Computational approaches to interpreting genomic sequence variation

Role of non-coding variants in cardiovascular disease

Cardiovascular diseases (CVDs) constitute one of the significant causes of death worldwide. Different pathological states are linked to CVDs, which despite interventions and treatments, still have poor prognoses. The genetic component, as a beneficial tool in the risk stratification of CVD development, plays a role in the pathogenesis of this group of diseases. The emergence of genome-wide association studies (GWAS) have led to the identification of non-coding parts associated with cardiovascular traits and disorders. Variants located in functional non-coding regions, including promoters/enhancers, introns, miRNAs and 5'/3' UTRs, account for 90% of all identified single-nucleotide polymorphisms associated with CVDs. Here, for the first time, we conducted a comprehensive review on the reported non-coding variants for different CVDs, including hypercholesterolemia, cardiomyopathies, congenital heart diseases, thoracic aortic aneurysms/dissections and coronary artery diseases. Additionally, we present the most commonly reported genes involved in each CVD. In total, 1469 non-coding variants constitute most reports on familial hypercholesterolemia, hypertrophic cardiomyopathy and dilated cardiomyopathy. The application and identification of non-coding variants are beneficial for the genetic diagnosis and better therapeutic management of CVDs.

SNP-Target Genes Interaction Perturbing the Cancer Risk in the Post-GWAS

Cancer ranks as the second leading cause of death worldwide, and, being a genetic disease, it is highly heritable. Over the past few decades, genome-wide association studies (GWAS) have identified many risk-associated loci harboring hundreds of single nucleotide polymorphisms (SNPs). Some of these cancer-associated SNPs have been revealed as causal, and the functional characterization of the mechanisms underlying the cancer risk association has been illuminated in some instances. In this review, based on the different positions of SNPs and their modes of action, we discuss the mechanisms underlying how SNPs regulate the expression of target genes to consequently affect tumorigenesis and the development of cancer.

Insights into the role of DNA methylation and gene expression in Graves' orbitopathy

CONTEXT

A role of DNA methylation in Graves' orbitopathy (GO) has been proposed. Design. To investigate DNA methylation and gene expression in orbital fibroblasts from control and GO patients, under basal conditions or following challenge with an anti-TSH receptor antibody (M22) or cytokines involved in GO; to investigate the relationship between DNA methylation and cell function (proliferation); to perform a methylome analysis. Setting. Referral Center. Materials. Orbital fibroblasts from six GO and six control patients. Intervention. None. Main Outcome Measure. Methylome analysis of the whole genome. Results. Global DNA methylation increased significantly both in control and GO fibroblasts upon incubation with M22. Expression of two selected genes (CYP19A1 and AIFM2) was variably affected by M22 and interleukin-6. M22 increased cell proliferation in control and GO fibroblasts, which correlated with global DNA methylation. Methylome analysis revealed 19,869 DNA regions differently methylated in GO fibroblasts, encompassing 3,957 genes and involving CpG islands, shores and shelves. One-hundred and nineteen gene families and subfamilies, 89 protein groups, 402 biological processes and seven pathways were involved. Three genes found to be differentially expressed were concordantly hyper- or hypomethylated. Among the differently methylated genes, insulin-like growth factor-1 receptor and several fibroblast growth factors and receptors were included.

CONCLUSIONS

We propose that, when exposed to an autoimmune environment, orbital fibroblasts undergo hyper- or hypomethylation of certain genes, involving CpG promoters, which results in differential gene expression, which may be responsible for functional alterations, in particular higher proliferation, and ultimately for the GO phenotype in vivo.

A machine learning approach based on ACMG/AMP guidelines for genomic variant classification and prioritization

Genomic variant interpretation is a critical step of the diagnostic procedure, often supported by the application of tools that may predict the damaging impact of each variant or provide a guidelines-based classification. We propose the application of Machine Learning methodologies, in particular Penalized Logistic Regression, to support variant classification and prioritization. Our approach combines ACMG/AMP guidelines for germline variant interpretation as well as variant annotation features and provides a probabilistic score of pathogenicity, thus supporting the prioritization and classification of variants that would be interpreted as uncertain by the ACMG/AMP guidelines. We compared different approaches in terms of variant prioritization and classification on different datasets, showing that our data-driven approach is able to solve more variant of uncertain significance (VUS) cases in comparison with guidelines-based approaches and in silico prediction tools.

Clinical, Genetic and Functional Characterization of a Novel AVPR2 Missense Mutation in a Woman with X-Linked Recessive Nephrogenic Diabetes Insipidus

Nephrogenic diabetes insipidus (NDI) is a rare disorder characterized by renal unresponsiveness to the hormone vasopressin, leading to excretion of large volumes of diluted urine. Mutations in the arginine vasopressin receptor-2 (AVPR2) gene cause congenital NDI and have an X-linked recessive inheritance. The disorder affects almost exclusively male family members, but female carriers occasionally present partial phenotypes due to skewed inactivation of the X-chromosome. Here, we report a rare case of a woman affected with X-linked recessive NDI, presenting an average urinary output of 12 L/day. Clinical and biochemical studies showed incomplete responses to water deprivation and vasopressin stimulation tests. Genetic analyses revealed a novel heterozygous missense mutation (c.493G > C, p.Ala165Pro) in the AVPR2 gene. Using a combination of in-silico protein modeling with human cellular models and molecular phenotyping, we provide functional evidence for phenotypic effects. The mutation destabilizes the helical structure of the AVPR2 transmembrane domains and disrupts its plasma membrane localization and downstream intracellular signaling pathways upon activation with its agonist vasopressin. These defects lead to deficient aquaporin 2 (AQP2) membrane translocation, explaining the inability to concentrate urine in this patient.

Conserved noncoding sequences provide insights into regulatory sequence and loss of gene expression in maize

Thousands of species will be sequenced in the next few years; however, understanding how their genomes work, without an unlimited budget, requires both molecular and novel evolutionary approaches. We developed a sensitive sequence alignment pipeline to identify conserved noncoding sequences (CNSs) in the Andropogoneae tribe (multiple crop species descended from a common ancestor ∼18 million years ago). The Andropogoneae share similar physiology while being tremendously genomically diverse, harboring a broad range of ploidy levels, structural variation, and transposons. These contribute to the potential of Andropogoneae as a powerful system for studying CNSs and are factors we leverage to understand the function of maize CNSs. We found that 86% of CNSs were comprised of annotated features, including introns, UTRs, putative cis-regulatory elements, chromatin loop anchors, noncoding RNA (ncRNA) genes, and several transposable element superfamilies. CNSs were enriched in active regions of DNA replication in the early S phase of the mitotic cell cycle and showed different DNA methylation ratios compared to the genome-wide background. More than half of putative cis-regulatory sequences (identified via other methods) overlapped with CNSs detected in this study. Variants in CNSs were associated with gene expression levels, and CNS absence contributed to loss of gene expression. Furthermore, the evolution of CNSs was associated with the functional diversification of duplicated genes in the context of maize subgenomes. Our results provide a quantitative understanding of the molecular processes governing the evolution of CNSs in maize.

Genetic Profiling of Orbital Fibroblasts from Patients with Graves' Orbitopathy

CONTEXT

Graves' orbitopathy (GO) is an autoimmune disease that persists when immunosuppression is achieved. Orbital fibroblasts from GO patients display peculiar phenotypes even if not exposed to autoimmunity, possibly reflecting genetic or epigenetic mechanisms, which we investigated here.

OBJECTIVE

We aimed to explore potential genetic or epigenetic differences using primary cultures of orbital fibroblasts from GO and control patients.

METHODS

Cell proliferation, hyaluronic acid (HA) secretion, and HA synthases (HAS) were measured. Next-generation sequencing and gene expression analysis of the whole genome were performed, as well as real-time-PCR of selected genes and global DNA methylation assay on orbital fibroblasts from 6 patients with GO and 6 control patients from a referral center.

RESULTS

Cell proliferation was higher in GO than in control fibroblasts. Likewise, HA in the cell medium was higher in GO fibroblasts. HAS-1 and HAS-2 did not differ between GO and control fibroblasts, whereas HAS-3 was more expressed in GO fibroblasts. No relevant gene variants were detected by whole-genome sequencing. However, 58 genes were found to be differentially expressed in GO compared with control fibroblasts, and RT-PCR confirmed the findings in 10 selected genes. We postulated that the differential gene expression was related to an epigenetic mechanism, reflecting diverse DNA methylation, which we therefore measured. In support of our hypothesis, global DNA methylation was significantly higher in GO fibroblasts.

CONCLUSIONS

We propose that, following an autoimmune insult, DNA methylation elicits differential gene expression and sustains the maintenance of GO.

Hypoparathyroidism, deafness, and renal dysplasia syndrome: 20 Years after the identification of the first GATA3 mutations

The hypoparathyroidism, deafness, and renal dysplasia (HDR) syndrome is an autosomal dominant disorder caused by heterozygous mutations of the GATA3 gene. In the last 20 years, since the identification of the genetic cause of the HDR syndrome, GATA3 mutations have been reported in 124 families (177 patients). The clinical aspects and molecular genetics of the HDR syndrome are reviewed here together with the reported mutations and phenotypes. Reported mutations consist of 40% frameshift deletions or insertions, 23% missense mutations, 14% nonsense mutations, 6% splice-site mutations, 1% in-frame deletions or insertions, 15% whole-gene deletions, and 1% whole-gene duplication. Missense mutations were found to cluster in the regions encoding the two GATA3 zinc-finger domains. Patients showed great clinical variability and the penetrance of each HDR defect increased with age. The most frequently observed abnormality was deafness (93%), followed by hypoparathyroidism (87%) and renal defects (61%). The mean age of diagnosis of HDR was 15.3, 7.5, and 14.0 years, respectively. However, patients with whole-gene deletions and protein-truncating mutations were diagnosed earlier than patients with missense mutations.

Prediction of impacts of mutations on protein structure and interactions: SDM, a statistical approach, and mCSM, using machine learning

Next-generation sequencing methods have not only allowed an understanding of genome sequence variation during the evolution of organisms but have also provided invaluable information about genetic variants in inherited disease and the emergence of resistance to drugs in cancers and infectious disease. A challenge is to distinguish mutations that are drivers of disease or drug resistance, from passengers that are neutral or even selectively advantageous to the organism. This requires an understanding of impacts of missense mutations in gene expression and regulation, and on the disruption of protein function by modulating protein stability or disturbing interactions with proteins, nucleic acids, small molecule ligands, and other biological molecules. Experimental approaches to understanding differences between wild-type and mutant proteins are most accurate but are also time-consuming and costly. Computational tools used to predict the impacts of mutations can provide useful information more quickly. Here, we focus on two widely used structure-based approaches, originally developed in the Blundell lab: site-directed mutator (SDM), a statistical approach to analyze amino acid substitutions, and mutation cutoff scanning matrix (mCSM), which uses graph-based signatures to represent the wild-type structural environment and machine learning to predict the effect of mutations on protein stability. Here, we describe DUET that uses machine learning to combine the two approaches. We discuss briefly the development of mCSM for understanding the impacts of mutations on interfaces with other proteins, nucleic acids, and ligands, and we exemplify the wide application of these approaches to understand human genetic disorders and drug resistance mutations relevant to cancer and mycobacterial infections. STATEMENT FOR A BROADER AUDIENCE: Genetic or somatic changes in genes can lead to mutations in human proteins, which give rise to genetic disorders or cancer, or to genes of pathogens leading to drug resistance. Computer software described here, using statistical approaches or machine learning, uses the information from genome sequencing of humans and pathogens, together with experimental or modeled 3D structures of gene products, the proteins, to predict impacts of mutations in genetic disease, cancer and drug resistance.

Assessing concordance among human, in silico predictions and functional assays on genetic variant classification

MOTIVATION

A variety of in silico tools have been developed and frequently used to aid high-throughput rapid variant classification, but their performances vary, and their ability to classify variants of uncertain significance were not systemically assessed previously due to lack of validation data. This has been changed recently by advances of functional assays, where functional impact of genetic changes can be measured in single-nucleotide resolution using saturation genome editing (SGE) assay.

RESULTS

We demonstrated the neural network model AIVAR (Artificial Intelligent VARiant classifier) was highly comparable to human experts on multiple verified datasets. Although highly accurate on known variants, AIVAR together with CADD and PhyloP showed non-significant concordance with SGE function scores. Moreover, our results indicated that neural network model trained from functional assay data may not produce accurate prediction on known variants.

AVAILABILITY AND IMPLEMENTATION

All source code of AIVAR is deposited and freely available at https://github.com/TopGene/AIvar.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Combined Pituitary Hormone Deficiency Caused by a Synonymous HESX1 Gene Mutation

CONTEXT

Mutations in the HESX1 gene can give rise to complex phenotypes that involve variable pituitary hormone deficiencies and other developmental defects.

CASE DESCRIPTION

A 14-year-old boy presented with short stature and delayed puberty and received a diagnosis of GH deficiency, central hypothyroidism, hypogonadotropic hypogonadism, and secondary adrenal insufficiency. He had anterior pituitary hypoplasia, ectopic posterior pituitary, and an interrupted pituitary stalk. Genetic studies uncovered a heterozygous variant in exon 2 of the HESX1 gene (c.219C>T; p.Ser73Ser). This single base change was predicted to be synonymous at the translational level but was shown to cause skipping of exon 2 in the RNA transcript.

CONCLUSIONS

This study of a patient with combined pituitary hormone deficiency revealed an unusual synonymous mutation of the HESX1 gene leading to abnormal RNA processing and indicates the importance of investigating silent variants that at first glance appear to be benign.

Leveraging the power of new molecular technologies in the clinical setting requires unprecedented awareness of limitations and drawbacks: experience of one diagnostic laboratory

BACKGROUND

Advances in molecular technologies and in-silico variant prediction tools offer wide-ranging opportunities in diagnostic settings, yet they also present with significant limitations.

OBJECTIVE

Here, we contextualise the limitations of next-generation sequencing (NGS), multiplex ligation-dependent probe amplification (MLPA) and in-silico prediction tools routinely used by diagnostic laboratories by reviewing specific experiences from our diagnostic laboratory.

METHODS

We investigated discordant annotations and/or incorrect variant 'callings' in exons of 56 genes constituting our cardiomyopathy and connective tissue disorder NGS panels. Discordant variants and segmental duplications (SD) were queried using the National Center for Biotechnology Information (NCBI) Basic Local Alignment Search Tool and the University of California Santa Cruz genome browser, respectively, to identify regions of high homology. Discrepant variant analyses by in-silico models were re-evaluated using updated file entries.

RESULTS

We observed a 5% error rate in MYH7 variant 'calling' using MLPA, which resulted from >90% homology of the MYH7 probe-binding site to MYH6. SDs were detected in TTN, PKP2 and MYLK. SDs in MYLK presented the highest risk (15.7%) of incorrect variant 'calling'. The inaccurate 'callings' and discrepant in-silico predictions were resolved following detailed investigation into the source of error.

CONCLUSION

Recognising the limitations described here may help avoid incorrect diagnoses and leverage the power of new molecular technologies in diagnostic settings.

Key Concepts in Genetic Epidemiology

Genetic epidemiology is a discipline closely allied to traditional epidemiology that deals with the analysis of the familial distribution of traits. It emerged in the mid-1980s bringing together approaches and techniques developed in mathematical and quantitative genetics, medical and population genetics, statistics and epidemiology. The purpose of this chapter is to familiarize the reader with key concepts in genetic epidemiology as applied at present to unveil the familial and genetic determinants of disease and the joint effects of genes and environmental exposures.

Functional Assays Are Essential for Interpretation of Missense Variants Associated with Variable Expressivity

Missense DNA variants have variable effects upon protein function. Consequently, interpreting their pathogenicity is challenging, especially when they are associated with disease variability. To determine the degree to which functional assays inform interpretation, we analyzed 48 CFTR missense variants associated with variable expressivity of cystic fibrosis (CF). We assessed function in a native isogenic context by evaluating CFTR mutants that were stably expressed in the genome of a human airway cell line devoid of endogenous CFTR expression. 21 of 29 variants associated with full expressivity of the CF phenotype generated <10% wild-type CFTR (WT-CFTR) function, a conservative threshold for the development of life-limiting CF lung disease, and five variants had moderately decreased function (10% to ∼25% WT-CFTR). The remaining three variants in this group unexpectedly had >25% WT-CFTR function; two were higher than 75% WT-CFTR. As expected, 14 of 19 variants associated with partial expressivity of CF had >25% WT-CFTR function; however, four had minimal to no effect on CFTR function (>75% WT-CFTR). Thus, 6 of 48 (13%) missense variants believed to be disease causing did not alter CFTR function. Functional studies substantially refined pathogenicity assignment with expert annotation and criteria from the American College of Medical Genetics and Genomics and Association for Molecular Pathology. However, four algorithms (CADD, REVEL, SIFT, and PolyPhen-2) could not differentiate between variants that caused severe, moderate, or minimal reduction in function. In the setting of variable expressivity, these results indicate that functional assays are essential for accurate interpretation of missense variants and that current prediction tools should be used with caution.

Inferring the effect of genomic variation in the new era of genomics

Accurate and detailed understanding of the effects of variants in the coding and noncoding regions of the genome is the next big challenge in the new genomic era of personalized medicine, especially to tackle newer findings of genetic and phenotypic heterogeneity of diseases. This is necessary to resolve the gene-variant-disease relationship, the pathogenic variant spectrum of genes, pathogenic variants with variable clinical consequences, and multiloci diseases. In turn, this will facilitate patient recruitment for relevant clinical trials. In this review, we describe the trends in research at the intersection of basic and clinical genomics aiming to (a) overcome molecular diagnostic challenges and increase the clinical utility of next-generation sequencing (NGS) platforms, (b) elucidate variants associated with disease, (c) determine overall genomic complexity including epistasis, complex inheritance patterns such as "synergistic heterozygosity," digenic/multigenic inheritance, modifier effect, and rare variant load. We describe the newly emerging field of integrated functional genomics, in vivo or in vitro large-scale functional approaches, statistical bioinformatics algorithms that support NGS genomics data to interpret variants for timely clinical diagnostics and disease management. Thus, facilitating the discovery of new therapeutic or biomarker options, and their roles in the future of personalized medicine.

Clinical implications and considerations for evaluation of in silico algorithms for use with ACMG/AMP clinical variant interpretation guidelines

Clinical genetics laboratories have recently adopted guidelines for the interpretation of sequence variants set by the American College of Medical Genetics (ACMG) and Association for Molecular Pathology (AMP). The use of in silico algorithms to predict whether amino acid substitutions result in human disease is inconsistent across clinical laboratories. The clinical genetics community must carefully consider how in silico predictions can be incorporated into variant interpretation in clinical practice.

Please see related Research article: https://doi.org/10.1186/s13059-017-1353-5

Reverse epitope mapping of the E2 glycoprotein in antibody associated hepatitis C virus

The humoral immune system responds to chronic hepatitis C virus (HCV) infection by producing neutralising antibodies (nAb). In this study we generated three HCV pseudoparticles in which E1E2 glycoprotein sequence was targeted by the host humoral immune system. We used patient derived virus free Fabs (VF-Fabs) obtained from HCV genotype 1a (n = 3), genotype 1b (n = 7) and genotype 3a (n = 1) for neutralisation of HCVpp produced in this study both individually and in combination. Based on the available anti-HCV monoclonal nAb mapping information we selected amino acid region 384-619 for conformational epitope mapping. Amongst our notable findings, we observed significant reduction in HCVpp infectivity (p<0.05) when challenged with a combination of inter genotype and subtype VF-Fabs. We also identified five binding motifs targeted by patient derived VF-Fab upon peptide mapping, of which two shared the residues with previously reported epitopes. One epitope lies within an immunodominant HVR1 and two were novel. In summary, we used a reverse epitope mapping strategy to identify preferred epitopes by the host humoral immune system. Additionally, we have combined different VF-Fabs to further reduce the HCVpp infectivity. Our data indicates that combining the antigen specificity of antibodies may be a useful strategy to reduce (in-vitro) infectivity.

Mining the Unknown: Assigning Function to Noncoding Single Nucleotide Polymorphisms

One of the formative goals of genetics research is to understand how genetic variation leads to phenotypic differences and human disease. Genome-wide association studies (GWASs) bring us closer to this goal by linking variation with disease faster than ever before. Despite this, GWASs alone are unable to pinpoint disease-causing single nucleotide polymorphisms (SNPs). Noncoding SNPs, which represent the majority of GWAS SNPs, present a particular challenge. To address this challenge, an array of computational tools designed to prioritize and predict the function of noncoding GWAS SNPs have been developed. However, fewer than 40% of GWAS publications from 2015 utilized these tools. We discuss several leading methods for annotating noncoding variants and how they can be integrated into research pipelines in hopes that they will be broadly applied in future GWAS analyses.

Recent Advances in the Molecular Genetics of Familial Hypertrophic Cardiomyopathy in South Asian Descendants

The South Asian population, numbered at 1.8 billion, is estimated to comprise around 20% of the global population and 1% of the American population, and has one of the highest rates of cardiovascular disease. While South Asians show increased classical risk factors for developing heart failure, the role of population-specific genetic risk factors has not yet been examined for this group. Hypertrophic cardiomyopathy (HCM) is one of the major cardiac genetic disorders among South Asians, leading to contractile dysfunction, heart failure, and sudden cardiac death. This disease displays autosomal dominant inheritance, and it is associated with a large number of variants in both sarcomeric and non-sarcomeric proteins. The South Asians, a population with large ethnic diversity, potentially carries region-specific polymorphisms. There is high variability in disease penetrance and phenotypic expression of variants associated with HCM. Thus, extensive studies are required to decipher pathogenicity and the physiological mechanisms of these variants, as well as the contribution of modifier genes and environmental factors to disease phenotypes. Conducting genotype-phenotype correlation studies will lead to improved understanding of HCM and, consequently, improved treatment options for this high-risk population. The objective of this review is to report the history of cardiovascular disease and HCM in South Asians, present previously published pathogenic variants, and introduce current efforts to study HCM using induced pluripotent stem cell-derived cardiomyocytes, next-generation sequencing, and gene editing technologies. The authors ultimately hope that this review will stimulate further research, drive novel discoveries, and contribute to the development of personalized medicine with the aim of expanding therapeutic strategies for HCM.

POSTAR: a platform for exploring post-transcriptional regulation coordinated by RNA-binding proteins

We present POSTAR (http://POSTAR.ncrnalab.org), a resource of POST-trAnscriptional Regulation coordinated by RNA-binding proteins (RBPs). Precise characterization of post-transcriptional regulatory maps has accelerated dramatically in the past few years. Based on new studies and resources, POSTAR supplies the largest collection of experimentally probed (∼23 million) and computationally predicted (approximately 117 million) RBP binding sites in the human and mouse transcriptomes. POSTAR annotates every transcript and its RBP binding sites using extensive information regarding various molecular regulatory events (e.g., splicing, editing, and modification), RNA secondary structures, disease-associated variants, and gene expression and function. Moreover, POSTAR provides a friendly, multi-mode, integrated search interface, which helps users to connect multiple RBP binding sites with post-transcriptional regulatory events, phenotypes, and diseases. Based on our platform, we were able to obtain novel insights into post-transcriptional regulation, such as the putative association between CPSF6 binding, RNA structural domains, and Li-Fraumeni syndrome SNPs. In summary, POSTAR represents an early effort to systematically annotate post-transcriptional regulatory maps and explore the putative roles of RBPs in human diseases.

A Whole-Genome Analysis Framework for Effective Identification of Pathogenic Regulatory Variants in Mendelian Disease

The interpretation of non-coding variants still constitutes a major challenge in the application of whole-genome sequencing in Mendelian disease, especially for single-nucleotide and other small non-coding variants. Here we present Genomiser, an analysis framework that is able not only to score the relevance of variation in the non-coding genome, but also to associate regulatory variants to specific Mendelian diseases. Genomiser scores variants through either existing methods such as CADD or a bespoke machine learning method and combines these with allele frequency, regulatory sequences, chromosomal topological domains, and phenotypic relevance to discover variants associated to specific Mendelian disorders. Overall, Genomiser is able to identify causal regulatory variants as the top candidate in 77% of simulated whole genomes, allowing effective detection and discovery of regulatory variants in Mendelian disease.

Looking beyond the genes: the role of non-coding variants in human disease

Over the past decades the search for disease causing variants has been focusing exclusively on the coding genome. This highly selective approach has been extremely successful resulting in the identification of thousands of disease genes, but ignores the functional and therefore disease relevance of the rest of the genome. Dropping sequencing costs and new high-throughput technologies such as ChIP-seq and chromosome conformation capture have opened new possibilities for the systematic investigation of the non-coding genome. These data have revealed the importance of non-coding DNA in fundamental processes such as gene regulation and 3D chromatin folding. Research into the principles of chromatin folding has revealed a domain structure of the genome, called topologically associated domains that provide a scaffold for enhancer promoter contacts. Non-coding mutations that affect regulatory elements can affect gene regulation by a loss of function, resulting in reduced gene expression, or a gain of function resulting in gene mis- or overexpression. Structural variations such as deletions, inversions or duplications have the potential to disturb normal chromatin folding. This may lead to the repositioning or disruption of topological associating domains and the relocation of enhancer elements with consecutive gene misexpression. Several recent studies highlight this as important disease mechanisms in developmental disorders and cancer. Therefore, the regulatory landscape of the genome has to be taken into consideration when investigating the pathology of human disease. In this review, we will discuss the recent discoveries in the field of non-coding variation, gene regulation, 3D genome architecture, and their implications for human genetics.

KinMutRF: a random forest classifier of sequence variants in the human protein kinase superfamily

Background

The association between aberrant signal processing by protein kinases and human diseases such as cancer was established long time ago. However, understanding the link between sequence variants in the protein kinase superfamily and the mechanistic complex traits at the molecular level remains challenging: cells tolerate most genomic alterations and only a minor fraction disrupt molecular function sufficiently and drive disease.

Results

KinMutRF is a novel random-forest method to automatically identify pathogenic variants in human kinases. Twenty six decision trees implemented as a random forest ponder a battery of features that characterize the variants: a) at the gene level, including membership to a Kinbase group and Gene Ontology terms; b) at the PFAM domain level; and c) at the residue level, the types of amino acids involved, changes in biochemical properties, functional annotations from UniProt, Phospho.ELM and FireDB. KinMutRF identifies disease-associated variants satisfactorily (Acc: 0.88, Prec:0.82, Rec:0.75, F-score:0.78, MCC:0.68) when trained and cross-validated with the 3689 human kinase variants from UniProt that have been annotated as neutral or pathogenic. All unclassified variants were excluded from the training set. Furthermore, KinMutRF is discussed with respect to two independent kinase-specific sets of mutations no included in the training and testing, Kin-Driver (643 variants) and Pon-BTK (1495 variants). Moreover, we provide predictions for the 848 protein kinase variants in UniProt that remained unclassified.

A public implementation of KinMutRF, including documentation and examples, is available online (http://kinmut2.bioinfo.cnio.es). The source code for local installation is released under a GPL version 3 license, and can be downloaded from https://github.com/Rbbt-Workflows/KinMut2.

Conclusions

KinMutRF is capable of classifying kinase variation with good performance. Predictions by KinMutRF compare favorably in a benchmark with other state-of-the-art methods (i.e. SIFT, Polyphen-2, MutationAssesor, MutationTaster, LRT, CADD, FATHMM, and VEST). Kinase-specific features rank as the most elucidatory in terms of information gain and are likely the improvement in prediction performance. This advocates for the development of family-specific classifiers able to exploit the discriminatory power of features unique to individual protein families.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-2723-1) contains supplementary material, which is available to authorized users.

In Silico Functional Annotation of Genomic Variation

This unit describes the concepts and practical techniques for annotating genomic variants in the human genome to estimate their functional significance. With the rapid increase of available whole exome and whole genome sequencing information for human studies, annotation techniques have become progressively more important for highlighting and prioritizing nucleotide variants and their potential impact on genes and other genetic constructs. Here, we present an overview of different types of variant annotation approaches and elaborate on their foundations, assumptions, and the downstream consequences of their use. Computational approaches and tools to assign annotations and to identify variants are reviewed. Further, the general philosophy of assigning potential function to a genetic change within the biological context of a disease is discussed.

Introduction to the analysis of next generation sequencing data and its application to venous thromboembolism

Despite knowledge of various inherited risk factors associated with venous thromboembolism (VTE), no definite cause can be found in about 50% of patients. The application of data-driven searches such as GWAS has not been able to identify genetic variants with implications for clinical care, and unexplained heritability remains. In the past years, the development of several so-called next generation sequencing (NGS) platforms is offering the possibility of generating fast, inexpensive and accurate genomic information. However, so far their application to VTE has been very limited. Here we review basic concepts of NGS data analysis and explore the application of NGS technology to VTE. We provide both computational and biological viewpoints to discuss potentials and challenges of NGS-based studies.

The genetic landscape of cardiomyopathy and its role in heart failure

Heart failure is highly influenced by heritability, and nearly 100 genes link to familial cardiomyopathy. Despite the marked genetic diversity that underlies these complex cardiovascular phenotypes, several key genes and pathways have emerged. Hypertrophic cardiomyopathy is characterized by increased contractility and a greater energetic cost of cardiac output. Dilated cardiomyopathy is often triggered by mutations that disrupt the giant protein titin. The energetic consequences of these mutations offer molecular targets and opportunities for new drug development and gene correction therapies.