The NIH Common Fund/Roadmap Epigenomics Program: Successes of a comprehensive consortium

Mapping chromatin interactions at melanoma susceptibility loci uncovers distant cis-regulatory gene targets

Genome-wide association studies (GWASs) of melanoma risk have identified 68 independent signals at 54 loci. For most loci, specific functional variants and their respective target genes remain to be established. Capture-HiC is an assay that links fine-mapped risk variants to candidate target genes by comprehensively mapping chromatin interactions. We performed a melanoma GWAS region-focused capture-HiC assay in human primary melanocytes to identify physical interactions between fine-mapped risk variants and potential causal melanoma-susceptibility genes. Overall, chromatin-interaction data alone nominated potential causal genes for 61 of the 68 melanoma risk signals, identifying many candidates beyond those reported by previous studies. We further integrated these data with epigenomic (chromatin state, accessibility), gene expression (expression quantitative trait locus [eQTL]/transcriptome-wide association study [TWAS]), DNA methylation (methylation QTL [meQTL]/methylome-wide association study [MWAS]), and massively parallel reporter assay (MPRA) data generated from melanoma-relevant cell types to prioritize potentially cis-regulatory variants and their respective candidate gene targets. From the set of fine-mapped variants across these loci, we identified 140 prioritized credible causal variants linked to 195 candidate genes at 42 risk signals. In addition, we developed an integrative scoring system to facilitate candidate gene prioritization, integrating melanocyte and melanoma datasets. Notably, at several GWAS risk signals, we observed long-range chromatin connections (500 kb to >1 Mb) with distant candidate target genes. We validated several such cis-regulatory interactions using CRISPR inhibition, providing evidence for known cancer driver genes MDM4 and CBL, as well as the SRY-box transcription factor SOX4, as likely melanoma risk genes.

Genomics and multiomics in the age of precision medicine

Precision medicine is a transformative healthcare model that utilizes an understanding of a person's genome, environment, lifestyle, and interplay to deliver customized healthcare. Precision medicine has the potential to improve the health and productivity of the population, enhance patient trust and satisfaction in healthcare, and accrue health cost-benefits both at an individual and population level. Through faster and cost-effective genomics data, next-generation sequencing has provided us the impetus to understand the nuances of complex interactions between genes, diet, and lifestyle that are heterogeneous across the population. The emergence of multiomics technologies, including transcriptomics, proteomics, epigenomics, metabolomics, and microbiomics, has enhanced the knowledge necessary for maximizing the applicability of genomics data for better health outcomes. Integrative multiomics, the combination of multiple 'omics' data layered over each other, including the interconnections and interactions between them, helps us understand human health and disease better than any of them separately. Integration of these multiomics data is possible today with the phenomenal advancements in bioinformatics, data sciences, and artificial intelligence. Our review presents a broad perspective on the utility and feasibility of a genomics-first approach layered with other omics data, offering a practical model for adopting an integrated multiomics approach in pediatric health care and research. IMPACT: Precision medicine provides a paradigm shift from a conventional, reactive disease control approach to proactive disease prevention and health preservation. Phenomenal advancements in bioinformatics, data sciences, and artificial intelligence have made integrative multiomics feasible and help us understand human health and disease better than any of them separately. The genotype-first approach or reverse phenotyping has the potential to overcome the limitations of the phenotype-first approach by identifying new genotype-phenotype associations, enhancing the subclassification of diseases by widening the phenotypic spectrum of genetic variants, and understanding functional mechanisms of genetic variations.

Genetic predisposition to Behcet's disease mediated by a IL10RA enhancer polymorphism

Background

Several studies suggested the genetic association between IL10RA variants and susceptibility to Behcet's disease (BD). However, the precise mechanism of the association is still unknown. The purpose of this study was to investigate the mechanism underlying the genetic associations between IL10RA polymorphisms and the risk of BD.

Methods

To analyse the genetic susceptibility to BD mediated by IL10RA causal polymorphisms, we performed a study on data from our previous genome-wide association studies (GWAS), the bioinformatic analysis of post-annotation of GWAS and relevant mechanism verification experiments, including chromatin immunoprecipitation, luciferase gene-reporter assay, electrophoretic mobility shift assays, and enzyme-linked immunosorbent assay.

Results

Among 125 single nucleotide polymorphisms (SNPs) with P < 1 × 10-5 identified in our previous GWAS study on BD, rs4936415 (G/C) was predicted with the highest conserved score as an expression quantitative-trait-locus SNP for IL10RA in whole blood. There were H3K27ac and H3K4Me1 enhancer-specific enrichments around SNP rs4936415. Luciferase gene-reporter assays revealed that the rs4936415 G-allele construct showed a higher enhancer activity as compared to the empty and the C-allele construct. NF-κB1 was identified to bind the C-allele rather than the G-allele, although the enhancer SNP (rs4936415) region was found to control transcription factor binding sites. Interaction of C-allele and NF-κB1 gene construct resulted in an increased enhancer activity. BD patients showed a significantly lower serum level of the IL-10Rα.

Conclusions

This study identified a single functional causal SNP, rs4936415, in the IL10RA super-enhancer, conferring BD susceptibility. The protective G-allele of non-coding rs4936415 located inside an enhancer region of IL10RA promoted the enhancer activity and increased the expression of IL10RA.The risk C-allele is able to specifically bind NF-κB1 and, in turn, promotes enhancer activity of IL10RA. This subsequently leads to an increased expression of IL10RA. Low expression of IL-10RA suggests a relative deficiency of NF-κB1 in BD.

Mapping chromatin interactions at melanoma susceptibility loci and cell-type specific dataset integration uncovers distant gene targets of cis-regulation

Genome-wide association studies (GWAS) of melanoma risk have identified 68 independent signals at 54 loci. For most loci, specific functional variants and their respective target genes remain to be established. Capture-HiC is an assay that links fine-mapped risk variants to candidate target genes by comprehensively mapping cell-type specific chromatin interactions. We performed a melanoma GWAS region-focused capture-HiC assay in human primary melanocytes to identify physical interactions between fine-mapped risk variants and potential causal melanoma susceptibility genes. Overall, chromatin interaction data alone nominated potential causal genes for 61 of the 68 melanoma risk signals, identifying many candidates beyond those reported by previous studies. We further integrated these data with cell-type specific epigenomic (chromatin state, accessibility), gene expression (eQTL/TWAS), DNA methylation (meQTL/MWAS), and massively parallel reporter assay (MPRA) data to prioritize potentially cis-regulatory variants and their respective candidate gene targets. From the set of fine-mapped variants across these loci, we identified 140 prioritized candidate causal variants linked to 195 candidate genes at 42 risk signals. In addition, we developed an integrative scoring system to facilitate candidate gene prioritization, integrating melanocyte and melanoma datasets. Notably, at several GWAS risk signals we observed long-range chromatin connections (500 kb to >1 Mb) with distant candidate target genes. We validated several such cis-regulatory interactions using CRISPR inhibition, providing evidence for known cancer driver genes MDM4 and CBL, as well as the SRY-box transcription factor SOX4, as likely melanoma risk genes.

Large-scale genomic analysis of the domestic dog informs biological discovery

Recent advances in genomics, coupled with a unique population structure and remarkable levels of variation, have propelled the domestic dog to new levels as a system for understanding fundamental principles in mammalian biology. Central to this advance are more than 350 recognized breeds, each a closed population that has undergone selection for unique features. Genetic variation in the domestic dog is particularly well characterized compared with other domestic mammals, with almost 3000 high-coverage genomes publicly available. Importantly, as the number of sequenced genomes increases, new avenues for analysis are becoming available. Herein, we discuss recent discoveries in canine genomics regarding behavior, morphology, and disease susceptibility. We explore the limitations of current data sets for variant interpretation, tradeoffs between sequencing strategies, and the burgeoning role of long-read genomes for capturing structural variants. In addition, we consider how large-scale collections of whole-genome sequence data drive rare variant discovery and assess the geographic distribution of canine diversity, which identifies Asia as a major source of missing variation. Finally, we review recent comparative genomic analyses that will facilitate annotation of the noncoding genome in dogs.

Mechanism of Histone Arginine Methylation Dynamic Change in Cellular Stress

Histone arginine residue methylation is crucial for individual development and gene regulation. However, the dynamics of histone arginine methylation in response to cellular stress remains largely unexplored. In addition, the interplay and regulatory mechanisms between this and other histone modifications are important scientific questions that require further investigation. This study aimed to investigate the changes in histone arginine methylation in response to DNA damage. We report a global decrease in histone H3R26 symmetric dimethylation (H3R26me2s) and hypoacetylation at the H3K27 site in response to DNA damage. Notably, H3R26me2s exhibits a distribution pattern similar to that of H3K27ac across the genome, both of which are antagonistic to H3K27me3. Additionally, histone deacetylase 1 (HDAC1) may be recruited to the H3R26me2s demethylation region to mediate H3K27 deacetylation. These findings suggest crosstalk between H3R26me2s and H3K27ac in regulating gene expression.

Advances in Integrated Multi-omics Analysis for Drug-Target Identification

As an essential component of modern drug discovery, the role of drug-target identification is growing increasingly prominent. Additionally, single-omics technologies have been widely utilized in the process of discovering drug targets. However, it is difficult for any single-omics level to clearly expound the causal connection between drugs and how they give rise to the emergence of complex phenotypes. With the progress of large-scale sequencing and the development of high-throughput technologies, the tendency in drug-target identification has shifted towards integrated multi-omics techniques, gradually replacing traditional single-omics techniques. Herein, this review centers on the recent advancements in the domain of integrated multi-omics techniques for target identification, highlights the common multi-omics analysis strategies, briefly summarizes the selection of multi-omics analysis tools, and explores the challenges of existing multi-omics analyses, as well as the applications of multi-omics technology in drug-target identification.

NCAD v1.0: a database for non-coding variant annotation and interpretation

The application of whole genome sequencing is expanding in clinical diagnostics across various genetic disorders, and the significance of non-coding variants in penetrant diseases is increasingly being demonstrated. Therefore, it is urgent to improve the diagnostic yield by exploring the pathogenic mechanisms of variants in non-coding regions. However, the interpretation of non-coding variants remains a significant challenge, due to the complex functional regulatory mechanisms of non-coding regions and the current limitations of available databases and tools. Hence, we develop the non-coding variant annotation database (NCAD, http://www.ncawdb.net/), encompassing comprehensive insights into 665,679,194 variants, regulatory elements, and element interaction details. Integrating data from 96 sources, spanning both GRCh37 and GRCh38 versions, NCAD v1.0 provides vital information to support the genetic diagnosis of non-coding variants, including allele frequencies of 12 diverse populations, with a particular focus on the population frequency information for 230,235,698 variants in 20,964 Chinese individuals. Moreover, it offers prediction scores for variant functionality, five categories of regulatory elements, and four types of non-coding RNAs. With its rich data and comprehensive coverage, NCAD serves as a valuable platform, empowering researchers and clinicians with profound insights into non-coding regulatory mechanisms while facilitating the interpretation of non-coding variants.

The science of uncertainty guides fetal-neonatal neurology principles and practice: diagnostic-prognostic opportunities and challenges

Fetal-neonatal neurologists (FNNs) consider diagnostic, therapeutic, and prognostic decisions strengthened by interdisciplinary collaborations. Bio-social perspectives of the woman's health influence evaluations of maternal-placental-fetal (MPF) triad, neonate, and child. A dual cognitive process integrates "fast thinking-slow thinking" to reach shared decisions that minimize bias and maintain trust. Assessing the science of uncertainty with uncertainties in science improves diagnostic choices across the developmental-aging continuum. Three case vignettes highlight challenges that illustrate this approach. The first maternal-fetal dyad involved a woman who had been recommended to terminate her pregnancy based on an incorrect diagnosis of an encephalocele. A meningocele was subsequently identified when she sought a second opinion with normal outcome for her child. The second vignette involved two pregnancies during which fetal cardiac rhabdomyoma was identified, suggesting tuberous sclerosis complex (TSC). One woman sought an out-of-state termination without confirmation using fetal brain MRI or postmortem examination. The second woman requested pregnancy care with postnatal evaluations. Her adult child experiences challenges associated with TSC sequelae. The third vignette involved a prenatal diagnosis of an open neural tube defect with arthrogryposis multiplex congenita. The family requested prenatal surgical closure of the defect at another institution at their personal expense despite receiving a grave prognosis. The subsequent Management of Myelomeningocele Study (MOMS) would not have recommended this procedure. Their adult child requires medical care for global developmental delay, intractable epilepsy, and autism. These three evaluations involved uncertainties requiring shared clinical decisions among all stakeholders. Falsely negative or misleading positive interpretation of results reduced chances for optimal outcomes. FNN diagnostic skills require an understanding of dynamic gene-environment interactions affecting reproductive followed by pregnancy exposomes that influence the MPF triad health with fetal neuroplasticity consequences. Toxic stressor interplay can impair the neural exposome, expressed as anomalous and/or destructive fetal brain lesions. Functional improvements or permanent sequelae may be expressed across the lifespan. Equitable and compassionate healthcare for women and families require shared decisions that preserve pregnancy health, guided by person-specific racial-ethnic, religious, and bio-social perspectives. Applying developmental origins theory to neurologic principles and practice supports a brain health capital strategy for all persons across each generation.

Opportunities for basic, clinical, and bioethics research at the intersection of machine learning and genomics

The data-intensive fields of genomics and machine learning (ML) are in an early stage of convergence. Genomics researchers increasingly seek to harness the power of ML methods to extract knowledge from their data; conversely, ML scientists recognize that genomics offers a wealth of large, complex, and well-annotated datasets that can be used as a substrate for developing biologically relevant algorithms and applications. The National Human Genome Research Institute (NHGRI) inquired with researchers working in these two fields to identify common challenges and receive recommendations to better support genomic research efforts using ML approaches. Those included increasing the amount and variety of training datasets by integrating genomic with multiomics, context-specific (e.g., by cell type), and social determinants of health datasets; reducing the inherent biases of training datasets; prioritizing transparency and interpretability of ML methods; and developing privacy-preserving technologies for research participants' data.

EndoQuad: a comprehensive genome-wide experimentally validated endogenous G-quadruplex database

G-quadruplexes (G4s) are non-canonical four-stranded structures and are emerging as novel genetic regulatory elements. However, a comprehensive genomic annotation of endogenous G4s (eG4s) and systematic characterization of their regulatory network are still lacking, posing major challenges for eG4 research. Here, we present EndoQuad (https://EndoQuad.chenzxlab.cn/) to address these pressing issues by integrating high-throughput experimental data. First, based on high-quality genome-wide eG4s mapping datasets (human: 1181; mouse: 24; chicken: 2) generated by G4 ChIP-seq/CUT&Tag, we generate a reference set of genome-wide eG4s. Our multi-omics analyses show that most eG4s are identified in one or a few cell types. The eG4s with higher occurrences across samples are more structurally stable, evolutionarily conserved, enriched in promoter regions, mark highly expressed genes and associate with complex regulatory programs, demonstrating higher confidence level for further experiments. Finally, we integrate millions of functional genomic variants and prioritize eG4s with regulatory functions in disease and cancer contexts. These efforts have culminated in the comprehensive and interactive database of experimentally validated DNA eG4s. As such, EndoQuad enables users to easily access, download and repurpose these data for their own research. EndoQuad will become a one-stop resource for eG4 research and lay the foundation for future functional studies.

Genome-wide enhancer-gene regulatory maps link causal variants to target genes underlying human cancer risk

Genome-wide association studies have identified numerous variants associated with human complex traits, most of which reside in the non-coding regions, but biological mechanisms remain unclear. However, assigning function to the non-coding elements is still challenging. Here we apply Activity-by-Contact (ABC) model to evaluate enhancer-gene regulation effect by integrating multi-omics data and identified 544,849 connections across 20 cancer types. ABC model outperforms previous approaches in linking regulatory variants to target genes. Furthermore, we identify over 30,000 enhancer-gene connections in colorectal cancer (CRC) tissues. By integrating large-scale population cohorts (23,813 cases and 29,973 controls) and multipronged functional assays, we demonstrate an ABC regulatory variant rs4810856 associated with CRC risk (Odds Ratio = 1.11, 95%CI = 1.05-1.16, P = 4.02 × 10-5) by acting as an allele-specific enhancer to distally facilitate PREX1, CSE1L and STAU1 expression, which synergistically activate p-AKT signaling. Our study provides comprehensive regulation maps and illuminates a single variant regulating multiple genes, providing insights into cancer etiology.

Functional Enrichment Analysis Identifying Regulatory Information Associated with Human Fracture

Dozens of loci associated with fracture have been identified by genome-wide association studies (GWASs). However, most of these variants are located in the noncoding regions including introns, long terminal repeats, and intergenic regions. Although combining regulation information helps to identify the causal SNPs and interpret the involvement of these variants in the etiology of human fracture, regulation information which was truly associated with fracture was unknown. A novel functional enrichment method GARFIELD (GWAS Analysis of Regulatory of Functional Information Enrichment with LD correction) was applied to identify fracture-associated regulation information, including transcript factor binding sites, expression quantitative trait loci (eQTLs), chromatin states, enhancer, promoter, dyadic, super enhancer and Epigenome marks. Fracture SNPs were significantly enriched in exon (Bonferroni correction, p value < 7.14 × 10-3) at two GWAS p value thresholds through GARFIELD. High level of fold-enrichment was observed in super enhancer of monocyte and the enhancer of chondrocyte (Bonferroni correction, p value < 4.45 × 10-3). eQTLs of 44 tissues/cells and 10 transcription factors (TFs) were identified to be associated with human fracture. These results provide new insight into the etiology of human fracture, which might increase the identification of the causal SNPs through the fine-mapping study combined with functional annotation, as well as polygenic risk score.

DECIPHER: Improving Genetic Diagnosis Through Dynamic Integration of Genomic and Clinical Data

DECIPHER (Database of Genomic Variation and Phenotype in Humans Using Ensembl Resources) shares candidate diagnostic variants and phenotypic data from patients with genetic disorders to facilitate research and improve the diagnosis, management, and therapy of rare diseases. The platform sits at the boundary between genomic research and the clinical community. DECIPHER aims to ensure that the most up-to-date data are made rapidly available within its interpretation interfaces to improve clinical care. Newly integrated cardiac case-control data that provide evidence of gene-disease associations and inform variant interpretation exemplify this mission. New research resources are presented in a format optimized for use by a broad range of professionals supporting the delivery of genomic medicine. The interfaces within DECIPHER integrate and contextualize variant and phenotypic data, helping to determine a robust clinico-molecular diagnosis for rare-disease patients, which combines both variant classification and clinical fit. DECIPHER supports discovery research, connecting individuals within the rare-disease community to pursue hypothesis-driven research.

Harnessing male germline epigenomics for the genetic improvement in cattle

Sperm is essential for successful artificial insemination in dairy cattle, and its quality can be influenced by both epigenetic modification and epigenetic inheritance. The bovine germline differentiation is characterized by epigenetic reprogramming, while intergenerational and transgenerational epigenetic inheritance can influence the offspring's development through the transmission of epigenetic features to the offspring via the germline. Therefore, the selection of bulls with superior sperm quality for the production and fertility traits requires a better understanding of the epigenetic mechanism and more accurate identifications of epigenetic biomarkers. We have comprehensively reviewed the current progress in the studies of bovine sperm epigenome in terms of both resources and biological discovery in order to provide perspectives on how to harness this valuable information for genetic improvement in the cattle breeding industry.

Dynamic Spatial-temporal Expression Ratio of X Chromosome to Autosomes but Stable Dosage Compensation in Mammals

In the evolutionary model of dosage compensation, per-allele expression level of the X chromosome has been proposed to have twofold up-regulation to compensate its dose reduction in males (XY) compared to females (XX). However, the expression regulation of X-linked genes is still controversial, and comprehensive evaluations are still lacking. By integrating multi-omics datasets in mammals, we investigated the expression ratios including X to autosomes (X:AA ratio) and X to orthologs (X:XX ratio) at the transcriptome, translatome, and proteome levels. We revealed a dynamic spatial-temporal X:AA ratio during development in humans and mice. Meanwhile, by tracing the evolution of orthologous gene expression in chickens, platypuses, and opossums, we found a stable expression ratio of X-linked genes in humans to their autosomal orthologs in other species (X:XX ≈ 1) across tissues and developmental stages, demonstrating stable dosage compensation in mammals. We also found that different epigenetic regulations contributed to the high tissue specificity and stage specificity of X-linked gene expression, thus affecting X:AA ratios. It could be concluded that the dynamics of X:AA ratios were attributed to the different gene contents and expression preferences of the X chromosome, rather than the stable dosage compensation.

POSTRE: a tool to predict the pathological effects of human structural variants

Understanding the pathological impact of non-coding genetic variation is a major challenge in medical genetics. Accumulating evidences indicate that a significant fraction of genetic alterations, including structural variants (SVs), can cause human disease by altering the function of non-coding regulatory elements, such as enhancers. In the case of SVs, described pathomechanisms include changes in enhancer dosage and long-range enhancer-gene communication. However, there is still a clear gap between the need to predict and interpret the medical impact of non-coding variants, and the existence of tools to properly perform these tasks. To reduce this gap, we have developed POSTRE (Prediction Of STRuctural variant Effects), a computational tool to predict the pathogenicity of SVs implicated in a broad range of human congenital disorders. By considering disease-relevant cellular contexts, POSTRE identifies SVs with either coding or long-range pathological consequences with high specificity and sensitivity. Furthermore, POSTRE not only identifies pathogenic SVs, but also predicts the disease-causative genes and the underlying pathological mechanism (e.g, gene deletion, enhancer disconnection, enhancer adoption, etc.). POSTRE is available at https://github.com/vicsanga/Postre.

The Canadian Consortium for Research in Pediatric Surgery: Roadmap for Creation and Implementation of a National Subspecialty Research Consortium

Clinical practice should be driven by high-quality research that produces evidence to inform best practices. Generation of such evidence is often challenging, particularly for smaller specialties, such as pediatric surgery, that treat many patients with rare diseases. Multi-institutional collaboration is seen as a major strategy to address these challenges. We have recently created the Canadian Consortium for Research in Pediatric Surgery, a national consortium that includes all major pediatric surgical services across Canada. The mission of the Consortium is to improve pediatric surgical care through high-quality collaborative research. In this article, we describe the rationale and methodology for creation of the Canadian Consortium for Research in Pediatric Surgery, demonstrate its achievements to date, and share a number of foundational concepts that are integral to its success. Our aim is to provide a model for creation of such consortia, ultimately leading to improvements in the quality of clinical research and patient care.

Immune and spermatogenesis-related loci are involved in the development of extreme patterns of male infertility

We conducted a genome-wide association study in a large population of infertile men due to unexplained spermatogenic failure (SPGF). More than seven million genetic variants were analysed in 1,274 SPGF cases and 1,951 unaffected controls from two independent European cohorts. Two genomic regions were associated with the most severe histological pattern of SPGF, defined by Sertoli cell-only (SCO) phenotype, namely the MHC class II gene HLA-DRB1 (rs1136759, P = 1.32E-08, OR = 1.80) and an upstream locus of VRK1 (rs115054029, P = 4.24E-08, OR = 3.14), which encodes a protein kinase involved in the regulation of spermatogenesis. The SCO-associated rs1136759 allele (G) determines a serine in the position 13 of the HLA-DRβ1 molecule located in the antigen-binding pocket. Overall, our data support the notion of unexplained SPGF as a complex trait influenced by common variation in the genome, with the SCO phenotype likely representing an immune-mediated condition.

A GWAS in a large case-control cohort of European ancestry identifies two genomic regions, the MHC class II gene HLA-DRB1 and an upstream locus of VRK1, that are associated with the most severe phenotype of spermatogenic failure.

Potential mechanisms and prognostic model of eRNAs-regulated genes in stomach adenocarcinoma

Gastric Carcinoma is the fourth leading cause of cancer deaths worldwide, in which stomach adenocarcinoma (STAD) is the most common histological type. A growing amount of evidence has suggested the importance of enhancer RNAs (eRNAs) in the cancer. However, the potential mechanism of eRNAs in STAD remains unclear. The eRNAs-regulated genes (eRRGs) were identified through four different enhancer resources. The differentially expressed eRRGs were obtained by ‘DESeq2’ R package. The prognosis prediction model was constructed by Cox and Lasso regression analysis. The ‘ChAMP’ R package and ‘maftools’ R package were used to investigate the multi-omics characters. In this study, combining the concept of contact domain, a total of 9014 eRRGs including 4926 PCGs and 4088 lncRNAs were identified and these eRRGs showed higher and more stable expression. Besides, the functions of these genes were mainly associated with tumor-related biological processes. Then, a prognostic prediction model was constructed and the AUC values of the 1-, 3- and 5-year survival prediction reached 0.76, 0.84 and 0.84, respectively, indicating that this model has a high accuracy. Finally, the difference between high-risk group and low-risk group were investigated using multi-omics data including gene expression, DNA methylation and somatic mutations. Our study provides significant clues for the elucidation of eRNAs in STAD and may help improve the overall survival for STAD patients.

DNA methylation detection and site analysis by using an electrochemical biosensor constructed based on toehold-mediated strand displacement reaction

DNA methylation has become a novel target for early diagnosis and prognosis of cancer as well as other related diseases. The accurate detection of the methylation sites of specific genes proved to be of great significance. However, the complex biological nature of clinical samples and the detection of low-abundance targets led to higher requirements for the testing technology. It has been found that by virtue of high sensitivity, rapid response, low cost, facile operation and applicability to microanalysis, electrochemical sensors have greatly contributed to the process of clinical diagnosis. In this study, a facile, rapid and highly sensitive electrochemical biosensor based on the peak current change was developed on the basis of high selectivity of toehold and greater efficiency of PNA strand displacement and used for the detection and site analysis of DNA methylation. Moreover, compared with non-methylated DNA sequences, methylated DNA sequences could be readily invaded by PNA probes, thereby resulting in the strand displacement and significant electrical signals. Therefore, methylation of cytosine sites was primarily analyzed based on electrical signals. Strand displacement by the target DNA sequences with different methylated sites can lead to substantial changes of strand displacement efficiency. As a result, the methylation sites can be analyzed on the basis of corresponding peak current response relation. This method has a detection limit of 0.075 pM and does not involve various complicated steps such as bisulfite treatment, enzyme digestion and PCR amplification. Indeed, one detection cycle can be completed in 60 min. The proposed technology might exhibit great potential in early clinical diagnosis and risk assessment of cancers and related diseases.

Computational and experimental methods for classifying variants of unknown clinical significance

The increase in sequencing capacity, reduction in costs, and national and international coordinated efforts have led to the widespread introduction of next-generation sequencing (NGS) technologies in patient care. More generally, human genetics and genomic medicine are gaining importance for more and more patients. Some communities are already discussing the prospect of sequencing each individual's genome at time of birth. Together with digital health records, this shall enable individualized treatments and preventive measures, so-called precision medicine. A central step in this process is the identification of disease causal mutations or variant combinations that make us more susceptible for diseases. Although various technological advances have improved the identification of genetic alterations, the interpretation and ranking of the identified variants remains a major challenge. Based on our knowledge of molecular processes or previously identified disease variants, we can identify potentially functional genetic variants and, using different lines of evidence, we are sometimes able to demonstrate their pathogenicity directly. However, the vast majority of variants are classified as variants of uncertain clinical significance (VUSs) with not enough experimental evidence to determine their pathogenicity. In these cases, computational methods may be used to improve the prioritization and an increasing toolbox of experimental methods is emerging that can be used to assay the molecular effects of VUSs. Here, we discuss how computational and experimental methods can be used to create catalogs of variant effects for a variety of molecular and cellular phenotypes. We discuss the prospects of integrating large-scale functional data with machine learning and clinical knowledge for the development of accurate pathogenicity predictions for clinical applications.

Ethical implications of epigenetics in the era of personalized medicine

Given the increasing research activity on epigenetics to monitor human diseases and its connection with lifestyle and environmental expositions, the field of epigenetics has attracted a great deal of interest also at the ethical and societal level. In this review, we will identify and discuss current ethical, legal and social issues of epigenetics research in the context of personalized medicine. The review covers ethical aspects such as how epigenetic information should impact patient autonomy and the ability to generate an intentional and voluntary decision, the measures of data protection related to privacy and confidentiality derived from epigenome studies (e.g., risk of discrimination, patient re-identification and unexpected findings) or the debate in the distribution of responsibilities for health (i.e., personal versus public responsibilities). We pay special attention to the risk of social discrimination and stigmatization as a consequence of inferring information related to lifestyle and environmental exposures potentially contained in epigenetic data. Furthermore, as exposures to the environment and individual habits do not affect all populations equally, the violation of the principle of distributive justice in the access to the benefits of clinical epigenetics is discussed. In this regard, epigenetics represents a great opportunity for the integration of public policy measures aimed to create healthier living environments. Whether these public policies will coexist or, in contrast, compete with strategies reinforcing the personalized medicine interventions needs to be considered. The review ends with a reflection on the main challenges in epigenetic research, some of them in a technical dimension (e.g., assessing causality or establishing reference epigenomes) but also in the ethical and social sphere (e.g., risk to add an epigenetic determinism on top of the current genetic one). In sum, integration into life science investigation of social experiences such as exposure to risk, nutritional habits, prejudice and stigma, is imperative to understand epigenetic variation in disease. This pragmatic approach is required to locate clinical epigenetics out of the experimental laboratories and facilitate its implementation into society.

Bioinformatics pipeline to guide late-onset Alzheimer's disease (LOAD) post-GWAS studies: Prioritizing transcription regulatory variants within LOAD-associated regions

INTRODUCTION

As new late-onset Alzheimer's disease (LOAD) genetic risk loci are identified and brain cell-type specific omics data becomes available, there is an unmet need for a bioinformatics framework to prioritize genes and variants for testing in single-cell molecular profiling experiments and validation using disease models and gene editing technologies. Prior work has characterized and prioritized active enhancers located in LOAD-genome-wide association study (GWAS) regions and their potential interactions with candidate genes. The current study extends this work by focusing on single nucleotide polymorphisms (SNPs) within these LOAD enhancers and their impact on altering transcription factor (TF) binding. The proposed bioinformatics pipeline progresses from SNPs located in LOAD-GWAS regions to a filtered set of candidate regulatory SNPs that have a predicted strong effect on TF binding.

METHODS

Active enhancers within LOAD-associated regions were identified and SNPs located in the enhancers were catalogued. SNPs that disrupt TF binding sites were prioritized and the respective TFs were filtered to include only those that were expressed in brain tissues relevant to LOAD. The TFs binding to the corresponding sequence was further confirmed by ChIP-seq signals. Finally, the high-priority candidate SNPs were evaluated as expression quantitative trait loci (eQTLs) in disease-relevant tissues.

RESULTS

We catalogued 61 strong enhancers in LOAD-GWAS regions encompassing 326 SNPs and 104 TF binding sites. Seventy-seven and 78 of the TFs were expressed in brain and monocytes, respectively, out of which 19 TF-binding sites showed ChIP-seq signals. Eleven SNPs were found to interrupt with TF binding out of which three SNPs were also significant eQTL.

DISCUSSION

This study provides a framework to catalogue noncoding variations in enhancers located in LOAD-GWAS loci and characterize their likelihood to perturb TF binding. The approach integrates multiple data types to characterize and prioritize SNPs for putative regulatory function using single-cell multi-omics assays and gene editing.

echolocatoR: an automated end-to-end statistical and functional genomic fine-mapping pipeline

SUMMARY

echolocatoR integrates a diverse suite of statistical and functional fine-mapping tools to identify, test enrichment in, and visualize high-confidence causal consensus variants in any phenotype. It requires minimal input from users (a summary statistics file), can be run in a single R function, and provides extensive access to relevant datasets (e.g. reference linkage disequilibrium panels, quantitative trait loci, genome-wide annotations, cell-type-specific epigenomics), thereby enabling rapid, robust and scalable end-to-end fine-mapping investigations.

AVAILABILITY AND IMPLEMENTATION

echolocatoR is an open-source R package available through GitHub under the GNU General Public License (Version 3) license: https://github.com/RajLabMSSM/echolocatoR.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Variations in DNA methylation and allograft rejection

PURPOSE OF REVIEW

DNA methylation is involved in gene transcription and as such important for cellular function. Here, the literature on DNA methylation in relation to acute rejection is summarized with a focus on the potential clinical utility of DNA methylation for monitoring transplant rejection.

RECENT FINDINGS

The tight transcriptional control of DNA methylation in immune cell function, e.g. demethylation in regulatory T-cell-specific genes for stable immunosuppressive capacities, suggests an important role for DNA methylation variations in the antidonor-directed immune response. Until today, differentially methylated DNA in immune cells, however, has not been described at the moment of allograft rejection. The ability to locus-specific modify DNA methylation could facilitate the generation of stable cells for cellular therapy purposes. The unique cell-specific characteristics of DNA methylation provide the opportunity to identify its cellular origin. Examining methylation of cell-free DNA in blood or urine may serve as a 'liquid biopsy' enabling minimally invasive detection of allograft rejection.

SUMMARY

Actual research publications on DNA methylation in relation to allograft rejection are scarce, which makes it challenging to determine its potential clinical value. Extensive research is needed to investigate the value of DNA methylation in early recognition, diagnosis, and/or successful treatment of allograft rejection.

Review of multi-omics data resources and integrative analysis for human brain disorders

In the last decade, massive omics datasets have been generated for human brain research. It is evolving so fast that a timely update is urgently needed. In this review, we summarize the main multi-omics data resources for the human brains of both healthy controls and neuropsychiatric disorders, including schizophrenia, autism, bipolar disorder, Alzheimer's disease, Parkinson's disease, progressive supranuclear palsy, etc. We also review the recent development of single-cell omics in brain research, such as single-nucleus RNA-seq, single-cell ATAC-seq and spatial transcriptomics. We further investigate the integrative multi-omics analysis methods for both tissue and single-cell data. Finally, we discuss the limitations and future directions of the multi-omics study of human brain disorders.

Strategies to Uplift Novel Mendelian Gene Discovery for Improved Clinical Outcomes

Rare genetic disorders, while individually rare, are collectively common. They represent some of the most severe disorders affecting patients worldwide with significant morbidity and mortality. Over the last decade, advances in genomic methods have significantly uplifted diagnostic rates for patients and facilitated novel and targeted therapies. However, many patients with rare genetic disorders still remain undiagnosed as the genetic etiology of only a proportion of Mendelian conditions has been discovered to date. This article explores existing strategies to identify novel Mendelian genes and how these discoveries impact clinical care and therapeutics. We discuss the importance of data sharing, phenotype-driven approaches, patient-led approaches, utilization of large-scale genomic sequencing projects, constraint-based methods, integration of multi-omics data, and gene-to-patient methods. We further consider the health economic advantages of novel gene discovery and speculate on potential future methods for improved clinical outcomes.

Conceptual models for implementing solution-oriented team science in large research consortia

Large translational research initiatives can strengthen efficiencies and support science with enhanced impact when practical conceptual models guide their design, implementation, and evaluation. The National Institutes of Health (NIH) Environmental influences on Child Health Outcomes (ECHO) program brings together data from 72 ongoing maternal–child cohort studies – involving more than 50,000 children and over 1200 investigators – to conduct transdisciplinary solution-oriented research that addresses how early environmental exposures influence child health. ECHO uses a multi-team system approach to consortium-wide data collection and analysis to generate original research that informs programs, policies, and practices to enhance children’s health. Here, we share two conceptual models informed by ECHO’s experiences and the Science of Team Science. The first conceptual model illuminates a system of teams and associated tasks that support collaboration toward shared scientific goals. The second conceptual model provides a framework for designing evaluations for continuous quality improvement of manuscript writing teams. Together, the two conceptual models offer guidance for the design, implementation, and evaluation of translational and transdisciplinary multi-team research initiatives.

What can clinical immunology learn from inborn errors of epigenetic regulators?

The epigenome is at the interface between environmental factors and the genome, regulating gene transcription, DNA repair, and replication. Epigenetic modifications play a crucial role in establishing and maintaining cell identity and are especially crucial for neurology, musculoskeletal integrity, and the function of the immune system. Mutations in genes encoding for the components of the epigenetic machinery lead to the development of distinct disorders, especially involving the central nervous system and host defense. In this review, we focus on the role of epigenetic modifications for the function of the immune system. By studying the immune phenotype of patients with monogenic mutations in components of the epigenetic machinery (inborn errors of epigenetic regulators), we demonstrate the importance of DNA methylation, histone modifications, chromatin remodeling, noncoding RNAs, and mRNA processing for immunity. Moreover, we give a short overview on therapeutic strategies targeting the epigenome.

MassArray analysis of genomic susceptibility variants in ovarian cancer

Ovarian cancer (OC), a multifaceted and genetically heterogeneous malignancy is one of the most common cancers among women. The aim of the study is to unravel the genetic factors associated with OC and the extent of genetic heterogeneity in the populations of Jammu and Kashmir (J&K).Using the high throughput Agena MassARRAY platform, present case control study was designed which comprises 200 histopathological confirmed OC patients and 400 age and ethnicity matched healthy controls to ascertain the association of previously reported eleven single nucleotide polymorphisms (SNPs) spread over ten genes (DNMT3A, PIK3CA, FGFR2, GSTP1, ERCC5, AKT1, CASC16, CYP19A1, BCL2 and ERCC1) within the OC population of Jammu and Kashmir, India. The association of each variant was estimated using logistic regression analyses. Out of the 11 SNPs the odds ratio observed for three SNPs; rs2699887 was (1.72 at 95% CI: 1.19-2.48, p = 0.004), rs1695 was (1.87 at 95% CI: 1.28-2.71, p = 0.001), and rs2298881 was (0.66 at 95% CI: 0.46-0.96, p = 0.03) were found significantly associated with the OC after correction with confounding factors i.e. age & BMI. Furthermore, the estimation of interactive analyses was performed and odds ratio observed was 2.44 (1.72-3.47), p value < 0. 001 suggests that there was a strong existence of interplay between the selected genetic variants in OC, which demonstrate that interactive analysis highlights the role of gene-gene interaction that provides an insight among multiple little effects of various polymorphisms in OC.

Characteristics of mutational signatures of unknown etiology

Although not all somatic mutations are cancer drivers, their mutational signatures, i.e. the patterns of genomic alterations at a genome-wide scale, provide insights into past exposure to mutagens, DNA damage and repair processes. Computational deconvolution of somatic mutation patterns and expert curation pan-cancer studies have identified a number of mutational signatures associated with point mutations, dinucleotide substitutions, insertions and deletions, and rearrangements, and have established etiologies for a subset of these signatures. However, the mechanisms underlying nearly one-third of all mutational signatures are not yet understood. The signatures with established etiology and those with hitherto unknown origin appear to have some differences in strand bias, GC content and nucleotide context diversity. It is possible that some of the hitherto ‘unknown’ signatures predominantly occur outside gene regions. While nucleotide contexts might be adequate to establish etiologies of some mutational signatures, in other cases additional features, such as broader (epi)genomic contexts, including chromatin, replication timing, processivity and local mutational patterns, may help fully understand the underlying DNA damage and repair processes. Nonetheless, remarkable progress in characterization of mutational signatures has provided fundamental insights into the biology of cancer, informed disease etiology and opened up new opportunities for cancer prevention, risk management, and therapeutic decision making.

Computational Approaches for Unraveling the Effects of Variation in the Human Genome and Microbiome

The past two decades of analytical efforts have highlighted how much more remains to be learned about the human genome and, particularly, its complex involvement in promoting disease development and progression. While numerous computational tools exist for the assessment of the functional and pathogenic effects of genome variants, their precision is far from satisfactory, particularly for clinical use. Accumulating evidence also suggests that the human microbiome's interaction with the human genome plays a critical role in determining health and disease states. While numerous microbial taxonomic groups and molecular functions of the human microbiome have been associated with disease, the reproducibility of these findings is lacking. The human microbiome–genome interaction in healthy individuals is even less well understood. This review summarizes the available computational methods built to analyze the effect of variation in the human genome and microbiome. We address the applicability and precision of these methods across their possible uses. We also briefly discuss the exciting, necessary, and now possible integration of the two types of data to improve the understanding of pathogenicity mechanisms.

EPD in 2020: enhanced data visualization and extension to ncRNA promoters

The Eukaryotic Promoter Database (EPD), available online at https://epd.epfl.ch, provides accurate transcription start site (TSS) information for promoters of 15 model organisms plus corresponding functional genomics data that can be viewed in a genome browser, queried or analyzed via web interfaces, or exported in standard formats (FASTA, BED, CSV) for subsequent analysis with other tools. Recent work has focused on the improvement of the EPD promoter viewers, which use the UCSC Genome Browser as visualization platform. Thousands of high-resolution tracks for CAGE, ChIP-seq and similar data have been generated and organized into public track hubs. Customized, reproducible promoter views, combining EPD-supplied tracks with native UCSC Genome Browser tracks, can be accessed from the organism summary pages or from individual promoter entries. Moreover, thanks to recent improvements and stabilization of ncRNA gene catalogs, we were able to release promoter collections for certain classes of ncRNAs from human and mouse. Furthermore, we developed automatic computational protocols to assign orphan TSS peaks to downstream genes based on paired-end (RAMPAGE) TSS mapping data, which enabled us to add nearly 9000 new entries to the human promoter collection. Since our last article in this journal, EPD was extended to five more model organisms: rhesus monkey, rat, dog, chicken and Plasmodium falciparum.

Factors Influencing Epigenetic Mechanisms: Is There A Role for Bariatric Surgery?

Epigenetics is the interaction between the genome and environmental stimuli capable of influencing gene expression during development and aging. A large number of studies have shown that metabolic diseases are highly associated with epigenetic alterations, suggesting that epigenetic factors may play a central role in obesity. To investigate these relationships, we focus our attention on the most common epigenetic modifications that occur in obesity, including DNA methylation and post-translational modifications of histones. We also consider bariatric surgery as an epigenetic factor, evaluating how the anatomic and physiologic modifications induced by these surgical techniques can change gene expression. Here we discuss the importance of epigenetic mechanisms in chronic disease and cancer, and the role of epigenetic disturbances in obesity, with a focus on the role of bariatric surgery.