socioeconomic circumstances affect health.

scholarly journals HLA imputation and its application to genetic and molecular fine-mapping of the MHC region in autoimmune diseases

2021 ◽  
Author(s):  
Tatsuhiko Naito ◽  
Yukinori Okada
Keyword(s):  
Autoimmune Diseases ◽  
Fine Mapping ◽  
Disease Risk ◽  
Human Leukocyte ◽  
Nucleotide Polymorphisms ◽  
Imputation Methods ◽  
Leukocyte Antigen ◽  
Sequencing Technologies ◽  
Computational Performance ◽  
Causal Variants

AbstractVariations of human leukocyte antigen (HLA) genes in the major histocompatibility complex region (MHC) significantly affect the risk of various diseases, especially autoimmune diseases. Fine-mapping of causal variants in this region was challenging due to the difficulty in sequencing and its inapplicability to large cohorts. Thus, HLA imputation, a method to infer HLA types from regional single nucleotide polymorphisms, has been developed and has successfully contributed to MHC fine-mapping of various diseases. Different HLA imputation methods have been developed, each with its own advantages, and recent methods have been improved in terms of accuracy and computational performance. Additionally, advances in HLA reference panels by next-generation sequencing technologies have enabled higher resolution and a more reliable imputation, allowing a finer-grained evaluation of the association between sequence variations and disease risk. Risk-associated variants in the MHC region would affect disease susceptibility through complicated mechanisms including alterations in peripheral responses and central thymic selection of T cells. The cooperation of reliable HLA imputation methods, informative fine-mapping, and experimental validation of the functional significance of MHC variations would be essential for further understanding of the role of the MHC in the immunopathology of autoimmune diseases.

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