Processed Pseudogene Confounding Deletion/Duplication Assays for SMAD4

A validated heart-specific model for splice-disrupting variants in childhood heart disease

BACKGROUND

Congenital heart disease (CHD) is the most common congenital anomaly. Almost 90% of isolated cases have an unexplained genetic etiology after clinical testing. Non-canonical splice variants that disrupt mRNA splicing through the loss or creation of exon boundaries are not routinely captured and/or evaluated by standard clinical genetic tests. Recent computational algorithms such as SpliceAI have shown an ability to predict such variants, but are not specific to cardiac-expressed genes and transcriptional isoforms.

METHODS

We used genome sequencing (GS) (n = 1101 CHD probands) and myocardial RNA-Sequencing (RNA-Seq) (n = 154 CHD and n = 43 cardiomyopathy probands) to identify and validate splice disrupting variants, and to develop a heart-specific model for canonical and non-canonical splice variants that can be applied to patients with CHD and cardiomyopathy. Two thousand five hundred seventy GS samples from the Medical Genome Reference Bank were analyzed as healthy controls.

RESULTS

Of 8583 rare DNA splice-disrupting variants initially identified using SpliceAI, 100 were associated with altered splice junctions in the corresponding patient myocardium affecting 95 genes. Using strength of myocardial gene expression and genome-wide DNA variant features that were confirmed to affect splicing in myocardial RNA, we trained a machine learning model for predicting cardiac-specific splice-disrupting variants (AUC 0.86 on internal validation). In a validation set of 48 CHD probands, the cardiac-specific model outperformed a SpliceAI model alone (AUC 0.94 vs 0.67 respectively). Application of this model to an additional 947 CHD probands with only GS data identified 1% patients with canonical and 11% patients with non-canonical splice-disrupting variants in CHD genes. Forty-nine percent of predicted splice-disrupting variants were intronic and > 10 bp from existing splice junctions. The burden of high-confidence splice-disrupting variants in CHD genes was 1.28-fold higher in CHD cases compared with healthy controls.

CONCLUSIONS

A new cardiac-specific in silico model was developed using complementary GS and RNA-Seq data that improved genetic yield by identifying a significant burden of non-canonical splice variants associated with CHD that would not be detectable through panel or exome sequencing.

PΨFinder: a practical tool for the identification and visualization of novel pseudogenes in DNA sequencing data

BACKGROUND

Processed pseudogenes (PΨgs) are disabled gene copies that are transcribed and may affect expression of paralogous genes. Moreover, their insertion in the genome can disrupt the structure or the regulatory region of a gene, affecting its expression level. These events have been identified as occurring mutations during cancer development, thus being able to identify PΨgs and their location will improve their impact on diagnostic testing, not only in cancer but also in inherited disorders.

RESULTS

We have implemented PΨFinder (P-psy-finder), a tool that identifies PΨgs, annotates known ones and predicts their insertion site(s) in the genome. The tool screens alignment files and provides user-friendly summary reports and visualizations. To demonstrate its applicability, we scanned 218 DNA samples from patients screened for hereditary colorectal cancer. We detected 423 PΨgs distributed in 96% of the samples, comprising 7 different parent genes. Among these, we confirmed the well-known insertion site of the SMAD4-PΨg within the last intron of the SCAI gene in one sample. While for the ubiquitous CBX3-PΨg, present in 82.6% of the samples, we found it reversed inserted in the second intron of the C15ORF57 gene.

CONCLUSIONS

PΨFinder is a tool that can automatically identify novel PΨgs from DNA sequencing data and determine their location in the genome with high sensitivity (95.92%). It generates high quality figures and tables that facilitate the interpretation of the results and can guide the experimental validation. PΨFinder is a complementary analysis to any mutational screening in the identification of disease-causing mutations within cancer and other diseases.

Detecting copy number variation in next generation sequencing data from diagnostic gene panels

BACKGROUND

Detection of copy number variation (CNV) in genes associated with disease is important in genetic diagnostics, and next generation sequencing (NGS) technology provides data that can be used for CNV detection. However, CNV detection based on NGS data is in general not often used in diagnostic labs as the data analysis is challenging, especially with data from targeted gene panels. Wet lab methods like MLPA (MRC Holland) are widely used, but are expensive, time consuming and have gene-specific limitations. Our aim has been to develop a bioinformatic tool for CNV detection from NGS data in medical genetic diagnostic samples.

RESULTS

Our computational pipeline for detection of CNVs in NGS data from targeted gene panels utilizes coverage depth of the captured regions and calculates a copy number ratio score for each region. This is computed by comparing the mean coverage of the sample with the mean coverage of the same region in other samples, defined as a pool. The pipeline selects pools for comparison dynamically from previously sequenced samples, using the pool with an average coverage depth that is nearest to the one of the samples. A sliding window-based approach is used to analyze each region, where length of sliding window and sliding distance can be chosen dynamically to increase or decrease the resolution. This helps in detecting CNVs in small or partial exons. With this pipeline we have correctly identified the CNVs in 36 positive control samples, with sensitivity of 100% and specificity of 91%. We have detected whole gene level deletion/duplication, single/multi exonic level deletion/duplication, partial exonic deletion and mosaic deletion. Since its implementation in mid-2018 it has proven its diagnostic value with more than 45 CNV findings in routine tests.

CONCLUSIONS

With this pipeline as part of our diagnostic practices it is now possible to detect partial, single or multi-exonic, and intragenic CNVs in all genes in our target panel. This has helped our diagnostic lab to expand the portfolio of genes where we offer CNV detection, which previously was limited by the availability of MLPA kits.

Intronic Breakpoint Signatures Enhance Detection and Characterization of Clinically Relevant Germline Structural Variants

The relevance of large copy number variants (CNVs) to hereditary disorders has been long recognized, and population sequencing efforts have chronicled many common structural variants (SVs). However, limited data are available on the clinical contribution of rare germline SVs. Here, a detailed characterization of SVs identified using targeted next-generation sequencing was performed. Across 50 genes associated with hereditary cancer and cardiovascular disorders, a minimum of 828 unique SVs were reported, including 584 fully characterized SVs. Almost 40% of CNVs were <5 kb, with one in three deletions impacting a single exon. Additionally, 36 mid-range deletions/duplications (50 to 250 bp), 21 mobile element insertions, 6 inversions, and 27 complex rearrangements were detected. This data set was used to model SV detection in a bioinformatics pipeline solely relying on read depth, which revealed that genome sequencing (30×) allows detection of 71%, a 500× panel only targeting coding regions 53%, and exome sequencing (100×) <20% of characterized SVs. SVs accounted for 14.1% of all unique pathogenic variants, supporting the importance of SVs in hereditary disorders. Robust SV detection requires an ensemble of variant-calling algorithms that utilize sequencing of intronic regions. These algorithms should use distinct data features representative of each class of mutational mechanism, including recombination between two sequences sharing high similarity, covariants inserted between CNV breakpoints, and complex rearrangements containing inverted sequences.

Development and Validation of a 34-Gene Inherited Cancer Predisposition Panel Using Next-Generation Sequencing

The use of genetic testing to identify individuals with hereditary cancer syndromes has been widely adopted by clinicians for management of inherited cancer risk. The objective of this study was to develop and validate a 34-gene inherited cancer predisposition panel using targeted capture-based next-generation sequencing (NGS). The panel incorporates genes underlying well-characterized cancer syndromes, such as BRCA1 and BRCA2 (BRCA1/2), along with more recently discovered genes associated with increased cancer risk. We performed a validation study on 133 unique specimens, including 33 with known variant status; known variants included single nucleotide variants (SNVs) and small insertions and deletions (Indels), as well as copy-number variants (CNVs). The analytical validation study achieved 100% sensitivity and specificity for SNVs and small Indels, with 100% sensitivity and 98.0% specificity for CNVs using in-house developed CNV flagging algorithm. We employed a microarray comparative genomic hybridization (aCGH) method for all specimens that the algorithm flags as CNV-positive for confirmation. In combination with aCGH confirmation, CNV detection specificity improved to 100%. We additionally report results of the first 500 consecutive specimens submitted for clinical testing with the 34-gene panel, identifying 53 deleterious variants in 13 genes in 49 individuals. Half of the detected pathogenic/likely pathogenic variants were found in BRCA1 (23%), BRCA2 (23%), or the Lynch syndrome-associated genes PMS2 (4%) and MLH1 (2%). The other half were detected in 9 other genes: MUTYH (17%), CHEK2 (15%), ATM (4%), PALB2 (4%), BARD1 (2%), CDH1 (2%), CDKN2A (2%), RAD51C (2%), and RET (2%). Our validation studies and initial clinical data demonstrate that a 34-gene inherited cancer predisposition panel can provide clinically significant information for cancer risk assessment.

Re-recognition of pseudogenes: From molecular to clinical applications

Pseudogenes were initially regarded as "nonfunctional" genomic elements that did not have protein-coding abilities due to several endogenous inactivating mutations. Although pseudogenes are widely expressed in prokaryotes and eukaryotes, for decades, they have been largely ignored and classified as gene "junk" or "relics". With the widespread availability of high-throughput sequencing analysis, especially omics technologies, knowledge concerning pseudogenes has substantially increased. Pseudogenes are evolutionarily conserved and derive primarily from a mutation or retrotransposon, conferring the pseudogene with a "gene repository" role to store and expand genetic information. In contrast to previous notions, pseudogenes have a variety of functions at the DNA, RNA and protein levels for broadly participating in gene regulation to influence the development and progression of certain diseases, especially cancer. Indeed, some pseudogenes have been proven to encode proteins, strongly contradicting their "trash" identification, and have been confirmed to have tissue-specific and disease subtype-specific expression, indicating their own value in disease diagnosis. Moreover, pseudogenes have been correlated with the life expectancy of patients and exhibit great potential for future use in disease treatment, suggesting that they are promising biomarkers and therapeutic targets for clinical applications. In this review, we summarize the natural properties, functions, disease involvement and clinical value of pseudogenes. Although our knowledge of pseudogenes remains nascent, this field deserves more attention and deeper exploration.

Identification of mobile retrocopies during genetic testing: Consequences for routine diagnosis

Human retrocopies, that is messenger RNA transcripts benefitting from the long interspersed element 1 machinery for retrotransposition, may have specific consequences for genomic testing. Next genetration sequencing (NGS) techniques allow the detection of such mobile elements but they may be misinterpreted as genomic duplications or be totally overlooked. We report eight observations of retrocopies detected during diagnostic NGS analyses of targeted gene panels, exome, or genome sequencing. For seven cases, while an exons-only copy number gain was called, read alignment inspection revealed a depth of coverage shift at every exon-intron junction where indels were also systematically called. Moreover, aberrant chimeric read pairs spanned entire introns or were paired with another locus for terminal exons. The 8th retrocopy was present in the reference genome and thus showed a normal NGS profile. We emphasize the existence of retrocopies and strategies to accurately detect them at a glance during genetic testing and discuss pitfalls for genetic testing.

Identification of prognostic molecular biomarkers in 157 HPV-positive and HPV-negative squamous cell carcinomas of the oropharynx

The incidence of oropharyngeal squamous cell carcinoma (OPSCC) has been increasing due to high-risk HPV infection. We explored the significance of genetic alterations in HPV-positive (HPV-P) and HPV-negative (HPV-N) OPSCC patients on long-term outcome. A total of 157 cases of primary resected OPSCC diagnosed from 1978 to 2005 were subjected to a targeted exome sequencing by MSK-IMPACT™ interrogating somatic mutations in 410 cancer-related genes. Mutational profiles were correlated to recurrence and survival outcomes. OPSCC included 47% HPV-positive (HPV-P) and 53% HPV-negative (HPV-N) tumors arising in the base of tongue (BOT, 43%), palatine tonsil (30%) and soft palate (SP, 27%). HPV negative status, SP location and smoking were associated with poorer outcome. Poorer overall survival was found in NOTCH1-mutated HPV-P (p = 0.039), and in SOX2-amplified HPV-N cases (p = 0.036). Chromosomal arm gains in 8p and 8q, and 16q loss were more common in HPV-P (p = 0.005, 0.04 and 0.01, respectively), while 9p, 18q and 21q losses were more frequent in HPV-N OPSCC (p = 0.006, 0.002 and 0.01, respectively). Novel, potentially functional JAK3, MYC and EP300 intragenic deletions were found in HPV-P, and FOXP1, CDKN2A, CCND1 and RUNX1 intragenic deletions and one FGFR3 inversion were detected in HPV-N tumors. HPV-N/TP53-wild-type OPSCC harbored recurrent mutations in NOTCH1/3/4 (39%), PIK3CA, FAT1 and TERT. In comparison to their oral and laryngeal counterparts, HPV-N OPSCC were genetically distinct. In OPSCC, HPV status, tumor subsite and smoking determine outcome. Risk-stratification can be further refined based on the mutational signature, namely, NOTCH1 and SOX2 mutation status.

Detection of structural variation using target captured next-generation sequencing data for genetic diagnostic testing

PURPOSE

Structural variation (SV) is associated with inherited diseases. Next-generation sequencing (NGS) is an efficient method for SV detection because of its high-throughput, low cost, and base-pair resolution. However, due to lack of standard NGS protocols and a limited number of clinical samples with pathogenic SVs, comprehensive standards for SV detection, interpretation, and reporting are to be established.

METHODS

We performed SV assessment on 60,000 clinical samples tested with hereditary cancer NGS panels spanning 48 genes. To evaluate NGS results, NGS and orthogonal methods were used separately in a blinded fashion for SV detection in all samples.

RESULTS

A total of 1,037 SVs in coding sequence (CDS) or untranslated regions (UTRs) and 30,847 SVs in introns were detected and validated. Across all variant types, NGS shows 100% sensitivity and 99.9% specificity. Overall, 64% of CDS/UTR SVs were classified as pathogenic/likely pathogenic, and five deletions/duplications were reclassified as pathogenic using breakpoint information from NGS.

CONCLUSION

The SVs presented here can be used as a valuable resource for clinical research and diagnostics. The data illustrate NGS as a powerful tool for SV detection. Application of NGS and confirmation technologies in genetic testing ensures delivering accurate and reliable results for diagnosis and patient care.

Characterization and Genomic Localization of a SMAD4 Processed Pseudogene

Like many clinical diagnostic laboratories, the Yorkshire Regional Genetics Service undertakes routine investigation of cancer-predisposed individuals by high-throughput sequencing of patient DNA that has been target-enriched for genes associated with hereditary cancer. Accurate diagnosis using such reagents requires alertness regarding rare nonpathogenic variants that may interfere with variant calling. In a cohort of 2042 such cases, we identified 5 that initially appeared to be carriers of a 95-bp deletion of SMAD4 intron 6. More detailed analysis indicated that these individuals all carried one copy of a SMAD4 processed gene. Because of its interference with diagnostic analysis, we characterized this processed gene in detail. Whole-genome sequencing and confirmatory Sanger sequencing of junction PCR products were used to show that in each of the 5 cases, the SMAD4 processed gene was integrated at the same position on chromosome 9, located within the last intron of the SCAI gene. This rare polymorphic processed gene therefore reflects the occurrence of a single ancestral retrotransposition event. Compared to the reference SMAD4 mRNA sequence NM_005359.5 (https://www.ncbi.nlm.nih.gov/nucleotide), the 5' and 3' untranslated regions of the processed gene are both truncated, but its open reading frame is unaltered. Our experience leads us to advocate the use of an RNA-seq aligner as part of diagnostic assay quality assurance, since this allows recognition of processed pseudogenes in a comparatively facile automated fashion.

Sanger Confirmation Is Required to Achieve Optimal Sensitivity and Specificity in Next-Generation Sequencing Panel Testing

Next-generation sequencing (NGS) has rapidly replaced Sanger sequencing as the method of choice for diagnostic gene-panel testing. For hereditary-cancer testing, the technical sensitivity and specificity of the assay are paramount as clinicians use results to make important clinical management and treatment decisions. There is significant debate within the diagnostics community regarding the necessity of confirming NGS variant calls by Sanger sequencing, considering that numerous laboratories report having 100% specificity from the NGS data alone. Here we report our results from 20,000 hereditary-cancer NGS panels spanning 47 genes, in which all 7845 nonpolymorphic variants were Sanger- sequenced. Of these, 98.7% were concordant between NGS and Sanger sequencing and 1.3% were identified as NGS false-positives, located mainly in complex genomic regions (A/T-rich regions, G/C-rich regions, homopolymer stretches, and pseudogene regions). Simulating a false-positive rate of zero by adjusting the variant-calling quality-score thresholds decreased the sensitivity of the assay from 100% to 97.8%, resulting in the missed detection of 176 Sanger-confirmed variants, the majority in complex genomic regions (n = 114) and mosaic mutations (n = 7). The data illustrate the importance of setting quality thresholds for panel testing only after thousands of samples have been processed and the necessity of Sanger confirmation of NGS variants to maintain the highest possible sensitivity.

Navigating highly homologous genes in a molecular diagnostic setting: a resource for clinical next-generation sequencing

PURPOSE

Next-generation sequencing (NGS) is now routinely used to interrogate large sets of genes in a diagnostic setting. Regions of high sequence homology continue to be a major challenge for short-read technologies and can lead to false-positive and false-negative diagnostic errors. At the scale of whole-exome sequencing (WES), laboratories may be limited in their knowledge of genes and regions that pose technical hurdles due to high homology. We have created an exome-wide resource that catalogs highly homologous regions that is tailored toward diagnostic applications.

METHODS

This resource was developed using a mappability-based approach tailored to current Sanger and NGS protocols.

RESULTS

Gene-level and exon-level lists delineate regions that are difficult or impossible to analyze via standard NGS. These regions are ranked by degree of affectedness, annotated for medical relevance, and classified by the type of homology (within-gene, different functional gene, known pseudogene, uncharacterized noncoding region). Additionally, we provide a list of exons that cannot be analyzed by short-amplicon Sanger sequencing.

CONCLUSION

This resource can help guide clinical test design, supplemental assay implementation, and results interpretation in the context of high homology.Genet Med 18 12, 1282-1289.

Blepharospasm in a multiplex African-American pedigree

BACKGROUND

Isolated blepharospasm (BSP) is a late-onset focal dystonia characterized by involuntary contractions of the orbicularis oculi muscles. Genetic studies of BSP have been limited by the paucity of large multiplex pedigrees. Although sequence variants (SVs) in THAP1 have been reported in rare cases of BSP, the genetic causes of this focal dystonia remain largely unknown. Moreover, in the absence of family history and strong in silico or in vitro evidence of deleteriousness, the pathogenicity of novel SVs in THAP1 and other dystonia-associated genes can be indeterminate.

METHODS

A large African-American pedigree with BSP was phenotypically characterized and screened for mutations in THAP1, TOR1A and GNAL with Sanger sequencing. Whole-exome sequencing of the proband was used to examine other dystonia-associated genes for potentially pathogenic SVs. In silico and co-segregation analyses were performed for a novel THAP1 SV identified in the proband.

RESULTS

Seven family members exhibited increased blinking and/or stereotyped bilateral and synchronous orbicularis oculi spasms with age of onset ranging from early childhood to late adult life (7 to 54 years). The proband was found to harbor a novel THAP1 SV (c.314T>C, p.L105S). However, the p.L105S SV did not co-segregate with blepharospasm in the pedigree. Moreover, in silico analyses suggest that p.L105S is benign. No pathogenic or likely pathogenic SVs in other dystonia-associated genes were identified with whole-exome sequencing.

CONCLUSIONS

Blepharospasm can be familial and may be hereditary in African-Americans. A comprehensive array of in silico tools, and, if possible, co-segregation analysis should be used to classify SVs in dystonia-associated genes.