Variant Prioritization Research Articles

O48: DITTO4NF: In silico classification and prioritization of likely pathogenic variants for NF1 using explainable machine learning

O48: DITTO4NF: In silico classification and prioritization of likely pathogenic variants for NF1 using explainable machine learning

CFDP1 is a neuroblastoma susceptibility gene that regulates transcription factors of the noradrenergic cell identity.

Pleiotropic genetic factors (e.g., DNA polymorphisms) may be involved in the initiation of neuroblastoma (NB) and coronary artery disease (CAD) given their common origin from defects in neural crest development. To discover novel NB susceptibility genes, we conducted a three-stage survey including a meta-analysis of NB and CAD genome-wide association data, prioritization of NB causal variants, and validation in an independent cohort of affected individuals-control subjects. The lead SNP, rs13337397 at the 16q23.1 locus, associated with both diseases in the meta-analysis and with NB in the validation study. All the SNPs in linkage disequilibrium with rs13337397 were annotated using the H3K27ac epigenetic marker of neural crest cells (NCC) and NB cell lines. Indeed, we identified the functional SNP rs13337017, mapping within an enhancer of NCCs and NB cell lines and showing long-range interactions with CFDP1 by Hi-C analysis. Luciferase assays indicated that the risk allele of rs13337017 increased CFDP1 expression in NB cell lines. Of note, CFDP1 high expression associated with unfavorable prognostic markers in an analysis including 498 NB transcriptomes. Moreover, depletion of CFDP1 markedly decreased viability and migration and increased apoptotic rates in NB cell lines. Finally, transcriptome and qPCR analyses revealed that the depletion of CFDP1 may affect noradrenergic neuron differentiation by downregulating master regulators of sympathetic noradrenergic identity, including PHOX2B, HAND2, and GATA3. Our data strongly suggest that CFDP1 acts as oncogene in NB. In addition, we provide evidence that genetic predisposition to NB can be mediated by the alteration of noradrenergic lineage-specific gene expression.

P106: Population based reporting of ACMG78 secondary findings using semi-automated variant interpretation software for variant prioritization and curation*

P106: Population based reporting of ACMG78 secondary findings using semi-automated variant interpretation software for variant prioritization and curation*

Klarigi: Characteristic explanations for semantic biomedical data

Annotation of biomedical entities with ontology classes provides for formal semantic analysis and mobilisation of background knowledge in determining their relationships. To date, enrichment analysis has been routinely employed to identify classes that are over-represented in annotations across sets of groups, such as biosample gene expression profiles or patient phenotypes, and is useful for a range of tasks including differential diagnosis and causative variant prioritisation. These approaches, however, usually consider only univariate relationships, make limited use of the semantic features of ontologies, and provide limited information and evaluation of the explanatory power of both singular and grouped candidate classes. Moreover, they are not designed to solve the problem of deriving cohesive, characteristic, and discriminatory sets of classes for entity groups. We have developed a new tool, called Klarigi, which introduces multiple scoring heuristics for identification of classes that are both compositional and discriminatory for groups of entities annotated with ontology classes. The tool includes a novel algorithm for derivation of multivariable semantic explanations for entity groups, makes use of semantic inference through live use of an ontology reasoner, and includes a classification method for identifying the discriminatory power of candidate sets, in addition to significance testing apposite to traditional enrichment approaches. We describe the design and implementation of Klarigi, including its scoring and explanation determination methods, and evaluate its use in application to two test cases with clinical significance, comparing and contrasting methods and results with literature-based and enrichment analysis methods. We demonstrate that Klarigi produces characteristic and discriminatory explanations for groups of biomedical entities in two settings. We also show that these explanations recapitulate and extend the knowledge held in existing biomedical databases and literature for several diseases. We conclude that Klarigi provides a distinct and valuable perspective on biomedical datasets when compared with traditional enrichment methods, and therefore constitutes a new method by which biomedical datasets can be explored, contributing to improved insight into semantic data.

Molecular characterization of variants in mitochondrial DNA encoded genes using next generation sequencing analysis and mitochondrial dysfunction in women with PCOS

Emerging studies indicates mitochondrial dysfunction and involvement of mitochondrial DNA (mtDNA) variants in the pathogenesis of polycystic ovary syndrome (PCOS). Cumulative effect of mtDNA rare variants are now gaining considerable interest apart from common variants in the pathogenesis of complex diseases. Rare variants may modify the effect of polymorphism or in combination with the common variants may affect the risk of disease. With the evolution of high throughput sequencing techniques, which can be utilized to identify common as well as rare variants along with heteroplasmy levels, comprehensive characterization of the mtDNA variants is possible. Till date, few studies reported common mtDNA variants using traditional sequencing techniques but rare variants in mtDNA encoding genes remain unexplored in women with PCOS. These mtDNA variants may be responsible for mitochondrial dysfunction and may contribute in PCOS pathogenesis. In this study we determined mtDNA copy number, a biomarker of mitochondrial dysfunction and first time analysed variants in mtDNA encoded genes in women with PCOS using mitochondrial Next Generation sequencing (NGS) approach and compared allele frequency from mitochondrial 1000 genome dataset. Variant annotation and prioritization was done using highly automated pipeline, MToolBox that excludes reads mapped from nuclear mitochondrial DNA sequences (NumtS) to identify unique mtDNA reads. The present study identified significant reduction in mtDNA copy number in women with PCOS compared to non-PCOS women. A total of unique 214 prioritized common to rare variants were identified in mtDNA encoded genes, 183 variants in OXPHOS complexes, 14 variants in MT-tRNA and 17 variants in MT-rRNA genes that may be involved in mitochondrial dysfunction in PCOS. Numerous variants were heteroplasmic, pathogenic in nature and occurred in evolutionary conserved region. Heteroplasmic variants were more frequently occurred in MT-CO3 gene. Non-synonymous variants were more than synonymous variants and mainly occurred in OXPHOS complex I and IV. Few variants were found to be associated with diseases in MITOMAP database. The study provides a better understanding towards pathogenesis of PCOS from novel aspects focusing on mitochondrial genetic defects as underlying cause for contributing mitochondrial dysfunction in women with PCOS.

Whole‐exome sequencing of a Saudi epilepsy cohort reveals association signals in known and potentially novel loci

BackgroundEpilepsy, a serious chronic neurological condition effecting up to 100 million people globally, has clear genetic underpinnings including common and rare variants. In Saudi Arabia, the prevalence of epilepsy is high and caused mainly by perinatal and genetic factors. No whole-exome sequencing (WES) studies have been performed to date in Saudi Arabian epilepsy cohorts. This offers a unique opportunity for the discovery of rare genetic variants impacting this disease as there is a high rate of consanguinity among large tribal pedigrees.ResultsWe performed WES on 144 individuals diagnosed with epilepsy, to interrogate known epilepsy-related genes for known and functional novel variants. We also used an American College of Medical Genetics (ACMG) guideline-based variant prioritization approach in an attempt to discover putative causative variants. We identified 32 potentially causative pathogenic variants across 30 different genes in 44/144 (30%) of these Saudi epilepsy individuals. We also identified 232 variants of unknown significance (VUS) across 101 different genes in 133/144 (92%) subjects. Strong enrichment of variants of likely pathogenicity was observed in previously described epilepsy-associated loci, and a number of putative pathogenic variants in novel loci are also observed.ConclusionSeveral putative pathogenic variants in known epilepsy-related loci were identified for the first time in our population, in addition to several potential new loci which may be prioritized for further investigation.

510 GENETIC CHARACTERIZATION OF BIOPSY-PROVEN MYOCARDITIS: A PILOT STUDY

Abstract Genetic characterization of biopsy-proven myocarditis: a pilot study Background Myocarditis is characterized by the presence of an inflammatory infiltrate in the myocardium with degenerative/necrotic changes of cardiomyocytes, not-related to ischemic damage. Its clinical presentation is extremely heterogenous. Endomyocardial Biopsy (EMB) is the diagnostic gold standard and provides etiopathogenetic diagnosis. Biopsy-proven myocarditis may be infectious, mainly viral, toxic or non-infectious immune-mediated/autoimmune. A complex interplay between genetic factors, environmental triggers (i.e. viral infection) and the immune response of the host is postulated at the basis of different disease evolution. Purpose To determine the prevalence of pathogenic/likely pathogenic (P/LP) variants in cardiomyopathy-related genes in a well-characterized cohort of biopsy-proven myocarditis. Methods Seventy-three biopsy-proven myocarditis-affected patients (mean age 51±9, 41 males) underwent screening of 200 genes related to inherited cardiomyopathies. Definite/moderate gene association with Dilated Cardiomyopathy (DCM) and Arrhythmogenic Cardiomyopathy was based on the ClinGen framework. Variant prioritization was carried out using American College of Medical Genetics and Genomics rules. Correlation with presence of virus on endomyocardial biopsy by polymerase chain reaction, family history and serum anti-heart autoantibodies (AHA) and/or aintiintercalated-disk autoantibodies (AIDA), among other clinical features, was appraised. Results Nineteen of the 73 biopsy-proven myocarditis patients (26%) carried a P/LP variant in cardiac-related genes. Titin (TTN) was the most overrepresented gene accounting for 11% of cases (8/73), followed by Myosin Binding Protein C3 (MYBPC3) and Desmoplakin (DSP), each accounting for 3% of cases (2/73), respectively. No differences were observed in the distribution of disease onset, heart failure classification (NYHA class), ejection fraction, presence of virus and presence of anti-heart autoantibodies between genotype-positive and genotype-negative cohorts. However, 12 of the 19 genotype-positive index cases (63%) referred family history for cardiomyopathy and/or sudden cardiac death; whereas only 4 of the 54 genotype-negative declared positive family history, demonstrating a statistically significant difference. On the other hand, 37% of genotype positive patients declared negative family history, suggesting a non-familial or sporadic form of myocarditis. Conclusion The prevalence of clinically actionable P/LP variants in cardiomyopathy-related genes is nearly one third of biopsy-proven viral or autoimmune myocarditis patients, most of them associated with DCM, but clinical parameters do not differ between genotype-positive and genotype-negative patients. On the other hand, positive family history in genotype-positive patients indicate that family history assessment is mandatory to identify the genetic substrate of inherited inflammatory cardiomyopathies.

Genome sequencing with gene panel-based analysis for rare inherited conditions in a publicly funded healthcare system: implications for future testing

NHS genetics centres in Scotland sought to investigate the Genomics England 100,000 Genomes Project diagnostic utility to evaluate genome sequencing for in rare, inherited conditions. Four regional services recruited 999 individuals from 394 families in 200 rare phenotype categories, with negative historic genetic testing. Genome sequencing was performed at Edinburgh Genomics, and phenotype and sequence data were transferred to Genomics England for variant calling, gene-based filtering and variant prioritisation. NHS Scotland genetics laboratories performed interpretation, validation and reporting. New diagnoses were made in 23% cases – 19% in genes implicated in disease at the time of variant prioritisation, and 4% from later review of additional genes. Diagnostic yield varied considerably between phenotype categories and was minimal in cases with prior exome testing. Genome sequencing with gene panel filtering and reporting achieved improved diagnostic yield over previous historic testing but not over now routine trio-exome sequence tests. Re-interpretation of genomic data with updated gene panels modestly improved diagnostic yield at minimal cost. However, to justify the additional costs of genome vs exome sequencing, efficient methods for analysis of structural variation will be required and / or cost of genome analysis and storage will need to decrease.

A concurrent dual analysis of genomic data augments diagnoses: Experiences of 2 clinical sites in the Undiagnosed Diseases Network

Next-generation sequencing (NGS) has revolutionized the diagnostic process for rare/ultrarare conditions. However, diagnosis rates differ between analytical pipelines. In the National Institutes of Health-Undiagnosed Diseases Network (UDN) study, each individual's NGS data are concurrently analyzed by the UDN sequencing core laboratory and the clinical sites. We examined the outcomes of this practice. A retrospective review was performed at 2 UDN clinical sites to compare the variants and diagnoses/candidate genes identified with the dual analyses of the NGS data. In total, 95 individuals had 100 diagnoses/candidate genes. There was 59% concordance between the UDN sequencing core laboratories and the clinical sites in identifying diagnoses/candidate genes. The core laboratory provided more diagnoses, whereas the clinical sites prioritized more research variants/candidate genes (P < .001). The clinical sites solely identified 15% of the diagnoses/candidate genes. The differences between the 2 pipelines were more often because of variant prioritization disparities than variant detection. The unique dual analysis of NGS data in the UDN synergistically enhances outcomes. The core laboratory provided a clinical analysis with more diagnoses and the clinical sites prioritized more research variants/candidate genes. Implementing such concurrent dual analyses in other genomic research studies and clinical settings can improve both variant detection and prioritization.

In silico interpretation of variants identifies novel causes of inherited eye disease in the 100 000 genomes project

AbstractInherited retinal dystrophy (IRD) describes a widely heterogeneous spectrum of disorders with over 270 genes implicated to date across all inheritance patterns and an estimated prevalence of around 1 in 2000 people. Genetic testing for IRD had evolved dramatically since the discovery of the first pathogenic variant in 1990: the advent of massively parallel sequencing means that the more than half of patients are now able to receive a molecular diagnosis although there is still significant missing heritability (up to ~40% of patients). Novel genes, non‐coding variants, structural variants/copy number variants, novel gene/disease associations, non‐Mendelian inheritance and non‐genetic disease are some of the mechanisms thought to account for the missing heritability of rare disease. This presentation will focus on non‐coding variant analysis.Whole genome sequencing (WGS) enables the capture and interrogation of all variants covering up to approximately 95% of the genome, and advances in genomic analysis technology, genome and variant databases, bioinformatics and in silico tools mean that nowadays variant prioritization pipelines are able to filter the huge variant numbers leaving only a handful of candidate pathogenic coding variants per patient.For the missing heritability in patients whose phenotype cannot be fully explained by this approach, however, non‐coding variant analysis is more challenging. Many more variants exist outside of coding exons and our understanding of their effect on transcripts and proteins is limited. Functional assays, especially for retina specific genes may be complex and difficult to establish, so accurate variant effect predictions are critical to enable molecular geneticists and clinical laboratories to maximize their ability to provide a definitive molecular diagnosis.This presentation will focus on the analysis of non‐coding variants in IRD genes in a cohort of patients who underwent WGS in the UK's 100 000 genomes project. Key to this study, patients were identified for study following multidisciplinary team discussion to highlight the most likely gene to be implicated in their disease. Identification of candidate non‐coding variants, filtering and prioritization were performed to identify the most compelling variant, followed by molecular investigation and confirmation of the predicted effect. Exemplar case studies will demonstrate the power of this analysis pipeline and accuracy of in silico predictions.

Identification of novel exonic variants contributing to hereditary breast and ovarian cancer in west Indian population

Breast and ovarian cancers are the most common cancer types in females worldwide and in India. Patients with these cancers require an early diagnosis which is essential for better prognosis, treatment and improved patient survival. Recently, the utilization of next-generation sequencing (NGS)-based screening has accelerated molecular diagnosis of various cancers. In the present study, we performed whole-exome sequencing (WES) of 30 patients who had a first or second-degree relative with breast or ovarian cancer and are tested negative for BRCA1/2 or other high and moderate-risk genes reported for HBOC. WES data from patients were analyzed and variants were called using bcftools. Functional annotation of variants and variant prioritization was performed by Exomiser. The clinical significance of variants was determined as per ACMG classification using Varsome tool. The functional analysis of genes was determined by STRING analysis and disease association was determined by open target tool. We found novel variants and gene candidates having significant association with HBOC conditions. The genes identified by exomiser (phenotype score > 0.75) are associated with various biological processes such as DNA integrity maintenance, transcription regulation, cell cycle regulation, and apoptosis. Our findings provide novel and prevalent gene variants associated with the HBOC condition in the West Indian population which could be further studied for early diagnosis and better prognosis of HBOC.

From Samples to Germline and Somatic Sequence Variation: A Focus on Next-Generation Sequencing in Melanoma Research.

Next-generation sequencing (NGS) applications have flourished in the last decade, permitting the identification of cancer driver genes and profoundly expanding the possibilities of genomic studies of cancer, including melanoma. Here we aimed to present a technical review across many of the methodological approaches brought by the use of NGS applications with a focus on assessing germline and somatic sequence variation. We provide cautionary notes and discuss key technical details involved in library preparation, the most common problems with the samples, and guidance to circumvent them. We also provide an overview of the sequence-based methods for cancer genomics, exposing the pros and cons of targeted sequencing vs. exome or whole-genome sequencing (WGS), the fundamentals of the most common commercial platforms, and a comparison of throughputs and key applications. Details of the steps and the main software involved in the bioinformatics processing of the sequencing results, from preprocessing to variant prioritization and filtering, are also provided in the context of the full spectrum of genetic variation (SNVs, indels, CNVs, structural variation, and gene fusions). Finally, we put the emphasis on selected bioinformatic pipelines behind (a) short-read WGS identification of small germline and somatic variants, (b) detection of gene fusions from transcriptomes, and (c) de novo assembly of genomes from long-read WGS data. Overall, we provide comprehensive guidance across the main methodological procedures involved in obtaining sequencing results for the most common short- and long-read NGS platforms, highlighting key applications in melanoma research.

Deep learning approaches for noncoding variant prioritization in neurodegenerative diseases.

Determining how noncoding genetic variants contribute to neurodegenerative dementias is fundamental to understanding disease pathogenesis, improving patient prognostication, and developing new clinical treatments. Next generation sequencing technologies have produced vast amounts of genomic data on cell type-specific transcription factor binding, gene expression, and three-dimensional chromatin interactions, with the promise of providing key insights into the biological mechanisms underlying disease. However, this data is highly complex, making it challenging for researchers to interpret, assimilate, and dissect. To this end, deep learning has emerged as a powerful tool for genome analysis that can capture the intricate patterns and dependencies within these large datasets. In this review, we organize and discuss the many unique model architectures, development philosophies, and interpretation methods that have emerged in the last few years with a focus on using deep learning to predict the impact of genetic variants on disease pathogenesis. We highlight both broadly-applicable genomic deep learning methods that can be fine-tuned to disease-specific contexts as well as existing neurodegenerative disease research, with an emphasis on Alzheimer's-specific literature. We conclude with an overview of the future of the field at the intersection of neurodegeneration, genomics, and deep learning.

SCIP: software for efficient clinical interpretation of copy number variants detected by whole-genome sequencing

Copy number variants (CNVs) represent major etiologic factors in rare genetic diseases. Current clinical CNV interpretation workflows require extensive back-and-forth with multiple tools and databases. This increases complexity and time burden, potentially resulting in missed genetic diagnoses. We present the Suite for CNV Interpretation and Prioritization (SCIP), a software package for the clinical interpretation of CNVs detected by whole-genome sequencing (WGS). The SCIP Visualization Module near-instantaneously displays all information necessary for CNV interpretation (variant quality, population frequency, inheritance pattern, and clinical relevance) on a single page—supported by modules providing variant filtration and prioritization. SCIP was comprehensively evaluated using WGS data from 1027 families with congenital cardiac disease and/or autism spectrum disorder, containing 187 pathogenic or likely pathogenic (P/LP) CNVs identified in previous curations. SCIP was efficient in filtration and prioritization: a median of just two CNVs per case were selected for review, yet it captured all P/LP findings (92.5% of which ranked 1st). SCIP was also able to identify one pathogenic CNV previously missed. SCIP was benchmarked against AnnotSV and a spreadsheet-based manual workflow and performed superiorly than both. In conclusion, SCIP is a novel software package for efficient clinical CNV interpretation, substantially faster and more accurate than previous tools (available at https://github.com/qd29/SCIP, a video tutorial series is available at https://bit.ly/SCIPVideos).

De novo and inherited variants in coding and regulatory regions in genetic cardiomyopathies

BackgroundCardiomyopathies are a leading cause of progressive heart failure and sudden cardiac death; however, their genetic aetiology remains poorly understood. We hypothesised that variants in noncoding regulatory regions and oligogenic inheritance mechanisms may help close the diagnostic gap.MethodsWe first analysed whole-genome sequencing data of 143 parent–offspring trios from Genomics England 100,000 Genomes Project. We used gene panel testing and a phenotype-based, variant prioritisation framework called Exomiser to identify candidate genes in trios. To assess the contribution of noncoding DNVs to cardiomyopathies, we intersected DNVs with open chromatin sequences from single-cell ATAC-seq data of cardiomyocytes. We also performed a case–control analysis in an exome-negative cohort, including 843 probands and 19,467 controls, to assess the association between noncoding variants in known cardiomyopathy genes and disease.ResultsIn the trio analysis, a definite or probable genetic diagnosis was identified in 21 probands according to the American College of Medical Genetics guidelines. We identified novel DNVs in diagnostic-grade genes (RYR2, TNNT2, PTPN11, MYH7, LZR1, NKX2-5), and five cases harbouring a combination of prioritised variants, suggesting that oligogenic inheritance and genetic modifiers contribute to cardiomyopathies. Phenotype-based ranking of candidate genes identified in noncoding DNV analysis revealed JPH2 as the top candidate. Moreover, a case–control analysis revealed an enrichment of rare noncoding variants in regulatory elements of cardiomyopathy genes (p = .035, OR = 1.43, 95% Cl = 1.095–1.767) versus controls. Of the 25 variants associated with disease (p< 0.5), 23 are novel and nine are predicted to disrupt transcription factor binding motifs.ConclusionOur results highlight complex genetic mechanisms in cardiomyopathies and reveal novel genes for future investigations.

QTLbase2: an enhanced catalog of human quantitative trait loci on extensive molecular phenotypes.

Deciphering the fine-scale molecular mechanisms that shape the genetic effects at disease-associated loci from genome-wide association studies (GWAS) remains challenging. The key avenue is to identify the essential molecular phenotypes that mediate the causal variant and disease under particular biological conditions. Therefore, integrating GWAS signals with context-specific quantitative trait loci (QTLs) (such as different tissue/cell types, disease states, and perturbations) from extensive molecular phenotypes would present important strategies for full understanding of disease genetics. Via persistent curation and systematic data processing of large-scale human molecular trait QTLs (xQTLs), we updated our previous QTLbase database (now QTLbase2, http://mulinlab.org/qtlbase) to comprehensively analyze and visualize context-specific QTLs across 22 molecular phenotypes and over 95 tissue/cell types. Overall, the resource features the following major updates and novel functions: (i) 960 more genome-wide QTL summary statistics from 146 independent studies; (ii) new data for 10 previously uncompiled QTL types; (iii) variant query scope expanded to fit 195 QTL datasets based on whole-genome sequencing; (iv) supports filtering and comparison of QTLs for different biological conditions, such as stimulation types and disease states; (v) a new linkage disequilibrium viewer to facilitate variant prioritization across tissue/cell types and QTL types.

Systematic analysis and prediction of genes associated with monogenic disorders on human chromosome X

Disease gene discovery on chromosome (chr) X is challenging owing to its unique modes of inheritance. We undertook a systematic analysis of human chrX genes. We observe a higher proportion of disorder-associated genes and an enrichment of genes involved in cognition, language, and seizures on chrX compared to autosomes. We analyze gene constraints, exon and promoter conservation, expression, and paralogues, and report 127 genes sharing one or more attributes with known chrX disorder genes. Using machine learning classifiers trained to distinguish disease-associated from dispensable genes, we classify 247 genes, including 115 of the 127, as having high probability of being disease-associated. We provide evidence of an excess of variants in predicted genes in existing databases. Finally, we report damaging variants in CDK16 and TRPC5 in patients with intellectual disability or autism spectrum disorders. This study predicts large-scale gene-disease associations that could be used for prioritization of X-linked pathogenic variants.

Improving variant prioritization in exome analysis by entropy-weighted ensemble of multiple tools.

Variant prioritization is a crucial step in the analysis of exome and genome sequencing. Multiple phenotype-driven tools have been developed to automate the variant prioritization process, but the efficacy of these tools in clinical setting with fuzzy phenotypic information and whether ensemble of these tools could outperform single algorithm remains to be assessed. A large rare disease cohort with heterogeneous phenotypic information, including a primary cohort of 1614 patients and a replication cohort of 1904 patients referred to exome sequencing, were recruited to assess the efficacy of variant prioritization and their ensemble. Three freely available tools-Exomiser, Xrare, and DeepPVP-and their ensemble were evaluated. The performance of all three tools was influenced by the attributes of phenotypic input. When combining these three tools by weighted-sum entropy method (EWE3), the ensemble outperformed any single algorithm, achieving a rate of 78% diagnostic variants in top 3 (13% improvement over current best performer, compared to Exomiser: 63%, Xrare: 65%, and DeepPVP: 51%), 88% in top 10 and 96% in top 30. The results were replicated in another independent cohort. Our study supports using entropy-weighted ensemble of multiple tools to improve variant prioritization and accelerate molecular diagnosis in exome/genome sequencing.

SEPT–GD: A decision tree to prioritise potential RNA splice variants in cardiomyopathy genes for functional splicing assays in diagnostics

BackgroundSplice prediction algorithms currently used in routine DNA diagnostics have limited sensitivity and specificity, therefore many potential splice variants are classified as variants of uncertain significance (VUSs). However, functional assessment of VUSs to test splicing is labour-intensive and time-consuming. We developed a decision tree to prioritise potential splice variants for functional studies and functionally verified the outcome of the decision tree. Materials and methodsWe built the decision tree, SEPT–GD, by setting thresholds for the splice prediction programs implemented in Alamut. A set of 343 variants with known effects on splicing was used as control for sensitivity and specificity. We tested SEPT–GD using variants from a Dutch cardiomyopathy cohort of 2002 patients that were previously classified as VUS and predicted to have a splice effect according to diagnostic rules. We then selected 12 VUSs ranked by SEPT–GD to functionally verify the predicted effect on splicing using a minigene assay: 10 variants predicted to have a strong effect and 2 with a weak effect. RT-PCR was performed for nine variants. Variant classification was re-evaluated based on the functional test outcome. ResultsCompared to similar individually tested algorithms, SEPT–GD shows higher sensitivity (91 %) and comparable specificity (88 %) for both consensus (dinucleotides at the start and end of the intron, GT at the 5′ end and AG at the 3′ end) and non-consensus splice-site variants (excluding middle of exon variants). Using clinical diagnostic criteria, 1295 unique variants in our cardiomyopathy cohort had originally been classified as VUSs, with 57 predicted by Alamut to have an effect on splicing. Using SEPT–GD, we prioritised 31 variants in 40 patients. In the minigene assay, all 12 variants showed results concordant with SEPT-GD predictions. RT-PCR confirmed the minigene results for two variants, TMEM43 c.1000 + 5G > T and TTN c.25922–6 T > G. Based on all outcomes, the SGCD c.4-1G > A and CSRP3 c.282-5_285del variants were reclassified as likely pathogenic. ConclusionSEPT–GD outperforms the tools commonly used for RNA splicing prediction and improves prioritisation of variants in cardiomyopathy genes for functional splicing analysis in a diagnostic setting.

The Cancermuts software package for the prioritization of missense cancer variants: a case study of AMBRA1 in melanoma

Cancer genomics and cancer mutation databases have made an available wealth of information about missense mutations found in cancer patient samples. Contextualizing by means of annotation and predicting the effect of amino acid change help identify which ones are more likely to have a pathogenic impact. Those can be validated by means of experimental approaches that assess the impact of protein mutations on the cellular functions or their tumorigenic potential. Here, we propose the integrative bioinformatic approach Cancermuts, implemented as a Python package. Cancermuts is able to gather known missense cancer mutations from databases such as cBioPortal and COSMIC, and annotate them with the pathogenicity score REVEL as well as information on their source. It is also able to add annotations about the protein context these mutations are found in, such as post-translational modification sites, structured/unstructured regions, presence of short linear motifs, and more. We applied Cancermuts to the intrinsically disordered protein AMBRA1, a key regulator of many cellular processes frequently deregulated in cancer. By these means, we classified mutations of AMBRA1 in melanoma, where AMBRA1 is highly mutated and displays a tumor-suppressive role. Next, based on REVEL score, position along the sequence, and their local context, we applied cellular and molecular approaches to validate the predicted pathogenicity of a subset of mutations in an in vitro melanoma model. By doing so, we have identified two AMBRA1 mutations which show enhanced tumorigenic potential and are worth further investigation, highlighting the usefulness of the tool. Cancermuts can be used on any protein targets starting from minimal information, and it is available at https://www.github.com/ELELAB/cancermuts as free software.