The dbGaP data browser: a new tool for browsing dbGaP controlled-access genomic data

Integrated analysis of transcriptome and epigenome reveals ENSR00000272060 as a potential biomarker in gastric cancer

Background: Enhancer RNAs (eRNAs) are involved in gene expression regulation. Although functional roles of eRNAs in the pathophysiology of neoplasms have been reported, their involvement in gastric cancer (GC) is less known. Materials & methods: A network-based integrative approach was utilized for analyzing transcriptome and epigenome alterations in GC, and an eRNA was selected for experimental validation. Survival analysis and clinicopathological associations were also performed. Results: A hub eRNA, ENSR00000272060, showed significantly increased expression in tumor versus nontumor tissues, as well as an association with clinicopathological features. A seven-gene prognostic model was also constructed. Conclusion: The constructed network provides a comprehensive understanding of the underlying processes implicated in the progression of GC, along with a starting point from which to derive potential diagnostic/prognostic biomarkers.

GeniePool: genomic database with corresponding annotated samples based on a cloud data lake architecture

In recent years, there are a huge influx of genomic data and a growing need for its phenotypic correlations, yet existing genomic databases do not allow easy storage and accessibility to the combined phenotypic-genotypic information. Freely accessible allele frequency (AF) databases, such as gnomAD, are crucial for evaluating variants but lack correlated phenotype data. The Sequence Read Archive (SRA) accumulates hundreds of thousands of next-generation sequencing (NGS) samples tagged by their submitters and various attributes. However, samples are stored in large raw format files, inaccessible for a common user. To make thousands of NGS samples and their corresponding additional attributes easily available to clinicians and researchers, we generated a pipeline that continuously downloads raw human NGS data uploaded to SRA using SRAtoolkit and preprocesses them using GATK pipeline. Data are then stored efficiently in a cloud data lake and can be accessed via a representational state transfer application programming interface (REST API) and a user-friendly website. We thus generated GeniePool, a simple and intuitive web service and API for querying NGS data from SRA with direct access to information related to each sample and related studies, providing significant advantages over existing databases for both clinical and research usages. Utilizing data lake infrastructure, we were able to generate a multi-purpose tool that can serve many clinical and research use cases. We expect users to explore the meta-data served via GeniePool both in daily clinical practice and in versatile research endeavours. Database URL https://geniepool.link.

Construction of a three-component regulatory network of transcribed ultraconserved regions for the identification of prognostic biomarkers in gastric cancer

Altered expression and functional roles of the transcribed ultraconserved regions (T-UCRs), as genomic sequences with 100% conservation between the genomes of human, mouse, and rat, in the pathophysiology of neoplasms has already been investigated. Nevertheless, the relevance of the functions for T-UCRs in gastric cancer (GC) is still the subject of inquiry. In the current study, we first used a genome-wide profiling approach to analyze the expression of T-UCRs in GC patients. Then, we constructed a three-component regulatory network and investigated potential diagnostic and prognostic values of the T-UCRs. The Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD) dataset was used as a resource for the RNA-sequencing data. FeatureCounts was utilized to quantify the number of reads mapped to each T-UCR. Differential expression analysis was then conducted using DESeq2. In the following, interactions between T-UCRs, microRNAs (miRNAs), and messenger RNAs (mRNAs) were combined into a three-component network. Enrichment analyses were performed and a protein-protein interaction (PPI) network was constructed. The R Survival package was utilized to identify survival-related significantly differentially expressed T-UCRs (DET-UCRs). Using an in-house cohort of GC tissues, expression of two DET-UCRs was furthermore experimentally verified. Our results showed that several T-UCRs were dysregulated in TCGA-STAD tumoral samples compared to nontumoral counterparts. The three-component network was constructed which composed of DET-UCRs, miRNAs, and mRNAs nodes. Functional enrichment and PPI network analyses revealed important enriched signaling pathways and gene ontologies such as "pathway in cancer" and regulation of cell proliferation and apoptosis. Five T-UCRs were significantly correlated with the overall survival of GC patients. While no expression of uc.232 was observed in our in-house cohort of GC tissues, uc.343 showed an increased expression, although not statistically significant, in gastric tumoral tissues. The constructed three-component regulatory network of T-UCRs in GC presents a comprehensive understanding of the underlying gene expression regulation processes involved in tumor development and can serve as a basis to investigate potential prognostic biomarkers and therapeutic targets.

Consent Codes: Maintaining Consent in an Ever-expanding Open Science Ecosystem

We previously proposed a structure for recording consent-based data use 'categories' and 'requirements' - Consent Codes - with a view to supporting maximum use and integration of genomic research datasets, and reducing uncertainty about permissible re-use of shared data. Here we discuss clarifications and subsequent updates to the Consent Codes (v4) based on new areas of application (e.g., the neurosciences, biobanking, H3Africa), policy developments (e.g., return of research results), and further practical considerations, including developments in automated approaches to consent management.

Using expression quantitative trait loci data and graph-embedded neural networks to uncover genotype–phenotype interactions

Motivation: A central goal of current biology is to establish a complete functional link between the genotype and phenotype, known as the so-called genotype–phenotype map. With the continuous development of high-throughput technology and the decline in sequencing costs, multi-omics analysis has become more widely employed. While this gives us new opportunities to uncover the correlation mechanisms between single-nucleotide polymorphism (SNP), genes, and phenotypes, multi-omics still faces certain challenges, specifically: 1) When the sample size is large enough, the number of omics types is often not large enough to meet the requirements of multi-omics analysis; 2) each omics’ internal correlations are often unclear, such as the correlation between genes in genomics; 3) when analyzing a large number of traits (p), the sample size (n) is often smaller than p, n << p, hindering the application of machine learning methods in the classification of disease outcomes.

Results: To solve these issues with multi-omics and build a robust classification model, we propose a graph-embedded deep neural network (G-EDNN) based on expression quantitative trait loci (eQTL) data, which achieves sparse connectivity between network layers to prevent overfitting. The correlation within each omics is also considered such that the model more closely resembles biological reality. To verify the capabilities of this method, we conducted experimental analysis using the GSE28127 and GSE95496 data sets from the Gene Expression Omnibus (GEO) database, tested various neural network architectures, and used prior data for feature selection and graph embedding. Results show that the proposed method could achieve a high classification accuracy and easy-to-interpret feature selection. This method represents an extended application of genotype–phenotype association analysis in deep learning networks.

Multi-Omic Approaches to Identify Genetic Factors in Metabolic Syndrome

Metabolic syndrome (MetS) is a highly heritable disease and a major public health burden worldwide. MetS diagnosis criteria are met by the simultaneous presence of any three of the following: high triglycerides, low HDL/high LDL cholesterol, insulin resistance, hypertension, and central obesity. These diseases act synergistically in people suffering from MetS and dramatically increase risk of morbidity and mortality due to stroke and cardiovascular disease, as well as certain cancers. Each of these component features is itself a complex disease, as is MetS. As a genetically complex disease, genetic risk factors for MetS are numerous, but not very powerful individually, often requiring specific environmental stressors for the disease to manifest. When taken together, all sequence variants that contribute to MetS disease risk explain only a fraction of the heritable variance, suggesting additional, novel loci have yet to be discovered. In this article, we will give a brief overview on the genetic concepts needed to interpret genome-wide association studies (GWAS) and quantitative trait locus (QTL) data, summarize the state of the field of MetS physiological genomics, and to introduce tools and resources that can be used by the physiologist to integrate genomics into their own research on MetS and any of its component features. There is a wealth of phenotypic and molecular data in animal models and humans that can be leveraged as outlined in this article. Integrating these multi-omic QTL data for complex diseases such as MetS provides a means to unravel the pathways and mechanisms leading to complex disease and promise for novel treatments. © 2022 American Physiological Society. Compr Physiol 12:1-40, 2022.

Big Data in Nephrology

A huge array of data in nephrology is collected through patient registries, large epidemiological studies, electronic health records, administrative claims, clinical trial repositories, mobile health devices and molecular databases. Application of these big data, particularly using machine-learning algorithms, provides a unique opportunity to obtain novel insights into kidney diseases, facilitate personalized medicine and improve patient care. Efforts to make large volumes of data freely accessible to the scientific community, increased awareness of the importance of data sharing and the availability of advanced computing algorithms will facilitate the use of big data in nephrology. However, challenges exist in accessing, harmonizing and integrating datasets in different formats from disparate sources, improving data quality and ensuring that data are secure and the rights and privacy of patients and research participants are protected. In addition, the optimism for data-driven breakthroughs in medicine is tempered by scepticism about the accuracy of calibration and prediction from in silico techniques. Machine-learning algorithms designed to study kidney health and diseases must be able to handle the nuances of this specialty, must adapt as medical practice continually evolves, and must have global and prospective applicability for external and future datasets.

Current status of use of high throughput nucleotide sequencing in rheumatology

OBJECTIVE

Here, we assess the usage of high throughput sequencing (HTS) in rheumatic research and the availability of public HTS data of rheumatic samples.

METHODS

We performed a semiautomated literature review on PubMed, consisting of an R-script and manual curation as well as a manual search on the Sequence Read Archive for public available HTS data.

RESULTS

Of the 699 identified articles, rheumatoid arthritis (n=182 publications, 26%), systemic lupus erythematous (n=161, 23%) and osteoarthritis (n=152, 22%) are among the rheumatic diseases with the most reported use of HTS assays. The most represented assay is RNA-Seq (n=457, 65%) for the identification of biomarkers in blood or synovial tissue. We also find, that the quality of accompanying clinical characterisation of the sequenced patients differs dramatically and we propose a minimal set of clinical data necessary to accompany rheumatological-relevant HTS data.

CONCLUSION

HTS allows the analysis of a broad spectrum of molecular features in many samples at the same time. It offers enormous potential in novel personalised diagnosis and treatment strategies for patients with rheumatic diseases. Being established in cancer research and in the field of Mendelian diseases, rheumatic diseases are about to become the third disease domain for HTS, especially the RNA-Seq assay. However, we need to start a discussion about reporting of clinical characterisation accompany rheumatological-relevant HTS data to make clinical meaningful use of this data.

Mendelian Randomization in Stroke: A Powerful Approach to Causal Inference and Drug Target Validation

Stroke is a leading cause of death and disability worldwide. However, our understanding of its underlying biology and the number of available treatment options remain limited. Mendelian randomization (MR) offers a powerful approach to identify novel biological pathways and therapeutic targets for this disease. Around ~100 MR studies have been conducted so far to explore, confirm, and quantify causal relationships between several exposures and risk of stroke. In this review, we summarize the current evidence arising from these studies, including those investigating ischemic stroke, hemorrhagic stroke, or both. We highlight the different types of exposures that are currently under study, ranging from well-known cardiovascular risk factors to less established inflammation-related mechanisms. Finally, we provide an overview of future avenues of research and novel approaches, including drug target validation MR, which is poised to have a substantial impact on drug development and drug repurposing.

African Global Representation in Biomedical Sciences

African populations are diverse in their ethnicity, language, culture, and genetics. Although plagued by high disease burdens, until recently the continent has largely been excluded from biomedical studies. Along with limitations in research and clinical infrastructure, human capacity, and funding, this omission has resulted in an underrepresentation of African data and disadvantaged African scientists. This review interrogates the relative abundance of biomedical data from Africa, primarily in genomics and other omics. The visibility of African science through publications is also discussed. A challenge encountered in this review is the relative lack of annotation of data on their geographical or population origin, with African countries represented as a single group. In addition to the abovementioned limitations,the global representation of African data may also be attributed to the hesitation to deposit data in public repositories. Whatever the reason, the disparity should be addressed, as African data have enormous value for scientists in Africa and globally.

Concept libraries for automatic electronic health record based phenotyping: A review

Introduction

Electronic health records (EHR) are linked together to examine disease history and to undertake research into the causes and outcomes of disease. However, the process of constructing algorithms for phenotyping (e.g., identifying disease characteristics) or health characteristics (e.g., smoker) is very time consuming and resource costly. In addition, results can vary greatly between researchers. Reusing or building on algorithms that others have created is a compelling solution to these problems. However, sharing algorithms is not a common practice and many published studies do not detail the clinical code lists used by the researchers in the disease/characteristic definition. To address these challenges, a number of centres across the world have developed health data portals which contain concept libraries (e.g., algorithms for defining concepts such as disease and characteristics) in order to facilitate disease phenotyping and health studies.

Objectives

This study aims to review the literature of existing concept libraries, examine their utilities, identify the current gaps, and suggest future developments.

Methods

The five-stage framework of Arksey and O'Malley was used for the literature search. This approach included defining the research questions, identifying relevant studies through literature review, selecting eligible studies, charting and extracting data, and summarising and reporting the findings.

Results

This review identified seven publicly accessible Electronic Health data concept libraries which were developed in different countries including UK, USA, and Canada. The concept libraries (n = 7) investigated were either general libraries that hold phenotypes of multiple specialties (n = 4) or specialized libraries that manage only certain specialities such as rare diseases (n = 3). There were some clear differences between the general libraries such as archiving data from different electronic sources, and using a range of different types of coding systems. However, they share some clear similarities such as enabling users to upload their own code lists, and allowing users to use/download the publicly accessible code. In addition, there were some differences between the specialized libraries such as difference in ability to search, and if it was possible to use different searching queries such as simple or complex searches. Conversely, there were some similarities between the specialized libraries such as enabling users to upload their own concepts into the libraries and to show where they were published, which facilitates assessing the validity of the concepts. All the specialized libraries aimed to encourage the reuse of research methods such as lists of clinical code and/or metadata.

Conclusion

The seven libraries identified have been developed independently and appear to replicate similar concepts but in different ways. Collaboration between similar libraries would greatly facilitate the use of these libraries for the user. The process of building code lists takes time and effort. Access to existing code lists increases consistency and accuracy of definitions across studies. Concept library developers should collaborate with each other to raise awareness of their existence and of their various functions, which could increase users’ contributions to those libraries and promote their wide-ranging adoption.

Effect of MACC1 Genetic Polymorphisms and Environmental Risk Factors in the Occurrence of Oral Squamous Cell Carcinoma

MACC1 (Metastasis Associated in Colon Cancer 1) is found to regulate the hepatocyte growth factor (HGF)/Met signal pathway, and plays an important role in tumor proliferation, angiogenesis, and metastasis. However, the relationships between MACC1 SNPs (single nucleotide polymorphisms) and oral cancer are still blurred. In this study, five SNPs (rs3095007, rs1990172, rs4721888, rs975263, and rs3735615) were genotyped in 911 oral cancer patients and 1200 healthy individuals by real-time polymerase chain reaction (PCR), and the associations of oral cancer with the SNP genotypes, environmental risk factors, and clinicopathological characteristics were further analyzed. Our results showed that individuals who had GC genotype or C-allele (GC + CC) in rs4721888 would have a higher risk for oral cancer incidence than GG genotype after adjustment for betel quid chewing, cigarette smoking, and alcohol drinking. Moreover, the 715 oral cancer patients with a betel quid chewing habit, who had C-allele (TC + CC) in rs975263, would have a higher risk for lymph node metastasis. Further analyses of the sequences of rs4721888 revealed that the C-allele of rs4721888 would be a putative exonic splicing enhancer. In conclusion, MACC1 SNP rs4721888 would elevate the susceptibility for oral cancer, and SNP rs975263 would increase the metastasis risk for oral cancer patients with a betel quid chewing habit. Our data suggest that SNP rs4721888 could be a putative genetic marker for oral cancer, and SNP rs975362 may have the potential to be a prognostic marker of metastasis in an oral cancer patient.

MarkVCID cerebral small vessel consortium: I. Enrollment, clinical, fluid protocols

The concept of vascular contributions to cognitive impairment and dementia (VCID) derives from more than two decades of research indicating that (1) most older individuals with cognitive impairment have post mortem evidence of multiple contributing pathologies and (2) along with the preeminent role of Alzheimer's disease (AD) pathology, cerebrovascular disease accounts for a substantial proportion of this contribution. Contributing cerebrovascular processes include both overt strokes caused by etiologies such as large vessel occlusion, cardioembolism, and embolic infarcts of unknown source, and frequently asymptomatic brain injuries caused by diseases of the small cerebral vessels. Cerebral small vessel diseases such as arteriolosclerosis and cerebral amyloid angiopathy, when present at moderate or greater pathologic severity, are independently associated with worse cognitive performance and greater likelihood of dementia, particularly in combination with AD and other neurodegenerative pathologies. Based on this evidence, the US National Alzheimer's Project Act explicitly authorized accelerated research in vascular and mixed dementia along with frontotemporal and Lewy body dementia and AD itself. Biomarker development has been consistently identified as a key step toward translating scientific advances in VCID into effective prevention and treatment strategies. Validated biomarkers can serve a range of purposes in trials of candidate interventions, including (1) identifying individuals at increased VCID risk, (2) diagnosing the presence of cerebral small vessel disease or specific small vessel pathologies, (3) stratifying study participants according to their prognosis for VCID progression or treatment response, (4) demonstrating an intervention's target engagement or pharmacodynamic mechanism of action, and (5) monitoring disease progression during treatment. Effective biomarkers allow academic and industry investigators to advance promising interventions at early stages of development and discard interventions with low success likelihood. The MarkVCID consortium was formed in 2016 with the goal of developing and validating fluid- and imaging-based biomarkers for the cerebral small vessel diseases associated with VCID. MarkVCID consists of seven project sites and a central coordinating center, working with the National Institute of Neurologic Diseases and Stroke and National Institute on Aging under cooperative agreements. Through an internal selection process, MarkVCID has identified a panel of 11 candidate biomarker "kits" (consisting of the biomarker measure and the clinical and cognitive data used to validate it) and established a range of harmonized procedures and protocols for participant enrollment, clinical and cognitive evaluation, collection and handling of fluid samples, acquisition of neuroimaging studies, and biomarker validation. The overarching goal of these protocols is to generate rigorous validating data that could be used by investigators throughout the research community in selecting and applying biomarkers to multi-site VCID trials. Key features of MarkVCID participant enrollment, clinical/cognitive testing, and fluid biomarker procedures are summarized here, with full details in the following text, tables, and supplemental material, and a description of the MarkVCID imaging biomarker procedures in a companion paper, "MarkVCID Cerebral small vessel consortium: II. Neuroimaging protocols." The procedures described here address a range of challenges in MarkVCID's design, notably: (1) acquiring all data under informed consent and enrollment procedures that allow unlimited sharing and open-ended analyses without compromising participant privacy rights; (2) acquiring the data in a sufficiently wide range of study participants to allow assessment of candidate biomarkers across the various patient groups who might ultimately be targeted in VCID clinical trials; (3) defining a common dataset of clinical and cognitive elements that contains all the key outcome markers and covariates for VCID studies and is realistically obtainable during a practical study visit; (4) instituting best fluid-handling practices for minimizing avoidable sources of variability; and (5) establishing rigorous procedures for testing the reliability of candidate fluid-based biomarkers across replicates, assay runs, sites, and time intervals (collectively defined as the biomarker's instrumental validity). Participant Enrollment Project sites enroll diverse study cohorts using site-specific inclusion and exclusion criteria so as to provide generalizable validation data across a range of cognitive statuses, risk factor profiles, small vessel disease severities, and racial/ethnic characteristics representative of the diverse patient groups that might be enrolled in a future VCID trial. MarkVCID project sites include both prospectively enrolling centers and centers providing extant data and samples from preexisting community- and population-based studies. With approval of local institutional review boards, all sites incorporate MarkVCID consensus language into their study documents and informed consent agreements. The consensus language asks prospectively enrolled participants to consent to unrestricted access to their data and samples for research analysis within and outside MarkVCID. The data are transferred and stored as a de-identified dataset as defined by the Health Insurance Portability and Accountability Act Privacy Rule. Similar human subject protection and informed consent language serve as the basis for MarkVCID Research Agreements that act as contracts and data/biospecimen sharing agreements across the consortium. Clinical and Cognitive Data Clinical and cognitive data are collected across prospectively enrolling project sites using common MarkVCID instruments. The clinical data elements are modified from study protocols already in use such as the Alzheimer's Disease Center program Uniform Data Set Version 3 (UDS3), with additional focus on VCID-related items such as prior stroke and cardiovascular disease, vascular risk factors, focal neurologic findings, and blood testing for vascular risk markers and kidney function including hemoglobin A1c, cholesterol subtypes, triglycerides, and creatinine. Cognitive assessments and rating instruments include the Clinical Dementia Rating Scale, Geriatric Depression Scale, and most of the UDS3 neuropsychological battery. The cognitive testing requires ≈60 to 90 minutes. Study staff at the prospectively recruiting sites undergo formalized training in all measures and review of their first three UDS3 administrations by the coordinating center. Collection and Handling of Fluid Samples Fluid sample types collected for MarkVCID biomarker kits are serum, ethylenediaminetetraacetic acid-plasma, platelet-poor plasma, and cerebrospinal fluid (CSF) with additional collection of packed cells to allow future DNA extraction and analyses. MarkVCID fluid guidelines to minimize variability include fasting morning fluid collections, rapid processing, standardized handling and storage, and avoidance of CSF contact with polystyrene. Instrumental Validation for Fluid-Based Biomarkers Instrumental validation of MarkVCID fluid-based biomarkers is operationally defined as determination of intra-plate and inter-plate repeatability, inter-site reproducibility, and test-retest repeatability. MarkVCID study participants both with and without advanced small vessel disease are selected for these determinations to assess instrumental validity across the full biomarker assay range. Intra- and inter-plate repeatability is determined by repeat assays of single split fluid samples performed at individual sites. Inter-site reproducibility is determined by assays of split samples distributed to multiple sites. Test-retest repeatability is determined by assay of three samples acquired from the same individual, collected at least 5 days apart over a 30-day period and assayed on a single plate. The MarkVCID protocols are designed to allow direct translation of the biomarker validation results to multicenter trials. They also provide a template for outside groups to perform analyses using identical methods and therefore allow direct comparison of results across studies and centers. All MarkVCID protocols are available to the biomedical community and intended to be shared. In addition to the instrumental validation procedures described here, each of the MarkVCID kits will undergo biological validation to determine whether the candidate biomarker measures important aspects of VCID such as cognitive function. Analytic methods and results of these validation studies for the 11 MarkVCID biomarker kits will be published separately. The results of this rigorous validation process will ultimately determine each kit's potential usefulness for multicenter interventional trials aimed at preventing or treating small vessel disease related VCID.

Identification of novel susceptibility loci for non-syndromic cleft lip with or without cleft palate

Although several genome‐wide association studies (GWAS) of non‐syndromic cleft lip with or without cleft palate (NSCL/P) have been reported, more novel association signals are remained to be exploited. Here, we performed an in‐depth analysis of our previously published Chinese GWAS cohort study with replication in an extra dbGaP case‐parent trios and another in‐house Nanjing cohort, and finally identified five novel significant association signals (rs11119445: 3’ of SERTAD4, P = 6.44 × 10⁻¹⁴; rs227227 and rs12561877: intron of SYT14, P = 5.02 × 10⁻¹³ and 2.80 × 10⁻¹¹, respectively; rs643118: intron of TRAF3IP3, P = 4.45 × 10⁻⁶; rs2095293: intron of NR6A1, P = 2.98 × 10⁻⁵). The mean (standard deviation) of the weighted genetic risk score (wGRS) from these SNPs was 1.83 (0.65) for NSCL/P cases and 1.58 (0.68) for controls, respectively (P = 2.67 × 10⁻¹⁶). Rs643118 was identified as a shared susceptible factor of NSCL/P among Asians and Europeans, while rs227227 may contribute to the risk of NSCL/P as well as NSCPO. In addition, sertad4 knockdown zebrafish models resulted in down‐regulation of sox2 and caused oedema around the heart and mandibular deficiency, compared with control embryos. Taken together, this study has improved our understanding of the genetic susceptibility to NSCL/P and provided further clues to its aetiology in the Chinese population.

dbgap2x: an R package to explore and extract data from the database of Genotypes and Phenotypes (dbGaP)

SUMMARY

Based on the Genomic Data Sharing Policy issued in August 2007, the National Institutes of Health (NIH) has supported several repositories such as the database of Genotypes and Phenotypes (dbGaP). dbGaP is an online repository that provides access to large-scale genetic and phenotypic datasets with more than 1000 studies. However, navigating the website and understanding the relationship between the studies are not easy tasks. Moreover, the decryption of the files is a complex procedure. In this study we propose the dbgap2x R package that covers a broad range of functions for searching dbGaP studies, exploring the characteristics of a study and easily decrypting the files from dbGaP.

AVAILABILITY AND IMPLEMENTATION

dbgap2x is an R package with the code available at https://github.com/gversmee/dbgap2x. A containerized version including the package, a Jupyter server and with a Notebook example is available at https://hub.docker.com/r/gversmee/dbgap2x.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Computational resources associating diseases with genotypes, phenotypes and exposures

The causes of a disease and its therapies are not only related to genotypes, but also associated with other factors, including phenotypes, environmental exposures, drugs and chemical molecules. Distinguishing disease-related factors from many neutral factors is critical as well as difficult. Over the past two decades, bioinformaticians have developed many computational resources to integrate the omics data and discover associations among these factors. However, researchers and clinicians are experiencing difficulties in choosing appropriate resources from hundreds of relevant databases and software tools. Here, in order to assist the researchers and clinicians, we systematically review the public computational resources of human diseases related to genotypes, phenotypes, environment factors, drugs and chemical exposures. We briefly describe the development history of these computational resources, followed by the details of the relevant databases and software tools. We finally conclude with a discussion of current challenges and future opportunities as well as prospects on this topic.

PDX Finder: A portal for patient-derived tumor xenograft model discovery

Patient-derived tumor xenograft (PDX) mouse models are a versatile oncology research platform for studying tumor biology and for testing chemotherapeutic approaches tailored to genomic characteristics of individual patients’ tumors. PDX models are generated and distributed by a diverse group of academic labs, multi-institution consortia and contract research organizations. The distributed nature of PDX repositories and the use of different metadata standards for describing model characteristics presents a significant challenge to identifying PDX models relevant to specific cancer research questions. The Jackson Laboratory and EMBL-EBI are addressing these challenges by co-developing PDX Finder, a comprehensive open global catalog of PDX models and their associated datasets. Within PDX Finder, model attributes are harmonized and integrated using a previously developed community minimal information standard to support consistent searching across the originating resources. Links to repositories are provided from the PDX Finder search results to facilitate model acquisition and/or collaboration. The PDX Finder resource currently contains information for 1985 PDX models of diverse cancers including those from large resources such as the Patient-Derived Models Repository, PDXNet and EurOPDX. Individuals or organizations that generate and distribute PDXs are invited to increase the ‘findability’ of their models by participating in the PDX Finder initiative at www.pdxfinder.org.

DDBJ update: the Genomic Expression Archive (GEA) for functional genomics data

The Genomic Expression Archive (GEA) for functional genomics data from microarray and high-throughput sequencing experiments has been established at the DNA Data Bank of Japan (DDBJ) Center (https://www.ddbj.nig.ac.jp), which is a member of the International Nucleotide Sequence Database Collaboration (INSDC) with the US National Center for Biotechnology Information and the European Bioinformatics Institute. The DDBJ Center collects nucleotide sequence data and associated biological information from researchers and also services the Japanese Genotype–phenotype Archive (JGA) with the National Bioscience Database Center for collecting human data. To automate the submission process, we have implemented the DDBJ BioSample validator which checks submitted records, auto-corrects their format, and issues error messages and warnings if necessary. The DDBJ Center also operates the NIG supercomputer, prepared for analyzing large-scale genome sequences. We now offer a secure platform specifically to handle personal human genomes. This report describes database activities for INSDC and JGA over the past year, the newly launched GEA, submission, retrieval, and analysis services available in our supercomputer system and their recent developments.

Detection of Allosteric Effects of lncRNA Secondary Structures Altered by SNPs in Human Diseases

Recent studies have shown that structuralized long non-coding RNAs (lncRNAs) play important roles in genetic and epigenetic processes. The spatial structures of most lncRNAs can be altered by distinct in vivo and in vitro cellular environments, as well as by DNA structural variations, such as single-nucleotide polymorphisms (SNPs) and variants (SNVs). In the present study, we extended candidate SNPs that had linkage disequilibria with those significantly associated with lung diseases in genome-wide association studies in order to investigate potential disease mechanisms originating from SNP structural changes of host lncRNAs. Following accurate alignments, we recognized 115 ternary-relationship pairs among 41 SNPs, 10 lncRNA transcripts, and 1 type of lung disease (adenocarcinoma of the lung). Then, we evaluated the structural heterogeneity induced by SNP alleles by developing a local-RNA-structure alignment algorithm and employing randomized strategies to determine the significance of structural variation. We identified four ternary-relationship pairs that were significantly associated with SNP-induced lncRNA allosteric effects. Moreover, these conformational changes disrupted the interactive regions and binding affinities of lncRNA-HCG23 and TF-E2F6, suggesting that these may represent regulatory mechanisms in lung diseases. Taken together, our findings support that SNP-induced changes in lncRNA conformations regulate many biological processes, providing novel insight into the role of the lncRNA “structurome” in human diseases.

Genetic Variation and Response to Neurocritical Illness: a Powerful Approach to Identify Novel Pathophysiological Mechanisms and Therapeutic Targets

Disease-specific therapeutic options for critically ill neurological patients are limited. The identification of new preventive, therapeutic, and rehabilitation strategies is of the utmost importance in the field of neurocritical care research. Population genetics offers powerful tools to identify and prioritize biological pathways to be targeted by novel interventions. New treatments with supportive genetic evidence have twice the chances of obtaining final FDA approval compared to those without this support. Large collaborations, public access to data, reproducible science, and innovative analytical methods have exponentially increased the pace of discoveries related to neurocritical care genetics.

Potential prognostic impact of EBV RNA-seq reads in gastric cancer: a reanalysis of The Cancer Genome Atlas cohort

Epstein-Barr virus (EBV)-associated gastric cancer (EBVaGC), whose prognosis remains controversial, is diagnosed by in situ hybridization of EBV-derived EBER1/2 small RNAs. In The Cancer Genome Atlas (TCGA) Stomach Adenocarcinoma (STAD) project, the EBV molecular subtype was determined through a combination of multiple next-generation sequencing methods, but not by the gold standard in situ hybridization method. This leaves unanswered questions regarding the discordance of EBV positivity detected by different approaches and the threshold of sequencing reads. Therefore, we reanalyzed the TCGA-STAD RNA sequencing (RNA-seq) dataset including 375 tumor and 32 normal samples, using our analysis pipeline. We defined a reliable threshold for EBV-derived next-generation sequencing reads by mapping them to the EBV genome with three different random arbitrary alignments. We analyzed the prognostic impact of EBV status on the histopathological subtypes of gastric cancer. EBV-positive cases identified by reanalysis comprised nearly half of the cases (49.6%) independent from infiltrating lymphocyte signatures, and showed significantly longer overall survival for adenocarcinomas of the 'not-otherwise-specified' type [P = 0.016 (log-rank test); hazard ratios (HR): 0.476; 95% CI: 0.260-0.870, P = 0.016 (Cox univariate analysis)], but shorter overall survival for the tubular adenocarcinoma type [P = 0.005 (log-rank test); HR: 3.329; 95% CI: 1.406-7.885, P = 0.006 (Cox univariate analysis)]. These results demonstrate that the EBV positivity rates were higher when determined by RNA-seq than when determined by EBER1/2 in situ hybridization. The RNA-seq-based EBV positivity demonstrated distinct results for gastric cancer prognosis depending on the histopathological subtype, suggesting its potential to be used in clinical prognoses.

DDBJ Database updates and computational infrastructure enhancement

The Bioinformation and DDBJ Center (https://www.ddbj.nig.ac.jp) in the National Institute of Genetics (NIG) maintains a primary nucleotide sequence database as a member of the International Nucleotide Sequence Database Collaboration (INSDC) in partnership with the US National Center for Biotechnology Information and the European Bioinformatics Institute. The NIG operates the NIG supercomputer as a computational basis for the construction of DDBJ databases and as a large-scale computational resource for Japanese biologists and medical researchers. In order to accommodate the rapidly growing amount of deoxyribonucleic acid (DNA) nucleotide sequence data, NIG replaced its supercomputer system, which is designed for big data analysis of genome data, in early 2019. The new system is equipped with 30 PB of DNA data archiving storage; large-scale parallel distributed file systems (13.8 PB in total) and 1.1 PFLOPS computation nodes and graphics processing units (GPUs). Moreover, as a starting point of developing multi-cloud infrastructure of bioinformatics, we have also installed an automatic file transfer system that allows users to prevent data lock-in and to achieve cost/performance balance by exploiting the most suitable environment from among the supercomputer and public clouds for different workloads.

Rare disease data stewardship in Canada

The Canadian Genomics Partnership for Rare Diseases, spearheaded by Genome Canada, will integrate genome-wide sequencing to rare disease clinical care in Canada. Centralized and tiered models of data stewardship are proposed to ensure that the data generated can be shared for secondary clinical, research, and quality assurance purposes in compliance with ethics and law. The principal ethico-legal obligations of clinicians, researchers, and institutions are synthesized. Governance infrastructures such as registered access platforms, data access compliance offices, and Beacon systems are proposed as potential organizational and technical foundations of responsible rare disease data sharing. The appropriate delegation of responsibilities, the transparent communication of rights and duties, and the integration of data privacy safeguards into infrastructure design are proposed as the cornerstones of rare disease data stewardship.

Phyotochemical candidates repurposing for cancer therapy and their molecular mechanisms

Though limited success through chemotherapy, radiotherapy and surgery has been obtained for efficient cancer therapy for modern decades, cancers are still considered high burden to human health worldwide to date. Recently repurposing drugs are attractive with lower cost and shorter time compared to classical drug discovery, just as Metformin from Galega officinalis, originally approved for treating Type 2 diabetes by FDA, is globally valued at millions of US dollars for cancer therapy. As most previous reviews focused on FDA approved drugs and synthetic agents, current review discussed the anticancer potential of phytochemicals originally approved for treatment of cardiovascular diseases, diabetes, infectious diarrhea, depression and malaria with their molecular mechanisms and efficacies and suggested future research perspectives.

Substantial batch effects in TCGA exome sequences undermine pan-cancer analysis of germline variants

BACKGROUND

In recent years, research on cancer predisposition germline variants has emerged as a prominent field. The identity of somatic mutations is based on a reliable mapping of the patient germline variants. In addition, the statistics of germline variants frequencies in healthy individuals and cancer patients is the basis for seeking candidates for cancer predisposition genes. The Cancer Genome Atlas (TCGA) is one of the main sources of such data, providing a diverse collection of molecular data including deep sequencing for more than 30 types of cancer from > 10,000 patients.

METHODS

Our hypothesis in this study is that whole exome sequences from blood samples of cancer patients are not expected to show systematic differences among cancer types. To test this hypothesis, we analyzed common and rare germline variants across six cancer types, covering 2241 samples from TCGA. In our analysis we accounted for inherent variables in the data including the different variant calling protocols, sequencing platforms, and ethnicity.

RESULTS

We report on substantial batch effects in germline variants associated with cancer types. We attribute the effect to the specific sequencing centers that produced the data. Specifically, we measured 30% variability in the number of reported germline variants per sample across sequencing centers. The batch effect is further expressed in nucleotide composition and variant frequencies. Importantly, the batch effect causes substantial differences in germline variant distribution patterns across numerous genes, including prominent cancer predisposition genes such as BRCA1, RET, MAX, and KRAS. For most of known cancer predisposition genes, we found a distinct batch-dependent difference in germline variants.

CONCLUSION

TCGA germline data is exposed to strong batch effects with substantial variabilities among TCGA sequencing centers. We claim that those batch effects are consequential for numerous TCGA pan-cancer studies. In particular, these effects may compromise the reliability and the potency to detect new cancer predisposition genes. Furthermore, interpretation of pan-cancer analyses should be revisited in view of the source of the genomic data after accounting for the reported batch effects.

DNA Data Bank of Japan: 30th anniversary

The DNA Data Bank of Japan (DDBJ) Center (http://www.ddbj.nig.ac.jp) has been providing public data services for 30 years since 1987. We are collecting nucleotide sequence data and associated biological information from researchers as a member of the International Nucleotide Sequence Database Collaboration (INSDC), in collaboration with the US National Center for Biotechnology Information and the European Bioinformatics Institute. The DDBJ Center also services the Japanese Genotype-phenotype Archive (JGA) with the National Bioscience Database Center to collect genotype and phenotype data of human individuals. Here, we outline our database activities for INSDC and JGA over the past year, and introduce submission, retrieval and analysis services running on our supercomputer system and their recent developments. Furthermore, we highlight our responses to the amended Japanese rules for the protection of personal information and the launch of the DDBJ Group Cloud service for sharing pre-publication data among research groups.

Interactions of PVT1 and CASC11 on Prostate Cancer Risk in African Americans

BACKGROUND

African American (AA) men have a higher risk of developing prostate cancer than white men. SNPs are known to play an important role in developing prostate cancer. The impact of PVT1 and its neighborhood genes (CASC11 and MYC) on prostate cancer risk are getting more attention recently. The interactions among these three genes associated with prostate cancer risk are understudied, especially for AA men. The objective of this study is to investigate SNP-SNP interactions in the CASC11-MYC-PVT1 region associated with prostate cancer risk in AA men.

METHODS

We evaluated 205 SNPs using the 2,253 prostate cancer patients and 2,423 controls and applied multiphase (discovery-validation) design. In addition to SNP individual effects, SNP-SNP interactions were evaluated using the SNP Interaction Pattern Identifier, which assesses 45 patterns.

RESULTS

Three SNPs (rs9642880, rs16902359, and rs12680047) and 79 SNP-SNP pairs were significantly associated with prostate cancer risk. These two SNPs (rs16902359 and rs9642880) in CASC11 interacted frequently with other SNPs with 56 and 9 pairs, respectively. We identified the novel interaction of CASC11-PVT1, which is the most common gene interaction (70%) in the top 79 pairs. Several top SNP interactions have a moderate to large effect size (OR, 0.27-0.68) and have a higher prediction power to prostate cancer risk than SNP individual effects.

CONCLUSIONS

Novel SNP-SNP interactions in the CASC11-MYC-PVT1 region have a larger impact than SNP individual effects on prostate cancer risk in AA men.

IMPACT

This gene-gene interaction between CASC11 and PVT1 can provide valuable information to reveal potential biological mechanisms of prostate cancer development.

Registered access: authorizing data access

The Global Alliance for Genomics and Health (GA4GH) proposes a data access policy model-"registered access"-to increase and improve access to data requiring an agreement to basic terms and conditions, such as the use of DNA sequence and health data in research. A registered access policy would enable a range of categories of users to gain access, starting with researchers and clinical care professionals. It would also facilitate general use and reuse of data but within the bounds of consent restrictions and other ethical obligations. In piloting registered access with the Scientific Demonstration data sharing projects of GA4GH, we provide additional ethics, policy and technical guidance to facilitate the implementation of this access model in an international setting.

Best practice data life cycle approaches for the life sciences

Throughout history, the life sciences have been revolutionised by technological advances; in our era this is manifested by advances in instrumentation for data generation, and consequently researchers now routinely handle large amounts of heterogeneous data in digital formats. The simultaneous transitions towards biology as a data science and towards a 'life cycle' view of research data pose new challenges. Researchers face a bewildering landscape of data management requirements, recommendations and regulations, without necessarily being able to access data management training or possessing a clear understanding of practical approaches that can assist in data management in their particular research domain. Here we provide an overview of best practice data life cycle approaches for researchers in the life sciences/bioinformatics space with a particular focus on 'omics' datasets and computer-based data processing and analysis. We discuss the different stages of the data life cycle and provide practical suggestions for useful tools and resources to improve data management practices.

Sorting Five Human Tumor Types Reveals Specific Biomarkers and Background Classification Genes

We applied two state-of-the-art, knowledge independent data-mining methods - Dynamic Quantum Clustering (DQC) and t-Distributed Stochastic Neighbor Embedding (t-SNE) - to data from The Cancer Genome Atlas (TCGA). We showed that the RNA expression patterns for a mixture of 2,016 samples from five tumor types can sort the tumors into groups enriched for relevant annotations including tumor type, gender, tumor stage, and ethnicity. DQC feature selection analysis discovered 48 core biomarker transcripts that clustered tumors by tumor type. When these transcripts were removed, the geometry of tumor relationships changed, but it was still possible to classify the tumors using the RNA expression profiles of the remaining transcripts. We continued to remove the top biomarkers for several iterations and performed cluster analysis. Even though the most informative transcripts were removed from the cluster analysis, the sorting ability of remaining transcripts remained strong after each iteration. Further, in some iterations we detected a repeating pattern of biological function that wasn't detectable with the core biomarker transcripts present. This suggests the existence of a "background classification" potential in which the pattern of gene expression after continued removal of "biomarker" transcripts could still classify tumors in agreement with the tumor type.

Gene Variation of Endoplasmic Reticulum Aminopeptidases 1 and 2, and Risk of Blood Pressure Progression and Incident Hypertension among 17,255 Initially Healthy Women

Recent studies have demonstrated the importance of endoplasmic reticulum aminopeptidase (ERAP) in blood pressure (BP) homeostasis. To date, no large prospective, genetic–epidemiological data are available on genetic variation within ERAP and hypertension risk. The association of 45 genetic variants of ERAP1 and ERAP2 was investigated in 17,255 Caucasian female participants from the Women's Genome Health Study. All subjects were free of hypertension at baseline. During an 18-year follow-up period, 10,216 incident hypertensive cases were identified. Multivariable linear, logistic, and Cox regression analyses were performed to assess the relationship of genotypes with baseline BP levels, BP progression at 48 months, and incident hypertension assuming an additive genetic model. Linear regression analyses showed associations of four tSNPs (ERAP1: rs27524; ERAP2: rs3733904, rs4869315, and rs2549782; all p < 0.05) with baseline systolic BP levels. Three tSNPs (ERAP1: rs27851, rs27429, and rs34736, all p < 0.05) were associated with baseline diastolic BP levels. Multivariable logistic regression analysis showed that ERAP1 rs27772 was associated with BP progression at 48 months (p = 0.0366). Multivariable Cox regression analysis showed an association of three tSNPs (ERAP1: rs469783 and rs10050860; ERAP2: rs2927615; all p < 0.05) with risk of incident hypertension. Analyses of dbGaP for genotype–phenotype association and GTEx Portal for gene expression quantitative trait loci revealed five tSNPs with differential association of BP and nine tSNPs with lower ERAP1 and ERAP2 mRNA expression levels, respectively. The present study suggests that ERAP1 and ERAP2 gene variation may be useful for risk assessment of BP progression and the development of hypertension.

A FAIR guide for data providers to maximise sharing of human genomic data

It is generally acknowledged that, for reproducibility and progress of human genomic research, data sharing is critical. For every sharing transaction, a successful data exchange is produced between a data consumer and a data provider. Providers of human genomic data (e.g., publicly or privately funded repositories and data archives) fulfil their social contract with data donors when their shareable data conforms to FAIR (findable, accessible, interoperable, reusable) principles. Based on our experiences via Repositive (https://repositive.io), a leading discovery platform cataloguing all shared human genomic datasets, we propose guidelines for data providers wishing to maximise their shared data's FAIRness.

Informed Consent in Translational Genomics: Insufficient Without Trustworthy Governance

Neither the range of potential results from genomic research that might be returned to participants nor future uses of stored data and biospecimens can be fully predicted at the outset of a study. Informed consent procedures require clear explanations about how and by whom decisions are made and what principles and criteria apply. To ensure trustworthy research governance, there is also a need for empirical studies incorporating public input to evaluate and strengthen these processes.

Validation of genotype imputation in Southeast Asian populations and the effect of single nucleotide polymorphism annotation on imputation outcome

Background

Imputation involves the inference of untyped single nucleotide polymorphisms (SNPs) in genome-wide association studies. The haplotypic reference of choice for imputation in Southeast Asian populations is unclear. Moreover, the influence of SNP annotation on imputation results has not been examined.

Methods

This study was divided into two parts. In the first part, we applied imputation to genotyped SNPs from Southeast Asian populations from the Pan-Asian SNP database. Five percent of the total SNPs were removed. The remaining SNPs were applied to imputation with IMPUTE2. The imputed outcomes were verified with the removed SNPs. We compared imputation references from Chinese and Japanese haplotypes from the HapMap phase II (HMII) and the complete set of haplotypes from the 1000 Genomes Project (1000G). The second part was imputation accuracy and yield in Thai patient dataset. Half of the autosomal SNPs was removed to create Set 1. Another dataset, Set 2, was then created where we switched which half of the SNPs were removed. Both Set 1 and Set 2 were imputed with HMII to create a complete imputed SNPs dataset. The dataset was used to validate association testing, SNPs annotation and imputation outcome.

Results

The accuracy was highest for all populations when using the HMII reference, but at the cost of a lower yield. Thai genotypes showed the highest accuracy over other populations in both HMII and 1000G panels, although accuracy and yield varied across chromosomes. Imputation was tested in a clinical dataset to compare accuracy in gene-related regions, and coding regions were found to have a higher accuracy and yield.

Conclusions

This work provides the first evidence of imputation reference selection for Southeast Asian studies and highlights the effects of SNP locations respective to genes on imputation outcome. Researchers will need to consider the trade-off between accuracy and yield in future imputation studies.

Electronic supplementary material

The online version of this article (10.1186/s12881-018-0534-8) contains supplementary material, which is available to authorized users.

Best practice data life cycle approaches for the life sciences

Throughout history, the life sciences have been revolutionised by technological advances; in our era this is manifested by advances in instrumentation for data generation, and consequently researchers now routinely handle large amounts of heterogeneous data in digital formats. The simultaneous transitions towards biology as a data science and towards a 'life cycle' view of research data pose new challenges. Researchers face a bewildering landscape of data management requirements, recommendations and regulations, without necessarily being able to access data management training or possessing a clear understanding of practical approaches that can assist in data management in their particular research domain. Here we provide an overview of best practice data life cycle approaches for researchers in the life sciences/bioinformatics space with a particular focus on 'omics' datasets and computer-based data processing and analysis. We discuss the different stages of the data life cycle and provide practical suggestions for useful tools and resources to improve data management practices.