Pathogenic Non-coding Variants Research Articles

NCBoost classifies pathogenic non-coding variants in Mendelian diseases through supervised learning on purifying selection signals in humans

State-of-the-art methods assessing pathogenic non-coding variants have mostly been characterized on common disease-associated polymorphisms, yet with modest accuracy and strong positional biases. In this study, we curated 737 high-confidence pathogenic non-coding variants associated with monogenic Mendelian diseases. In addition to interspecies conservation, a comprehensive set of recent and ongoing purifying selection signals in humans is explored, accounting for lineage-specific regulatory elements. Supervised learning using gradient tree boosting on such features achieves a high predictive performance and overcomes positional bias. NCBoost performs consistently across diverse learning and independent testing data sets and outperforms other existing reference methods.

PINES: phenotype-informed tissue weighting improves prediction of pathogenic noncoding variants

Functional characterization of the noncoding genome is essential for biological understanding of gene regulation and disease. Here, we introduce the computational framework PINES (Phenotype-Informed Noncoding Element Scoring), which predicts the functional impact of noncoding variants by integrating epigenetic annotations in a phenotype-dependent manner. PINES enables analyses to be customized towards genomic annotations from cell types of the highest relevance given the phenotype of interest. We illustrate that PINES identifies functional noncoding variation more accurately than methods that do not use phenotype-weighted knowledge, while at the same time being flexible and easy to use via a dedicated web portal.

Validation of an Expanded Carrier Screen that Optimizes Sensitivity via Full-Exon Sequencing and Panel-wide Copy Number Variant Identification

By identifying pathogenic variants across hundreds of genes, expanded carrier screening (ECS) enables prospective parents to assess the risk of transmitting an autosomal recessive or X-linked condition. Detection of at-risk couples depends on the number of conditions tested, the prevalence of the respective diseases, and the screen's analytical sensitivity for identifying disease-causing variants. Disease-level analytical sensitivity is often <100% in ECS tests because copy number variants (CNVs) are typically not interrogated because of their technical complexity. We present an analytical validation and preliminary clinical characterization of a 235-gene sequencing-based ECS with full coverage across coding regions, targeted assessment of pathogenic noncoding variants, panel-wide CNV calling, and specialized assays for technically challenging genes. Next-generation sequencing, customized bioinformatics, and expert manual call review were used to identify single-nucleotide variants, short insertions and deletions, and CNVs for all genes except FMR1 and those whose low disease incidence or high technical complexity precluded novel variant identification or interpretation. Screening of 36859 patients' blood or saliva samples revealed the substantial impact on fetal disease-risk detection attributable to novel CNVs (9.19% of risk) and technically challenging conditions (20.2% of risk), such as congenital adrenal hyperplasia. Of the 7498 couples screened, 335 were identified as at risk for an affected pregnancy, underscoring the clinical importance of the test. Validation of our ECS demonstrated >99% analytical sensitivity and >99% analytical specificity. Validated high-fidelity identification of different variant types-especially for diseases with complicated molecular genetics-maximizes at-risk couple detection.

Target 5000: Target Capture Sequencing for Inherited Retinal Degenerations.

There are an estimated 5000 people in Ireland who currently have an inherited retinal degeneration (IRD). It is the goal of this study, through genetic diagnosis, to better enable these 5000 individuals to obtain a clearer understanding of their condition and improved access to potentially applicable therapies. Here we show the current findings of a target capture next-generation sequencing study of over 750 patients from over 520 pedigrees currently situated in Ireland. We also demonstrate how processes can be implemented to retrospectively analyse patient datasets for the detection of structural variants in previously obtained sequencing reads. Pathogenic or likely pathogenic mutations were detected in 68% of pedigrees tested. We report nearly 30 novel mutations including three large structural variants. The population statistics related to our findings are presented by condition and credited to their respective candidate gene mutations. Rediagnosis rates of clinical phenotypes after genotyping are discussed. Possible causes of failure to detect a candidate mutation are evaluated. Future elements of this project, with a specific emphasis on structural variants and non-coding pathogenic variants, are expected to increase detection rates further and thereby produce an even more comprehensive representation of the genetic landscape of IRDs in Ireland.

Annotating pathogenic non-coding variants in genic regions

Identifying the underlying causes of disease requires accurate interpretation of genetic variants. Current methods ineffectively capture pathogenic non-coding variants in genic regions, resulting in overlooking synonymous and intronic variants when searching for disease risk. Here we present the Transcript-inferred Pathogenicity (TraP) score, which uses sequence context alterations to reliably identify non-coding variation that causes disease. High TraP scores single out extremely rare variants with lower minor allele frequencies than missense variants. TraP accurately distinguishes known pathogenic and benign variants in synonymous (AUC = 0.88) and intronic (AUC = 0.83) public datasets, dismissing benign variants with exceptionally high specificity. TraP analysis of 843 exomes from epilepsy family trios identifies synonymous variants in known epilepsy genes, thus pinpointing risk factors of disease from non-coding sequence data. TraP outperforms leading methods in identifying non-coding variants that are pathogenic and is therefore a valuable tool for use in gene discovery and the interpretation of personal genomes.

Quantifying the Impact of Non-coding Variants on Transcription Factor-DNA Binding.

Many recent studies have emphasized the importance of genetic variants and mutations in cancer and other complex human diseases. The overwhelming majority of these variants occur in non-coding portions of the genome, where they can have a functional impact by disrupting regulatory interactions between transcription factors (TFs) and DNA. Here, we present a method for assessing the impact of non-coding mutations on TF-DNA interactions, based on regression models of DNA-binding specificity trained on high-throughput in vitro data. We use ordinary least squares (OLS) to estimate the parameters of the binding model for each TF, and we show that our predictions of TF-binding changes due to DNA mutations correlate well with measured changes in gene expression. In addition, by leveraging distributional results associated with OLS estimation, for each predicted change in TF binding we also compute a normalized score (z-score) and a significance value (p-value) reflecting our confidence that the mutation affects TF binding. We use this approach to analyze a large set of pathogenic non-coding variants, and we show that these variants lead to significant differences in TF binding between alleles, compared to a control set of common variants. Thus, our results indicate that there is a strong regulatory component to the pathogenic non-coding variants identified thus far.

Prioritization of non-coding disease-causing variants and long non-coding RNAs in liver cancer

There are multiple bioinformatics tools available for the detection of coding driver mutations in cancers. However, the prioritization of pathogenic non-coding variants remains a challenging and demanding task. The present study was performed to discriminate non-coding disease-causing mutations and prioritize potential cancer-implicated long non-coding RNAs (lncRNAs) in liver cancer using a logistic regression model. A logistic regression model was constructed by combining 19,153 disease-associated ClinVar and human gene mutation database pathogenic variants as the response variable and non-coding features as the predictor variable. Genome-wide association study (GWAS) disease or trait-associated variants and recurrent somatic mutations were used to validate the model. Non-coding gene features with the highest fractions of load were characterized and potential cancer-associated lncRNA candidates were prioritized by combining the fraction of high-scoring regions and average score predicted by the logistic regression model. H3K9me3 and conserved regions were the most negatively and positively informative for the model, respectively. The area under the receiver operating characteristic curve of the model was 0.92. The average score of GWAS disease-associated variants was significantly increased compared with neutral single nucleotide polymorphisms (5.8642 vs. 5.4707; P<0.001), the average score of recurrent somatic mutations of liver cancer was significantly increased compared with non-recurrent somatic mutations (5.4101 vs. 5.2768; P=0.0125). The present study found regions in lncRNAs and introns/untranslated regions of protein coding genes where mutations are most likely to be damaging. In total, 847 lncRNAs were filtered out from the background. Characterization of this subset of lncRNAs showed that these lncRNAs are more conservative, less mutated and more highly expressed compared with other control lncRNAs. In addition, 23 of these lncRNAs were differentially expressed between 12 pairs of liver cancer and adjacent normal specimens. The logistic regression model is a useful tool to prioritize non-coding pathogenic variants and lncRNAs, and paves the way for the detection of non-coding driver lncRNAs in liver cancer.

A non-coding variant in the 5' UTR of DLG3 attenuates protein translation to cause non-syndromic intellectual disability.

Intellectual disability (ID) is a clinically complex and heterogeneous disorder, which has variable severity and may be associated with additional dysmorphic, metabolic, neuromuscular or psychiatric features. Although many coding variants have been implicated in ID, identification of pathogenic non-coding regulatory variants has only been achieved in a few cases to date. We identified a duplication of a guanine on chromosome X, NC_000023.10:g.69665044dupG 7 nucleotides upstream of the translational start site in the 5' untranslated region (UTR) of the known ID gene DLG3 that encodes synapse-associated protein 102 (SAP102). The dupG variant segregated with affected status in a large multigenerational family with non-syndromic X-linked ID and was predicted to disrupt folding of the mRNA. When tested on blood cells from the affected individuals, DLG3 mRNA levels were not altered, however, DLG3/SAP102 protein levels were. We also showed by dual luciferase reporter assay that the dupG variant interfered with translation. All currently known pathogenic DLG3 variants are predicted to be null, however the dupG variant likely leads to only a modest reduction of SAP102 levels accounting for the milder phenotype seen in this family.

Functional annotation of noncoding variants and prioritization of cancer-associated lncRNAs in lung cancer.

Multiple computational tools have been widely applied to the detection of coding driver mutations in cancer; however, the prioritization of pathogenic non-coding variants remains a difficult and demanding task. The present study was performed to distinguish non-coding disease-causing mutations from neutral ones, and to prioritize potential cancer-associated long non-coding RNAs (lncRNAs) with a logistic regression model in lung cancer. A logistic regression model was constructed, combining 19,153 disease-associated ClinVar and Human Gene Mutation Database pathogenic variants as the response variable and non-coding features as the predictor variable. Validation of the model was conducted with genome-wide association study (GWAS) disease- or trait-associated single nucleotide polymorphisms (SNPs) and recurrent somatic mutations. High scoring regions were characterized with respect to their distribution in various features and gene classes; potential cancer-associated lncRNA candidates were prioritized, combining the fraction of high-scoring regions and average score predicted by the logistic regression model. H3K79me2 was the most negative factor that contributed to the model, while conserved regions were most positively informative to the model. The area under the receiver operating characteristic curve of the model was 0.89. The model assigned a significantly higher score to GWAS SNPs and recurrent somatic mutations compared with neutral SNPs (mean, 5.9012 vs. 5.5238; P<0.001, Mann-Whitney U test) and non-recurrent mutations (mean, 5.4677 vs. 5.2277, P<0.001, Mann-Whitney U test), respectively. It was observed that regions, including splicing sites and untranslated regions, and gene classes, including cancer genes and cancer-associated lncRNAs, had an increased enrichment of high-scoring regions. In total, 2,679 cancer-associated lncRNAs were determined and characterized. A total of 104 of these lncRNAs were differentially expressed between lung cancer and normal specimens. The logistic regression model is a useful and efficient scoring system to prioritize non-coding pathogenic variants and lncRNAs, and may provide the basis for detecting non-coding driver lncRNAs in lung cancer.

A unified analytic framework for prioritization of non-coding variants of uncertain significance in heritable breast and ovarian cancer.

BackgroundSequencing of both healthy and disease singletons yields many novel and low frequency variants of uncertain significance (VUS). Complete gene and genome sequencing by next generation sequencing (NGS) significantly increases the number of VUS detected. While prior studies have emphasized protein coding variants, non-coding sequence variants have also been proven to significantly contribute to high penetrance disorders, such as hereditary breast and ovarian cancer (HBOC). We present a strategy for analyzing different functional classes of non-coding variants based on information theory (IT) and prioritizing patients with large intragenic deletions.MethodsWe captured and enriched for coding and non-coding variants in genes known to harbor mutations that increase HBOC risk. Custom oligonucleotide baits spanning the complete coding, non-coding, and intergenic regions 10 kb up- and downstream of ATM, BRCA1, BRCA2, CDH1, CHEK2, PALB2, and TP53 were synthesized for solution hybridization enrichment. Unique and divergent repetitive sequences were sequenced in 102 high-risk, anonymized patients without identified mutations in BRCA1/2. Aside from protein coding and copy number changes, IT-based sequence analysis was used to identify and prioritize pathogenic non-coding variants that occurred within sequence elements predicted to be recognized by proteins or protein complexes involved in mRNA splicing, transcription, and untranslated region (UTR) binding and structure. This approach was supplemented by in silico and laboratory analysis of UTR structure.Results15,311 unique variants were identified, of which 245 occurred in coding regions. With the unified IT-framework, 132 variants were identified and 87 functionally significant VUS were further prioritized. An intragenic 32.1 kb interval in BRCA2 that was likely hemizygous was detected in one patient. We also identified 4 stop-gain variants and 3 reading-frame altering exonic insertions/deletions (indels).ConclusionsWe have presented a strategy for complete gene sequence analysis followed by a unified framework for interpreting non-coding variants that may affect gene expression. This approach distills large numbers of variants detected by NGS to a limited set of variants prioritized as potential deleterious changes.Electronic supplementary materialThe online version of this article (doi:10.1186/s12920-016-0178-5) contains supplementary material, which is available to authorized users.

Mining the coding and non-coding genome for cancer drivers

Progress in next-generation sequencing provides unprecedented opportunities to fully characterize the spectrum of somatic mutations of cancer genomes. Given the large number of somatic mutations identified by such technologies, the prioritization of cancer-driving events is a consistent bottleneck. Most bioinformatics tools concentrate on driver mutations in the coding fraction of the genome, those causing changes in protein products. As more non-coding pathogenic variants are identified and characterized, the development of computational approaches to effectively prioritize cancer-driving variants within the non-coding fraction of human genome is becoming critical. After a short summary of methods for coding variant prioritization, we here review the highly diverse non-coding elements that may act as cancer drivers and describe recent methods that attempt to evaluate the deleteriousness of sequence variation in these elements. With such tools, the prioritization and identification of cancer-implicated regulatory elements and non-coding RNAs is becoming a reality.