Predicting mendelian disease-causing non-synonymous single nucleotide variants in exome sequencing studies

Ensemble and consensus approaches to prediction of recessive inheritance for missense variants in human disease

Mode of inheritance (MOI) is necessary for clinical interpretation of pathogenic variants; however, the majority of variants lack this information. Furthermore, variant effect predictors are fundamentally insensitive to recessive-acting diseases. Here, we present MOI-Pred, a variant pathogenicity prediction tool that accounts for MOI, and ConMOI, a consensus method that integrates variant MOI predictions from three independent tools. MOI-Pred integrates evolutionary and functional annotations to produce variant-level predictions that are sensitive to both dominant-acting and recessive-acting pathogenic variants. Both MOI-Pred and ConMOI show state-of-the-art performance on standard benchmarks. Importantly, dominant and recessive predictions from both tools are enriched in individuals with pathogenic variants for dominant- and recessive-acting diseases, respectively, in a real-world electronic health record (EHR)-based validation approach of 29,981 individuals. ConMOI outperforms its component methods in benchmarking and validation, demonstrating the value of consensus among multiple prediction methods. Predictions for all possible missense variants are provided in the "Data and code availability" section.

AI-derived comparative assessment of the performance of pathogenicity prediction tools on missense variants of breast cancer genes

Single nucleotide variants (SNVs) can exert substantial and extremely variable impacts on various cellular functions, making accurate predictions of their consequences challenging, albeit crucial especially in clinical settings such as in oncology. Laboratory-based experimental methods for assessing these effects are time-consuming and often impractical, highlighting the importance of in-silico tools for variant impact prediction. However, the performance metrics of currently available tools on breast cancer missense variants from benchmarking databases have not been thoroughly investigated, creating a knowledge gap in the accurate prediction of pathogenicity. In this study, the benchmarking datasets ClinVar and HGMD were used to evaluate 21 Artificial Intelligence (AI)-derived in-silico tools. Missense variants in breast cancer genes were extracted from ClinVar and HGMD professional v2023.1. The HGMD dataset focused on pathogenic variants only, to ensure balance, benign variants for the same genes were included from the ClinVar database. Interestingly, our analysis of both datasets revealed variants across genes with varying penetrance levels like low and moderate in addition to high, reinforcing the value of disease-specific tools. The top-performing tools on ClinVar dataset identified were MutPred (Accuracy = 0.73), Meta-RNN (Accuracy = 0.72), ClinPred (Accuracy = 0.71), Meta-SVM, REVEL, and Fathmm-XF (Accuracy = 0.70). While on HGMD dataset they were ClinPred (Accuracy = 0.72), MetaRNN (Accuracy = 0.71), CADD (Accuracy = 0.69), Fathmm-MKL (Accuracy = 0.68), and Fathmm-XF (Accuracy = 0.67). These findings offer clinicians and researchers valuable insights for selecting, improving, and developing effective in-silico tools for breast cancer pathogenicity prediction. Bridging this knowledge gap contributes to advancing precision medicine and enhancing diagnostic and therapeutic approaches for breast cancer patients with potential implications for other conditions.

Variant Impact Predictor database (VIPdb), version 2: trends from three decades of genetic variant impact predictors

BACKGROUND

Variant interpretation is essential for identifying patients' disease-causing genetic variants amongst the millions detected in their genomes. Hundreds of Variant Impact Predictors (VIPs), also known as Variant Effect Predictors (VEPs), have been developed for this purpose, with a variety of methodologies and goals. To facilitate the exploration of available VIP options, we have created the Variant Impact Predictor database (VIPdb).

RESULTS

The Variant Impact Predictor database (VIPdb) version 2 presents a collection of VIPs developed over the past three decades, summarizing their characteristics, ClinGen calibrated scores, CAGI assessment results, publication details, access information, and citation patterns. We previously summarized 217 VIPs and their features in VIPdb in 2019. Building upon this foundation, we identified and categorized an additional 190 VIPs, resulting in a total of 407 VIPs in VIPdb version 2. The majority of the VIPs have the capacity to predict the impacts of single nucleotide variants and nonsynonymous variants. More VIPs tailored to predict the impacts of insertions and deletions have been developed since the 2010s. In contrast, relatively few VIPs are dedicated to the prediction of splicing, structural, synonymous, and regulatory variants. The increasing rate of citations to VIPs reflects the ongoing growth in their use, and the evolving trends in citations reveal development in the field and individual methods.

CONCLUSIONS

VIPdb version 2 summarizes 407 VIPs and their features, potentially facilitating VIP exploration for various variant interpretation applications. VIPdb is available at  https://genomeinterpretation.org/vipdb.

Variant Impact Predictor database (VIPdb), version 2: Trends from 25 years of genetic variant impact predictors

Background

Variant interpretation is essential for identifying patients' disease-causing genetic variants amongst the millions detected in their genomes. Hundreds of Variant Impact Predictors (VIPs), also known as Variant Effect Predictors (VEPs), have been developed for this purpose, with a variety of methodologies and goals. To facilitate the exploration of available VIP options, we have created the Variant Impact Predictor database (VIPdb).

Results

The Variant Impact Predictor database (VIPdb) version 2 presents a collection of VIPs developed over the past 25 years, summarizing their characteristics, ClinGen calibrated scores, CAGI assessment results, publication details, access information, and citation patterns. We previously summarized 217 VIPs and their features in VIPdb in 2019. Building upon this foundation, we identified and categorized an additional 186 VIPs, resulting in a total of 403 VIPs in VIPdb version 2. The majority of the VIPs have the capacity to predict the impacts of single nucleotide variants and nonsynonymous variants. More VIPs tailored to predict the impacts of insertions and deletions have been developed since the 2010s. In contrast, relatively few VIPs are dedicated to the prediction of splicing, structural, synonymous, and regulatory variants. The increasing rate of citations to VIPs reflects the ongoing growth in their use, and the evolving trends in citations reveal development in the field and individual methods.

Conclusions

VIPdb version 2 summarizes 403 VIPs and their features, potentially facilitating VIP exploration for various variant interpretation applications.

Availability

VIPdb version 2 is available at https://genomeinterpretation.org/vipdb.

DVA: predicting the functional impact of single nucleotide missense variants

BACKGROUND

In the past decade, single nucleotide variants (SNVs) have been identified as having a significant relationship with the development and treatment of diseases. Among them, prioritizing missense variants for further functional impact investigation is an essential challenge in the study of common disease and cancer. Although several computational methods have been developed to predict the functional impacts of variants, the predictive ability of these methods is still insufficient in the Mendelian and cancer missense variants.

RESULTS

We present a novel prediction method called the disease-related variant annotation (DVA) method that predicts the effect of missense variants based on a comprehensive feature set of variants, notably, the allele frequency and protein-protein interaction network feature based on graph embedding. Benchmarked against datasets of single nucleotide missense variants, the DVA method outperforms the state-of-the-art methods by up to 0.473 in the area under receiver operating characteristic curve. The results demonstrate that the proposed method can accurately predict the functional impact of single nucleotide missense variants and substantially outperforms existing methods.

CONCLUSIONS

DVA is an effective framework for identifying the functional impact of disease missense variants based on a comprehensive feature set. Based on different datasets, DVA shows its generalization ability and robustness, and it also provides innovative ideas for the study of the functional mechanism and impact of SNVs.

eyeVarP: A computational framework for the identification of pathogenic variants specific to eye disease

PURPOSE

Disease-specific pathogenic variant prediction tools that differentiate pathogenic variants from benign have been improved through disease specificity recently. However, they have not been evaluated on disease-specific pathogenic variants compared with other diseases, which would help to prioritize disease-specific variants from several genes or novel genes. Thus, we hypothesize that features of pathogenic variants alone would provide a better model.

METHODS

We developed an eye disease-specific variant prioritization tool (eyeVarP), which applied the random forest algorithm to the data set of pathogenic variants of eye diseases and other diseases. We also developed the VarP tool and generalized pipeline to filter missense and insertion-deletion variants and predict their pathogenicity from exome or genome sequencing data, thus we provide a complete computational procedure.

RESULTS

eyeVarP outperformed pan disease-specific tools in identifying eye disease-specific pathogenic variants under the top 10. VarP outperformed 12 pathogenicity prediction tools with an accuracy of 95% in correctly identifying the pathogenicity of missense and insertion-deletion variants. The complete pipeline would help to develop disease-specific tools for other genetic disorders.

CONCLUSION

eyeVarP performs better in identifying eye disease-specific pathogenic variants using pathogenic variant features and gene features. Implementing such complete computational procedure would significantly improve the clinical variant interpretation for specific diseases.

Genomic Profiling of Metastatic Basal cell Carcinoma Reveals Candidate Drivers of Disease and Therapeutic Targets

Basal cell carcinomas (BCCs) are human beings' most common malignant tumors. Most are easily managed by surgery or topical therapies, and metastasis is rare. Although BCCs can become locally advanced, metastatic BCCs are very uncommon and may be biologically distinct. We assessed the clinicopathologic characteristics of 17 patients with metastatic BCC and pursued whole-exome sequencing of tumor and germline DNA from 8 patients. Genomic profiling revealed aberrant activation of Hedgehog signaling and alterations in GLI transcriptional regulators and Notch and Hippo signaling. Matched local recurrences of primary BCCs and metastases from 3 patients provided evidence of a clonal origin in all cases. Mutations associated with YAP inhibition were found exclusively in 2 hematogenously-spread lung metastases, and metastatic BCCs were enriched for mutations in the YAP/TAZ-binding domain of TEAD genes. Accordingly, YAP/TAZ nuclear localization was associated with metastatic types and Hippo mutations, suggesting an enhanced oncogenic role in hematogenously-spread metastases. Mutations in RET, HGF, and phosphatidylinositol 3‑kinase (PI3K)/protein kinase B (AKT) signaling were enriched compared with a cohort of low clinical-risk BCCs. Our results implicate Hippo and PI3K/AKT dysregulation in metastatic progression of BCCs, making these potential therapeutic targets in metastatic disease. The common clonal origin of matched recurrent and metastatic BCCs suggests that molecular profiling can assist in determining the nature/origin of poorly differentiated metastatic tumors of uncertain type. Genes and pathways enriched for mutations in this cohort are candidate drivers of metastasis and can be used to identify patients at high risk of metastasis who may benefit from aggressive local treatment and careful clinical follow-up.

AllelePred: A Simple Allele Frequencies Ensemble Predictor for Different Single Nucleotide Variants

BACKGROUND & OBJECTIVE

Genomic medicine stands to be revolutionized by understanding single nucleotide variants (SNVs) and their expression in single-gene disorders (Mendelian diseases). Computational tools can play a vital role in the exploration of such variations and their pathogenicity. Consequently, we developed the ensemble prediction tool AllelePred to identify deleterious SNVs and disease causative genes.

RESULTS

The model utilizes different population genetics backgrounds and restricted criteria for features selection to help generate high accuracy results. In comparison to other tools, such as Eigen, PROVEAN, and fathmm-MKL our classifier achieves higher accuracy (98%), precision (96%), F1 score (93%), and coverage (100%) for different types of coding variants. The new method was also compared against a bioinformatics analytical workflow, which uses gnomAD overall AFs (less than 1%) and CADD (scaled C-score of at least 15). Furthermore, this research highlights the stature of genetic variant sharing and curation. We accumulated a list of highly probable deleterious variants and recommended further experimental validation before medical diagnostic usage.

CONCLUSIONS

The ensemble prediction tool AllelePred enables increased accuracy in recognizing deleterious SNVs and the genetic determinants in real clinical data.

Pathogenicity Prediction of Single Amino Acid Variants With Machine Learning Model Based on Protein Structural Energies

The most popular tools for predicting pathogenicity of single amino acid variants (SAVs) were developed based on sequence-based techniques. SAVs may change protein structure and function. In the context of van der Waals force and disulfide bridge calculations, no method directly predicts the impact of mutations on the energies of the protein structure. Here, we combined machine learning methods and energy scores of protein structures calculated by Rosetta Energy Function 2015 to predict SAV pathogenicity. The accuracy level of our model (0.76) is higher than that of six prediction tools. Further analyses revealed that the differential reference energies, attractive energies, and solvation of polar atoms between wildtype and mutant side-chains played essential roles in distinguishing benign from pathogenic variants. These features indicated the physicochemical properties of amino acids, which were observed in 3D structures instead of sequences. We added 16 features to Rhapsody (the prediction tool we used for our data set) and consequently improved its performance. The results indicated that these energy scores were more appropriate and more detailed representations of the pathogenicity of SAVs.

A whole genome sequencing approach to anterior cruciate ligament rupture-a twin study in two unrelated families

Predisposition to anterior cruciate ligament (ACL) rupture is multi-factorial, with variation in the genome considered a key intrinsic risk factor. Most implicated loci have been identified from candidate gene-based approach using case-control association settings. Here, we leverage a hypothesis-free whole genome sequencing in two two unrelated families (Family A and B) each with twins with a history of recurrent ACL ruptures acquired playing rugby as their primary sport, aimed to elucidate biologically relevant function-altering variants and genetic modifiers in ACL rupture. Family A monozygotic twin males (Twin 1 and Twin 2) both sustained two unilateral non-contact ACL ruptures of the right limb while playing club level touch rugby. Their male sibling sustained a bilateral non-contact ACL rupture while playing rugby union was also recruited. The father had sustained a unilateral non-contact ACL rupture on the right limb while playing professional amateur level football and mother who had participated in dancing for over 10 years at a social level, with no previous ligament or tendon injuries were both recruited. Family B monozygotic twin males (Twin 3 and Twin 4) were recruited with Twin 3 who had sustained a unilateral non-contact ACL rupture of the right limb and Twin 4 sustained three non-contact ACL ruptures (two in right limb and one in left limb), both while playing provincial level rugby union. Their female sibling participated in karate and swimming activities; and mother in hockey (4 years) horse riding (15 years) and swimming, had both reported no previous history of ligament or tendon injury. Variants with potential deleterious, loss-of-function and pathogenic effects were prioritised. Identity by descent, molecular dynamic simulation and functional partner analyses were conducted. We identified, in all nine affected individuals, including twin sets, non-synonymous SNPs in three genes: COL12A1 and CATSPER2, and KCNJ12 that are commonly enriched for deleterious, loss-of-function mutations, and their dysfunctions are known to be involved in the development of chronic pain, and represent key therapeutic targets. Notably, using Identity By Decent (IBD) analyses a long shared identical sequence interval which included the LINC01250 gene, around the telomeric region of chromosome 2p25.3, was common between affected twins in both families, and an affected brother'. Overall gene sets were enriched in pathways relevant to ACL pathophysiology, including complement/coagulation cascades (p = 3.0e-7), purine metabolism (p = 6.0e-7) and mismatch repair (p = 6.9e-5) pathways. Highlighted, is that this study fills an important gap in knowledge by using a WGS approach, focusing on potential deleterious variants in two unrelated families with a historical record of ACL rupture; and providing new insights into the pathophysiology of ACL, by identifying gene sets that contribute to variability in ACL risk.

Whole-exome Sequencing Identifies SLC52A1 and ZNF106 Variants as Novel Genetic Risk Factors for (Early) Multiple-organ Failure in Acute Pancreatitis

OBJECTIVE

The aim of this study was to identify genetic variants associated with early multiple organ failure (MOF) in acute pancreatitis.

SUMMARY BACKGROUND DATA

MOF is a life-threatening complication of acute pancreatitis, and risk factors are largely unknown, especially in early persistent MOF. Genetic risk factors are thought to enhance severity in complex diseases such as acute pancreatitis.

METHODS

A 2-phase study design was conducted. First, we exome sequenced 9 acute pancreatitis patients with early persistent MOF and 9 case-matched patients with mild edematous pancreatitis (phenotypic extremes) from our initial Dutch cohort of 387 patients. Secondly, 48 candidate variants that were overrepresented in MOF patients and 10 additional variants known from literature were genotyped in a replication cohort of 286 Dutch and German patients.

RESULTS

Exome sequencing resulted in 161,696 genetic variants, of which the 38,333 non-synonymous variants were selected for downstream analyses. Of these, 153 variants were overrepresented in patients with multiple-organ failure, as compared with patients with mild acute pancreatitis. In total, 58 candidate variants were genotyped in the joined Dutch and German replication cohort. We found the rs12440118 variant of ZNF106 to be overrepresented in patients with MOF (minor allele frequency 20.4% vs 11.6%, Padj=0.026). Additionally, SLC52A1 rs346821 was found to be overrepresented (minor allele frequency 48.0% vs 42.4%, Padj= 0.003) in early MOF. None of the variants known from literature were associated.Conclusions: This study indicates that SLC52A1, a riboflavin plasma membrane transporter, and ZNF106, a zinc finger protein, may be involved in disease progression toward (early) MOF in acute pancreatitis.

Variant pathogenic prediction by locus variability: the importance of the current picture of evolution

Accurate detection of pathogenic single nucleotide variants (SNVs) is a key challenge in whole exome and whole genome sequencing studies. To date, several in silico tools have been developed to predict deleterious variants from this type of data. However, these tools have limited power to detect new pathogenic variants, especially in non-coding regions. In this study, we evaluate the use of a new metric, the Shannon Entropy of Locus Variability (SELV), calculated as the Shannon entropy of the variant frequencies reported in genome-wide population studies at a given locus, as a new predictor of potentially pathogenic variants in non-coding nuclear and mitochondrial DNA and also in coding regions with a selective pressure other than that imposed by the genetic code, e.g splice-sites. For benchmarking, SELV was compared to predictors of pathogenicity in different genomic contexts. In nuclear non-coding DNA, SELV outperformed CDTS (AUC_SELV = 0.97 in ROC curve and PR-AUC_SELV = 0.96 in Precision-recall curve). For non-coding mitochondrial variants (AUC_SELV = 0.98 in ROC curve and PR-AUC_SELV = 1.00 in Precision-recall curve) SELV outperformed HmtVar. Moreover, SELV was compared against two state-of-the-art ensemble predictors of pathogenicity in splice-sites, ada-score, and rf-score, matching their overall performance both in ROC (AUC_SELV = 0.95) and Precision-recall curves (PR-AUC = 0.97), with the advantage that SELV can be easily calculated for every position in the genome, as opposite to ada-score and rf-score. Therefore, we suggest that the information about the observed genetic variability in a locus reported from large scale population studies could improve the prioritization of SNVs in splice-sites and in non-coding regions.

StrVCTVRE: A supervised learning method to predict the pathogenicity of human genome structural variants

Whole-genome sequencing resolves many clinical cases where standard diagnostic methods have failed. However, at least half of these cases remain unresolved after whole-genome sequencing. Structural variants (SVs; genomic variants larger than 50 base pairs) of uncertain significance are the genetic cause of a portion of these unresolved cases. As sequencing methods using long or linked reads become more accessible and SV detection algorithms improve, clinicians and researchers are gaining access to thousands of reliable SVs of unknown disease relevance. Methods to predict the pathogenicity of these SVs are required to realize the full diagnostic potential of long-read sequencing. To address this emerging need, we developed StrVCTVRE to distinguish pathogenic SVs from benign SVs that overlap exons. In a random forest classifier, we integrated features that capture gene importance, coding region, conservation, expression, and exon structure. We found that features such as expression and conservation are important but are absent from SV classification guidelines. We leveraged multiple resources to construct a size-matched training set of rare, putatively benign and pathogenic SVs. StrVCTVRE performs accurately across a wide SV size range on independent test sets, which will allow clinicians and researchers to eliminate about half of SVs from consideration while retaining a 90% sensitivity. We anticipate clinicians and researchers will use StrVCTVRE to prioritize SVs in probands where no SV is immediately compelling, empowering deeper investigation into novel SVs to resolve cases and understand new mechanisms of disease. StrVCTVRE runs rapidly and is publicly available.

Answer ALS, a large-scale resource for sporadic and familial ALS combining clinical and multi-omics data from induced pluripotent cell lines

Answer ALS is a biological and clinical resource of patient-derived, induced pluripotent stem (iPS) cell lines, multi-omic data derived from iPS neurons and longitudinal clinical and smartphone data from over 1,000 patients with ALS. This resource provides population-level biological and clinical data that may be employed to identify clinical-molecular-biochemical subtypes of amyotrophic lateral sclerosis (ALS). A unique smartphone-based system was employed to collect deep clinical data, including fine motor activity, speech, breathing and linguistics/cognition. The iPS spinal neurons were blood derived from each patient and these cells underwent multi-omic analytics including whole-genome sequencing, RNA transcriptomics, ATAC-sequencing and proteomics. The intent of these data is for the generation of integrated clinical and biological signatures using bioinformatics, statistics and computational biology to establish patterns that may lead to a better understanding of the underlying mechanisms of disease, including subgroup identification. A web portal for open-source sharing of all data was developed for widespread community-based data analytics.

A comparison on predicting functional impact of genomic variants

Single-nucleotide polymorphism (SNPs) may cause the diverse functional impact on RNA or protein changing genotype and phenotype, which may lead to common or complex diseases like cancers. Accurate prediction of the functional impact of SNPs is crucial to discover the ‘influential’ (deleterious, pathogenic, disease-causing, and predisposing) variants from massive background polymorphisms in the human genome. Increasing computational methods have been developed to predict the functional impact of variants. However, predictive performances of these computational methods on massive genomic variants are still unclear. In this regard, we systematically evaluated 14 important computational methods including specific methods for one type of variant and general methods for multiple types of variants from several aspects; none of these methods achieved excellent (AUC ≥ 0.9) performance in both data sets. CADD and REVEL achieved excellent performance on multiple types of variants and missense variants, respectively. This comparison aims to assist researchers and clinicians to select appropriate methods or develop better predictive methods.

Deviation from baseline mutation burden provides powerful and robust rare-variants association test for complex diseases

Identifying rare variants that contribute to complex diseases is challenging because of the low statistical power in current tests comparing cases with controls. Here, we propose a novel and powerful rare variants association test based on the deviation of the observed mutation burden of a gene in cases from a baseline predicted by a weighted recursive truncated negative-binomial regression (RUNNER) on genomic features available from public data. Simulation studies show that RUNNER is substantially more powerful than state-of-the-art rare variant association tests and has reasonable type 1 error rates even for stratified populations or in small samples. Applied to real case-control data, RUNNER recapitulates known genes of Hirschsprung disease and Alzheimer's disease missed by current methods and detects promising new candidate genes for both disorders. In a case-only study, RUNNER successfully detected a known causal gene of amyotrophic lateral sclerosis. The present study provides a powerful and robust method to identify susceptibility genes with rare risk variants for complex diseases.

An integrated multi-omic analysis of iPSC-derived motor neurons from C9ORF72 ALS patients

Neurodegenerative diseases are challenging for systems biology because of the lack of reliable animal models or patient samples at early disease stages. Induced pluripotent stem cells (iPSCs) could address these challenges. We investigated DNA, RNA, epigenetics, and proteins in iPSC-derived motor neurons from patients with ALS carrying hexanucleotide expansions in C9ORF72. Using integrative computational methods combining all omics datasets, we identified novel and known dysregulated pathways. We used a C9ORF72 Drosophila model to distinguish pathways contributing to disease phenotypes from compensatory ones and confirmed alterations in some pathways in postmortem spinal cord tissue of patients with ALS. A different differentiation protocol was used to derive a separate set of C9ORF72 and control motor neurons. Many individual -omics differed by protocol, but some core dysregulated pathways were consistent. This strategy of analyzing patient-specific neurons provides disease-related outcomes with small numbers of heterogeneous lines and reduces variation from single-omics to elucidate network-based signatures.

Accurate Sequence-Based Prediction of Deleterious nsSNPs with Multiple Sequence Profiles and Putative Binding Residues

Non-synonymous single nucleotide polymorphisms (nsSNPs) may result in pathogenic changes that are associated with human diseases. Accurate prediction of these deleterious nsSNPs is in high demand. The existing predictors of deleterious nsSNPs secure modest levels of predictive performance, leaving room for improvements. We propose a new sequence-based predictor, DMBS, which addresses the need to improve the predictive quality. The design of DMBS relies on the observation that the deleterious mutations are likely to occur at the highly conserved and functionally important positions in the protein sequence. Correspondingly, we introduce two innovative components. First, we improve the estimates of the conservation computed from the multiple sequence profiles based on two complementary databases and two complementary alignment algorithms. Second, we utilize putative annotations of functional/binding residues produced by two state-of-the-art sequence-based methods. These inputs are processed by a random forests model that provides favorable predictive performance when empirically compared against five other machine-learning algorithms. Empirical results on four benchmark datasets reveal that DMBS achieves AUC > 0.94, outperforming current methods, including protein structure-based approaches. In particular, DMBS secures AUC = 0.97 for the SNPdbe and ExoVar datasets, compared to AUC = 0.70 and 0.88, respectively, that were obtained by the best available methods. Further tests on the independent HumVar dataset shows that our method significantly outperforms the state-of-the-art method SNPdryad. We conclude that DMBS provides accurate predictions that can effectively guide wet-lab experiments in a high-throughput manner.

C2orf69 mutations disrupt mitochondrial function and cause a multisystem human disorder with recurring autoinflammation

BACKGROUNDDeciphering the function of the many genes previously classified as uncharacterized open reading frame (ORF) would complete our understanding of a cell's function and its pathophysiology.METHODSWhole-exome sequencing, yeast 2-hybrid and transcriptome analyses, and molecular characterization were performed in this study to uncover the function of the C2orf69 gene.RESULTSWe identified loss-of-function mutations in the uncharacterized C2orf69 gene in 8 individuals with brain abnormalities involving hypomyelination and microcephaly, liver dysfunction, and recurrent autoinflammation. C2orf69 contains an N-terminal signal peptide that is required and sufficient for mitochondrial localization. Consistent with mitochondrial dysfunction, the patients showed signs of respiratory chain defects, and a CRISPR/Cas9-KO cell model of C2orf69 had similar respiratory chain defects. Patient-derived cells revealed alterations in immunological signaling pathways. Deposits of periodic acid-Schiff-positive (PAS-positive) material in tissues from affected individuals, together with decreased glycogen branching enzyme 1 (GBE1) activity, indicated an additional impact of C2orf69 on glycogen metabolism.CONCLUSIONSOur study identifies C2orf69 as an important regulator of human mitochondrial function and suggests that this gene has additional influence on other metabolic pathways.

Rhapsody: predicting the pathogenicity of human missense variants

MOTIVATION

The biological effects of human missense variants have been studied experimentally for decades but predicting their effects in clinical molecular diagnostics remains challenging. Available computational tools are usually based on the analysis of sequence conservation and structural properties of the mutant protein. We recently introduced a new machine learning method that demonstrated for the first time the significance of protein dynamics in determining the pathogenicity of missense variants.

RESULTS

Here, we present a new interface (Rhapsody) that enables fully automated assessment of pathogenicity, incorporating both sequence coevolution data and structure- and dynamics-based features. Benchmarked against a dataset of about 20 000 annotated variants, the methodology is shown to outperform well-established and/or advanced prediction tools. We illustrate the utility of Rhapsody by in silico saturation mutagenesis studies of human H-Ras, phosphatase and tensin homolog and thiopurine S-methyltransferase.

AVAILABILITY AND IMPLEMENTATION

The new tool is available both as an online webserver at http://rhapsody.csb.pitt.edu and as an open-source Python package (GitHub repository: https://github.com/prody/rhapsody; PyPI package installation: pip install prody-rhapsody). Links to additional resources, tutorials and package documentation are provided in the 'Python package' section of the website.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Whole exome sequencing reveals pathogenic variants in MYO3A, MYO15A and COL9A3 and differential frequencies in ancestral alleles in hearing impairment genes among individuals from Cameroon

There is scarcity of known gene variants of hearing impairment (HI) in African populations. This knowledge deficit is ultimately affecting the development of genetic diagnoses. We used whole exome sequencing to investigate gene variants, pathways of interactive genes and the fractions of ancestral overderived alleles for 159 HI genes among 18 Cameroonian patients with non-syndromic HI (NSHI) and 129 ethnically matched controls. Pathogenic and likely pathogenic (PLP) variants were found in MYO3A, MYO15A and COL9A3, with a resolution rate of 50% (9/18 patients). The study identified significant genetic differentiation in novel population-specific gene variants at FOXD4L2, DHRS2L6, RPL3L and VTN between HI patients and controls. These gene variants are found in functional/co-expressed interactive networks with other known HI-associated genes and in the same pathways with VTN being a hub protein, that is, focal adhesion pathway and regulation of the actin cytoskeleton (P-values <0.05). The results suggest that these novel population-specific gene variants are possible modifiers of the HI phenotypes. We found a high proportion of ancestral allele versus derived at low HI patients-specific minor allele frequency in the range of 0.0–0.1. The results showed a relatively low pickup rate of PLP variants in known genes in this group of Cameroonian patients with NSHI. In addition, findings may signal an evolutionary enrichment of some variants of HI genes in patients, as the result of polygenic adaptation, and suggest the possibility of multigenic influence on the phenotype of congenital HI, which deserves further investigations.

Assembling and Validating Bioinformatic Pipelines for Next-Generation Sequencing Clinical Assays

CONTEXT.—

Clinical next-generation sequencing (NGS) is being rapidly adopted, but analysis and interpretation of large data sets prompt new challenges for a clinical laboratory setting. Clinical NGS results rely heavily on the bioinformatics pipeline for identifying genetic variation in complex samples. The choice of bioinformatics algorithms, genome assembly, and genetic annotation databases are important for determining genetic alterations associated with disease. The analysis methods are often tuned to the assay to maximize accuracy. Once a pipeline has been developed, it must be validated to determine accuracy and reproducibility for samples similar to real-world cases. In silico proficiency testing or institutional data exchange will ensure consistency among clinical laboratories.

OBJECTIVE.—

To provide molecular pathologists a step-by-step guide to bioinformatics analysis and validation design in order to navigate the regulatory and validation standards of implementing a bioinformatic pipeline as a part of a new clinical NGS assay.

DATA SOURCES.—

This guide uses published studies on genomic analysis, bioinformatics methods, and methods comparison studies to inform the reader on what resources, including open source software tools and databases, are available for genetic variant detection and interpretation.

CONCLUSIONS.—

This review covers 4 key concepts: (1) bioinformatic analysis design for detecting genetic variation, (2) the resources for assessing genetic effects, (3) analysis validation assessment experiments and data sets, including a diverse set of samples to mimic real-world challenges that assess accuracy and reproducibility, and (4) if concordance between clinical laboratories will be improved by proficiency testing designed to test bioinformatic pipelines.

Excessive deubiquitination of NLRP3-R779C variant contributes to very-early-onset inflammatory bowel disease development

BACKGROUND

Very-early-onset inflammatory bowel disease (VEOIBD) is a chronic inflammatory disease of the gastrointestinal tract occurring during infancy or early childhood. NOD-like receptor family, pyrin domain containing 3 (NLRP3) inflammasome has emerged as a crucial regulator of intestinal homeostasis; however, whether NLRP3 variants may modify VEOIBD risk is unknown.

OBJECTIVE

We sought to investigate whether and how a rare NLRP3 variant, found in 3 patients with gastrointestinal symptoms, contributes to VEOIBD development.

METHODS

Whole-exome sequencing and bioinformatic analysis were performed to screen disease-associated NLRP3 variants from a cohort of children with VEOIBD. Inflammasome activation was determined in reconstituted HEK293T human embryonic kidney cells with NLRP3 inflammasome components, doxycycline-inducible NLRP3 macrophages, as well as PBMCs and biopsies from patients with NLRP3 variants. Pathogenesis of the variants was determined using a dextran sulfate sodium-induced acute colitis model.

RESULTS

We identified a dominant gain-of-function missense variant of NLRP3, encoded by rs772009059 (R779C), in 3 patients with gastrointestinal symptoms. Functional analysis revealed that R779C increased NLRP3 inflammasome activation and pyroptosis in macrophages. This was mediated by enhanced deubiquitination of NLRP3 via binding with deubiquitinases BRCC3 and JOSD2, which are highly expressed in myeloid cells. In a dextran sulfate sodium-induced acute colitis model, NLRP3-R779C in hematopoietic cells resulted in more severe colitis, which can be ameliorated via knockdown of BRCC3 or JOSD2.

CONCLUSIONS

BRCC3 and JOSD2 mediate NLRP3-R779C deubiquitination, which promotes NLRP3 inflammasome activation and the risk of developing VEOIBD.

Whole exome sequencing identified mutations causing hearing loss in five consanguineous Pakistani families

Background

Hearing loss is the most common sensory defect, and it affects over 6% of the population worldwide. Approximately 50–60% of hearing loss patients are attributed to genetic causes. Currently, more than 100 genes have been reported to cause non-syndromic hearing loss. It is possible and efficient to screen all potential disease-causing genes for hereditary hearing loss by whole exome sequencing (WES).

Methods

We collected 5 consanguineous pedigrees from Pakistan with hearing loss and applied WES in selected patients for each pedigree, followed by bioinformatics analysis and Sanger validation to identify the causal genes.

Results

Variants in 7 genes were identified and validated in these pedigrees. We identified single candidate variant for 3 pedigrees: GIPC3 (c.937 T > C), LOXHD1 (c.6136G > A) and TMPRSS3 (c.941 T > C). The remaining 2 pedigrees each contained two candidate variants: TECTA (c.4045G > A) and MYO15A (c.3310G > T and c.9913G > C) for one pedigree and DFNB59 (c.494G > A) and TRIOBP (c.1952C > T) for the other pedigree. The candidate variants were validated in all available samples by Sanger sequencing.

Conclusion

The candidate variants in hearing-loss genes were validated to be co-segregated in the pedigrees, and they may indicate the aetiologies of hearing loss in such patients. We also suggest that WES may be a suitable strategy for hearing-loss gene screening in clinical detection.

Novel and reported variants in Parkinson's disease genes confer high disease burden among Indians

BACKGROUND

Genetic heterogeneity in Parkinson's disease (PD) has been unambiguously reported across different populations. Assuming a higher genetic load, we tested variant burden in PD genes to an early onset PD cohort from India.

METHODS

Whole exome sequencing was performed in 250 PD patients recruited following MDS-UPDRS criteria. The number of rare variants in the 20 known PD genes per exome were used to calculate average rare variant burden with the 616 non-PD exomes available in-house as a comparison group. SKAT-O test was used for gene level analysis.

RESULTS

80 patients harboured rare variants in 20 PD genes, of which six had known pathogenic variants accounting for 2.4% of the cohort. Of 80 patients, 12 had homozygous and nine had likely compound heterozygous variants in recessive PD genes and 59 had heterozygous variants in only dominant PD genes. Of the 16 novel variants of as yet unknown significance identified, four homozygous across ATP13A2, PRKN, SYNJ1 and PARK7; and 12 heterozygous among LRRK2, VPS35, EIF4G1 and CHCHD2 were observed. SKAT-O test suggested a higher burden in GBA (p_unadjusted = 0.002). Aggregate rare variant analysis including 75 more individuals with only heterozygous variants in recessive PD genes (excluding GBA), with an average of 0.85 protein-altering rare variants per PD patient exome versus 0.51 in the non-PD group, revealed a significant enrichment (p < 0.0001).

CONCLUSION

This first study in an early onset PD cohort among Indians identified 16 novel variants in known genes and also provides evidence for a high genetic burden in this ethnically distinct population.

Identifying genetic variants and pathways associated with extreme levels of fetal hemoglobin in sickle cell disease in Tanzania

BACKGROUND

Sickle cell disease (SCD) is a blood disorder caused by a point mutation on the beta globin gene resulting in the synthesis of abnormal hemoglobin. Fetal hemoglobin (HbF) reduces disease severity, but the levels vary from one individual to another. Most research has focused on common genetic variants which differ across populations and hence do not fully account for HbF variation.

METHODS

We investigated rare and common genetic variants that influence HbF levels in 14 SCD patients to elucidate variants and pathways in SCD patients with extreme HbF levels (≥7.7% for high HbF) and (≤2.5% for low HbF) in Tanzania. We performed targeted next generation sequencing (Illumina_Miseq) covering exonic and other significant fetal hemoglobin-associated loci, including BCL11A, MYB, HOXA9, HBB, HBG1, HBG2, CHD4, KLF1, MBD3, ZBTB7A and PGLYRP1.

RESULTS

Results revealed a range of genetic variants, including bi-allelic and multi-allelic SNPs, frameshift insertions and deletions, some of which have functional importance. Notably, there were significantly more deletions in individuals with high HbF levels (11% vs 0.9%). We identified frameshift deletions in individuals with high HbF levels and frameshift insertions in individuals with low HbF. CHD4 and MBD3 genes, interacting in the same sub-network, were identified to have a significant number of pathogenic or non-synonymous mutations in individuals with low HbF levels, suggesting an important role of epigenetic pathways in the regulation of HbF synthesis.

CONCLUSIONS

This study provides new insights in selecting essential variants and identifying potential biological pathways associated with extreme HbF levels in SCD interrogating multiple genomic variants associated with HbF in SCD.

A report on DNA sequence determinants in gene expression

The biased usage of nucleotides in coding sequence and its correlation with gene expression has been observed in several studies. A complex set of interactions between genes and other components of the expression system determine the amount of proteins produced from coding sequences. It is known that the elongation rate of polypeptide chain is affected by both codon usage bias and specific amino acid compositional constraints. Therefore, it is of interest to review local DNA-sequence elements and other positional as well as combinatorial constraints that play significant role in gene expression.

Classification of VUS and unclassified variants in BRCA1 BRCT repeats by molecular dynamics simulation

Pathogenic mutation in BRCA1 gene is one of the most penetrant genetic predispositions towards cancer. Identification of the mutation provides important aspect in prevention and treatment of the mutation-caused cancer. Of the large quantity of genetic variants identified in human BRCA1, substantial portion is classified as Variant of Uncertain Significance (VUS) or unclassified variants due to the lack of functional evidence. In this study, we focused on the VUS and unclassified variants in BRCT repeat located at BRCA1 C-terminal. Utilizing the well-determined structure of BRCT repeats, we measured the influence of the variants on the structural conformations of BRCT repeats by using molecular dynamics simulation (MDS) consisting of RMSD (Root-mean-square-deviation), RMSF (Root-mean-square-fluctuations), Rg (Radius of gyration), SASA (Solvent accessible surface area), NH bond (hydrogen bond) and Covariance analysis. Using this approach, we analyzed 131 variants consisting of 89 VUS (Variant of Uncertain Significance) and 42 unclassified variants (unclassifiable by current methods) within BRCT repeats and were able to differentiate them into 78 Deleterious and 53 Tolerated variants. Comparing the results made by the saturation genome editing assay, multiple experimental assays, and BRCA1 reference databases shows that our approach provides high specificity, sensitivity and robust. Our study opens an avenue to classify VUS and unclassified variants in many cancer predisposition genes with known protein structure.

A Synonymous Variant c.579A>G in the ETFDH Gene Caused Exon Skipping in a Patient With Late-Onset Multiple Acyl-CoA Dehydrogenase Deficiency: A Case Report

Background: Multiple acyl-CoA dehydrogenase deficiency (MADD) is an autosomal recessive disorder characterized by a wide range of clinical features, including muscle weakness, hypoglycemia, metabolic acidosis, and multisystem dysfunctions. Loss-of-function mutations in the electron transfer flavoprotein dehydrogenase (ETFDH) gene are associated with MADD. Disease-causing synonymous variants in the ETFDH gene have not been reported so far. Methods: We reported the clinical course of a Chinese girl who was diagnosed with late-onset MADD by the whole exome sequencing. The effects of variants on mRNA splicing were analyzed through transcript analysis in vivo and minigene splice assay in vitro. Results: The 6-month-old girl initially showed muscle weakness, muscular hypotonia, mild myogenic damage, and fatty liver. The blood and urine metabolic screening by tandem mass spectrometry suggested MADD. Molecular analysis of ETFDH gene revealed two novel heterozygous variants, a frameshift mutation c.1812delG (p.V605Yfs^*34) in exon 13 and a synonymous variant c.579A>G (p.E193E) in exon 5. The transcript analysis in vivo exhibited that the synonymous variant c.579A>G caused exon 5 skipping. The minigene splice assay in vitro confirmed the alteration of ETFDH mRNA splicing which could lead to the production of a truncated protein. Supplementation of riboflavin, carnitine and low-fat diet improved the clinical symptoms. Conclusion: We firstly report a rare case of MADD with a pathogenic synonymous variant in the ETFDH gene which highlights the importance and necessity of bioinformatic analysis and functional testing for synonymous variants when searching for causative gene mutations. The results expand the spectrum of pathogenic variants in MADD.

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.

Systematic quantification of the anion transport function of pendrin (SLC26A4) and its disease-associated variants

Thanks to the advent of rapid DNA sequencing technology and its prevalence, many disease-associated genetic variants are rapidly identified in many genes from patient samples. However, the subsequent effort to experimentally validate and define their pathological roles is extremely slow. Consequently, the pathogenicity of most disease-associated genetic variants is solely speculated in silico, which is no longer deemed compelling. We developed an experimental approach to efficiently quantify the pathogenic effects of disease-associated genetic variants with a focus on SLC26A4, which is essential for normal inner ear function. Alterations of this gene are associated with both syndromic and nonsyndromic hereditary hearing loss with various degrees of severity. We established HEK293T-based stable cell lines that express pendrin missense variants in a doxycycline-dependent manner, and systematically determined their anion transport activities with high accuracy in a 96-well plate format using a high throughput plate reader. Our doxycycline dosage-dependent transport assay objectively distinguishes missense variants that indeed impair the function of pendrin from those that do not (functional variants). We also found that some of these putative missense variants disrupt normal messenger RNA splicing. Our comprehensive experimental approach helps determine the pathogenicity of each pendrin variant, which should guide future efforts to benefit patients.

Mutations in PIGU Impair the Function of the GPI Transamidase Complex, Causing Severe Intellectual Disability, Epilepsy, and Brain Anomalies

The glycosylphosphatidylinositol (GPI) anchor links over 150 proteins to the cell surface and is present on every cell type. Many of these proteins play crucial roles in neuronal development and function. Mutations in 18 of the 29 genes implicated in the biosynthesis of the GPI anchor have been identified as the cause of GPI biosynthesis deficiencies (GPIBDs) in humans. GPIBDs are associated with intellectual disability and seizures as their cardinal features. An essential component of the GPI transamidase complex is PIGU, along with PIGK, PIGS, PIGT, and GPAA1, all of which link GPI-anchored proteins (GPI-APs) onto the GPI anchor in the endoplasmic reticulum (ER). Here, we report two homozygous missense mutations (c.209T>A [p.Ile70Lys] and c.1149C>A [p.Asn383Lys]) in five individuals from three unrelated families. All individuals presented with global developmental delay, severe-to-profound intellectual disability, muscular hypotonia, seizures, brain anomalies, scoliosis, and mild facial dysmorphism. Using multicolor flow cytometry, we determined a characteristic profile for GPI transamidase deficiency. On granulocytes this profile consisted of reduced cell-surface expression of fluorescein-labeled proaerolysin (FLAER), CD16, and CD24, but not of CD55 and CD59; additionally, B cells showed an increased expression of free GPI anchors determined by T5 antibody. Moreover, computer-assisted facial analysis of different GPIBDs revealed a characteristic facial gestalt shared among individuals with mutations in PIGU and GPAA1. Our findings improve our understanding of the role of the GPI transamidase complex in the development of nervous and skeletal systems and expand the clinical spectrum of disorders belonging to the group of inherited GPI-anchor deficiencies.

Detecting genetic modifiers of spondyloepimetaphyseal dysplasia with joint laxity in the Caucasian Afrikaner community

Spondyloepimetaphyseal dysplasia with joint laxity (SEMDJL) is an autosomal-recessive skeletal dysplasia. A relatively large number of patients with SEMDJL have been identified in the Caucasian Afrikaans-speaking community in South Africa. We used a combination of Genome-Wide Human Single Nucleotide Polymorphism (SNP) Array 6.0 data and whole exomic data to potentially dissect genetic modifiers associated with SEMDJL in Caucasian Afrikaans-speaking patients. Leveraging the family-based association signal in prioritizing candidate mutations, we identified two potential modifier genes, COL1A2 and MATN1, and replicating previously identified mutation in KIF22. Importantly, our findings of genetic modifier genes and previously identified mutations are layered on the same sub-network implicated in syndromes characterized by skeletal abnormalities and intellectual disability, bone and connective tissue fragility. This study has potentially provided crucial insights in identifying the indirect modifying mutation(s) linked to the true causal mutation associated with SEMDJL. It is a critical lesson that one may use constructively especially when the pace of exomic sequencing of rare disorders continues apace.

Somatic Hypermutation of the YAP Oncogene in a Human Cutaneous Melanoma

Melanoma is usually driven by mutations in BRAF or NRAS, which trigger hyperactivation of MAPK signaling. However, MAPK-targeted therapies are not sustainably effective in most patients. Accordingly, characterizing mechanisms that co-operatively drive melanoma progression is key to improving patient outcomes. One possible mechanism is the Hippo signaling pathway, which regulates cancer progression via its central oncoproteins YAP and TAZ, although is thought to be only rarely affected by direct mutation. As YAP hyperactivation occurs in uveal melanoma, we investigated this oncogene in cutaneous melanoma. YAP protein expression was elevated in most benign nevi and primary cutaneous melanomas but present at only very low levels in normal melanocytes. In patient-derived xenografts and melanoma cell lines, we observed variable reliance of cell viability on Hippo pathway signaling that was independent of TAZ activity and also of classical melanoma driver mutations such as BRAF and NRAS. Finally, in genotyping studies of melanoma, we observed the first ever hyperactivating YAP mutations in a human cancer, manifest as seven distinct missense point mutations that caused serine to alanine transpositions. Strikingly, these mutate four serine residues known to be targeted by the Hippo pathway and we show that they lead to hyperactivation of YAP. IMPLICATIONS: Our studies highlight the YAP oncoprotein as a potential therapeutic target in select subgroups of melanoma patients, although successful treatment with anti-YAP therapies will depend on identification of biomarkers additional to YAP protein expression.

ALSgeneScanner: a pipeline for the analysis and interpretation of DNA sequencing data of ALS patients

Amyotrophic lateral sclerosis (ALS, MND) is a neurodegenerative disease of upper and lower motor neurons resulting in death from neuromuscular respiratory failure, typically within two years of first symptoms. Genetic factors are an important cause of ALS, with variants in more than 25 genes having strong evidence, and weaker evidence available for variants in more than 120 genes. With the increasing availability of next-generation sequencing data, non-specialists, including health care professionals and patients, are obtaining their genomic information without a corresponding ability to analyze and interpret it. Furthermore, the relevance of novel or existing variants in ALS genes is not always apparent. Here we present ALSgeneScanner, a tool that is easy to install and use, able to provide an automatic, detailed, annotated report, on a list of ALS genes from whole-genome sequencing (WGS) data in a few hours and whole exome sequence data in about 1 h on a readily available mid-range computer. This will be of value to non-specialists and aid in the interpretation of the relevance of novel and existing variants identified in DNA sequencing data.

Approaches to functionally validate candidate genetic variants involved in colorectal cancer predisposition

Most next generation sequencing (NGS) studies identified candidate genetic variants predisposing to colorectal cancer (CRC) but do not tackle its functional interpretation to unequivocally recognize a new hereditary CRC gene. Besides, germline variants in already established hereditary CRC-predisposing genes or somatic variants share the same need when trying to categorize those with relevant significance. Functional genomics approaches have an important role in identifying the causal links between genetic architecture and phenotypes, in order to decipher cellular function in health and disease. Therefore, functional interpretation of identified genetic variants by NGS platforms is now essential. Available approaches nowadays include bioinformatics, cell and molecular biology and animal models. Recent advances, such as the CRISPR-Cas9, ZFN and TALEN systems, have been already used as a powerful tool with this objective. However, the use of cell lines is of limited value due to the CRC heterogeneity and its close interaction with microenvironment. Access to tridimensional cultures or organoids and xenograft models that mimic the in vivo tissue architecture could revolutionize functional analysis. This review will focus on the application of state-of-the-art functional studies to better tackle new genes involved in germline predisposition to this neoplasm.

DEGS1-associated aberrant sphingolipid metabolism impairs nervous system function in humans

BACKGROUND

Sphingolipids are important components of cellular membranes and functionally associated with fundamental processes such as cell differentiation, neuronal signaling, and myelin sheath formation. Defects in the synthesis or degradation of sphingolipids leads to various neurological pathologies; however, the entire spectrum of sphingolipid metabolism disorders remains elusive.

METHODS

A combined approach of genomics and lipidomics was applied to identify and characterize a human sphingolipid metabolism disorder.

RESULTS

By whole-exome sequencing in a patient with a multisystem neurological disorder of both the central and peripheral nervous systems, we identified a homozygous p.Ala280Val variant in DEGS1, which catalyzes the last step in the ceramide synthesis pathway. The blood sphingolipid profile in the patient showed a significant increase in dihydro sphingolipid species that was further recapitulated in patient-derived fibroblasts, in CRISPR/Cas9-derived DEGS1-knockout cells, and by pharmacological inhibition of DEGS1. The enzymatic activity in patient fibroblasts was reduced by 80% compared with wild-type cells, which was in line with a reduced expression of mutant DEGS1 protein. Moreover, an atypical and potentially neurotoxic sphingosine isomer was identified in patient plasma and in cells expressing mutant DEGS1.

CONCLUSION

We report DEGS1 dysfunction as the cause of a sphingolipid disorder with hypomyelination and degeneration of both the central and peripheral nervous systems.

TRIAL REGISTRATION

Not applicable.

FUNDING

Seventh Framework Program of the European Commission, Swiss National Foundation, Rare Disease Initiative Zurich.

TSLP Polymorphisms in Atopic Dermatitis and Atopic March in Koreans

Background

Atopic march (AM) is the progression from atopic dermatitis (AD) to allergic rhinitis and asthma. The development of AD is as high as 20% in children worldwide and continues to increase. AD seems to be caused by both genetic and environmental factors. Recently, polymorphisms of the thymic stromal lymphopoietin (TSLP) gene associated with allergic disorders were reported in ethnic groups from various countries.

Objective

Identification of TSLP polymorphisms in Koreans with AD or AM.

Methods

Whole-exome sequencing was performed in 20 AD and 20 AM patients.

Results

Nine single nucleotide polymorphisms (SNPs) of TSLP were detected (rs191607411, rs3806933, rs2289276, rs2289277, rs2289278, rs139817258, rs11466749, rs11466750, rs10073816). These SNPs have been correlated with susceptibility to allergic diseases in ethnic groups from China, Japan, Turkey, and Costa Rica in previous studies. Remarkably, one of 20 patients in the AD group lacked all SNPs, compared to six of 20 patients in the AM group. Odds ratios showed that Korean patients without the nine TSLP variants had an 8.14 times higher risk of moving from AD to AM. Two haplotype blocks were validated in 60 AD and 59 AM patients using Sanger sequencing. The haplotype blocks (rs3806933 and rs2289276) and (rs11466749 and rs11466750) were in high linkage disequilibrium, respectively (D′=0.97, D′=1).

Conclusion

The increase of major allele frequency of respective nine TSLP variants may enhance the risk of AM. These data will contribute to improved genetic surveillance system in the early diagnosis technology of allergic disease.

Identification of rare heterozygous missense mutations in FANCA in esophageal atresia patients using next-generation sequencing

Esophageal atresia and tracheoesophageal fistula (EA/TEF) are relatively common malformations in newborns, but the etiology of EA/TEF remains unknown. Fanconi anemia (FA) complementation group A (FANCA) is a key component of the FA core complex and is essential for the activation of the DNA repair pathway. The middle region (amino acids 674-1208) of FANCA is required for its interaction with FAAP20. We performed targeted sequencing of this binding region of FANCA (exons 23-36) in 40 EA/TEF patients. We also investigated the effect of the p.A958V mutation on the protein-protein interaction between FANCA and FAAP20 using an in vitro binding assay and co-immunoprecipitation. Immunolocalization analysis was performed to investigate the subcellular localization of FANCA, and tissue sections and immunohistochemistry were used to explore the expression of FANCA. We identified four rare missense variants in the FANCA binding region. FANCA mutations were significantly overrepresented in EA/TEF patients compared with 4300 control subjects from the NHLBI-ESP project (Fisher's exact p = 2.17 × 10^-5, odds ratio = 31.75). p.A958V, a novel de novo mutation in the FANCA gene, was identified in one patient with EA/TEF. We provide further evidence that the p.A958V mutation reduces the binding affinity of FANCA for FAAP20. Interestingly, the p.A958V mutation impaired the nuclear localization of the FANCA protein expressed in HeLa cells. We found that FANCA was more highly expressed in stratified squamous epithelium than in smooth muscle. In conclusion, mutations in the FANCA gene are associated with EA/TEF in humans.

Variants in EXOSC9 Disrupt the RNA Exosome and Result in Cerebellar Atrophy with Spinal Motor Neuronopathy

The exosome is a conserved multi-protein complex that is essential for correct RNA processing. Recessive variants in exosome components EXOSC3, EXOSC8, and RBM7 cause various constellations of pontocerebellar hypoplasia (PCH), spinal muscular atrophy (SMA), and central nervous system demyelination. Here, we report on four unrelated affected individuals with recessive variants in EXOSC9 and the effect of the variants on the function of the RNA exosome in vitro in affected individuals' fibroblasts and skeletal muscle and in vivo in zebrafish. The clinical presentation was severe, early-onset, progressive SMA-like motor neuronopathy, cerebellar atrophy, and in one affected individual, congenital fractures of the long bones. Three affected individuals of different ethnicity carried the homozygous c.41T>C (p.Leu14Pro) variant, whereas one affected individual was compound heterozygous for c.41T>C (p.Leu14Pro) and c.481C>T (p.Arg161^∗). We detected reduced EXOSC9 in fibroblasts and skeletal muscle and observed a reduction of the whole multi-subunit exosome complex on blue-native polyacrylamide gel electrophoresis. RNA sequencing of fibroblasts and skeletal muscle detected significant >2-fold changes in genes involved in neuronal development and cerebellar and motor neuron degeneration, demonstrating the widespread effect of the variants. Morpholino oligonucleotide knockdown and CRISPR/Cas9-mediated mutagenesis of exosc9 in zebrafish recapitulated aspects of the human phenotype, as they have in other zebrafish models of exosomal disease. Specifically, portions of the cerebellum and hindbrain were absent, and motor neurons failed to develop and migrate properly. In summary, we show that variants in EXOSC9 result in a neurological syndrome combining cerebellar atrophy and spinal motoneuronopathy, thus expanding the list of human exosomopathies.

Structural dynamics is a determinant of the functional significance of missense variants

Discrimination of clinically relevant mutations from neutral mutations is of paramount importance in precision medicine and pharmacogenomics. Our study shows that current computational predictions of pathogenicity, mostly based on analysis of sequence conservation, may be improved by considering the changes in the structural dynamics of the protein due to point mutations. We introduce and demonstrate the utility of a classifier that takes advantage of efficient evaluation of structural dynamics by elastic network models.

Accurate evaluation of the effect of point mutations on protein function is essential to assessing the genesis and prognosis of many inherited diseases and cancer types. Currently, a wealth of computational tools has been developed for pathogenicity prediction. Two major types of data are used to this aim: sequence conservation/evolution and structural properties. Here, we demonstrate in a systematic way that another determinant of the functional impact of missense variants is the protein’s structural dynamics. Measurable improvement is shown in pathogenicity prediction by taking into consideration the dynamical context and implications of the mutation. Our study suggests that the class of dynamics descriptors introduced here may be used in conjunction with existing features to not only increase the prediction accuracy of the impact of variants on biological function, but also gain insight into the physical basis of the effect of missense variants.

Probability of phenotypically detectable protein damage by ENU-induced mutations in the Mutagenetix database

Computational inference of mutation effects is necessary for genetic studies in which many mutations must be considered as etiologic candidates. Programs such as PolyPhen-2 predict the relative severity of damage caused by missense mutations, but not the actual probability that a mutation will reduce/eliminate protein function. Based on genotype and phenotype data for 116,330 ENU-induced mutations in the Mutagenetix database, we calculate that putative null mutations, and PolyPhen-2-classified “probably damaging”, “possibly damaging”, or “probably benign” mutations have, respectively, 61%, 17%, 9.8%, and 4.5% probabilities of causing phenotypically detectable damage in the homozygous state. We use these probabilities in the estimation of genome saturation and the probability that individual proteins have been adequately tested for function in specific genetic screens. We estimate the proportion of essential autosomal genes in Mus musculus (C57BL/6J) and show that viable mutations in essential genes are more likely to induce phenotype than mutations in non-essential genes.

Programs such as PolyPhen-2 predict the relative severity of damage by missense mutations. Here, Wang et al estimate probabilities that putative null or missense alleles would reduce protein function to cause detectable phenotype by analyzing data from ENU-induced mouse mutations.

Synonymous Codon Usage Controls Various Molecular Aspects

Synonymous sites are generally considered to be functionally neutral. However, there are recent contradictory findings suggesting that synonymous alleles might have functional roles in various molecular aspects. For instance, a recent study demonstrated that synonymous single nucleotide polymorphisms have a similar effect size as nonsynonymous single nucleotide polymorphisms in human disease association studies. Researchers have recognized synonymous codon usage bias (SCUB) in the genomes of almost all species and have investigated whether SCUB is due to random nucleotide compositional bias or to natural selection of any functional exposure generated by synonymous mutations. One of the most prominent observations on the non-neutrality of synonymous codons is the correlation between SCUB and levels of gene expression, such that highly expressed genes tend to have a higher preference toward so-called optimal codons than lowly expressed genes. In relation, it is known that amounts of cognate tRNAs that bind to optimal codons are significantly higher than the amounts of cognate tRNAs that bind to non-optimal codons in genomes. In the present paper, we review various functions that synonymous codons might have other than regulating expression levels.

MAPPIN: a method for annotating, predicting pathogenicity and mode of inheritance for nonsynonymous variants

Nonsynonymous single nucleotide variants (nsSNVs) constitute about 50% of known disease-causing mutations and understanding their functional impact is an area of active research. Existing algorithms predict pathogenicity of nsSNVs; however, they are unable to differentiate heterozygous, dominant disease-causing variants from heterozygous carrier variants that lead to disease only in the homozygous state. Here, we present MAPPIN (Method for Annotating, Predicting Pathogenicity, and mode of Inheritance for Nonsynonymous variants), a prediction method which utilizes a random forest algorithm to distinguish between nsSNVs with dominant, recessive, and benign effects. We apply MAPPIN to a set of Mendelian disease-causing mutations and accurately predict pathogenicity for all mutations. Furthermore, MAPPIN predicts mode of inheritance correctly for 70.3% of nsSNVs. MAPPIN also correctly predicts pathogenicity for 87.3% of mutations from the Deciphering Developmental Disorders Study with a 78.5% accuracy for mode of inheritance. When tested on a larger collection of mutations from the Human Gene Mutation Database, MAPPIN is able to significantly discriminate between mutations in known dominant and recessive genes. Finally, we demonstrate that MAPPIN outperforms CADD and Eigen in predicting disease inheritance modes for all validation datasets. To our knowledge, MAPPIN is the first nsSNV pathogenicity prediction algorithm that provides mode of inheritance predictions, adding another layer of information for variant prioritization.

Dysfunction of Myosin Light-Chain 4 (MYL4) Leads to Heritable Atrial Cardiomyopathy With Electrical, Contractile, and Structural Components: Evidence From Genetically-Engineered Rats

BACKGROUND

There is increasing interest in the concept of atrial cardiomyopathy, but the underlying molecular and mechanistic determinants remain poorly defined. We identified a family with heritable atrial cardiomyopathy manifesting as progressive atrial-selective electromechanical dysfunction, tachyarrhythmias, and bradyarrhythmias requiring pacemaker implantation. Myosin light-chain 4 (MYL4), encoding the atrial-selective essential myosin light chain, was identified as a candidate gene. We used genetically modified rat models to investigate the role of MYL4 in atrial cardiomyopathy.

METHODS AND RESULTS

Exome sequencing and systematic bioinformatic analyses identified a rare missense variant of MYL4 (c.31G>A [p.E11K]) in a large multiplex atrial cardiomyopathy family pedigree. The mutation cosegregated with atrial standstill (selected as the principal presenting trait) with a logarithm of the odds score of 5.3. The phenotype of rats with MYL4 mutation knock-in confirmed the causative role of the mutation. MYL4 knockout rats showed a similar atrial cardiomyopathy phenotype, whereas rats with an adjacent 4-amino-acid deletion showed no phenotype. Both MYL4 p.E11K knock-in rats and MYL4 knockout rats showed progressive atrial electrophysiological, contractile, and fibrotic abnormalities, similar to affected patients. Biochemical analyses of MYL4 p.E11K mutation rats showed activation of proapoptotic and profibrotic signaling, along with increased atrial-cardiomyocyte terminal deoxynucleotidyl transferase dUTP nick end labeling staining, suggesting enhanced apoptotic cell death, findings that were mimicked by in vitro adenoviral transfer of the mutant gene to neonatal-rat cardiomyocytes.

CONCLUSIONS

Loss-of-function MYL4 gene variants cause progressive atrial cardiomyopathy in humans and rats. Our findings identify MYL4 as a key gene required for atrial contractile, electrical and structural integrity. These results improve our understanding of the molecular basis of atrial cardiomyopathy and introduce new models for further mechanistic analysis.

Robust and rapid algorithms facilitate large-scale whole genome sequencing downstream analysis in an integrative framework

Whole genome sequencing (WGS) is a promising strategy to unravel variants or genes responsible for human diseases and traits. However, there is a lack of robust platforms for a comprehensive downstream analysis. In the present study, we first proposed three novel algorithms, sequence gap-filled gene feature annotation, bit-block encoded genotypes and sectional fast access to text lines to address three fundamental problems. The three algorithms then formed the infrastructure of a robust parallel computing framework, KGGSeq, for integrating downstream analysis functions for whole genome sequencing data. KGGSeq has been equipped with a comprehensive set of analysis functions for quality control, filtration, annotation, pathogenic prediction and statistical tests. In the tests with whole genome sequencing data from 1000 Genomes Project, KGGSeq annotated several thousand more reliable non-synonymous variants than other widely used tools (e.g. ANNOVAR and SNPEff). It took only around half an hour on a small server with 10 CPUs to access genotypes of ∼60 million variants of 2504 subjects, while a popular alternative tool required around one day. KGGSeq's bit-block genotype format used 1.5% or less space to flexibly represent phased or unphased genotypes with multiple alleles and achieved a speed of over 1000 times faster to calculate genotypic correlation.

IDUA mutational profile and genotype-phenotype relationships in UK patients with Mucopolysaccharidosis Type I

Mucopolysaccharidosis Type I (MPS I) is a lysosomal storage disorder with varying degrees of phenotypic severity caused by mutations in IDUA. Over 200 disease-causing variants in IDUA have been reported. We describe the profile of disease-causing variants in 291 individuals with MPS I for whom IDUA sequencing was performed, focusing on the UK subset of the cohort. A total of 63 variants were identified, of which 20 were novel, and the functional significance of the novel variants is explored. The severe form of MPS I is treated with hematopoietic stem cell transplantation, known to have improved outcomes with earlier age at treatment. Developing genotype-phenotype relationships would therefore have considerable clinical utility, especially in the light of the development of newborn screening programs for MPS I. Associations between genotype and phenotype are examined in this cohort, particularly in the context of the profile of variants identified in UK individuals. Relevant associations can be made for the majority of UK individuals based on the presence of nonsense or truncating variants as well as other associations described in this report.

A novel splice donor site mutation in EPHA2 caused congenital cataract in a Chinese family

Background:

Congenital cataract is a rare disorder characterized by crystallin denaturation, which becomes a major cause of childhood blindness. Although more than fifty pathogenic genes for congenital cataract have been reported, the genetic causes of many cataract patients remain unknown. In this study, the aim is to identify the genetic cause of a five-generation Chinese autosomal dominant congenital cataract family.

Methods:

Whole exome sequencing (WES) was performed on three affected and one unaffected member of the family, known causative genes were scanned first. Sanger sequencing was used to validate co-segregation of the candidate variant in the family. The impact on the transcript and amino acid sequences of the variant was further analyzed.

Results:

We identified a novel splice donor site mutation c. 2825+1G >A in EPHA2 that was absent in public and in-house databases and showed co-segregation in the family. This variant resulted in an altered splice that led to protein truncation.

Conclusions:

The mutation we identified was responsible for congenital cataract in our studied family. Our findings broaden the spectrum of causative mutations in EPHA2 gene for congenital cataract and suggest that WES is an efficient strategy to scan variants in known causative genes for genetically heterogeneous diseases.