With the expanded availability of next generation sequencing (NGS)-based clinical genetic tests, clinicians seeking to test patients with Mendelian diseases must weigh the superior coverage of ...targeted gene panels with the greater number of genes included in whole exome sequencing (WES) when considering their first-tier testing approach. Here, we use an in silico analysis to predict the analytic sensitivity of WES using pathogenic variants identified on targeted NGS panels as a reference.
Corresponding nucleotide positions for 1533 different alterations classified as pathogenic or likely pathogenic identified on targeted NGS multi-gene panel tests in our laboratory were interrogated in data from 100 randomly-selected clinical WES samples to quantify the sequence coverage at each position. Pathogenic variants represented 91 genes implicated in hereditary cancer, X-linked intellectual disability, primary ciliary dyskinesia, Marfan syndrome/aortic aneurysms, cardiomyopathies and arrhythmias.
When assessing coverage among 100 individual WES samples for each pathogenic variant (153,300 individual assessments), 99.7% (n = 152,798) would likely have been detected on WES. All pathogenic variants had at least some coverage on exome sequencing, with a total of 97.3% (n = 1491) detectable across all 100 individuals. For the remaining 42 pathogenic variants, the number of WES samples with adequate coverage ranged from 35 to 99. Factors such as location in GC-rich, repetitive, or homologous regions likely explain why some of these alterations were not detected across all samples. To validate study findings, a similar analysis was performed against coverage data from 60,706 exomes available through the Exome Aggregation Consortium (ExAC). Results from this validation confirmed that 98.6% (91,743,296/93,062,298) of pathogenic variants demonstrated adequate depth for detection.
Results from this in silico analysis suggest that exome sequencing may achieve a diagnostic yield similar to panel-based testing for Mendelian diseases.
Many in silico predictors of genetic variant pathogenicity have been previously developed, but there is currently no standard application of these algorithms for variant assessment. Using 4,094 ...ClinVar-curated missense variants in clinically actionable genes, we evaluated the accuracy and yield of benign and deleterious evidence in 5 in silico meta-predictors, as well as agreement of SIFT and PolyPhen2, and report the derived thresholds for the best performing predictor(s). REVEL and BayesDel outperformed all other meta-predictors (CADD, MetaSVM, Eigen), with higher positive predictive value, comparable negative predictive value, higher yield, and greater overall prediction performance. Agreement of SIFT and PolyPhen2 resulted in slightly higher yield but lower overall prediction performance than REVEL or BayesDel. Our results support the use of gene-level rather than generalized thresholds, when gene-level thresholds can be estimated. Our results also support the use of 2-sided thresholds, which allow for uncertainty, rather than a single, binary cut-point for assigning benign and deleterious evidence. The gene-level 2-sided thresholds we derived for REVEL or BayesDel can be used to assess in silico evidence for missense variants in accordance with current classification guidelines.
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.
Background
Genome‐wide association studies have identified over 100 single‐nucleotide polymorphisms (SNPs) associated with prostate cancer (PrCa), and polygenic risk scores (PRS) based on their ...combined genotypes have been developed for risk stratification. We aimed to assess the contribution of PRS to PrCa risk in a large multisite study.
Methods
The sample included 1972 PrCa cases and 1919 unaffected controls. Next‐generation sequencing was used to assess pathogenic variants in 14 PrCa‐susceptibility genes and 72 validated PrCa‐associated SNPs. We constructed a population‐standardized PRS and tested its association with PrCa using logistic regression adjusted for age and family history of PrCa.
Results
The mean age of PrCa cases at diagnosis and age of controls at testing/last clinic visit was 59.5 ± 7.2 and 57.2 ± 13.0 years, respectively. Among 1740 cases with pathology data, 57.4% had Gleason score ≤ 6, while 42.6% had Gleason score ≥ 8. In addition, 39.6% cases and 20.1% controls had a family history of PrCa. The PRS was significantly higher in cases than controls (mean ± SD: 1.42 ± 1.11 vs 1.02 ± 0.76; P < .0001). Compared with men in the 1st quartile of age‐adjusted PRS, those in the 2nd, 3rd, and 4th quartile were 1.58 (95% confidence interval CI: 1.31‐1.90), 2.36 (95% CI: 1.96‐2.84), and 3.98 (95% CI: 3.29‐4.82) times as likely to have PrCa (all P < .0001). Adjustment for family history yielded similar results. PRS predictive performance was consistent with prior literature (area under the receiver operating curve = 0.64; 95% CI: 0.62‐0.66).
Conclusions
These data suggest that a 72‐SNP PRS is predictive of PrCa, supporting its potential use in clinical risk assessment.
Summary
Some patients with therapy‐related myeloid neoplasms (t‐MN) may have unsuspected inherited cancer predisposition syndrome (CPS). We propose a set of clinical criteria to identify t‐MN ...patients with high risk of CPS (HR‐CPS). Among 225 t‐MN patients with an antecedent non‐myeloid malignancy, our clinical criteria identified 52 (23%) HR‐CPS patients. Germline whole‐exome sequencing identified pathogenic or likely pathogenic variants in 10 of 27 HR‐CPS patients compared to 0 of 9 low‐risk CPS patients (37% vs. 0%, p = 0.04). These simple clinical criteria identify t‐MN patients most likely to benefit from genetic testing for inherited CPS.
Breast cancer is the most commonly diagnosed cancer in women, with 10% of disease attributed to hereditary factors. Although BRCA1 and BRCA2 account for a high percentage of hereditary cases, there ...are more than 25 susceptibility genes that differentially impact the risk for breast cancer. Traditionally, germline testing for breast cancer was performed by Sanger dideoxy terminator sequencing in a reflexive manner, beginning with BRCA1 and BRCA2. The introduction of next-generation sequencing (NGS) has enabled the simultaneous testing of all genes implicated in breast cancer resulting in diagnostic labs offering large, comprehensive gene panels. However, some physicians prefer to only test for those genes in which established surveillance and treatment protocol exists. The NGS based BRCAplus test utilizes a custom tiled PCR based target enrichment design and bioinformatics pipeline coupled with array comparative genomic hybridization (aCGH) to identify mutations in the six high-risk genes: BRCA1, BRCA2, PTEN, TP53, CDH1, and STK11. Validation of the assay with 250 previously characterized samples resulted in 100% detection of 3,025 known variants and analytical specificity of 99.99%. Analysis of the clinical performance of the first 3,000 BRCAplus samples referred for testing revealed an average coverage greater than 9,000X per target base pair resulting in excellent specificity and the sensitivity to detect low level mosaicism and allele-drop out. The unique design of the assay enabled the detection of pathogenic mutations missed by previous testing. With the abundance of NGS diagnostic tests being released, it is essential that clinicians understand the advantages and limitations of different test designs.
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.
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.
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.
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.
Diagnostic exome sequencing was immediately successful in diagnosing patients in whom traditional technologies were uninformative. Herein, we provide the results from the first 500 probands referred ...to a clinical laboratory for diagnostic exome sequencing.
Family-based exome sequencing included whole-exome sequencing followed by family inheritance-based model filtering, comprehensive medical review, familial cosegregation analysis, and analysis of novel genes.
A positive or likely positive result in a characterized gene was identified in 30% of patients (152/500). A novel gene finding was identified in 7.5% of patients (31/416). The highest diagnostic rates were observed among patients with ataxia, multiple congenital anomalies, and epilepsy (44, 36, and 35%, respectively). Twenty-three percent of positive findings were within genes characterized within the past 2 years. The diagnostic rate was significantly higher among families undergoing a trio (37%) as compared with a singleton (21%) whole-exome testing strategy.
Overall, we present results from the largest clinical cohort of diagnostic exome sequencing cases to date. These data demonstrate the utility of family-based exome sequencing and analysis to obtain the highest reported detection rate in an unselected clinical cohort, illustrating the utility of diagnostic exome sequencing as a transformative technology for the molecular diagnosis of genetic disease.
Computational studies of biological networks can help to identify components and wirings responsible for observed phenotypes. However, studying stochastic networks controlling many biological ...processes is challenging. Similar to Schrödinger’s equation in quantum mechanics, the chemical master equation (CME) provides a basic framework for understanding stochastic networks. However, except for simple problems, the CME cannot be solved analytically. Here we use a method called discrete chemical master equation (dCME) to compute directly the full steady-state probability landscape of the lysogeny maintenance network in phage lambda from its CME. Results show that wild-type phage lambda can maintain a constant level of repressor over a wide range of repressor degradation rate and is stable against UV irradiation, ensuring heritability of the lysogenic state. Furthermore, it can switch efficiently to the lytic state once repressor degradation increases past a high threshold by a small amount. We find that beyond bistability and nonlinear dimerization, cooperativity between repressors bound to O R 1 and O R 2 is required for stable and heritable epigenetic state of lysogeny that can switch efficiently. Mutants of phage lambda lack stability and do not possess a high threshold. Instead, they are leaky and respond to gradual changes in degradation rate. Our computation faithfully reproduces the hair triggers for UV-induced lysis observed in mutants and the limitation in robustness against mutations. The landscape approach computed from dCME is general and can be applied to study broad issues in systems biology.
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