Pathogenic Non-coding Variants Research Articles

Abstract 4628: Validation of an automated, scalable comprehensive genomic profiling assay for hematologic malignancies

Abstract Optimal diagnosis, prognosis, and treatment selection for hematologic malignancies requires assessment of somatic mutations across a subset of clinically relevant genes. Here we present the analytical validation results of an expanded targeted next generation sequencing (NGS) panel of 141 clinically relevant genes for myeloid and lymphoid malignancies. Based on review of clinical guidelines, 141 genes including 79 myeloid and 84 lymphoid associated genes were selected. The expanded hematological panel interrogates all coding exons of the 141 genes to detect single nucleotide variants (SNVs) and insertions/deletions (indels). The panel also identifies copy number aberrations (CNAs) in 16 genes, FLT3 internal tandem duplicates (ITDs), select non-coding pathogenic variants, and patient sex. The custom hybrid capture-based assay utilizes genomic libraries created from 250 ng gDNA extracted from peripheral blood, bone marrow, or cell suspensions, followed by sequencing on Illumina® instruments. Concordance studies were performed on clinical samples previously assessed using an orthogonal NGS-based laboratory developed test for SNVs/indels/FLT3-ITDs or digital multiplexed ligation-dependent probe amplification for CNAs. In total, 308 patient samples were evaluated and included 72 (23.4%) acute myeloid leukemia and 58 (18.8%) myelodysplastic syndromes samples among other indications, of which 240 (77.9%) were bone marrow specimens. Analysis of concordance demonstrated a positive percent agreement (PPA) of 99.7% for SNVs (750/752), 99.5% for indels <25bp (206/207), and 95.7% for indels ≥ 25bp (22/23). Variants were from 45 genes including TP53, NPM1, and IDH1/2. PPA was 100% for FLT3-ITDs (38/38; size range 18-300 bp) and sex (294/294). For CNAs, PPA was 96.4% (132/137) and negative percent agreement (NPA) was 98.3% (931/947). Assay precision was determined using three replicates of 15 clinical samples for both intra and inter-assay precision using multiple operators, instruments, and reagent lots. 100% concordance was observed for SNVs (36/36), indels (11/11), a 24 bp FLT3-ITD (1/1), CNAs (24/24), and sex (15/15). Analytical specificity was assessed using 5 replicates of NA12878 showing specificity >99.99% for SNVs/indels with variant allele frequency ≥ 3%, and >99.99% for CNAs with copy number ≤0.85 or ≥1.15. Dilution series to determine analytical sensitivity are in progress. This study highlights the analytical validation of the expanded NGS panel for the detection of clinically informative genomic alterations in hematologic malignancies. Results from this validation study, when complete, will include at least 576 samples plus orthogonal testing. These data describe the performance of the assay to enable a comprehensive evaluation of genomic alterations using a single sample, further facilitating the use of broad NGS assays in patients with hematologic malignancies. Citation Format: Grant Hogg, Tong Liu, Helen Cao, Adib Shafi, Ashraf Shabaneh, Jon Williams, John Howitt, Amanda Williamson, Rachel Dango, Xiaojun Guan, Heidi Hoffmann, Michael Mooney, John Pruitt, Scott Parker, Henry Dong, Stan Letovsky, Li Cai, Eric A. Severson, Shakti H. Ramkissoon, Anjen Chenn, Marcia Eisenberg, Brian Caveney, Eyad Almasri, Taylor J. Jensen. Validation of an automated, scalable comprehensive genomic profiling assay for hematologic malignancies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4628.

Multi-omics analysis in human retina uncovers ultraconserved cis-regulatory elements at rare eye disease loci

Cross-species genome comparisons have revealed a substantial number of ultraconserved non-coding elements (UCNEs). Several of these elements have proved to be essential tissue- and cell type-specific cis-regulators of developmental gene expression. Here, we characterize a set of UCNEs as candidate CREs (cCREs) during retinal development and evaluate the contribution of their genomic variation to rare eye diseases, for which pathogenic non-coding variants are emerging. Integration of bulk and single-cell retinal multi-omics data reveals 594 genes under potential cis-regulatory control of UCNEs, of which 45 are implicated in rare eye disease. Mining of candidate cis-regulatory UCNEs in WGS data derived from the rare eye disease cohort of Genomics England reveals 178 ultrarare variants within 84 UCNEs associated with 29 disease genes. Overall, we provide a comprehensive annotation of ultraconserved non-coding regions acting as cCREs during retinal development which can be targets of non-coding variation underlying rare eye diseases.

Abstract 6220: Development of a mutation profiling NGS assay to facilitate clinical decisions in hematologic malignancies

Abstract Introduction: Accurate diagnosis, prognosis and treatment of hematologic disorders from both myeloid and lymphoid origin requires assessing somatic mutation status in an increasingly large number of genes. Here we present the results of the assay development and validation of a targeted next generation sequencing panel which provides full exon sequencing of 141 genes associated with myeloid and lymphoid malignancies, as well as the ability to detect clinically relevant copy number alterations (CNAs) and certain sub-gene level CNAs. Methods: Based on review of clinical guidelines, 141 genes including 72 myeloid and 84 lymphoid associated genes were selected. The assay interrogates all coding exons of the 141 genes to detect single nucleotide variants (SNVs) and insertions/deletions (indels). The assay also identifies CNAs in 16 genes, FLT3 internal tandem duplicates (ITDs), and select non-coding pathogenic variants. The custom hybrid capture-based assay utilizes genomic libraries created from 250 ng gDNA extracted from peripheral blood or bone marrow followed by sequencing on Illumina sequencers. Concordance studies were performed on clinical samples previously assessed using an orthogonal NGS-based laboratory developed test for SNVs/Indels or digital multiplexed ligation-dependent probe amplification for CNAs. In addition, analytical sensitivity was assessed using cell line dilution series and specificity by reference samples from Genome in a Bottle (GIAB). Small indels were defined as <25bp while long indels are ≥ 25bp. Results: Interim estimates of positive percentage agreement (PPA) and specificity have been determined for SNV/Indels, FLT3-ITDs, and CNVs, with further testing ongoing. PPA of SNVs was 100% (218/218 SNVs; n = 134 samples). PPA was also 100% for both small indels (21/21; n = 14 samples) and long indels (6/6; n = 6 samples). A dilution series of 8 positive clinical samples at 4 different dilutions showed SNV and small indel detection of 100% at allele frequency (AF) ≥ 3%. Long indel detection was 87.5% at AF ≥ 5%. FLT3-ITD was evaluated with 2 cell line samples at 5 different dilutions with 100% detection at AF ≥ 5%. PPA of CNAs were 95.7% (157/164) in 46 clinical samples. CNA detection was also evaluated with 8 cell line samples at 4 different dilutions showing 92.3% detection at a copy number ratio threshold ± 0.15. A preliminary estimate of specificity was determined using 4 replicates of the GIAB sample, NA12878, and was >99.99% for SNVs and small indels (n=6.9 × 105). Finally, a model for gender classification was created using a training cohort of 70 clinical samples. The performance was evaluated on a test set of 144 clinical samples with 100% PPA (144/144). Conclusions: Taken together, these data demonstrate that the developed NGS assay is a sensitive, specific, and accurate assay with high clinical utility to inform treatment decision making in hematologic malignancies. Citation Format: Grant Hogg, Tong Liu, Helen Cao, Adib Shafi, Ashraf Shabaneh, John Howitt, Amanda Williamson, Rachel Dango, Xiaojun Guan, Heidi Hoffmann, Michael Mooney, John Pruitt, Scott Parker, Stan Letovsky, Li Cai, Eric A. Severson, Shakti Ramkissoon, Anjen Chenn, Marcia Eisenberg, Eyad Almasri, Taylor Jensen. Development of a mutation profiling NGS assay to facilitate clinical decisions in hematologic malignancies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6220.

Intronic variants in inborn errors of metabolism: Beyond the exome.

Non-coding regions are areas of the genome that do not directly encode protein and were initially thought to be of little biological relevance. However, subsequent identification of pathogenic variants in these regions indicates there are exceptions to this assertion. With the increasing availability of next generation sequencing, variants in non-coding regions are often considered when no causative exonic changes have been identified. There is still a lack of understanding of normal human variation in non-coding areas. As a result, potentially pathogenic non-coding variants are initially classified as variants of uncertain significance or are even overlooked during genomic analysis. In most cases where the phenotype is non-specific, clinical suspicion is not sufficient to warrant further exploration of these changes, partly due to the magnitude of non-coding variants identified. In contrast, inborn errors of metabolism (IEMs) are one group of genetic disorders where there is often high phenotypic specificity. The clinical and biochemical features seen often result in a narrow list of diagnostic possibilities. In this context, there have been numerous cases in which suspicion of a particular IEM led to the discovery of a variant in a non-coding region. We present four patients with IEMs where the molecular aetiology was identified within non-coding regions. Confirmation of the molecular diagnosis is often aided by the clinical and biochemical specificity associated with IEMs. Whilst the clinical severity associated with a non-coding variant can be difficult to predict, obtaining a molecular diagnosis is crucial as it ends diagnostic odysseys and assists in management.

Multidisciplinary team directed analysis of whole genome sequencing reveals pathogenic non-coding variants in molecularly undiagnosed inherited retinal dystrophies.

The purpose of this paper is to identify likely pathogenic non-coding variants in inherited retinal dystrophy (IRD) genes, using genome sequencing (GS). Patients with IRD were recruited to the study and underwent comprehensive ophthalmological evaluation and GS. The results of GS were investigated through virtual gene panel analysis, and plausible pathogenic variants and clinical phenotype evaluated by the multidisciplinary team (MDT) discussion. For unsolved patients in whom a specific gene was suspected to harbor a missed pathogenic variant, targeted re-analysis of non-coding regions was performed on GS data. Candidate variants were functionally tested by messenger RNA analysis, minigene or luciferase reporter assays. Previously unreported, likely pathogenic, non-coding variants in 7 genes (PRPF31, NDP, IFT140, CRB1, USH2A, BBS10 and GUCY2D), were identified in 11 patients. These were shown to lead to mis-splicing (PRPF31, IFT140, CRB1 and USH2A) or altered transcription levels (BBS10 and GUCY2D). MDT-led, phenotype-driven, non-coding variant re-analysis of GS is effective in identifying the missing causative alleles.

Noncanonical Splice Site and Deep Intronic FRMD7 Variants Activate Cryptic Exons in X-linked Infantile Nystagmus.

PurposeWe aim to report noncoding pathogenic variants in patients with FRMD7-related infantile nystagmus (FIN).MethodsGenome sequencing (n = 2 families) and reanalysis of targeted panel next generation sequencing (n = 2 families) was performed in genetically unsolved cases of suspected FIN. Previous sequence analysis showed no pathogenic coding variants in genes associated with infantile nystagmus. SpliceAI, SpliceRover, and Alamut consensus programs were used to annotate noncoding variants. Minigene splicing assay was performed to confirm aberrant splicing. In silico analysis of exonic splicing enhancer and silencer was also performed.Results FRMD7 intronic variants were identified based on genome sequencing and targeted next-generation sequencing analysis. These included c.285-12A>G (pedigree 1), c.284+63T>A (pedigrees 2 and 3), and c. 383-1368A>G (pedigree 4). All variants were absent in gnomAD, and the both c.285-12A>G and c.284+63T>A variants were predicted to enhance new splicing acceptor gains with SpliceAI, SpliceRover, and Alamut consensus approaches. However, the c.383-1368 A>G variant only had a significant impact score on the SpliceRover program. The c.383-1368A>G variant was predicted to promote pseudoexon inclusion by binding of exonic splicing enhancer. Aberrant exonizations were validated through minigene constructs, and all variants were segregated in the families.ConclusionsDeep learning–based annotation of noncoding variants facilitates the discovery of hidden genetic variations in patients with FIN. This study provides evidence of effectiveness of combined deep learning–based splicing tools to identify hidden pathogenic variants in previously unsolved patients with infantile nystagmus.Translational RelevanceThese results demonstrate robust analysis using two deep learning splicing predictions and in vitro functional study can lead to finding hidden genetic variations in unsolved patients.

First Identification of Rare Exonic and Deep Intronic Splice-Altering Variants in Patients With Beta-Sarcoglycanopathy.

BackgroundThe precise genetic diagnosis of a sarcoglycanopathy or dystrophinopathy is sometimes extremely challenging, as pathogenic non-coding variants and/or complex structural variants do exist in DMD or sarcoglycan genes. This study aimed to determine the genetic diagnosis of three patients from two unrelated families with a suspected sarcoglycanopathy or dystrophinopathy based on their clinical, radiological, and pathological features, for whom routine genomic detection approaches failed to yield a definite genetic diagnosis.MethodsMuscle-derived reverse transcription-polymerase chain reaction analysis and/or TA cloning of DMD, SGCA, SGCB, SGCD, and SGCG mRNA were performed to identify aberrant transcripts. Genomic Sanger sequencing around the aberrant transcripts was performed to detect possible splice-altering variants. Bioinformatic and segregation studies of the detected genomic variants were performed in both families.ResultsIn patients F1-II1 and F1-II2, we identified two novel pathogenic compound heterozygous variants in SGCB. One is a deep intronic splice-altering variant (DISV), c.243 + 1558C > T in intron 2 causing the activation of an 87-base pair (bp) pseudoexon, and the other one is a non-canonical splicing site variant, c.243 + 6T > A leading to the partial intron inclusion of 10-bp sequence. A novel DISV, c.243 + 1576C > G causing a 106-bp pseudoexon activation, and a nonsense variant in SGCB were identified in compound heterozygous state in patient F2-II1. Unexpectedly, the predicted nonsense variant, c.334C > T in exon 3, created a new donor splice site in exon 3 that was stronger than the natural one, resulting in a 97-bp deletion of exon 3 (r.333_429del).ConclusionThis is the first identification of rare exonic and DISVs in the SGCB gene.

Massively parallel identification of functionally consequential noncoding genetic variants in undiagnosed rare disease patients

Clinical whole genome sequencing has enabled the discovery of potentially pathogenic noncoding variants in the genomes of rare disease patients with a prior history of negative genetic testing. However, interpreting the functional consequences of noncoding variants and distinguishing those that contribute to disease etiology remains a challenge. Here we address this challenge by experimentally profiling the functional consequences of rare noncoding variants detected in a cohort of undiagnosed rare disease patients at scale using a massively parallel reporter assay. We demonstrate that this approach successfully identifies rare noncoding variants that alter the regulatory capacity of genomic sequences. In addition, we describe an integrative analysis that utilizes genomic features alongside patient clinical data to further prioritize candidate variants with an increased likelihood of pathogenicity. This work represents an important step towards establishing a framework for the functional interpretation of clinically detected noncoding variants.

Machine Learning Prediction of Non-Coding Variant Impact in Human Retinal cis-Regulatory Elements.

PurposePrior studies have demonstrated the significance of specific cis-regulatory variants in retinal disease; however, determining the functional impact of regulatory variants remains a major challenge. In this study, we utilized a machine learning approach, trained on epigenomic data from the adult human retina, to systematically quantify the predicted impact of cis-regulatory variants.MethodsWe used human retinal DNA accessibility data (ATAC-seq) to determine a set of 18.9k high-confidence, putative cis-regulatory elements. Eighty percent of these elements were used to train a machine learning model utilizing a gapped k-mer support vector machine–based approach. In silico saturation mutagenesis and variant scoring was applied to predict the functional impact of all potential single nucleotide variants within cis-regulatory elements. Impact scores were tested in a 20% hold-out dataset and compared to allele population frequency, phylogenetic conservation, transcription factor (TF) binding motifs, and existing massively parallel reporter assay data.ResultsWe generated a model that distinguishes between human retinal regulatory elements and negative test sequences with 95% accuracy. Among a hold-out test set of 3.7k human retinal CREs, all possible single nucleotide variants were scored. Variants with negative impact scores correlated with higher phylogenetic conservation of the reference allele, disruption of predicted TF binding motifs, and massively parallel reporter expression.ConclusionsWe demonstrated the utility of human retinal epigenomic data to train a machine learning model for the purpose of predicting the impact of non-coding regulatory sequence variants. Our model accurately scored sequences and predicted putative transcription factor binding motifs. This approach has the potential to expedite the characterization of pathogenic non-coding sequence variants in the context of unexplained retinal disease.Translational RelevanceThis workflow and resulting dataset serve as a promising genomic tool to facilitate the clinical prioritization of functionally disruptive non-coding mutations in the retina.

Genome sequencing reveals novel noncoding variants in PLA2G6 and LMNB1 causing progressive neurologic disease.

Neurodegenerative disorders and leukodystrophies are progressive neurologic conditions that can occur following the disruption of intricately coordinated patterns of gene expression. Exome sequencing has been adopted as an effective diagnostic tool for determining the underlying genetic etiology of Mendelian neurologic disorders, however genome sequencing offer advantages in its ability to identify and characterize copy number, structural, and sequence variants in noncoding regions. Genome sequencing from peripheral leukocytes was performed on two patients with progressive neurologic disease of unknown etiology following negative genetic investigations including exome sequencing. RNA sequencing from peripheral blood was performed to determine gene expression patterns in one of the patients. Potential causative variants were matched to the patients’ clinical presentation. The first proband was found to be heterozygous for a likely pathogenic missense variant in PLA2G6 (c.386T>C; p.Leu129Pro) and have an additional deep intronic variant in PLA2G6 (c.2035‐926G>A). RNA sequencing indicated this latter variant created a splice acceptor site leading to the incorporation of a pseudo‐exon introducing a premature termination codon. The second proband was heterozygous for a 261 kb deletion upstream of LMNB1 that included an enhancer region. Previous reports of copy number variants spanning this region of cis‐acting regulatory elements corroborated its pathogenicity. When combined with clinical presentations, these findings led to a definitive diagnosis of autosomal recessive infantile neuroaxonal dystrophy and autosomal dominant adult‐onset demyelinating leukodystrophy, respectively. In patients with progressive neurologic disease of unknown etiology, genome sequencing with the addition of RNA analysis where appropriate should be considered for the identification of causative noncoding pathogenic variants.

Characterizing the Copy Number Variation of Non-Coding RNAs Reveals Potential Therapeutic Targets and Prognostic Markers of LUSC.

Lung squamous cell carcinoma (LUSC) has a poor clinical prognosis and a lack of available targeted therapies. Therefore, there is an urgent need to identify novel prognostic markers and therapeutic targets to assist in the diagnosis and treatment of LUSC. With the development of high-throughput sequencing technology, integrated analysis of multi-omics data will provide annotation of pathogenic non-coding variants and the role of non-coding sequence variants in cancers. Here, we integrated RNA-seq profiles and copy number variation (CNV) data to study the effects of non-coding variations on gene regulatory network. Furthermore, the 372 long non-coding RNAs (lncRNA) regulated by CNV were used as candidate genes, which could be used as biomarkers for clinical application. Nine lncRNAs including LINC00896, MCM8-AS1, LINC01251, LNX1-AS1, GPRC5D-AS1, CTD-2350J17.1, LINC01133, LINC01121, and AC073130.1 were recognized as prognostic markers for LUSC. By exploring the association of the prognosis-related lncRNAs (pr-lncRNAs) with immune cell infiltration, GPRC5D-AS1 and LINC01133 were highlighted as markers of the immunosuppressive microenvironment. Additionally, the cascade response of pr-lncRNA-CNV-mRNA-physiological functions was revealed. Taken together, the identification of prognostic markers and carcinogenic regulatory mechanisms will contribute to the individualized treatment for LUSC and promote the development of precision medicine.

Genome sequencing in families with congenital limb malformations

The extensive clinical and genetic heterogeneity of congenital limb malformation calls for comprehensive genome-wide analysis of genetic variation. Genome sequencing (GS) has the potential to identify all genetic variants. Here we aim to determine the diagnostic potential of GS as a comprehensive one-test-for-all strategy in a cohort of undiagnosed patients with congenital limb malformations. We collected 69 cases (64 trios, 1 duo, 5 singletons) with congenital limb malformations with no molecular diagnosis after standard clinical genetic testing and performed genome sequencing. We also developed a framework to identify potential noncoding pathogenic variants. We identified likely pathogenic/disease-associated variants in 12 cases (17.4%) including four in known disease genes, and one repeat expansion in HOXD13. In three unrelated cases with ectrodactyly, we identified likely pathogenic variants in UBA2, establishing it as a novel disease gene. In addition, we found two complex structural variants (3%). We also identified likely causative variants in three novel high confidence candidate genes. We were not able to identify any noncoding variants. GS is a powerful strategy to identify all types of genomic variants associated with congenital limb malformation, including repeat expansions and complex structural variants missed by standard diagnostic approaches. In this cohort, no causative noncoding SNVs could be identified.

Different types of disease‐causing noncoding variants revealed by genomic and gene expression analyses in families with X‐linked intellectual disability

The pioneering discovery research of X-linked intellectual disability (XLID) genes has benefitted thousands of individuals worldwide; however, approximately 30% of XLID families still remain unresolved. We postulated that noncoding variants that affect gene regulation or splicing may account for the lack of a genetic diagnosis in some cases. Detecting pathogenic, gene-regulatory variants with the same sensitivity and specificity as structural and coding variants is a major challenge for Mendelian disorders. Here, we describe three pedigrees with suggestive XLID where distinctive phenotypes associated with known genes guided the identification of three different noncoding variants. We used comprehensive structural, single-nucleotide, and repeat expansion analyses of genome sequencing. RNA-Seq from patient-derived cell lines, reverse-transcription polymerase chain reactions, Western blots, and reporter gene assays were used to confirm the functional effect of three fundamentally different classes of pathogenic noncoding variants: a retrotransposon insertion, a novel intronic splice donor, and a canonical splice variant of an untranslated exon. In one family, we excluded a rare coding variant in ARX, a known XLID gene, in favor of a regulatory noncoding variant in OFD1 that correlated with the clinical phenotype. Our results underscore the value of genomic research on unresolved XLID families to aid novel, pathogenic noncoding variant discovery.

NcVarDB: a manually curated database for pathogenic non-coding variants and benign controls.

Variants within the non-coding genome are frequently associated with phenotypes in genome-wide association studies. These non-coding regions may be involved in the regulation of gene expression, encode functional non-coding RNAs, or influence splicing and other cellular functions. We have curated a list of characterized non-coding human genome variants based on the published evidence that indicates phenotypic consequences of the variation. In order to minimize annotation errors, two curators have independently verified the supporting evidence for pathogenicity of each non-coding variant in the published literature. The database consists of 721 non-coding variants linked to the published literature describing the evidence of functional consequences. We have also sampled 7228 covariate-matched benign controls, that have a population frequency of over 5%, from the single nucleotide polymorphism database (dbSNP151) database. These were sampled controlling for potential confounding factors such as linkage with pathogenic variants, annotation type (untranslated region, intron, intergenic, etc.) and variant type (substitution or indel). The dataset presented here represents a curated repository, with a potential use for the training or evaluation of algorithms used in the prediction of non-coding variant functionality. Database URL: https://github.com/Gardner-BinfLab/ncVarDB.

The challenge of genetically unresolved haemophilia A patients: Interest of the combination of whole F8 gene sequencing and functional assays.

The causative variant remains unidentified in 2%-5% of haemophilia A (HA) patients despite an exhaustive sequencing of the full F8 coding sequence, splice consensus sequences, 5'/3' untranslated regions and copy number variant (CNV) analysis. Next-generation sequencing (NGS) has provided significant improvements for a complete F8 analysis. The aim of this study was to identify and characterize pathogenic non-coding variants in F8 of 15 French and Canadian HA patients genetically unresolved, through the use of NGS, mRNA sequencing and functional confirmation of aberrant splicing. We sequenced the entire F8 gene using an NGS capture method. We analysed F8 mRNA in order to detect aberrant transcripts. The pathogenic effect of candidate intronic variants was further confirmed using a minigene assay. After bioinformatic analysis, 11 deep intronic variants were identified in 13 patients (8 new variants and 3 previously reported). Three variants were confirmed to be likely pathogenic with the presence of an aberrant transcript during mRNA analysis and minigene assay. We also found a small intronic deletion in 6 patients, recently described as causing mild HA. With this comprehensive work combining NGS and functional assays, we report new deep intronic variants that cause HA through splicing alteration mechanism. Functional analyses are critical to confirm the pathogenic effect of these variants and will be invaluable in the future to study the large number of variants of uncertain significance that may affect splicing that will be found in the human genome.

Ranking of non-coding pathogenic variants and putative essential regions of the human genome

A gene is considered essential if loss of function results in loss of viability, fitness or in disease. This concept is well established for coding genes; however, non-coding regions are thought less likely to be determinants of critical functions. Here we train a machine learning model using functional, mutational and structural features, including new genome essentiality metrics, 3D genome organization and enhancer reporter data to identify deleterious variants in non-coding regions. We assess the model for functional correlates by using data from tiling-deletion-based and CRISPR interference screens of activity of cis-regulatory elements in over 3 Mb of genome sequence. Finally, we explore two user cases that involve indels and the disruption of enhancers associated with a developmental disease. We rank variants in the non-coding genome according to their predicted deleteriousness. The model prioritizes non-coding regions associated with regulation of important genes and with cell viability, an in vitro surrogate of essentiality.

Contribution of noncoding pathogenic variants to RPGRIP1-mediated inherited retinal degeneration

PurposeWith the advent of gene therapies for inherited retinal degenerations (IRDs), genetic diagnostics will have an increasing role in clinical decision-making. Yet the genetic cause of disease cannot be identified using exon-based sequencing for a significant portion of patients. We hypothesized that noncoding pathogenic variants contribute significantly to the genetic causality of IRDs and evaluated patients with single coding pathogenic variants in RPGRIP1 to test this hypothesis. MethodsIRD families underwent targeted panel sequencing. Unsolved cases were explored by exome and genome sequencing looking for additional pathogenic variants. Candidate pathogenic variants were then validated by Sanger sequencing, quantitative polymerase chain reaction, and in vitro splicing assays in two cell lines analyzed through amplicon sequencing. ResultsAmong 1722 families, 3 had biallelic loss-of-function pathogenic variants in RPGRIP1 while 7 had a single disruptive coding pathogenic variants. Exome and genome sequencing revealed potential noncoding pathogenic variants in these 7 families. In 6, the noncoding pathogenic variants were shown to lead to loss of function in vitro. ConclusionNoncoding pathogenic variants were identified in 6 of 7 families with single coding pathogenic variants in RPGRIP1. The results suggest that noncoding pathogenic variants contribute significantly to the genetic causality of IRDs and RPGRIP1-mediated IRDs are more common than previously thought.

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.