Prioritizing causal disease genes using unbiased genomic features

Machine learning for identifying risk of death in patients with severe fever with thrombocytopenia syndrome

Background

Severe fever with thrombocytopenia syndrome (SFTS) has attracted attention due to the rising incidence and high severity and mortality rates. This study aims to construct a machine learning (ML) model to identify SFTS patients at high risk of death early in hospital admission, and to provide early intensive intervention with a view to reducing the risk of death.

Methods

Data of patients hospitalized for SFTS in two hospitals were collected as training and validation sets, respectively, and six ML methods were used to construct the models using the screened variables as features. The performance of the models was comprehensively evaluated and the best model was selected for interpretation and development of an online web calculator for application.

Results

A total of 483 participants were enrolled in the study and 96 (19.88%) patients died due to SFTS. After a comprehensive evaluation, the XGBoost-based model performs best: the AUC scores for the training and validation sets are 0.962 and 0.997.

Conclusion

Using ML can be a good way to identify high risk individuals in SFTS patients. We can use this model to identify patients at high risk of death early in their admission and manage them intensively at an early stage.

Genomic Innovation in Early Life Cardiovascular Disease Prevention and Treatment

Cardiovascular disease (CVD) is a leading cause of morbidity and mortality globally. Although CVD events do not typically manifest until older adulthood, CVD develops gradually across the life-course, beginning with the elevation of risk factors observed as early as childhood or adolescence and the emergence of subclinical disease that can occur in young adulthood or midlife. Genomic background, which is determined at zygote formation, is among the earliest risk factors for CVD. With major advances in molecular technology, including the emergence of gene-editing techniques, along with deep whole-genome sequencing and high-throughput array-based genotyping, scientists now have the opportunity to not only discover genomic mechanisms underlying CVD but use this knowledge for the life-course prevention and treatment of these conditions. The current review focuses on innovations in the field of genomics and their applications to monogenic and polygenic CVD prevention and treatment. With respect to monogenic CVD, we discuss how the emergence of whole-genome sequencing technology has accelerated the discovery of disease-causing variants, allowing comprehensive screening and early, aggressive CVD mitigation strategies in patients and their families. We further describe advances in gene editing technology, which might soon make possible cures for CVD conditions once thought untreatable. In relation to polygenic CVD, we focus on recent innovations that leverage findings of genome-wide association studies to identify druggable gene targets and develop predictive genomic models of disease, which are already facilitating breakthroughs in the life-course treatment and prevention of CVD. Gaps in current research and future directions of genomics studies are also discussed. In aggregate, we hope to underline the value of leveraging genomics and broader multiomics information for characterizing CVD conditions, work which promises to expand precision approaches for the life-course prevention and treatment of CVD.

Large scale genome-wide association analyses identify novel genetic loci and mechanisms in hypertrophic cardiomyopathy

Hypertrophic cardiomyopathy (HCM) is an important cause of morbidity and mortality with both monogenic and polygenic components. We here report results from the largest HCM genome-wide association study (GWAS) and multi-trait analysis (MTAG) including 5,900 HCM cases, 68,359 controls, and 36,083 UK Biobank (UKB) participants with cardiac magnetic resonance (CMR) imaging. We identified a total of 70 loci (50 novel) associated with HCM, and 62 loci (32 novel) associated with relevant left ventricular (LV) structural or functional traits. Amongst the common variant HCM loci, we identify a novel HCM disease gene, SVIL, which encodes the actin-binding protein supervillin, showing that rare truncating SVIL variants cause HCM. Mendelian randomization analyses support a causal role of increased LV contractility in both obstructive and non-obstructive forms of HCM, suggesting common disease mechanisms and anticipating shared response to therapy. Taken together, the findings significantly increase our understanding of the genetic basis and molecular mechanisms of HCM, with potential implications for disease management.

How data science and AI-based technologies impact genomics

Advancements in high-throughput sequencing have yielded vast amounts of genomic data, which are studied using genome-wide association study (GWAS)/phenome-wide association study (PheWAS) methods to identify associations between the genotype and phenotype. The associated findings have contributed to pharmacogenomics and improved clinical decision support at the point of care in many healthcare systems. However, the accumulation of genomic data from sequencing and clinical data from electronic health records (EHRs) poses significant challenges for data scientists. Following the rise of artificial intelligence (AI) technology such as machine learning and deep learning, an increasing number of GWAS/PheWAS studies have successfully leveraged this technology to overcome the aforementioned challenges. In this review, we focus on the application of data science and AI technology in three areas, including risk prediction and identification of causal single-nucleotide polymorphisms, EHR-based phenotyping and CRISPR guide RNA design. Additionally, we highlight a few emerging AI technologies, such as transfer learning and multi-view learning, which will or have started to benefit genomic studies.

Dilated cardiomyopathy in the era of precision medicine: latest concepts and developments

Dilated cardiomyopathy (DCM) is an umbrella term entailing a wide variety of genetic and non-genetic etiologies, leading to left ventricular systolic dysfunction and dilatation, not explained by abnormal loading conditions or coronary artery disease. The clinical presentation can vary from asymptomatic to heart failure symptoms or sudden cardiac death (SCD) even in previously asymptomatic individuals. In the last 2 decades, there has been striking progress in the understanding of the complex genetic basis of DCM, with the discovery of additional genes and genotype-phenotype correlation studies. Rigorous clinical work-up of DCM patients, meticulous family screening, and the implementation of advanced imaging techniques pave the way for a more efficient and earlier diagnosis as well as more precise indications for implantable cardioverter defibrillator implantation and prevention of SCD. In the era of precision medicine, genotype-directed therapies have started to emerge. In this review, we focus on updates of the genetic background of DCM, characteristic phenotypes caused by recently described pathogenic variants, specific indications for prevention of SCD in those individuals and genotype-directed treatments under development. Finally, the latest developments in distinguishing athletic heart syndrome from subclinical DCM are described.

Genetic Insights into Primary Restrictive Cardiomyopathy

Restrictive cardiomyopathy is a rare cardiac disease causing severe diastolic dysfunction, ventricular stiffness and dilated atria. In consequence, it induces heart failure often with preserved ejection fraction and is associated with a high mortality. Since it is a poor clinical prognosis, patients with restrictive cardiomyopathy frequently require heart transplantation. Genetic as well as non-genetic factors contribute to restrictive cardiomyopathy and a significant portion of cases are of unknown etiology. However, the genetic forms of restrictive cardiomyopathy and the involved molecular pathomechanisms are only partially understood. In this review, we summarize the current knowledge about primary genetic restrictive cardiomyopathy and describe its genetic landscape, which might be of interest for geneticists as well as for cardiologists.

A New Algorithm for Multivariate Genome Wide Association Studies Based on Differential Evolution and Extreme Learning Machines

Genome-wide association studies (GWAS) are observational studies of a large set of genetic variants, whose aim is to find those that are linked to a certain trait or illness. Due to the multivariate nature of these kinds of studies, machine learning methodologies have been already applied in them, showing good performance. This work presents a new methodology for GWAS that makes use of extreme learning machines and differential evolution. The proposed methodology was tested with the help of the genetic information (370,750 single-nucleotide polymorphisms) of 2049 individuals, 1076 of whom suffer from colorectal cancer. The possible relationship of 10 different pathways with this illness was tested. The results achieved showed that the proposed methodology is suitable for detecting relevant pathways for the trait under analysis with a lower computational cost than other machine learning methodologies previously proposed.

Filamin C in cardiomyopathy: from physiological roles to DNA variants

Cardiomyopathy affects approximately 1 in 500 adults and is the leading cause of death. Familial cases are common, and mutations in many genes are involved in cardiomyopathy, especially those in genes encoding cytoskeletal, sarcomere, and nuclear envelope proteins. Filamin C is an actin-binding protein encoded by filamin C (FLNC) gene and participates in sarcomere stability maintenance. FLNC was first demonstrated to be a causal gene of myofibrillar myopathy; recently, it has been found that FLNC mutation plays a critical role in the pathogenesis of cardiomyopathy. In this review, we summarized the physiological roles of filamin C in cardiomyocytes and the genetic evidence for links between FLNC mutations and cardiomyopathies. Truncated FLNC is enriched in dilated cardiomyopathy and arrhythmogenic right ventricular cardiomyopathy. Non-truncated FLNC is enriched in hypertrophic cardiomyopathy and restrictive cardiomyopathy. Two major pathomechanisms in FLNC-related cardiomyopathy have been described: protein aggregation resulting from non-truncating mutations and haploinsufficiency triggered by filamin C truncation. Therefore, it is important to understand the cellular biology and molecular regulation of FLNC to design new therapies to treat patients with FLNC-related cardiomyopathy.

Cardiomyopathy, Proximal Myopathy, Camptocormia, and Novel Filamin C (FLNC) Variant: A Case Report

Patient: Male, 56-year-old

Final Diagnosis: Camptocormia • cardiomyopathy • proximal myopathy

Symptoms: Truncal weakness • weakness

Medication: —

Clinical Procedure: —

Specialty: Neurology

Identifying potential association on gene-disease network via dual hypergraph regularized least squares

BACKGROUND

Identifying potential associations between genes and diseases via biomedical experiments must be the time-consuming and expensive research works. The computational technologies based on machine learning models have been widely utilized to explore genetic information related to complex diseases. Importantly, the gene-disease association detection can be defined as the link prediction problem in bipartite network. However, many existing methods do not utilize multiple sources of biological information; Additionally, they do not extract higher-order relationships among genes and diseases.

RESULTS

In this study, we propose a novel method called Dual Hypergraph Regularized Least Squares (DHRLS) with Centered Kernel Alignment-based Multiple Kernel Learning (CKA-MKL), in order to detect all potential gene-disease associations. First, we construct multiple kernels based on various biological data sources in gene and disease spaces respectively. After that, we use CAK-MKL to obtain the optimal kernels in the two spaces respectively. To specific, hypergraph can be employed to establish higher-order relationships. Finally, our DHRLS model is solved by the Alternating Least squares algorithm (ALSA), for predicting gene-disease associations.

CONCLUSION

Comparing with many outstanding prediction tools, DHRLS achieves best performance on gene-disease associations network under two types of cross validation. To verify robustness, our proposed approach has excellent prediction performance on six real-world networks. Our research work can effectively discover potential disease-associated genes and provide guidance for the follow-up verification methods of complex diseases.

PRYNT: a tool for prioritization of disease candidates from proteomics data using a combination of shortest-path and random walk algorithms

The urinary proteome is a promising pool of biomarkers of kidney disease. However, the protein changes observed in urine only partially reflect the deregulated mechanisms within kidney tissue. In order to improve on the mechanistic insight based on the urinary protein changes, we developed a new prioritization strategy called PRYNT (PRioritization bY protein NeTwork) that employs a combination of two closeness-based algorithms, shortest-path and random walk, and a contextualized protein-protein interaction (PPI) network, mainly based on clique consolidation of STRING network. To assess the performance of our approach, we evaluated both precision and specificity of PRYNT in prioritizing kidney disease candidates. Using four urinary proteome datasets, PRYNT prioritization performed better than other prioritization methods and tools available in the literature. Moreover, PRYNT performed to a similar, but complementary, extent compared to the upstream regulator analysis from the commercial Ingenuity Pathway Analysis software. In conclusion, PRYNT appears to be a valuable freely accessible tool to predict key proteins indirectly from urinary proteome data. In the future, PRYNT approach could be applied to other biofluids, molecular traits and diseases. The source code is freely available on GitHub at: https://github.com/Boizard/PRYNT and has been integrated as an interactive web apps to improved accessibility ( https://github.com/Boizard/PRYNT/tree/master/AppPRYNT ).

Machine and Deep Learning in Molecular and Genetic Aspects of Sleep Research

Epidemiological sleep research strives to identify the interactions and causal mechanisms by which sleep affects human health, and to design intervention strategies for improving sleep throughout the lifespan. These goals can be advanced by further focusing on the environmental and genetic etiology of sleep disorders, and by development of risk stratification algorithms, to identify people who are at risk or are affected by, sleep disorders. These studies rely on comprehensive sleep-related data which often contains complex multi-dimensional physiological and molecular measurements across multiple timepoints. Thus, sleep research is well-suited for the application of computational approaches that can handle high-dimensional data. Here, we survey recent advances in machine and deep learning together with the availability of large human cohort studies with sleep data that can jointly drive the next breakthroughs in the sleep-research field. We describe sleep-related data types and datasets, and present some of the tasks in the field that can be targets for algorithmic approaches, as well as the challenges and opportunities in pursuing them.

Loss of supervillin causes myopathy with myofibrillar disorganization and autophagic vacuoles

The muscle specific isoform of the supervillin protein (SV2), encoded by the SVIL gene, is a large sarcolemmal myosin II- and F-actin-binding protein. Supervillin (SV2) binds and co-localizes with costameric dystrophin and binds nebulin, potentially attaching the sarcolemma to myofibrillar Z-lines. Despite its important role in muscle cell physiology suggested by various in vitro studies, there are so far no reports of any human disease caused by SVIL mutations. We here report four patients from two unrelated, consanguineous families with a childhood/adolescence onset of a myopathy associated with homozygous loss-of-function mutations in SVIL. Wide neck, anteverted shoulders and prominent trapezius muscles together with variable contractures were characteristic features. All patients showed increased levels of serum creatine kinase but no or minor muscle weakness. Mild cardiac manifestations were observed. Muscle biopsies showed complete loss of large supervillin isoforms in muscle fibres by western blot and immunohistochemical analyses. Light and electron microscopic investigations revealed a structural myopathy with numerous lobulated muscle fibres and considerable myofibrillar alterations with a coarse and irregular intermyofibrillar network. Autophagic vacuoles, as well as frequent and extensive deposits of lipoproteins, including immature lipofuscin, were observed. Several sarcolemma-associated proteins, including dystrophin and sarcoglycans, were partially mis-localized. The results demonstrate the importance of the supervillin (SV2) protein for the structural integrity of muscle fibres in humans and show that recessive loss-of-function mutations in SVIL cause a distinctive and novel myopathy.

Clinical Implications of the Genetic Architecture of Dilated Cardiomyopathy

PURPOSE OF REVIEW

Dilated cardiomyopathy (DCM) frequently involves an underlying genetic etiology, but the clinical approach for genetic diagnosis and application of results in clinical practice can be complex.

RECENT FINDINGS

International sequence databases described the landscape of genetic variability across populations, which informed guidelines for the interpretation of DCM gene variants. New evidence indicates that loss-of-function mutations in filamin C (FLNC) contribute to DCM and portend high risk of ventricular arrhythmia. A clinical framework aids in referring patients for DCM genetic testing and applying results to patient care. Results of genetic testing can change medical management, particularly in a subset of genes that increase risk for life-threatening ventricular arrhythmias, and can influence decisions for defibrillator therapy. Clinical screening and cascade genetic testing of family members should be diligently pursued to identify those at risk of developing DCM.

Reaching the End-Game for GWAS: Machine Learning Approaches for the Prioritization of Complex Disease Loci

Genome-wide association studies (GWAS) have revealed thousands of genetic loci that underpin the complex biology of many human traits. However, the strength of GWAS - the ability to detect genetic association by linkage disequilibrium (LD) - is also its limitation. Whilst the ever-increasing study size and improved design have augmented the power of GWAS to detect effects, differentiation of causal variants or genes from other highly correlated genes associated by LD remains the real challenge. This has severely hindered the biological insights and clinical translation of GWAS findings. Although thousands of disease susceptibility loci have been reported, causal genes at these loci remain elusive. Machine learning (ML) techniques offer an opportunity to dissect the heterogeneity of variant and gene signals in the post-GWAS analysis phase. ML models for GWAS prioritization vary greatly in their complexity, ranging from relatively simple logistic regression approaches to more complex ensemble models such as random forests and gradient boosting, as well as deep learning models, i.e., neural networks. Paired with functional validation, these methods show important promise for clinical translation, providing a strong evidence-based approach to direct post-GWAS research. However, as ML approaches continue to evolve to meet the challenge of causal gene identification, a critical assessment of the underlying methodologies and their applicability to the GWAS prioritization problem is needed. This review investigates the landscape of ML applications in three parts: selected models, input features, and output model performance, with a focus on prioritizations of complex disease associated loci. Overall, we explore the contributions ML has made towards reaching the GWAS end-game with consequent wide-ranging translational impact.

Structure and Function of Filamin C in the Muscle Z-Disc

Filamin C (FLNC) is one of three filamin proteins (Filamin A (FLNA), Filamin B (FLNB), and FLNC) that cross-link actin filaments and interact with numerous binding partners. FLNC consists of a N-terminal actin-binding domain followed by 24 immunoglobulin-like repeats with two intervening calpain-sensitive hinges separating R15 and R16 (hinge 1) and R23 and R24 (hinge-2). The FLNC subunit is dimerized through R24 and calpain cleaves off the dimerization domain to regulate mobility of the FLNC subunit. FLNC is localized in the Z-disc due to the unique insertion of 82 amino acid residues in repeat 20 and necessary for normal Z-disc formation that connect sarcomeres. Since phosphorylation of FLNC by PKC diminishes the calpain sensitivity, assembly, and disassembly of the Z-disc may be regulated by phosphorylation of FLNC. Mutations of FLNC result in cardiomyopathy and muscle weakness. Although this review will focus on the current understanding of FLNC structure and functions in muscle, we will also discuss other filamins because they share high sequence similarity and are better characterized. We will also discuss a possible role of FLNC as a mechanosensor during muscle contraction.

Reduction in Filamin C transcript is associated with arrhythmogenic cardiomyopathy in Ashkenazi Jews

BACKGROUND

Filamin C is a cytoskeletal protein expressed in cardiac cells. Nonsense variations in the filamin C gene (FLNC) were associated with dilated and arrhythmogenic cardiomyopathies.

METHODS AND RESULTS

We identified an intronic variation in FLNC gene (c.3791-1G > C) in three unrelated Ashkenazi Jewish families with variable expression of arrhythmia and cardiomyopathy. cDNA was prepared from a mutation carrier's cultured skin fibroblasts. Quantitative PCR demonstrated a reduction in total FLNC transcript, and no other FLNC splice variants were found. Single-nucleotide polymorphism (SNP) analysis revealed heterozygous variations in the genomic DNA that were not expressed in the messenger RNA. Immunohistochemical analysis of cardiac sections detected a normal distribution of filamin C protein in the heart ventricles.

CONCLUSION

The transcript that included the FLNC variant was degraded. Haploinsufficiency in filamin C underlies arrhythmogenic cardiomyopathy with variable symptoms.

A mutation update for the FLNC gene in myopathies and cardiomyopathies

Filamin C (FLNC) variants are associated with cardiac and muscular phenotypes. Originally, FLNC variants were described in myofibrillar myopathy (MFM) patients. Later, high‐throughput screening in cardiomyopathy cohorts determined a prominent role for FLNC in isolated hypertrophic and dilated cardiomyopathies (HCM and DCM). FLNC variants are now among the more prevalent causes of genetic DCM. FLNC‐associated DCM is associated with a malignant clinical course and a high risk of sudden cardiac death. The clinical spectrum of FLNC suggests different pathomechanisms related to variant types and their location in the gene. The appropriate functioning of FLNC is crucial for structural integrity and cell signaling of the sarcomere. The secondary protein structure of FLNC is critical to ensure this function. Truncating variants with subsequent haploinsufficiency are associated with DCM and cardiac arrhythmias. Interference with the dimerization and folding of the protein leads to aggregate formation detrimental for muscle function, as found in HCM and MFM. Variants associated with HCM are predominantly missense variants, which cluster in the ROD2 domain. This domain is important for binding to the sarcomere and to ensure appropriate cell signaling. We here review FLNC genotype–phenotype correlations based on available evidence.

Metastable Atrial State Underlies the Primary Genetic Substrate for MYL4 Mutation-Associated Atrial Fibrillation

BACKGROUND

Atrial fibrillation (AF) is the most common clinical arrhythmia and is associated with heart failure, stroke, and increased mortality. The myocardial substrate for AF is poorly understood because of limited access to primary human tissue and mechanistic questions around existing in vitro or in vivo models.

METHODS

Using an MYH6:mCherry knock-in reporter line, we developed a protocol to generate and highly purify human pluripotent stem cell-derived cardiomyocytes displaying physiological and molecular characteristics of atrial cells. We modeled human MYL4 mutants, one of the few definitive genetic causes of AF. To explore non-cell-autonomous components of AF substrate, we also created a zebrafish Myl4 knockout model, which exhibited molecular, cellular, and physiologic abnormalities that parallel those in humans bearing the cognate mutations.

RESULTS

There was evidence of increased retinoic acid signaling in both human embryonic stem cells and zebrafish mutant models, as well as abnormal expression and localization of cytoskeletal proteins, and loss of intracellular nicotinamide adenine dinucleotide and nicotinamide adenine dinucleotide + hydrogen. To identify potentially druggable proximate mechanisms, we performed a chemical suppressor screen integrating multiple human cellular and zebrafish in vivo endpoints. This screen identified Cx43 (connexin 43) hemichannel blockade as a robust suppressor of the abnormal phenotypes in both models of MYL4 (myosin light chain 4)-related atrial cardiomyopathy. Immunofluorescence and coimmunoprecipitation studies revealed an interaction between MYL4 and Cx43 with altered localization of Cx43 hemichannels to the lateral membrane in MYL4 mutants, as well as in atrial biopsies from unselected forms of human AF. The membrane fraction from MYL4-/- human embryonic stem cell derived atrial cells demonstrated increased phospho-Cx43, which was further accentuated by retinoic acid treatment and by the presence of risk alleles at the Pitx2 locus. PKC (protein kinase C) was induced by retinoic acid, and PKC inhibition also rescued the abnormal phenotypes in the atrial cardiomyopathy models.

CONCLUSIONS

These data establish a mechanistic link between the transcriptional, metabolic and electrical pathways previously implicated in AF substrate and suggest novel avenues for the prevention or therapy of this common arrhythmia.

QTG-Finder: A Machine-Learning Based Algorithm To Prioritize Causal Genes of Quantitative Trait Loci in Arabidopsis and Rice

Linkage mapping is one of the most commonly used methods to identify genetic loci that determine a trait. However, the loci identified by linkage mapping may contain hundreds of candidate genes and require a time-consuming and labor-intensive fine mapping process to find the causal gene controlling the trait. With the availability of a rich assortment of genomic and functional genomic data, it is possible to develop a computational method to facilitate faster identification of causal genes. We developed QTG-Finder, a machine learning based algorithm to prioritize causal genes by ranking genes within a quantitative trait locus (QTL). Two predictive models were trained separately based on known causal genes in Arabidopsis and rice. An independent validation analysis showed that the models could recall about 64% of Arabidopsis and 79% of rice causal genes when the top 20% ranked genes were considered. The top 20% ranked genes can range from 10 to 100 genes, depending on the size of a QTL. The models can prioritize different types of traits though at different efficiency. We also identified several important features of causal genes including paralog copy number, being a transporter, being a transcription factor, and containing SNPs that cause premature stop codon. This work lays the foundation for systematically understanding characteristics of causal genes and establishes a pipeline to predict causal genes based on public data.

Prioritization of differentially expressed genes through integrating public expression data

Differentially expressed gene (DEG) analysis is a major approach for interpreting phenotype differences and produces a large number of candidate genes. Given that it is burdensome to validate too many genes through benchwork, an urgent need exists for DEG prioritization. Here, a novel method is proposed for prioritizing bona fide DEGs by constructing the normal range of gene expression through integrating public expression data. Prioritization was performed by ranking the differences in cumulative probability for genes in case and control groups. DEGs from a study on pig muscle tissue were used to evaluate the prioritization accuracy. The results showed that the method reached an area under the receiver operating characteristic curve of 96.42% and can effectively shorten the list of candidate genes from a differential expression experiment to find novel causal genes. Our method can be easily extended to other tissues or species to promote functional research in broad applications.

Gene prioritization using Bayesian matrix factorization with genomic and phenotypic side information

Motivation

Most gene prioritization methods model each disease or phenotype individually, but this fails to capture patterns common to several diseases or phenotypes. To overcome this limitation, we formulate the gene prioritization task as the factorization of a sparsely filled gene-phenotype matrix, where the objective is to predict the unknown matrix entries. To deliver more accurate gene-phenotype matrix completion, we extend classical Bayesian matrix factorization to work with multiple side information sources. The availability of side information allows us to make non-trivial predictions for genes for which no previous disease association is known.

Results

Our gene prioritization method can innovatively not only integrate data sources describing genes, but also data sources describing Human Phenotype Ontology terms. Experimental results on our benchmarks show that our proposed model can effectively improve accuracy over the well-established gene prioritization method, Endeavour. In particular, our proposed method offers promising results on diseases of the nervous system; diseases of the eye and adnexa; endocrine, nutritional and metabolic diseases; and congenital malformations, deformations and chromosomal abnormalities, when compared to Endeavour.

Availability and implementation

The Bayesian data fusion method is implemented as a Python/C++ package: https://github.com/jaak-s/macau. It is also available as a Julia package: https://github.com/jaak-s/BayesianDataFusion.jl. All data and benchmarks generated or analyzed during this study can be downloaded at https://owncloud.esat.kuleuven.be/index.php/s/UGb89WfkZwMYoTn.

Supplementary information

Supplementary data are available at Bioinformatics online.

Integrating Imaging and Omics: Computational Methods and Challenges

Fluorescence microscopy imaging has long been complementary to DNA sequencing- and mass spectrometry–based omics in biomedical research, but these approaches are now converging. On the one hand, omics methods are moving from in vitro methods that average across large cell populations to in situ molecular characterization tools with single-cell sensitivity. On the other hand, fluorescence microscopy imaging has moved from a morphological description of tissues and cells to quantitative molecular profiling with single-molecule resolution. Recent technological developments underpinned by computational methods have started to blur the lines between imaging and omics and have made their direct correlation and seamless integration an exciting possibility. As this trend continues rapidly, it will allow us to create comprehensive molecular profiles of living systems with spatial and temporal context and subcellular resolution. Key to achieving this ambitious goal will be novel computational methods and successfully dealing with the challenges of data integration and sharing as well as cloud-enabled big data analysis.

Analysis of 51 proposed hypertrophic cardiomyopathy genes from genome sequencing data in sarcomere negative cases has negligible diagnostic yield

PURPOSE

Increasing numbers of genes are being implicated in Mendelian disorders and incorporated into clinical test panels. However, lack of evidence supporting the gene-disease relationship can hinder interpretation. We explored the utility of testing 51 additional genes for hypertrophic cardiomyopathy (HCM), one of the most commonly tested Mendelian disorders.

METHODS

Using genome sequencing data from 240 sarcomere gene negative HCM cases and 6229 controls, we undertook case-control and individual variant analyses to assess 51 genes that have been proposed for HCM testing.

RESULTS

We found no evidence to suggest that rare variants in these genes are prevalent causes of HCM. One variant, in a single case, was categorized as likely to be pathogenic. Over 99% of variants were classified as a variant of uncertain significance (VUS) and 54% of cases had one or more VUS.

CONCLUSION

For almost all genes, the gene-disease relationship could not be validated and lack of evidence precluded variant interpretation. Thus, the incremental diagnostic yield of extending testing was negligible, and would, we propose, be outweighed by problems that arise with a high rate of uninterpretable findings. These findings highlight the need for rigorous, evidence-based selection of genes for clinical test panels.

Identify gene expression pattern change at transcriptional and post-transcriptional levels

Gene transcription is regulated with distinct sets of regulatory factors at multiple levels. Transcriptional and post-transcriptional regulation constitute two major regulation modes of gene expression to either activate or repress the initiation of transcription and thereby control the number of proteins synthesized during translation. Disruptions of the proper regulation patterns at transcriptional and post-transcriptional levels are increasingly recognized as causes of human diseases. Consequently, identifying the differential gene expression at transcriptional and post-transcriptional levels respectively is vital to identify potential disease-associated and/or causal genes and understand their roles in the disease development. Here, we proposed a novel method with a linear mixed model that can identify a set of differentially expressed genes at transcriptional and post-transcriptional levels. The simulation and real data analysis showed our method could provide an accurate way to identify genes subject to aberrant transcriptional and post-transcriptional regulation and reveal the potential causal genes that contributed to the diseases.

The G protein-coupled receptor GPR34 - The past 20 years of a grownup

Research on GPR34, which was discovered in 1999 as an orphan G protein-coupled receptor of the rhodopsin-like class, disclosed its physiologic relevance only piece by piece. Being present in all recent vertebrate genomes analyzed so far it seems to improve the fitness of species although it is not essential for life and reproduction as GPR34-deficient mice demonstrate. However, closer inspection of macrophages and microglia, where it is mainly expressed, revealed its relevance in immune cell function. Recent data clearly demonstrate that GPR34 function is required to arrest microglia in the M0 homeostatic non-phagocytic phenotype. Herein, we summarize the current knowledge on its evolution, genomic and structural organization, physiology, pharmacology and relevance in human diseases including neurodegenerative diseases and cancer, which accumulated over the last 20 years.

Filamin C Truncation Mutations Are Associated With Arrhythmogenic Dilated Cardiomyopathy and Changes in the Cell-Cell Adhesion Structures

OBJECTIVES

The purpose of this study was to assess the phenotype of Filamin C (FLNC) truncating variants in dilated cardiomyopathy (DCM) and understand the mechanism leading to an arrhythmogenic phenotype.

BACKGROUND

Mutations in FLNC are known to lead to skeletal myopathies, which may have an associated cardiac component. Recently, the clinical spectrum of FLNC mutations has been recognized to include a cardiac-restricted presentation in the absence of skeletal muscle involvement.

METHODS

A population of 319 U.S. and European DCM cardiomyopathy families was evaluated using whole-exome and targeted next-generation sequencing. FLNC truncation probands were identified and evaluated by clinical examination, histology, transmission electron microscopy, and immunohistochemistry.

RESULTS

A total of 13 individuals in 7 families (2.2%) were found to harbor 6 different FLNC truncation variants (2 stopgain, 1 frameshift, and 3 splicing). Of the 13 FLNC truncation carriers, 11 (85%) had either ventricular arrhythmias or sudden cardiac death, and 5 (38%) presented with evidence of right ventricular dilation. Pathology analysis of 2 explanted hearts from affected FLNC truncation carriers showed interstitial fibrosis in the right ventricle and epicardial fibrofatty infiltration in the left ventricle. Ultrastructural findings included occasional disarray of Z-discs within the sarcomere. Immunohistochemistry showed normal plakoglobin signal at cell-cell junctions, but decreased signals for desmoplakin and synapse-associated protein 97 in the myocardium and buccal mucosa.

CONCLUSIONS

We found FLNC truncating variants, present in 2.2% of DCM families, to be associated with a cardiac-restricted arrhythmogenic DCM phenotype characterized by a high risk of life-threatening ventricular arrhythmias and a pathological cellular phenotype partially overlapping with arrhythmogenic right ventricular cardiomyopathy.

LITTLE FISH, BIG DATA: ZEBRAFISH AS A MODEL FOR CARDIOVASCULAR AND METABOLIC DISEASE

The burden of cardiovascular and metabolic diseases worldwide is staggering. The emergence of systems approaches in biology promises new therapies, faster and cheaper diagnostics, and personalized medicine. However, a profound understanding of pathogenic mechanisms at the cellular and molecular levels remains a fundamental requirement for discovery and therapeutics. Animal models of human disease are cornerstones of drug discovery as they allow identification of novel pharmacological targets by linking gene function with pathogenesis. The zebrafish model has been used for decades to study development and pathophysiology. More than ever, the specific strengths of the zebrafish model make it a prime partner in an age of discovery transformed by big-data approaches to genomics and disease. Zebrafish share a largely conserved physiology and anatomy with mammals. They allow a wide range of genetic manipulations, including the latest genome engineering approaches. They can be bred and studied with remarkable speed, enabling a range of large-scale phenotypic screens. Finally, zebrafish demonstrate an impressive regenerative capacity scientists hope to unlock in humans. Here, we provide a comprehensive guide on applications of zebrafish to investigate cardiovascular and metabolic diseases. We delineate advantages and limitations of zebrafish models of human disease and summarize their most significant contributions to understanding disease progression to date.

Cellular network perturbations by disease-associated variants

Genetic and genome-wide association studies (GWAS) have identified a myriad of human disease-associated genomic variants. However, these studies do not reveal the mechanisms by which these variants perturb cellular networks, a necessary step to intervene and improve disease outcomes. This has been challenging because multiple variants are present in haplotype blocks, thereby confounding the identification of causal variants, and because most reside in noncoding regions. Here, we review recent advances in the identification of functional variants and gene-variant associations. In addition, we examine approaches used to study perturbations in protein-protein and protein-DNA interactions associated with disease, and discuss how these perturbations affect cellular networks.

GenToS: Use of Orthologous Gene Information to Prioritize Signals from Human GWAS

Genome-wide association studies (GWAS) evaluate associations between genetic variants and a trait or disease of interest free of prior biological hypotheses. GWAS require stringent correction for multiple testing, with genome-wide significance typically defined as association p-value <5*10-8. This study presents a new tool that uses external information about genes to prioritize SNP associations (GenToS). For a given list of candidate genes, GenToS calculates an appropriate statistical significance threshold and then searches for trait-associated variants in summary statistics from human GWAS. It thereby allows for identifying trait-associated genetic variants that do not meet genome-wide significance. The program additionally tests for enrichment of significant candidate gene associations in the human GWAS data compared to the number expected by chance. As proof of principle, this report used external information from a comprehensive resource of genetically manipulated and systematically phenotyped mice. Based on selected murine phenotypes for which human GWAS data for corresponding traits were publicly available, several candidate gene input lists were derived. Using GenToS for the investigation of candidate genes underlying murine skeletal phenotypes in data from a large human discovery GWAS meta-analysis of bone mineral density resulted in the identification of significantly associated variants in 29 genes. Index variants in 28 of these loci were subsequently replicated in an independent GWAS replication step, highlighting that they are true positive associations. One signal, COL11A1, has not been discovered through GWAS so far and represents a novel human candidate gene for altered bone mineral density. The number of observed genes that contained significant SNP associations in human GWAS based on murine candidate gene input lists was much greater than the number expected by chance across several complex human traits (enrichment p-value as low as 10-10). GenToS can be used with any candidate gene list, any GWAS summary file, runs on a desktop computer and is freely available.

FLNC Gene Splice Mutations Cause Dilated Cardiomyopathy

A genetic etiology has been identified in 30% to 40% of dilated cardiomyopathy (DCM) patients, yet only 50% of these cases are associated with a known causative gene variant. Thus, in order to understand the pathophysiology of DCM, it is necessary to identify and characterize additional genes. In this study, whole exome sequencing in combination with segregation analysis was used to identify mutations in a novel gene, filamin C (FLNC), resulting in a cardiac-restricted DCM pathology. Here we provide functional data via zebrafish studies and protein analysis to support a model implicating FLNC haploinsufficiency as a mechanism of DCM.

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Deoxyribonucleic acid obtained from 2 large DCM families was studied using whole-exome sequencing and cosegregation analysis resulting in the identification of a novel disease gene, FLNC. The 2 families, from the same Italian region, harbored the same FLNC splice-site mutation (FLNC c.7251+1G>A).

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A third U.S. family was then identified with a novel FLNC splice-site mutation (FLNC c.5669-1delG) that leads to haploinsufficiency as shown by the FLNC Western blot analysis of the heart muscle.

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The FLNC ortholog flncb morpholino was injected into zebrafish embryos, and when flncb was knocked down caused a cardiac dysfunction phenotype.

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On electron microscopy, the flncb morpholino knockdown zebrafish heart showed defects within the Z-discs and sarcomere disorganization.

Tools for Predicting the Functional Impact of Nonsynonymous Genetic Variation

As personal genome sequencing becomes a reality, understanding the effects of genetic variants on phenotype-particularly the impact of germline variants on disease risk and the impact of somatic variants on cancer development and treatment-continues to increase in importance. Because of their clear potential for affecting phenotype, nonsynonymous genetic variants (variants that cause a change in the amino acid sequence of a protein encoded by a gene) have long been the target of efforts to predict the effects of genetic variation. Whole-genome sequencing is identifying large numbers of nonsynonymous variants in each genome, intensifying the need for computational methods that accurately predict which of these are likely to impact disease phenotypes. This review focuses on nonsynonymous variant prediction with two aims in mind: (1) to review the prioritization methods that have been developed to date and the principles on which they are based and (2) to discuss the challenges to further improving these methods.

SoftPanel: a website for grouping diseases and related disorders for generation of customized panels

BACKGROUND

Targeted next-generation sequencing is playing an increasingly important role in biological research and clinical diagnosis by allowing researchers to sequence high priority genes at much higher depths and at a fraction of the cost of whole genome or exome sequencing. However, in designing the panel of genes to be sequenced, investigators need to consider the tradeoff between the better sensitivity of a broad panel and the higher specificity of a potentially more relevant panel. Although tools to prioritize candidate disease genes have been developed, the great majority of these require prior knowledge and a set of seed genes as input, which is only possible for diseases with a known genetic etiology.

RESULTS

To meet the demands of both researchers and clinicians, we have developed a user-friendly website called SoftPanel. This website is intended to serve users by allowing them to input a single disorder or a disorder group and generate a panel of genes predicted to underlie the disorder of interest. Various methods of retrieval including a keyword search, browsing of an arborized list of International Classification of Diseases, 10th revision (ICD-10) codes or using disorder phenotypic similarities can be combined to define a group of disorders and the genes known to be associated with them. Moreover, SoftPanel enables users to expand or refine a gene list by utilizing several biological data resources. In addition to providing users with the facility to create a "hard" panel that contains an exact gene list for targeted sequencing, SoftPanel also enables generation of a "soft" panel of genes, which may be used to further filter a significantly altered set of genes identified through whole genome or whole exome sequencing. The service and data provided by SoftPanel can be accessed at http://www.isb.pku.edu.cn/SoftPanel/ . A tutorial page is included for trying out sample data and interpreting results.

CONCLUSION

SoftPanel provides a convenient and powerful tool for creating a targeted panel of potential disease genes while supporting different forms of input. SoftPanel may be utilized in both genomics research and personalized medicine.

Machine Learning in Medicine

Spurred by advances in processing power, memory, storage, and an unprecedented wealth of data, computers are being asked to tackle increasingly complex learning tasks, often with astonishing success. Computers have now mastered a popular variant of poker, learned the laws of physics from experimental data, and become experts in video games - tasks that would have been deemed impossible not too long ago. In parallel, the number of companies centered on applying complex data analysis to varying industries has exploded, and it is thus unsurprising that some analytic companies are turning attention to problems in health care. The purpose of this review is to explore what problems in medicine might benefit from such learning approaches and use examples from the literature to introduce basic concepts in machine learning. It is important to note that seemingly large enough medical data sets and adequate learning algorithms have been available for many decades, and yet, although there are thousands of papers applying machine learning algorithms to medical data, very few have contributed meaningfully to clinical care. This lack of impact stands in stark contrast to the enormous relevance of machine learning to many other industries. Thus, part of my effort will be to identify what obstacles there may be to changing the practice of medicine through statistical learning approaches, and discuss how these might be overcome.

An internal promoter underlies the difference in disease severity between N- and C-terminal truncation mutations of Titin in zebrafish

Truncating mutations in the giant sarcomeric protein Titin result in dilated cardiomyopathy and skeletal myopathy. The most severely affected dilated cardiomyopathy patients harbor Titin truncations in the C-terminal two-thirds of the protein, suggesting that mutation position might influence disease mechanism. Using CRISPR/Cas9 technology, we generated six zebrafish lines with Titin truncations in the N-terminal and C-terminal regions. Although all exons were constitutive, C-terminal mutations caused severe myopathy whereas N-terminal mutations demonstrated mild phenotypes. Surprisingly, neither mutation type acted as a dominant negative. Instead, we found a conserved internal promoter at the precise position where divergence in disease severity occurs, with the resulting protein product partially rescuing N-terminal truncations. In addition to its clinical implications, our work may shed light on a long-standing mystery regarding the architecture of the sarcomere.

DOI:http://dx.doi.org/10.7554/eLife.09406.001

The heart is able to beat partly because of a large protein called Titin that helps to give heart muscle its elasticity. Mutations that shorten the gene that encodes Titin can cause part of the heart to become enlarged and weakened, a condition called dilated cardiomyopathy. Some people with shortened copies of this protein have a mild form of cardiomyopathy and are able to lead relatively normal lives. Others develop more severe symptoms that prevent the heart from pumping blood effectively and may even cause the individual to need a heart transplant.

Genetic studies have revealed that mutations that shorten the Titin protein by disrupting the portion of the gene corresponding to the latter two-thirds of the protein (which encodes the so-called “C-terminal” end of the protein) cause more severe symptoms than mutations that occur near the start of the gene. But it is not clear why the location of the mutation matters.

To investigate this problem, Zou et al. used a gene-editing tool called CRISPR to create genetically engineered zebrafish. These fish had mutations at one of six different points in the gene that encodes the zebrafish version of Titin. Just as with humans, mutations near the C-terminal end of the gene caused more severe muscle problems in the fish.

Specifically, Zou et al. found that the worst disease was associated with mutations that occurred at or after a “promoter” region within the gene and near this C-terminal end. Normally, the promoter produces an independent smaller form of the Titin protein, which helps to reduce the severity of muscle problems in zebrafish that have mutations near the start of the gene. However, mutations near the C-terminal end of the gene also damage this smaller form, preventing this failsafe from working, and so lead to more severe symptoms. Zou et al. also found this promoter to be active in both mouse and human hearts.

Future work will focus on learning how this smaller form of Titin works to help muscle develop and withstand stress and determine whether increasing its production can overcome the more severe forms of disease.

DOI:http://dx.doi.org/10.7554/eLife.09406.002