Constraints on Biological Mechanism from Disease Comorbidity Using Electronic Medical Records and Database of Genetic Variants

The Relationship Between Work-Related Stress and Depression: A Scoping Review

Objectives

Work-related stress is highly prevalent. Recent systematic reviews concluded on a significant association between common work-related stress measures and depression. Our scoping review aims to explore whether work-related psychosocial stress is generally associated with depression or depressiveness, the extent and methodology of the primary research undertaken on this topic and to elucidate inconsistencies or gaps in knowledge.

Methods

We searched for literature in Pubmed, PsycInfo and Web of Science including full reports in seven languages published between 1999 and 2022 and applied the PRISMA statement for scoping reviews criteria.

Results

Of 463 primarily identified articles, 125 were retained after abstract and full-text screening. The majority report significant associations between work-related stress and depression. Cross-sectional studies are most prevalent. Sufficient evidence exists only for job strain and effort-reward imbalance. Most studies are from Asia, North America and Europe. The health sector is the most studied. Several research gaps such as the lack of interventional studies were identified.

Conclusion

The consistency of most studies on the significant association between work-related stress and depression is remarkable. More studies are needed to improve evidence and to close research gaps.

Subjective response to alcohol in young adults with bipolar disorder and recent alcohol use: a within-subject randomized placebo-controlled alcohol administration study

Limited data exists on mechanisms contributing to elevated risk for alcohol use disorder (AUD) in bipolar disorder. Variation in subjective response to alcohol may relate to alcohol use and risk for AUD. This study used a randomized, placebo-controlled, cross-over, within-subjects design to investigate differences in subjective response to alcohol in 50 euthymic young adults (n = 24 with and n = 26 without bipolar disorder type I). Eighty-three percent of participants with bipolar disorder were medicated. Participants completed assessments of clinical history, alcohol expectancies, and recent alcohol use. Participants were dosed to a .08 g% breath alcohol concentration. The placebo condition occurred on a separate counter-balanced day. Subjective response to alcohol was investigated at similar time points during both conditions. Group, condition, and group-by-condition interactions were modeled, with condition and time of subjective response assessment as repeated within-subject variables, and subjective response to alcohol as the dependent variable. Greater stimulating effects and liking of alcohol were reported in people with bipolar disorder (group-by-condition interactions, p < .05) than healthy young adults. While young adults with bipolar disorder reported anticipating feeling less "mellow/relaxed" when drinking (p = .02), during both beverage conditions they reported feeling more "mellow/relaxed" (main effect of group, p = .006). Feeling more "mellow/relaxed" during the alcohol condition related to greater recent alcohol use in bipolar disorder (p = .001). Exploratory analyses suggested anticonvulsants and sedatives/antihistamines may relate to differences in subjective response to alcohol in bipolar disorder. Results suggest young adults with bipolar disorder may differ in alcohol expectancies and experience alcohol intoxication differently-with distinct relations between subjective response to alcohol and alcohol use-compared to healthy young adults.

Depression and suicidal ideations in relation to occupational stress in a sample of Egyptian medical residents

INTRODUCTION

Medical students, interns, and residents have higher rates of depression than the general population, according to previous literature.

OBJECTIVE

The objectives of this paper were to determine the rate of depression and its severity in a group of Egyptian residents of Ain Shams University hospitals.

METHODS

This is a cross-sectional comparative study that took place in El-Demerdash Hospital from March 1, 2019 to September 1, 2020, with 220 Egyptian residents of Ain Shams University Hospitals participating.

RESULTS

The socioeconomic stress scale had a statistically significant relationship with depression scores (p value = .008) and suicide scores (p-value = .010), according to the findings. Additionally, there was a statistically significant relationship between suicide scores and depression scores (p-value = .001). The relationship between obstetrics and gynecology and high suicide risk was statistically significant (p value = .010), with obstetrics and gynecology having the highest percentage of high suicide risk (10 out of 19 residents, 52.63%), followed by pediatrics with 4 out of 11 residents (36.36%).

CONCLUSION

The pediatrics department had the highest percentage of residents who were depressed, while the obstetrics and gynecology department had the highest suicide risk. Feeling underpaid, disruption of home life, having insufficient time, being concerned about keeping skills up to date, and having a large volume of work were also found to be the most stressful aspects of residency.

Visualizing novel connections and genetic similarities across diseases using a network-medicine based approach

Understanding the genetic relationships between human disorders could lead to better treatment and prevention strategies, especially for individuals with multiple comorbidities. A common resource for studying genetic-disease relationships is the GWAS Catalog, a large and well curated repository of SNP-trait associations from various studies and populations. Some of these populations are contained within mega-biobanks such as the Million Veteran Program (MVP), which has enabled the genetic classification of several diseases in a large well-characterized and heterogeneous population. Here we aim to provide a network of the genetic relationships among diseases and to demonstrate the utility of quantifying the extent to which a given resource such as MVP has contributed to the discovery of such relations. We use a network-based approach to evaluate shared variants among thousands of traits in the GWAS Catalog repository. Our results indicate many more novel disease relationships that did not exist in early studies and demonstrate that the network can reveal clusters of diseases mechanistically related. Finally, we show novel disease connections that emerge when MVP data is included, highlighting methodology that can be used to indicate the contributions of a given biobank.

Identification of genetic risk loci for depression and migraine comorbidity in Han Chinese residing in Taiwan

INTRODUCTION

The genetic association between depression and migraine has not been well investigated in Asian populations. Furthermore, the genetic basis of depression and comorbid migraine subtypes remains nebulous. Hence, in the current study we investigate the susceptibility loci associated with depression and migraine comorbidity in the Han Chinese population in Taiwan.

METHODS

We perform a genome-wide association study involving 966 migraine patients, with or without comorbid depression. Genotyping is performed using participant genomic DNA. Association analyses are performed for the entire migraine cohort (subgroups: episodic migraine, chronic migraine, and migraine with or without aura).

RESULTS

Results show that the single nucleotide polymorphism variants of the CDH4 intron region (rs78063755), NTRK3-AS1 downstream region (rs57729223), and between LINC01918 and GPR45 (rs2679891) are suggestively associated with depression. Twenty additional susceptibility loci occur within the subgroups. A multivariate association study demonstrated that a variant in the intron region of CDH4 rs78063755 was associated with Beck Depression Inventory and Migraine Disability Assessment scores.

DISCUSSION

The findings of this study identify several genetic loci suggestively associated with depression among migraine patients in the Han Chinese population. Moreover, a potential genetic basis has been characterized for depression and migraine comorbidity, thus providing genetic candidates for further investigation.

Decoding Neuromuscular Disorders Using Phenotypic Clusters Obtained From Co-Occurrence Networks

Neuromuscular disorders (NMDs) represent an important subset of rare diseases associated with elevated morbidity and mortality whose diagnosis can take years. Here we present a novel approach using systems biology to produce functionally-coherent phenotype clusters that provide insight into the cellular functions and phenotypic patterns underlying NMDs, using the Human Phenotype Ontology as a common framework. Gene and phenotype information was obtained for 424 NMDs in OMIM and 126 NMDs in Orphanet, and 335 and 216 phenotypes were identified as typical for NMDs, respectively. ‘Elevated serum creatine kinase’ was the most specific to NMDs, in agreement with the clinical test of elevated serum creatinine kinase that is conducted on NMD patients. The approach to obtain co-occurring NMD phenotypes was validated based on co-mention in PubMed abstracts. A total of 231 (OMIM) and 150 (Orphanet) clusters of highly connected co-occurrent NMD phenotypes were obtained. In parallel, a tripartite network based on phenotypes, diseases and genes was used to associate NMD phenotypes with functions, an approach also validated by literature co-mention, with KEGG pathways showing proportionally higher overlap than Gene Ontology and Reactome. Phenotype-function pairs were crossed with the co-occurrent NMD phenotype clusters to obtain 40 (OMIM) and 72 (Orphanet) functionally coherent phenotype clusters. As expected, many of these overlapped with known diseases and confirmed existing knowledge. Other clusters revealed interesting new findings, indicating informative phenotypes for differential diagnosis, providing deeper knowledge of NMDs, and pointing towards specific cell dysfunction caused by pleiotropic genes. This work is an example of reproducible research that i) can help better understand NMDs and support their diagnosis by providing a new tool that exploits existing information to obtain novel clusters of functionally-related phenotypes, and ii) takes us another step towards personalised medicine for NMDs.

Phenotype-genotype comorbidity analysis of patients with rare disorders provides insight into their pathological and molecular bases

Genetic and molecular analysis of rare disease is made difficult by the small numbers of affected patients. Phenotypic comorbidity analysis can help rectify this by combining information from individuals with similar phenotypes and looking for overlap in terms of shared genes and underlying functional systems. However, few studies have combined comorbidity analysis with genomic data. We present a computational approach that connects patient phenotypes based on phenotypic co-occurence and uses genomic information related to the patient mutations to assign genes to the phenotypes, which are used to detect enriched functional systems. These phenotypes are clustered using network analysis to obtain functionally coherent phenotype clusters. We applied the approach to the DECIPHER database, containing phenotypic and genomic information for thousands of patients with heterogeneous rare disorders and copy number variants. Validity was demonstrated through overlap with known diseases, co-mention within the biomedical literature, semantic similarity measures, and patient cluster membership. These connected pairs formed multiple phenotype clusters, showing functional coherence, and mapped to genes and systems involved in similar pathological processes. Examples include claudin genes from the 22q11 genomic region associated with a cluster of phenotypes related to DiGeorge syndrome and genes related to the GO term anterior/posterior pattern specification associated with abnormal development. The clusters generated can help with the diagnosis of rare diseases, by suggesting additional phenotypes for a given patient and potential underlying functional systems. Other tools to find causal genes based on phenotype were also investigated. The approach has been implemented as a workflow, named PhenCo, which can be adapted to any set of patients for which phenomic and genomic data is available. Full details of the analysis, including the clusters formed, their constituent functional systems and underlying genes are given. Code to implement the workflow is available from GitHub.

Although rare diseases each affect a small number of people, taken together they affect millions. Better diagnosis and understanding of the underlying mechanisms are needed. By combining phenotypic data for many rare disease patients, we can build clusters of comorbid phenotypes that tend to co-occur together. By using genomic information, we can supplement these clusters and look for related genes and functional systems, such as pathways and molecular mechanisms. We applied such an approach to thousands of rare disease patients from the DECIPHER resources. We were able to detect hundreds of pairs of comorbid phenotypes, and use them to build tens of phenotype clusters. By mapping genes to these phenotypes, based on data from the same patients, we were able to detect related genes and functional systems, such as genes mapping to the 22q11 genomic region underlying a cluster of phenotypes related to DiGeorge syndrome. To ensure that these clusters made sensible predictions, results were validated using literature co-mention, overlap with known disease and semantic similarity measures. These comorbidity patterns, along with their underlying molecular systems, can give important insights into disease mechanisms, moreover they can be used to direct differential-diagnosis of rare disease patients.

Comorbidity4j: a tool for interactive analysis of disease comorbidities over large patient datasets

SUMMARY

Pushed by the growing availability of Electronic Health Records for data mining, the identification of relevant patterns of co-occurring diseases over a population of individuals-referred to as comorbidity analysis-has become a common practice due to its great impact on life expectancy, quality of life and healthcare costs. In this scenario, the availability of scalable, easy-to-use software frameworks tailored to support the study of comorbidities over large datasets of patients is essential. We introduce Comorbidity4j, an open-source Java tool to perform systematic analyses of comorbidities by generating interactive Web visualizations to explore and refine results. Comorbidity4j processes user-provided clinical data by identifying significant disease co-occurrences and computing a comprehensive set of comorbidity indices. Patients can be stratified by sex, age and user-defined criteria. Comorbidity4j supports the analysis of the temporal directionality and the sex ratio of diseases. The incremental upload and validation of clinical input data and the customization of comorbidity analyses are performed by an interactive Web interface. With a Web browser, the results of such analyses can be filtered with respect to comorbidity indexes and disease names and explored by means of heat maps and network charts of disease associations. Comorbidity4j is optimized to efficiently process large datasets of clinical data. Besides a software tool for local execution, we provide Comorbidity4j as a Web service to enable users to perform online comorbidity analyses.

AVAILABILITY AND IMPLEMENTATION

Doc: http://comorbidity4j.readthedocs.io/; Source code: https://github.com/fra82/comorbidity4j, Web tool: http://comorbidity.eu/comorbidity4web/.

Sensitivity of comorbidity network analysis

OBJECTIVES

Comorbidity network analysis (CNA) is a graph-theoretic approach to systems medicine based on associations revealed from disease co-occurrence data. Researchers have used CNA to explore epidemiological patterns, differentiate populations, characterize disorders, and more; but these techniques have not been comprehensively evaluated. Our objectives were to assess the stability of common CNA techniques.

MATERIALS AND METHODS

We obtained seven co-occurrence data sets, most from previous CNAs, coded using several ontologies. We constructed comorbidity networks under various modeling procedures and calculated summary statistics and centrality rankings. We used regression, ordination, and rank correlation to assess these properties' sensitivity to the source of data and construction parameters.

RESULTS

Most summary statistics were robust to variation in link determination but somewhere sensitive to the association measure. Some more effectively than others discriminated among networks constructed from different data sets. Centrality rankings, especially among hubs, were somewhat sensitive to link determination and highly sensitive to ontology. As multivariate models incorporated additional effects, comorbid associations among low-prevalence disorders weakened while those between high-prevalence disorders shifted negative.

DISCUSSION

Pairwise CNA techniques are generally robust, but some analyses are highly sensitive to certain parameters. Multivariate approaches expose additional conceptual and technical limitations to the usual pairwise approach.

CONCLUSION

We conclude with a set of recommendations we believe will help CNA researchers improve the robustness of results and the potential of follow-up research.

Exploring the Association between Monoclonal Antibodies and Depression and Suicidal Ideation and Behavior: A VigiBase Study

Introduction

Several monoclonal antibodies (mAbs) have been linked to neuropsychiatric adverse effects in patients, including depression and suicidal ideation and behavior.

Objective

The aim of this study was to quantify and characterize spontaneously reported adverse drug reactions (ADRs) of depression and suicidal ideation and behavior related to mAb users, and to explore a possible association with their mechanism of action.

Methods

We included mAb ADRs that were reported in VigiBase, and identified those related to depression and suicidal ideation and behavior. Reporting odds ratios (RORs) were estimated for each mAb (bevacizumab as the reference) and according to their influence on the immune system (not directly targeting [reference], stimulating, or suppressing). Those suppressing the immune system were further divided into their intended indication (auto-immune diseases, cancer).

Results

Overall, 2,924,319 ADRs for 44 mAbs were included; 9455 ADRs were related to depression and 1770 were related to suicidal ideation and behavior. The association was strongest for natalizumab and belimumab, both for depression (ROR 5.7, 95% confidence interval [CI] 5.0–6.4; and ROR 5.1, 95% CI 4.2–6.2) and suicidal ideation and behavior (ROR 12.0, 95% CI 7.9–18.3; and ROR 20.2, 95% CI 12.4–33.0). Those suppressing the immune system showed higher ROR, i.e. 1.9 (95% CI 1.8–2.0) for depression and 3.6 (95% CI 3.0–4.4) for suicidal ideation and behavior. This finding was only seen for mAbs used for treating autoimmune diseases.

Conclusion

Depression and suicidal ideation and behavior are seen in patients using mAbs, particularly mAbs used for treating autoimmune diseases that suppress the immune system. For interpretation of these data, the indications for use and other characteristics require further consideration.

Electronic supplementary material

The online version of this article (10.1007/s40264-018-00789-9) contains supplementary material, which is available to authorized users.

Comorbidity of asthma and hypertension may be mediated by shared genetic dysregulation and drug side effects

Asthma and hypertension are complex diseases coinciding more frequently than expected by chance. Unraveling the mechanisms of comorbidity of asthma and hypertension is necessary for choosing the most appropriate treatment plan for patients with this comorbidity. Since both diseases have a strong genetic component in this article we aimed to find and study genes simultaneously associated with asthma and hypertension. We identified 330 shared genes and found that they form six modules on the interaction network. A strong overlap between genes associated with asthma and hypertension was found on the level of eQTL regulated genes and between targets of drugs relevant for asthma and hypertension. This suggests that the phenomenon of comorbidity of asthma and hypertension may be explained by altered genetic regulation or result from drug side effects. In this work we also demonstrate that not only drug indications but also contraindications provide an important source of molecular evidence helpful to uncover disease mechanisms. These findings give a clue to the possible mechanisms of comorbidity and highlight the direction for future research.

comoRbidity: an R package for the systematic analysis of disease comorbidities

Motivation

The study of comorbidities is a major priority due to their impact on life expectancy, quality of life and healthcare cost. The availability of electronic health records (EHRs) for data mining offers the opportunity to discover disease associations and comorbidity patterns from the clinical history of patients gathered during routine medical care. This opens the need for analytical tools for detection of disease comorbidities, including the investigation of their underlying genetic basis.

Results

We present comoRbidity, an R package aimed at providing a systematic and comprehensive analysis of disease comorbidities from both the clinical and molecular perspectives. comoRbidity leverages from (i) user provided clinical data from EHR databases (the clinical comorbidity analysis) and (ii) genotype-phenotype information of the diseases under study (the molecular comorbidity analysis) for a comprehensive analysis of disease comorbidities. The clinical comorbidity analysis enables identifying significant disease comorbidities from clinical data, including sex and age stratification and temporal directionality analyses, while the molecular comorbidity analysis supports the generation of hypothesis on the underlying mechanisms of the disease comorbidities by exploring shared genes among disorders. The open-source comoRbidity package is a software tool aimed at expediting the integrative analysis of disease comorbidities by incorporating several analytical and visualization functions.

Availability and implementation

https://bitbucket.org/ibi_group/comorbidity

Supplementary information

Supplementary data are available at Bioinformatics online.

Disease classification: from phenotypic similarity to integrative genomics and beyond

A fundamental challenge of modern biomedical research is understanding how diseases that are similar on the phenotypic level are similar on the molecular level. Integration of various genomic data sets with the traditionally used phenotypic disease similarity revealed novel genetic and molecular mechanisms and blurred the distinction between monogenic (Mendelian) and complex diseases. Network-based medicine has emerged as a complementary approach for identifying disease-causing genes, genetic mediators, disruptions in the underlying cellular functions and for drug repositioning. The recent development of machine and deep learning methods allow for leveraging real-life information about diseases to refine genetic and phenotypic disease relationships. This review describes the historical development and recent methodological advancements for studying disease classification (nosology).

Novel disease syndromes unveiled by integrative multiscale network analysis of diseases sharing molecular effectors and comorbidities

Background

Forty-two percent of patients experience disease comorbidity, contributing substantially to mortality rates and increased healthcare costs. Yet, the possibility of underlying shared mechanisms for diseases remains not well established, and few studies have confirmed their molecular predictions with clinical datasets.

Methods

In this work, we integrated genome-wide association study (GWAS) associating diseases and single nucleotide polymorphisms (SNPs) with transcript regulatory activity from expression quantitative trait loci (eQTL). This allowed novel mechanistic insights for noncoding and intergenic regions. We then analyzed pairs of SNPs across diseases to identify shared molecular effectors robust to multiple test correction (False Discovery Rate FDR_eRNA < 0.05). We hypothesized that disease pairs found to be molecularly convergent would also be significantly overrepresented among comorbidities in clinical datasets. To assess our hypothesis, we used clinical claims datasets from the Healthcare Cost and Utilization Project (HCUP) and calculated significant disease comorbidities (FDR_comorbidity < 0.05). We finally verified if disease pairs resulting molecularly convergent were also statistically comorbid more than by chance using the Fisher’s Exact Test.

Results

Our approach integrates: (i) 6175 SNPs associated with 238 diseases from ~ 1000 GWAS, (ii) eQTL associations from 19 tissues, and (iii) claims data for 35 million patients from HCUP. Logistic regression (controlled for age, gender, and race) identified comorbidities in HCUP, while enrichment analyses identified cis- and trans-eQTL downstream effectors of GWAS-identified variants. Among ~ 16,000 combinations of diseases, 398 disease-pairs were prioritized by both convergent eQTL-genetics (RNA overlap enrichment, FDR_eRNA < 0.05) and clinical comorbidities (OR > 1.5, FDR_comorbidity < 0.05). Case studies of comorbidities illustrate specific convergent noncoding regulatory elements. An intergenic architecture of disease comorbidity was unveiled due to GWAS and eQTL-derived convergent mechanisms between distinct diseases being overrepresented among observed comorbidities in clinical datasets (OR = 8.6, p-value = 6.4 × 10^− 5 FET).

Conclusions

These comorbid diseases with convergent eQTL genetic mechanisms suggest clinical syndromes. While it took over a decade to confirm the genetic underpinning of the metabolic syndrome, this study is likely highlighting hundreds of new ones. Further, this knowledge may improve the clinical management of comorbidities with precision and shed light on novel approaches of drug repositioning or SNP-guided precision molecular therapy inclusive of intergenic risks.

Electronic supplementary material

The online version of this article (10.1186/s12920-018-0428-9) contains supplementary material, which is available to authorized users.

The Alzheimer's comorbidity phenome: mining from a large patient database and phenome-driven genetics prediction

Objective

Alzheimer’s disease (AD) is a severe neurodegenerative disorder and has become a global public health problem. Intensive research has been conducted for AD. But the pathophysiology of AD is still not elucidated. Disease comorbidity often associates diseases with overlapping patterns of genetic markers. This may inform a common etiology and suggest essential protein targets. US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) collects large-scale postmarketing surveillance data that provide a unique opportunity to investigate disease co-occurrence pattern. We aim to construct a heterogeneous network that integrates disease comorbidity network (DCN) from FAERS with protein–protein interaction (PPI) to prioritize the AD risk genes using network-based ranking algorithm.

Materials and Methods

We built a DCN based on indication data from FAERS using association rule mining. DCN was further integrated with PPI network. We used random walk with restart ranking algorithm to prioritize AD risk genes.

Results

We evaluated the performance of our approach using AD risk genes curated from genetic association studies. Our approach achieved an area under a receiver operating characteristic curve of 0.770. Top 500 ranked genes achieved 5.53-fold enrichment for known AD risk genes as compared to random expectation. Pathway enrichment analysis using top-ranked genes revealed that two novel pathways, ERBB and coagulation pathways, might be involved in AD pathogenesis.

Conclusion

We innovatively leveraged FAERS, a comprehensive data resource for FDA postmarket drug safety surveillance, for large-scale AD comorbidity mining. This exploratory study demonstrated the potential of disease-comorbidities mining from FAERS in AD genetics discovery.

Enabling precision medicine in neonatology, an integrated repository for preterm birth research

Preterm birth, or the delivery of an infant prior to 37 weeks of gestation, is a significant cause of infant morbidity and mortality. In the last decade, the advent and continued development of molecular profiling technologies has enabled researchers to generate vast amount of 'omics' data, which together with integrative computational approaches, can help refine the current knowledge about disease mechanisms, diagnostics, and therapeutics. Here we describe the March of Dimes' Database for Preterm Birth Research (http://www.immport.org/resources/mod), a unique resource that contains a variety of 'omics' datasets related to preterm birth. The database is open publicly, and as of January 2018, links 13 molecular studies with data across tens of thousands of patients from 6 measurement modalities. The data in the repository are highly diverse and include genomic, transcriptomic, immunological, and microbiome data. Relevant datasets are augmented with additional molecular characterizations of almost 25,000 biological samples from public databases. We believe our data-sharing efforts will lead to enhanced research collaborations and coordination accelerating the overall pace of discovery in preterm birth research.

Applications of network analysis to routinely collected health care data: a systematic review

Objective

To survey network analyses of datasets collected in the course of routine operations in health care settings and identify driving questions, methods, needs, and potential for future research.

Materials and Methods

A search strategy was designed to find studies that applied network analysis to routinely collected health care datasets and was adapted to 3 bibliographic databases. The results were grouped according to a thematic analysis of their settings, objectives, data, and methods. Each group received a methodological synthesis.

Results

The search found 189 distinct studies reported before August 2016. We manually partitioned the sample into 4 groups, which investigated institutional exchange, physician collaboration, clinical co-occurrence, and workplace interaction networks. Several robust and ongoing research programs were discerned within (and sometimes across) the groups. Little interaction was observed between these programs, despite conceptual and methodological similarities.

Discussion

We use the literature sample to inform a discussion of good practice at this methodological interface, including the concordance of motivations, study design, data, and tools and the validation and standardization of techniques. We then highlight instances of positive feedback between methodological development and knowledge domains and assess the overall cohesion of the sample.

A Review of Recent Advances in Translational Bioinformatics: Bridges from Biology to Medicine

Objectives: To highlight and provide insights into key developments in translational bioinformatics between 2014 and 2016. Methods: This review describes some of the most influential bioinformatics papers and resources that have been published between 2014 and 2016 as well as the national genome sequencing initiatives that utilize these resources to routinely embed genomic medicine into healthcare. Also discussed are some applications of the secondary use of patient data followed by a comprehensive view of the open challenges and emergent technologies. Results: Although data generation can be performed routinely, analyses and data integration methods still require active research and standardization to improve streamlining of clinical interpretation. The secondary use of patient data has resulted in the development of novel algorithms and has enabled a refined understanding of cellular and phenotypic mechanisms. New data storage and data sharing approaches are required to enable diverse biomedical communities to contribute to genomic discovery. Conclusion: The translation of genomics data into actionable knowledge for use in healthcare is transforming the clinical landscape in an unprecedented way. Exciting and innovative models that bridge the gap between clinical and academic research are set to open up the field of translational bioinformatics for rapid growth in a digital era.

Comorbidities in the diseasome are more apparent than real: What Bayesian filtering reveals about the comorbidities of depression

Comorbidity patterns have become a major source of information to explore shared mechanisms of pathogenesis between disorders. In hypothesis-free exploration of comorbid conditions, disease-disease networks are usually identified by pairwise methods. However, interpretation of the results is hindered by several confounders. In particular a very large number of pairwise associations can arise indirectly through other comorbidity associations and they increase exponentially with the increasing breadth of the investigated diseases. To investigate and filter this effect, we computed and compared pairwise approaches with a systems-based method, which constructs a sparse Bayesian direct multimorbidity map (BDMM) by systematically eliminating disease-mediated comorbidity relations. Additionally, focusing on depression-related parts of the BDMM, we evaluated correspondence with results from logistic regression, text-mining and molecular-level measures for comorbidities such as genetic overlap and the interactome-based association score. We used a subset of the UK Biobank Resource, a cross-sectional dataset including 247 diseases and 117,392 participants who filled out a detailed questionnaire about mental health. The sparse comorbidity map confirmed that depressed patients frequently suffer from both psychiatric and somatic comorbid disorders. Notably, anxiety and obesity show strong and direct relationships with depression. The BDMM identified further directly co-morbid somatic disorders, e.g. irritable bowel syndrome, fibromyalgia, or migraine. Using the subnetwork of depression and metabolic disorders for functional analysis, the interactome-based system-level score showed the best agreement with the sparse disease network. This indicates that these epidemiologically strong disease-disease relations have improved correspondence with expected molecular-level mechanisms. The substantially fewer number of comorbidity relations in the BDMM compared to pairwise methods implies that biologically meaningful comorbid relations may be less frequent than earlier pairwise methods suggested. The computed interactive comprehensive multimorbidity views over the diseasome are available on the web at Co=MorNet: bioinformatics.mit.bme.hu/UKBNetworks.

Using genetics to inform new therapeutics for diabetes

The genetic architecture of diabetes has been extensively studied. Numerous genetic markers for diabetes have been reported. However, the translation of such knowledge into clinical interventions has been inadequate. Areas covered: We performed a literature search on various frontiers in diabetes treatment that could be improved using genetic information: (1) understanding the mechanisms of existing antidiabetic drugs, (2) repurposing existing drugs for the treatment of diabetes, (3) complementing clinical trial findings; (4) finding novel treatment approaches; (5) better estimation of the efficacy of metabolic surgery. Expert commentary: The translation of genetic information to clinical intervention requires further study, including the development of an appropriate genetic risk score algorithm for type 2 diabetes. Genomic studies provide empirical explanations for clinical trial findings. Moreover, the mechanisms of antidiabetic drugs should be thoroughly investigated to enable clinical trials and pharmacogenomics studies of these drugs. As metabolic surgery becomes more prevalent for the treatment of diabetes, genetic approaches may improve patient prioritization.