HGCS: an online tool for prioritizing disease-causing gene variants by biological distance

New genetic drivers in hemorrhagic hereditary telangiectasia

BACKGROUND

Hereditary hemorrhagic telangiectasia (HHT) is a rare vascular disease inherited in an autosomal dominant manner. Disease-causing variants in endoglin (ENG) and activin A receptor type II-like 1 (ACVRL1) genes are detected in around 90% of the patients; also 2% of patients harbor pathogenic variants at SMAD4 and GDF2. Importantly, the genetic cause of 8% of patients with clinical HHT remains unknown. Here, we present new putative genetic drivers of HHT.

METHODS

To identify new HHT genetic drivers, we performed exome sequencing of 19 HHT patients and relatives with unknown HHT genetic etiology. We applied a multistep filtration strategy to catalog deleterious variants and prioritize gene candidates based on their known relevance in endothelial cell biology. Additionally, we performed in vitro validation of one of the identified variants.

RESULTS

We identified variants in the INHA, HIF1A, JAK2, DNM2, POSTN, ANGPTL4, FOXO1 and SMAD6 genes as putative drivers in HHT. We have identified the SMAD6 p.(Glu407Lys) variant in one of the families; this is a loss-of-function variant leading to the activation of the BMP/TGFβ signaling in endothelial cells.

CONCLUSIONS

Variants in these genes should be considered for genetic testing in patients with HHT phenotype and negative for ACVRL1/ENG mutations.

Faster and more accurate pathogenic combination predictions with VarCoPP2.0

BACKGROUND

The prediction of potentially pathogenic variant combinations in patients remains a key task in the field of medical genetics for the understanding and detection of oligogenic/multilocus diseases. Models tailored towards such cases can help shorten the gap of missing diagnoses and can aid researchers in dealing with the high complexity of the derived data. The predictor VarCoPP (Variant Combinations Pathogenicity Predictor) that was published in 2019 and identified potentially pathogenic variant combinations in gene pairs (bilocus variant combinations), was the first important step in this direction. Despite its usefulness and applicability, several issues still remained that hindered a better performance, such as its False Positive (FP) rate, the quality of its training set and its complex architecture.

RESULTS

We present VarCoPP2.0: the successor of VarCoPP that is a simplified, faster and more accurate predictive model identifying potentially pathogenic bilocus variant combinations. Results from cross-validation and on independent data sets reveal that VarCoPP2.0 has improved in terms of both sensitivity (95% in cross-validation and 98% during testing) and specificity (5% FP rate). At the same time, its running time shows a significant 150-fold decrease due to the selection of a simpler Balanced Random Forest model. Its positive training set now consists of variant combinations that are more confidently linked with evidence of pathogenicity, based on the confidence scores present in OLIDA, the Oligogenic Diseases Database ( https://olida.ibsquare.be ). The improvement of its performance is also attributed to a more careful selection of up-to-date features identified via an original wrapper method. We show that the combination of different variant and gene pair features together is important for predictions, highlighting the usefulness of integrating biological information at different levels.

CONCLUSIONS

Through its improved performance and faster execution time, VarCoPP2.0 enables a more accurate analysis of larger data sets linked to oligogenic diseases. Users can access the ORVAL platform ( https://orval.ibsquare.be ) to apply VarCoPP2.0 on their data.

Identification of TAC1 Associated with Alzheimer's Disease Using a Robust Rank Aggregation Approach

BACKGROUND

Alzheimer's disease (AD) brings heavy burden to society and family. There is an urgent need to find effective methods for disease diagnosis and treatment. The robust rank aggregation (RRA) approach that could aggregate the resulting gene lists has been widely utilized in genomic data analysis.

OBJECTIVE

To identify hub genes using RRA approach in AD.

METHODS

Seven microarray datasets in frontal cortex from GEO database were used to identify differential expressed genes (DEGs) in AD patients using RRA approach. STRING was performed to explore the protein-to-protein interaction (PPI). Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analyses were utilized for enrichment analysis. Human Gene Connectome and Gene Set Enrichment Analysis were used for functional annotation. Finally, the expression levels of hub genes were validated in the cortex of 5xFAD mice by quantitative real-time polymerase chain reaction.

RESULTS

After RRA analysis, 473 DEGs (216 upregulated and 257 downregulated) were identified in AD samples. PPI showed that DEGs had a total of 416 nodes and 2750 edges. These genes were divided into 17 clusters, each of which contains at least three genes. After functional annotation and enrichment analysis, TAC1 is identified as the hub gene and may be related to synaptic function and inflammation. In addition, Tac1 was found downregulated in cortices of 5xFAD mice.

CONCLUSION

In the current study, TAC1 is identified as a key gene in the frontal cortex of AD, providing insight into the possible pathogenesis and potential therapeutic targets for this disease.

Phenotypic and Genotypic Characterization of Hereditary Angioedema in Saudi Arabia

Hereditary angioedema (HAE) is a potentially life-threatening autosomal dominant disorder affecting roughly 1:50,000 individuals. It is commonly characterized by swelling of the larynx, gastrointestinal tract, extremities, and skin. There is growing genetic heterogeneity associated with this disease but more than 95% of mutations are found in SERPING1, the gene which encodes complement 1 inhibitor (C1-INH). HAE cohorts from several populations have been published but no large scale study has been reported from the Arab world to date. Here we document the clinical and genetic findings of HAE patients from a single Saudi institution, which is a major referral center at the national level. A total of 51 patients across 17 unrelated families were recruited including two large multi-generational families, of which one contained an in-frame exonic deletion that was resolved through MLPA. Two cases were negative for all the genes we tested (including F12, PLG, ANGPT1, MYOF, KNG1, and HS3ST6). The predominant HAE subtype in our cohort was type I, at 76%. We were able to uncover a mutation in 49 patients (96%). No type III (normal C1-INH) patients were encountered in the clinic, suggesting that this subtype does not play a major role in HAE pathogenesis in Saudi Arabia. Additionally, the existence of four patients with consistently normal complement 4 (C4) levels alongside abnormal C1-INH profiles highlights the utility of dual screening for both proteins in suspected patients.

Candidate genes and sequence variants for susceptibility to mycobacterial infection identified by whole-exome sequencing

Inborn errors of immunity are known to influence susceptibility to mycobacterial infections. The aim of this study was to characterize the genetic profile of nine patients with mycobacterial infections (eight with BCGitis and one with disseminated tuberculosis) from the Republic of Moldova using whole-exome sequencing. In total, 12 variants in eight genes known to be associated with Mendelian Susceptibility to Mycobacterial Disease (MSMD) were detected in six out of nine patients examined. In particular, a novel splice site mutation c.373–2A>C in STAT1 gene was found and functionally confirmed in a patient with disseminated tuberculosis. Trio analysis was possible for seven out of nine patients, and resulted in 23 candidate variants in 15 novel genes. Four of these genes - GBP2, HEATR3, PPP1R9B and KDM6A were further prioritized, considering their elevated expression in immune-related tissues. Compound heterozygosity was found in GBP2 in a single patient, comprising a maternally inherited missense variant c.412G>A/p.(Ala138Thr) predicted to be deleterious and a paternally inherited intronic mutation c.1149+14T>C. Functional studies demonstrated that the intronic mutation affects splicing and the level of transcript. Finally, we analyzed pathogenicity of variant combinations in gene pairs and identified five patients with putative oligogenic inheritance. In summary, our study expands the spectrum of genetic variation contributing to susceptibility to mycobacterial infections in children and provides insight into the complex/oligogenic disease-causing mode.

An accurate prediction model of digenic interaction for estimating pathogenic gene pairs of human diseases

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Numerous pathogenic interactions are yet to be revealed efficiently due to dimension burden.

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Existing methods are underpowered and inaccurate to estimate pathogenic interactions.

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We developed an accurate bioinformatic method to predict digenic interaction effects based on gene-level features.

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The new method created a valuable resource of genome-wide pathogenic digenic interactions.

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We found that known causal genes in Mendelian and Oligogenic diseases may be enriched with interactive effects for the first time.

Increasing evidence shows that genetic interaction across the entire genome may explain a non-trivial fraction of genetic diseases. Digenic interaction is the simplest manifestation of genetic interaction among genes. However, systematic exploration of digenic interactive effects on the whole genome is often discouraged by the high dimension burden. Thus, numerous digenic interactions are yet to be identified for many diseases. Here, we propose a Digenic Interaction Effect Predictor (DIEP), an accurate machine-learning approach to identify the genome-wide pathogenic coding gene pairs with digenic interaction effects. This approach achieved high accuracy and sensitivity in independent testing datasets, outperforming another gene-level digenic predictor (DiGePred). DIEP was also able to discriminate digenic interaction effect from bi-locus effects dual molecular diagnosis (pseudo-digenic). Using DIEP, we provided a valuable resource of genome-wide digenic interactions and demonstrated the enrichment of the digenic interaction effect in Mendelian and Oligogenic diseases. Therefore, DIEP will play a useful role in facilitating the genomic mapping of interactive causal genes for human diseases.

Novel brown adipose tissue candidate genes predicted by the human gene connectome

Brown adipose tissue (BAT) is a promising therapeutic target against obesity. Therefore, research on the genetic architecture of BAT could be key for the development of successful therapies against this complex phenotype. Hypothesis-driven candidate gene association studies are useful for studying genetic determinants of complex traits, but they are dependent upon the previous knowledge to select candidate genes. Here, we predicted 107 novel-BAT candidate genes in silico using the uncoupling protein one (UCP1) as the hallmark of BAT activity. We first identified the top 1% of human genes predicted by the human gene connectome to be biologically closest to the UCP1, estimating 167 additional pathway genes (BAT connectome). We validated this prediction by showing that 60 genes already associated with BAT were included in the connectome and they were biologically closer to each other than expected by chance (p < 2.2 × 10^-16). The rest of genes (107) are potential candidates for BAT, being also closer to known BAT genes and more expressed in BAT biopsies than expected by chance (p < 2.2 × 10^-16; p = 4.39 × 10^-02). The resulting new list of predicted human BAT genes should be useful for the discovery of novel BAT genes and metabolic pathways.

Large-scale discovery of novel neurodevelopmental disorder-related genes through a unified analysis of single-nucleotide and copy number variants

Background

Previous large-scale studies of de novo variants identified a number of genes associated with neurodevelopmental disorders (NDDs); however, it was also predicted that many NDD-associated genes await discovery. Such genes can be discovered by integrating copy number variants (CNVs), which have not been fully considered in previous studies, and increasing the sample size.

Methods

We first constructed a model estimating the rates of de novo CNVs per gene from several factors such as gene length and number of exons. Second, we compiled a comprehensive list of de novo single-nucleotide variants (SNVs) in 41,165 individuals and de novo CNVs in 3675 individuals with NDDs by aggregating our own and publicly available datasets, including denovo-db and the Deciphering Developmental Disorders study data. Third, summing up the de novo CNV rates that we estimated and SNV rates previously established, gene-based enrichment of de novo deleterious SNVs and CNVs were assessed in the 41,165 cases. Significantly enriched genes were further prioritized according to their similarity to known NDD genes using a deep learning model that considers functional characteristics (e.g., gene ontology and expression patterns).

Results

We identified a total of 380 genes achieving statistical significance (5% false discovery rate), including 31 genes affected by de novo CNVs. Of the 380 genes, 52 have not previously been reported as NDD genes, and the data of de novo CNVs contributed to the significance of three genes (GLTSCR1, MARK2, and UBR3). Among the 52 genes, we reasonably excluded 18 genes [a number almost identical to the theoretically expected false positives (i.e., 380 × 0.05 = 19)] given their constraints against deleterious variants and extracted 34 “plausible” candidate genes. Their validity as NDD genes was consistently supported by their similarity in function and gene expression patterns to known NDD genes. Quantifying the overall similarity using deep learning, we identified 11 high-confidence (> 90% true-positive probabilities) candidate genes: HDAC2, SUPT16H, HECTD4, CHD5, XPO1, GSK3B, NLGN2, ADGRB1, CTR9, BRD3, and MARK2.

Conclusions

We identified dozens of new candidates for NDD genes. Both the methods and the resources developed here will contribute to the further identification of novel NDD-associated genes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-022-01042-w.

Network analysis reveals rare disease signatures across multiple levels of biological organization

Rare genetic diseases are typically caused by a single gene defect. Despite this clear causal relationship between genotype and phenotype, identifying the pathobiological mechanisms at various levels of biological organization remains a practical and conceptual challenge. Here, we introduce a network approach for evaluating the impact of rare gene defects across biological scales. We construct a multiplex network consisting of over 20 million gene relationships that are organized into 46 network layers spanning six major biological scales between genotype and phenotype. A comprehensive analysis of 3,771 rare diseases reveals distinct phenotypic modules within individual layers. These modules can be exploited to mechanistically dissect the impact of gene defects and accurately predict rare disease gene candidates. Our results show that the disease module formalism can be applied to rare diseases and generalized beyond physical interaction networks. These findings open up new venues to apply network-based tools for cross-scale data integration.

IFIH1 loss-of-function variants contribute to very early-onset inflammatory bowel disease

Genetic defects of innate immunity impairing intestinal bacterial sensing are linked to the development of Inflammatory Bowel Disease (IBD). Although much evidence supports a role of the intestinal virome in gut homeostasis, most studies focus on intestinal viral composition rather than on host intestinal viral sensitivity. To demonstrate the association between the development of Very Early Onset IBD (VEOIBD) and variants in the IFIH1 gene which encodes MDA5, a key cytosolic sensor for viral nucleic acids. Whole exome sequencing (WES) was performed in two independent cohorts of children with VEOIBD enrolled in Italy (n = 18) and USA (n = 24). Luciferase reporter assays were employed to assess MDA5 activity. An enrichment analysis was performed on IFIH1 comparing 42 VEOIBD probands with 1527 unrelated individuals without gastrointestinal or immunological issues. We identified rare, likely loss-of-function (LoF), IFIH1 variants in eight patients with VEOIBD from a combined cohort of 42 children. One subject, carrying a homozygous truncating variant resulting in complete LoF, experienced neonatal-onset, pan-gastrointestinal, IBD-like enteropathy plus multiple infectious episodes. The remaining seven subjects, affected by VEOIBD without immunodeficiency, were carriers of one LoF variant in IFIH1. Among these, two patients also carried a second hypomorphic variant, with partial function apparent when MDA5 was weakly stimulated. Furthermore, IFIH1 variants were significantly enriched in children with VEOIBD as compared to controls (p = 0.007). Complete and partial MDA5 deficiency is associated with VEOIBD with variable penetrance and expressivity, suggesting a role for impaired intestinal viral sensing in IBD pathogenesis.

Expanding the potential genes of inborn errors of immunity through protein interactions

BACKGROUND

Inborn errors of immunity (IEI) are a group of genetic disorders that impair the immune system, with over 400 genes described so far, and hundreds more to be discovered. To facilitate the search for new genes, we need a way to prioritize among all the genes in the genome those most likely to play an important role in immunity.

RESULTS

Here we identify a new list of genes by linking known IEI genes to new ones by using open-source databases of protein-protein interactions, post-translational modifications, and transcriptional regulation. We analyze this new set of 2,530 IEI-related genes for their tolerance of genetic variation and by their expression levels in various immune cell types.

CONCLUSIONS

By merging genes derived from protein interactions of known IEI genes with transcriptional data, we offer a new list of candidate genes that may play a role in as-yet undiscovered IEIs.

Increased burden of rare variants in genes of the endosomal Toll-like receptor pathway in patients with systemic lupus erythematosus

OBJECTIVE

To compare the frequency of rare variants in genes of the pathophysiologically relevant endosomal Toll-like receptor (eTLR) pathway and any quantifiable differences in variant rarity, predicted deleteriousness, or molecular proximity in patients with systemic lupus erythematosus (SLE) and healthy controls.

PATIENTS AND METHODS

65 genes associated with the eTLR pathway were identified by literature search and pathway analysis. Using next generation sequencing techniques, these were compared in two randomised cohorts of patients with SLE (n = 114 and n = 113) with 197 healthy controls. Genetically determined ethnicity was used to normalise minor allele frequencies (MAF) for the identified genetic variants and these were then compared by their frequency: rare (MAF < 0.005), uncommon (MAF 0.005-0.02), and common (MAF >0.02). This was compared to the results for 65 randomly selected genes.

RESULTS

Patients with SLE are more likely to carry a rare nonsynonymous variant affecting proteins within the eTLR pathway than healthy controls. Furthermore, individuals with SLE are more likely to have multiple rare variants in this pathway. There were no differences in rarity, Combined Annotation Dependent Depletion (CADD) score, or molecular proximity for rare eTLR pathway variants.

CONCLUSIONS

Rare non-synonymous variants are enriched in patients with SLE in the eTLR pathway. This supports the hypothesis that SLE arises from several rare variants of relatively large effect rather than many common variants of small effect.

The VRNetzer platform enables interactive network analysis in Virtual Reality

Networks provide a powerful representation of interacting components within complex systems, making them ideal for visually and analytically exploring big data. However, the size and complexity of many networks render static visualizations on typically-sized paper or screens impractical, resulting in proverbial ‘hairballs’. Here, we introduce a Virtual Reality (VR) platform that overcomes these limitations by facilitating the thorough visual, and interactive, exploration of large networks. Our platform allows maximal customization and extendibility, through the import of custom code for data analysis, integration of external databases, and design of arbitrary user interface elements, among other features. As a proof of concept, we show how our platform can be used to interactively explore genome-scale molecular networks to identify genes associated with rare diseases and understand how they might contribute to disease development. Our platform represents a general purpose, VR-based data exploration platform for large and diverse data types by providing an interface that facilitates the interaction between human intuition and state-of-the-art analysis methods.

Data-rich networks can be difficult to interpret beyond a certain size. Here, the authors introduce a platform that uses virtual reality to allow the visual exploration of large networks, while interfacing with data repositories and other analytical methods to improve the interpretation of big data.

Next-generation sequencing for inborn errors of immunity

Inborn errors of immunity (IEIs) include several hundred gene defects affecting various components of the immune system. As with other constitutional disorders, next-generation sequencing (NGS) is a powerful tool for the diagnosis of these diseases. While NGS can provide molecular confirmation of disease in a patient with a suspected or classic phenotype, it can also identify new molecular defects of the immune system, expand gene-disease phenotypes, clarify mechanism of disease, pattern of inheritance or identify new gene-disease associations. Multiple clinical specialties are involved in the diagnosis and management of patients with IEI, and most have no formal genetic training or expertise. To effectively utilize NGS tools and data in clinical practice, it is relevant and pragmatic to obtain a modicum of knowledge about genetic terminology, the variety of platforms and tools available for high-throughput genomic analysis, the interpretation and implementation of such data in clinical practice. There is considerable variability not only in the technologies and analytical tools used for NGS but in the bioinformatics approach to variant identification and interpretation. The ability to provide a molecular basis for disease has the potential to alter therapeutic management and longer-term treatment of the disease, including developing personalized approaches with molecularly targeted therapies. This review is intended for the clinical specialist or diagnostic immunologist who works in the area of inborn errors of immunity, and provides an overview of the need for genetic testing in these patients (the "why" aspect), the various technologies and analytical approaches, bioinformatics tools, resources, and challenges (the "how" aspect), and the clinical evidence for identifying which patients might be best served by such testing (the "when" aspect).

Human T-bet Governs Innate and Innate-like Adaptive IFN-γ Immunity against Mycobacteria

Inborn errors of human interferon gamma (IFN-γ) immunity underlie mycobacterial disease. We report a patient with mycobacterial disease due to inherited deficiency of the transcription factor T-bet. The patient has extremely low counts of circulating Mycobacterium-reactive natural killer (NK), invariant NKT (iNKT), mucosal-associated invariant T (MAIT), and Vδ2+ γδ T lymphocytes, and of Mycobacterium-non reactive classic TH1 lymphocytes, with the residual populations of these cells also producing abnormally small amounts of IFN-γ. Other lymphocyte subsets develop normally but produce low levels of IFN-γ, with the exception of CD8+ αβ T and non-classic CD4+ αβ TH1∗ lymphocytes, which produce IFN-γ normally in response to mycobacterial antigens. Human T-bet deficiency thus underlies mycobacterial disease by preventing the development of innate (NK) and innate-like adaptive lymphocytes (iNKT, MAIT, and Vδ2+ γδ T cells) and IFN-γ production by them, with mycobacterium-specific, IFN-γ-producing, purely adaptive CD8+ αβ T, and CD4+ αβ TH1∗ cells unable to compensate for this deficit.

Chronic mucocutaneous candidiasis and connective tissue disorder in humans with impaired JNK1-dependent responses to IL-17A/F and TGF-β

Genetic etiologies of chronic mucocutaneous candidiasis (CMC) disrupt human IL-17A/F-dependent immunity at mucosal surfaces, whereas those of connective tissue disorders (CTDs) often impair the TGF-β-dependent homeostasis of connective tissues. The signaling pathways involved are incompletely understood. We report a three-generation family with an autosomal dominant (AD) combination of CMC and a previously undescribed form of CTD that clinically overlaps with Ehlers-Danlos syndrome (EDS). The patients are heterozygous for a private splice-site variant of MAPK8, the gene encoding c-Jun N-terminal kinase 1 (JNK1), a component of the MAPK signaling pathway. This variant is loss-of-expression and loss-of-function in the patients' fibroblasts, which display AD JNK1 deficiency by haploinsufficiency. These cells have impaired, but not abolished, responses to IL-17A and IL-17F. Moreover, the development of the patients' T_H17 cells was impaired ex vivo and in vitro, probably due to the involvement of JNK1 in the TGF-β-responsive pathway and further accounting for the patients' CMC. Consistently, the patients' fibroblasts displayed impaired JNK1- and c-Jun/ATF-2-dependent induction of key extracellular matrix (ECM) components and regulators, but not of EDS-causing gene products, in response to TGF-β. Furthermore, they displayed a transcriptional pattern in response to TGF-β different from that of fibroblasts from patients with Loeys-Dietz syndrome caused by mutations of TGFBR2 or SMAD3, further accounting for the patients' complex and unusual CTD phenotype. This experiment of nature indicates that the integrity of the human JNK1-dependent MAPK signaling pathway is essential for IL-17A- and IL-17F-dependent mucocutaneous immunity to Candida and for the TGF-β-dependent homeostasis of connective tissues.

ORVAL: a novel platform for the prediction and exploration of disease-causing oligogenic variant combinations

A tremendous amount of DNA sequencing data is being produced around the world with the ambition to capture in more detail the mechanisms underlying human diseases. While numerous bioinformatics tools exist that allow the discovery of causal variants in Mendelian diseases, little to no support is provided to do the same for variant combinations, an essential task for the discovery of the causes of oligogenic diseases. ORVAL (the Oligogenic Resource for Variant AnaLysis), which is presented here, provides an answer to this problem by focusing on generating networks of candidate pathogenic variant combinations in gene pairs, as opposed to isolated variants in unique genes. This online platform integrates innovative machine learning methods for combinatorial variant pathogenicity prediction with visualization techniques, offering several interactive and exploratory tools, such as pathogenic gene and protein interaction networks, a ranking of pathogenic gene pairs, as well as visual mappings of the cellular location and pathway information. ORVAL is the first web-based exploration platform dedicated to identifying networks of candidate pathogenic variant combinations with the sole ambition to help in uncovering oligogenic causes for patients that cannot rely on the classical disease analysis tools. ORVAL is available at https://orval.ibsquare.be.

Identifying disease-causing mutations in genomes of single patients by computational approaches

Over the last decade next generation sequencing (NGS) has been extensively used to identify new pathogenic mutations and genes causing rare genetic diseases. The efficient analyses of NGS data is not trivial and requires a technically and biologically rigorous pipeline that addresses data quality control, accurate variant filtration to minimize false positives and false negatives, and prioritization of the remaining genes based on disease genomics and physiological knowledge. This review provides a pipeline including all these steps, describes popular software for each step of the analysis, and proposes a general framework for the identification of causal mutations and genes in individual patients of rare genetic diseases.

De novo variants in exomes of congenital heart disease patients identify risk genes and pathways

Background

Congenital heart disease (CHD) affects ~ 1% of live births and is the most common birth defect. Although the genetic contribution to the CHD has been long suspected, it has only been well established recently. De novo variants are estimated to contribute to approximately 8% of sporadic CHD.

Methods

CHD is genetically heterogeneous, making pathway enrichment analysis an effective approach to explore and statistically validate CHD-associated genes. In this study, we performed novel gene and pathway enrichment analyses of high-impact de novo variants in the recently published whole-exome sequencing (WES) data generated from a cohort of CHD 2645 parent-offspring trios to identify new CHD-causing candidate genes and mutations. We performed rigorous variant- and gene-level filtrations to identify potentially damaging variants, followed by enrichment analyses and gene prioritization.

Results

Our analyses revealed 23 novel genes that are likely to cause CHD, including HSP90AA1, ROCK2, IQGAP1, and CHD4, and sharing biological functions, pathways, molecular interactions, and properties with known CHD-causing genes.

Conclusions

Ultimately, these findings suggest novel genes that are likely to be contributing to CHD pathogenesis.

Diagnostic interpretation of genetic studies in patients with primary immunodeficiency diseases: A working group report of the Primary Immunodeficiency Diseases Committee of the American Academy of Allergy, Asthma & Immunology

Genetic testing has become an integral component of the diagnostic evaluation of patients with suspected primary immunodeficiency diseases. Results of genetic testing can have a profound effect on clinical management decisions. Therefore clinical providers must demonstrate proficiency in interpreting genetic data. Because of the need for increased knowledge regarding this practice, the American Academy of Allergy, Asthma & Immunology Primary Immunodeficiency Diseases Committee established a work group that reviewed and summarized information concerning appropriate methods, tools, and resources for evaluating variants identified by genetic testing. Strengths and limitations of tests frequently ordered by clinicians were examined. Summary statements and tables were then developed to guide the interpretation process. Finally, the need for research and collaboration was emphasized. Greater understanding of these important concepts will improve the diagnosis and management of patients with suspected primary immunodeficiency diseases.

Using game theory and decision decomposition to effectively discern and characterise bi-locus diseases

In order to gain insight into oligogenic disorders, understanding those involving bi-locus variant combinations appears to be key. In prior work, we showed that features at multiple biological scales can already be used to discriminate among two types, i.e. disorders involving true digenic and modifier combinations. The current study expands this machine learning work towards dual molecular diagnosis cases, providing a classifier able to effectively distinguish between these three types. To reach this goal and gain an in-depth understanding of the decision process, game theory and tree decomposition techniques are applied to random forest predictors to investigate the relevance of feature combinations in the prediction. A machine learning model with high discrimination capabilities was developed, effectively differentiating the three classes in a biologically meaningful manner. Combining prediction interpretation and statistical analysis, we propose a biologically meaningful characterization of each class relying on specific feature strengths. Figuring out how biological characteristics shift samples towards one of three classes provides clinically relevant insight into the underlying biological processes as well as the disease itself.

Predicting disease-causing variant combinations

Directly assessing the pathogenicity of variant combinations in multiple genes was until now difficult. Nonetheless, this type of assessment can provide important benefits in identifying the genetic causes of rare diseases. The work presented in this paper aims to resolve this problem by presenting a machine-learning method able to predict the pathogenicity of variant combinations in gene pairs, based on pathogenic data. We demonstrate the high accuracy of this method and its effective capacity to identify novel instances. The method’s decision-making process is also made explicit, a contribution that is useful for clinical interpretation. This pioneering work will lead to toolboxes for geneticists and clinicians that can aid them in counselling their patients more effectively.

Notwithstanding important advances in the context of single-variant pathogenicity identification, novel breakthroughs in discerning the origins of many rare diseases require methods able to identify more complex genetic models. We present here the Variant Combinations Pathogenicity Predictor (VarCoPP), a machine-learning approach that identifies pathogenic variant combinations in gene pairs (called digenic or bilocus variant combinations). We show that the results produced by this method are highly accurate and precise, an efficacy that is endorsed when validating the method on recently published independent disease-causing data. Confidence labels of 95% and 99% are identified, representing the probability of a bilocus combination being a true pathogenic result, providing geneticists with rational markers to evaluate the most relevant pathogenic combinations and limit the search space and time. Finally, the VarCoPP has been designed to act as an interpretable method that can provide explanations on why a bilocus combination is predicted as pathogenic and which biological information is important for that prediction. This work provides an important step toward the genetic understanding of rare diseases, paving the way to clinical knowledge and improved patient care.

CDG: An Online Server for Detecting Biologically Closest Disease-Causing Genes and its Application to Primary Immunodeficiency

High-throughput genomic technologies yield about 20,000 variants in the protein-coding exome of each individual. A commonly used approach to select candidate disease-causing variants is to test whether the associated gene has been previously reported to be disease-causing. In the absence of known disease-causing genes, it can be challenging to associate candidate genes with specific genetic diseases. To facilitate the discovery of novel gene-disease associations, we determined the putative biologically closest known genes and their associated diseases for 13,005 human genes not currently reported to be disease-associated. We used these data to construct the closest disease-causing genes (CDG) server, which can be used to infer the closest genes with an associated disease for a user-defined list of genes or diseases. We demonstrate the utility of the CDG server in five immunodeficiency patient exomes across different diseases and modes of inheritance, where CDG dramatically reduced the number of candidate genes to be evaluated. This resource will be a considerable asset for ascertaining the potential relevance of genetic variants found in patient exomes to specific diseases of interest. The CDG database and online server are freely available to non-commercial users at: http://lab.rockefeller.edu/casanova/CDG.

Evaluating the Genetics of Common Variable Immunodeficiency: Monogenetic Model and Beyond

Common variable immunodeficiency (CVID) is the most frequent symptomatic primary immunodeficiency characterized by recurrent infections, hypogammaglobulinemia and poor response to vaccines. Its diagnosis is made based on clinical and immunological criteria, after exclusion of other diseases that can cause similar phenotypes. Currently, less than 20% of cases of CVID have a known underlying genetic cause. We have analyzed whole-exome sequencing and copy number variants data of 36 children and adolescents diagnosed with CVID and healthy relatives to estimate the proportion of monogenic cases. We have replicated an association of CVID to p.C104R in TNFRSF13B and reported the second case of homozygous patient to date. Our results also identify five causative genetic variants in LRBA, CTLA4, NFKB1, and PIK3R1, as well as other very likely causative variants in PRKCD, MAPK8, or DOCK8 among others. We experimentally validate the effect of the LRBA stop-gain mutation which abolishes protein production and downregulates the expression of CTLA4, and of the frameshift indel in CTLA4 producing expression downregulation of the protein. Our results indicate a monogenic origin of at least 15–24% of the CVID cases included in the study. The proportion of monogenic patients seems to be lower in CVID than in other PID that have also been analyzed by whole exome or targeted gene panels sequencing. Regardless of the exact proportion of CVID monogenic cases, other genetic models have to be considered for CVID. We propose that because of its prevalence and other features as intermediate penetrancies and phenotypic variation within families, CVID could fit with other more complex genetic scenarios. In particular, in this work, we explore the possibility of CVID being originated by an oligogenic model with the presence of heterozygous mutations in interacting proteins or by the accumulation of detrimental variants in particular immunological pathways, as well as perform association tests to detect association with rare genetic functional variation in the CVID cohort compared to healthy controls.

Discovery of Variants Underlying Host Susceptibility to Virus Infection Using Whole-Exome Sequencing

The clinical course of any viral infection greatly differs in individuals. This variation results from various viral, host, and environmental factors. The identification of host genetic factors influencing inter-individual variation in susceptibility to several pathogenic viruses has tremendously increased our understanding of the mechanisms and pathways required for immunity. Next-generation sequencing of whole exomes represents a powerful tool in biomedical research. In this chapter, we briefly introduce whole-exome sequencing in the context of genetic approaches to identify host susceptibility genes to viral infections. We then describe general aspects of the workflow for whole-exome sequence analysis together with the tools and online resources that can be used to identify and annotate variant calls, and then prioritize them for their potential association to phenotypes of interest.

Mutations modifying sporadic Alzheimer's disease age of onset

The identification of mutations modifying the age of onset (AOO) in Alzheimer's disease (AD) is crucial for understanding the natural history of AD and, therefore, for early interventions. Patients with sporadic AD (sAD) from a genetic isolate in the extremes of the AOO distribution were whole-exome genotyped. Single- and multi-locus linear mixed-effects models were used to identify functional variants modifying AOO. A posteriori enrichment and bioinformatic analyses were applied to evaluate the non-random clustering of the associate variants to physiopathological pathways involved in AD. We identified more than 20 pathogenic, genome-wide statistically significant mutations of major modifier effect on the AOO. These variants are harbored in genes implicated in neuron apoptosis, neurogenesis, inflammatory processes linked to AD, oligodendrocyte differentiation, and memory processes. This set of new genes harboring these mutations could be of importance for prediction, follow-up and eventually as therapeutical targets of AD. © 2016 Wiley Periodicals, Inc.

Capturing the biology of disease severity in a PSC-based model of familial dysautonomia

Familial dysautonomia (FD) is a debilitating disorder that affects derivatives of the neural crest (NC). For unknown reasons, people with FD show marked differences in disease severity despite carrying an identical, homozygous point mutation in IKBKAP, encoding IκB kinase complex-associated protein. Here we present disease-related phenotypes in human pluripotent stem cells (PSCs) that capture FD severity. Cells from individuals with severe but not mild disease show impaired specification of NC derivatives, including autonomic and sensory neurons. In contrast, cells from individuals with severe and mild FD show defects in peripheral neuron survival, indicating that neurodegeneration is the main culprit for cases of mild FD. Although genetic repair of the FD-associated mutation reversed early developmental NC defects, sensory neuron specification was not restored, indicating that other factors may contribute to disease severity. Whole-exome sequencing identified candidate modifier genes for individuals with severe FD. Our study demonstrates that PSC-based modeling is sensitive in recapitulating disease severity, which presents an important step toward personalized medicine.

Exome and genome sequencing for inborn errors of immunity

The advent of next-generation sequencing (NGS) in 2010 has transformed medicine, particularly the growing field of inborn errors of immunity. NGS has facilitated the discovery of novel disease-causing genes and the genetic diagnosis of patients with monogenic inborn errors of immunity. Whole-exome sequencing (WES) is presently the most cost-effective approach for research and diagnostics, although whole-genome sequencing offers several advantages. The scientific or diagnostic challenge consists in selecting 1 or 2 candidate variants among thousands of NGS calls. Variant- and gene-level computational methods, as well as immunologic hypotheses, can help narrow down this genome-wide search. The key to success is a well-informed genetic hypothesis on 3 key aspects: mode of inheritance, clinical penetrance, and genetic heterogeneity of the condition. This determines the search strategy and selection criteria for candidate alleles. Subsequent functional validation of the disease-causing effect of the candidate variant is critical. Even the most up-to-date dry lab cannot clinch this validation without a seasoned wet lab. The multifariousness of variations entails an experimental rigor even greater than traditional Sanger sequencing-based approaches in order not to assign a condition to an irrelevant variant. Finding the needle in the haystack takes patience, prudence, and discernment.

Interferon-gamma-dependent Immunity in Bacillus Calmette-Guérin Vaccine Osteitis Survivors

BACKGROUND

Inborn errors of interferon-gamma (IFN-γ)-mediated immunity underlie disseminated disease caused by Mycobacterium bovis Bacillus Calmette-Guérin (BCG) live vaccines. We hypothesized that some patients with osteitis after BCG vaccination may have an impaired IFN-γ immunity. Our aim was to investigate interleukin (IL)-12 and IFN-γ ex vivo production stimulated with BCG and BCG + IFN-γ or BCG + IL-12, respectively, in BCG osteitis survivors.

METHODS

Fresh blood samples were collected from 132 former BCG osteitis Finnish patients now aged 21-49 years, and IL-12 and IFN-γ were measured in cell cultures with and without stimulation with BCG and with BCG + IFN-γ or BCG + IL-12, respectively. As a pilot study, known disease-causing genes controlling IFN-γ immunity (IFNGR1, IFNGR2, STAT1, IL12B, IL12RB1, ISG15, IRF8, NEMO and CYBB) were investigated in 20 selected patients by whole exome sequencing.

RESULTS

By the limit of <5th percentile, ex vivo IL-12 concentration and increase in concentration was low in 5 and ex vivo IFN-γ concentration and increase in concentration was low in 6 patients (including 2 samples with both IL-12 and IFN-γ findings). By the limit of <10th percentile, an additional 6 and 4 patients were, respectively, detected (including 2 samples with both findings). With 2 exceptions, low concentrations and low increases in concentrations picked-up the same cases. Mutations in known disease-causing IFN-γ-related genes were not found in any of these patients.

CONCLUSION

These findings call for searching of mutations in new genes governing IFN-γ-dependent immunity to live BCG vaccine.

Evolutionary Genomics

This supplement is intended to focus on evolutionary genomics. Evolutionary Bioinformatics aims to provide researchers working in this complex, quickly developing field with online, open access to highly relevant scholarly articles by leading international researchers. In a field where the literature is ever-expanding, researchers increasingly need access to up-to-date, high quality scholarly articles on areas of specific contemporary interest. This supplement aims to address this by presenting high-quality articles that allow readers to distinguish the signal from the noise. The editor in chief hopes that through this effort, practitioners and researchers will be aided in finding answers to some of the most complex and pressing issues of our time.

The human gene damage index as a gene-level approach to prioritizing exome variants

The protein-coding exome of a patient with a monogenic disease contains about 20,000 variants, only one or two of which are disease causing. We found that 58% of rare variants in the protein-coding exome of the general population are located in only 2% of the genes. Prompted by this observation, we aimed to develop a gene-level approach for predicting whether a given human protein-coding gene is likely to harbor disease-causing mutations. To this end, we derived the gene damage index (GDI): a genome-wide, gene-level metric of the mutational damage that has accumulated in the general population. We found that the GDI was correlated with selective evolutionary pressure, protein complexity, coding sequence length, and the number of paralogs. We compared GDI with the leading gene-level approaches, genic intolerance, and de novo excess, and demonstrated that GDI performed best for the detection of false positives (i.e., removing exome variants in genes irrelevant to disease), whereas genic intolerance and de novo excess performed better for the detection of true positives (i.e., assessing de novo mutations in genes likely to be disease causing). The GDI server, data, and software are freely available to noncommercial users from lab.rockefeller.edu/casanova/GDI.

Human TYK2 deficiency: Mycobacterial and viral infections without hyper-IgE syndrome

Kreins et al. report the identification and immunological characterization of a group of TYK2-deficient patients.

Autosomal recessive, complete TYK2 deficiency was previously described in a patient (P1) with intracellular bacterial and viral infections and features of hyper-IgE syndrome (HIES), including atopic dermatitis, high serum IgE levels, and staphylococcal abscesses. We identified seven other TYK2-deficient patients from five families and four different ethnic groups. These patients were homozygous for one of five null mutations, different from that seen in P1. They displayed mycobacterial and/or viral infections, but no HIES. All eight TYK2-deficient patients displayed impaired but not abolished cellular responses to (a) IL-12 and IFN-α/β, accounting for mycobacterial and viral infections, respectively; (b) IL-23, with normal proportions of circulating IL-17⁺ T cells, accounting for their apparent lack of mucocutaneous candidiasis; and (c) IL-10, with no overt clinical consequences, including a lack of inflammatory bowel disease. Cellular responses to IL-21, IL-27, IFN-γ, IL-28/29 (IFN-λ), and leukemia inhibitory factor (LIF) were normal. The leukocytes and fibroblasts of all seven newly identified TYK2-deficient patients, unlike those of P1, responded normally to IL-6, possibly accounting for the lack of HIES in these patients. The expression of exogenous wild-type TYK2 or the silencing of endogenous TYK2 did not rescue IL-6 hyporesponsiveness, suggesting that this phenotype was not a consequence of the TYK2 genotype. The core clinical phenotype of TYK2 deficiency is mycobacterial and/or viral infections, caused by impaired responses to IL-12 and IFN-α/β. Moreover, impaired IL-6 responses and HIES do not appear to be intrinsic features of TYK2 deficiency in humans.

Novel and rare functional genomic variants in multiple autoimmune syndrome and Sjögren's syndrome

Background

Multiple autoimmune syndrome (MAS), an extreme phenotype of autoimmune disorders, is a very well suited trait to tackle genomic variants of these conditions. Whole exome sequencing (WES) is a widely used strategy for detection of protein coding and splicing variants associated with inherited diseases.

Methods

The DNA of eight patients affected by MAS [all of whom presenting with Sjögren’s syndrome (SS)], four patients affected by SS alone and 38 unaffected individuals, were subject to WES. Filters to identify novel and rare functional (pathogenic–deleterious) homozygous and/or compound heterozygous variants in these patients and controls were applied. Bioinformatics tools such as the Human gene connectome as well as pathway and network analysis were applied to test overrepresentation of genes harbouring these variants in critical pathways and networks involved in autoimmunity.

Results

Eleven novel and rare functional variants were identified in cases but not in controls, harboured in: MACF1, KIAA0754, DUSP12, ICA1, CELA1, LRP1/STAT6, GRIN3B, ANKLE1, TMEM161A, and FKRP. These were subsequently subject to network analysis and their functional relatedness to genes already associated with autoimmunity was evaluated. Notably, the LRP1/STAT6 novel mutation was homozygous in one MAS affected patient and heterozygous in another. LRP1/STAT6 disclosed the strongest plausibility for autoimmunity. LRP1/STAT6 are involved in extracellular and intracellular anti-inflammatory pathways that play key roles in maintaining the homeostasis of the immune system. Further; networks, pathways, and interaction analyses showed that LRP1 is functionally related to the HLA-B and IL10 genes and it has a substantial impact within immunological pathways and/or reaction to bacterial and other foreign proteins (phagocytosis, regulation of phospholipase A2 activity, negative regulation of apoptosis and response to lipopolysaccharides). Further, ICA1 and STAT6 were also closely related to AIRE and IRF5, two very well known autoimmunity genes.

Conclusions

Novel and rare exonic mutations that may account for autoimmunity were identified. Among those, the LRP1/STAT6 novel mutation has the strongest case for being categorised as potentially causative of MAS given the presence of intriguing patterns of functional interaction with other major genes shaping autoimmunity.

Electronic supplementary material

The online version of this article (doi:10.1186/s12967-015-0525-x) contains supplementary material, which is available to authorized users.

Inherited IL-17RC deficiency in patients with chronic mucocutaneous candidiasis

Autosomal-recessive IL-17RA, IL-17RC, and ACT1 deficiencies and autosomal-dominant IL-17F deficiency in humans underlie susceptibility to chronic mucocutaneous candidiasis.

Chronic mucocutaneous candidiasis (CMC) is characterized by recurrent or persistent infections of the skin, nail, oral, and genital mucosae with Candida species, mainly C. albicans. Autosomal-recessive (AR) IL-17RA and ACT1 deficiencies and autosomal-dominant IL-17F deficiency, each reported in a single kindred, underlie CMC in otherwise healthy patients. We report three patients from unrelated kindreds, aged 8, 12, and 37 yr with isolated CMC, who display AR IL-17RC deficiency. The patients are homozygous for different nonsense alleles that prevent the expression of IL-17RC on the cell surface. The defect is complete, abolishing cellular responses to IL-17A and IL-17F homo- and heterodimers. However, in contrast to what is observed for the IL-17RA– and ACT1-deficient patients tested, the response to IL-17E (IL-25) is maintained in these IL-17RC–deficient patients. These experiments of nature indicate that human IL-17RC is essential for mucocutaneous immunity to C. albicans but is otherwise largely redundant.

Novel primary immunodeficiency candidate genes predicted by the human gene connectome

Germline genetic mutations underlie various primary immunodeficiency (PID) diseases. Patients with rare PID diseases (like most non-PID patients and healthy individuals) carry, on average, 20,000 rare and common coding variants detected by high-throughput sequencing. It is thus a major challenge to select only a few candidate disease-causing variants for experimental testing. One of the tools commonly used in the pipeline for estimating a potential PID-candidate gene is to test whether the specific gene is included in the list of genes that were already experimentally validated as PID-causing in previous studies. However, this approach is limited because it cannot detect the PID-causing mutation(s) in the many PID patients carrying causal mutations of as yet unidentified PID-causing genes. In this study, we expanded in silico the list of potential PID-causing candidate genes from 229 to 3,110. We first identified the top 1% of human genes predicted by the human genes connectome to be biologically close to the 229 known PID genes. We then further narrowed down the list of genes by retaining only the most biologically relevant genes, with functionally enriched gene ontology biological categories similar to those for the known PID genes. We validated this prediction by showing that 17 of the 21 novel PID genes published since the last IUIS classification fall into this group of 3,110 genes (p < 10⁻⁷). The resulting new extended list of 3,110 predicted PID genes should be useful for the discovery of novel PID genes in patients.