Human Mutation

In silico predictive tools can help determine the pathogenicity of variants. The 2015 American College of Medical Genetics and Genomics (ACMG) guidelines recommended that scores from these tools can be used as supporting evidence of pathogenicity. A subsequent publication by the ClinGen Sequence Variant Interpretation Working Group suggested that high scores from some tools were sufficiently predictive to be used as moderate or strong evidence of pathogenicity. REVEL is a widely used metapredictor that uses the scores of 13 individual in silico tools to calculate the pathogenicity of missense variants. Its ability to predict missense pathogenicity has been assessed extensively; however, no study has previously tested whether its performance is affected by whether the missense variant acts via a loss-of-function (LoF) or gain-of-function (GoF) mechanism. We used a highly curated dataset of 66 confirmed LoF and 65 confirmed GoF variants to evaluate whether this affected the performance of REVEL. 98% of LoF and 100% of GoF variants met the author-recommended REVEL threshold of 0.5 for pathogenicity, while 89% of LoF and 88% of GoF variants exceeded the 0.75 threshold. However, while 55% of LoF variants met the threshold recommended for a REVEL score to count as strong evidence of pathogenicity from the ACMG guidelines (0.932), only 35% of GoF variants met this threshold (). GoF variants are therefore less likely to receive the highest REVEL scores which would enable the REVEL score to be used as strong evidence of pathogenicity. This has implications for classification with the ACMG guidelines as GoF variants are less likely to meet the criteria for pathogenicity.

Amyotrophic lateral sclerosis (ALS) is a debilitating neurodegenerative disease affecting approximately two per 100,000 individuals globally. While there are many benefits to offering early genetic testing to people with ALS, this has also led to an increase in the yield of novel variants of uncertain significance in ALS-associated genes. Computational (in silico) predictors, including REVEL and CADD, are widely employed to provide supporting evidence of pathogenicity for variants in conjunction with clinical, molecular, and other genetic evidence. However, in silico predictors are developed to be broadly applied across the human genome; thus, their ability to evaluate the consequences of variation in ALS-associated genes remains unclear. To resolve this ambiguity, we surveyed 20 definitive and moderate ClinGen-defined ALS-associated genes from two large, open-access ALS sequencing datasets (total people with ; ) to investigate REVEL and CADD’s ability to predict which variants are most likely to be disease-causing in ALS. While our results indicate a predetermined pathogenicity threshold for REVEL that could be of clinical value for classifying variants in ALS-associated genes, an accurate threshold was not evident for CADD, and both in silico predictors were of limited value for resolving which variants of uncertain significance (VUS) may be likely pathogenic in ALS. Our findings allow us to provide important recommendations for the use of REVEL and CADD scores for variants and indicate that both tools should be used with caution when attempting to evaluate the pathogenicity of VUSs in ALS genetic testing.

Pediatric cardiomyopathy (CM) has significant childhood morbidity and mortality which is caused by both genetic and environmental factors. Previous research has focused on identifying genetic variants in pediatric CM for diagnostic purposes, but not for risk stratification. The current study was modeled after previous work which showed an association between CardioBoost-classified disease-causing variants and an increased risk for severe clinical outcomes in adults with CM to assess if the same association is true in pediatric CM. This was a retrospective, single-center cohort study that evaluated outcomes in pediatric CM patients who were evaluated by the Children’s Hospital of Philadelphia (CHOP). CardioBoost (CB) scores were generated for these patients, and scores were categorized as ≤0.1, 0.1-0.9, and ≥0.9. Composite endpoint was freedom from a major adverse cardiac event (MACE). 104 patients were included in the final analysis. 32 (31%) had DCM, 45 (43%) had HCM, and 27 (26%) had other CM. There was no significant association between CB score and clinical outcome in pediatric CM patients. Overall, this study highlights the continued deficits in variant interpretation for pediatric CM. We recommend using caution when applying this tool to stratify clinical outcomes in the pediatric population.

Purpose. Therapeutic advances in the treatment of spinal muscular atrophy (SMA) prompt the need for robust and efficient molecular diagnosis of this disease. Approximately five percent of SMA cases are attributable to one copy of SMN1 with a hypomorphic or inactivating variant in trans with a deleted or converted allele. These intragenic variants are challenging to definitively localize to SMN1 due to its sequence homology with the SMN2 gene. To enhance the clinical sensitivity of SMA diagnostic testing, we present an optimized gene-specific sequencing assay to localize variants to either SMN1 or SMN2. Methods. SMN1 and SMN2 genes are independently amplified by long-range allele-specific PCR. Long-range products are used in subsequent nested PCR reactions to amplify the coding exons of SMN1 and SMN2. The resulting products are sequenced using standard Sanger-based methodologies and analyzed for disease-associated alterations. Results. 83 probands suspicious for a clinical diagnosis of SMA with a nondiagnostic SMN dosage result were sequenced for intragenic variants in the SMN1 gene. Gene-specific sequencing revealed likely disease-associated variants in SMN1 in 42 cases (50.6%). Of the 42 variants, 27 are unique including 16 loss-of-function variants, 9 missense variants, 1 in-frame deletion variant, and 1 splice site variant. Conclusions. Herein, we describe an optimized assay for clinical sequencing of the full coding region of SMN1 and SMN2. This assay uses standard techniques and equipment readily available to most molecular diagnostic laboratories.

Objective. Increasingly rare thalassemia has been identified with the advanced use of long-read sequencing based on long-read technology. Here, we aim to present a novel δ/β-globin gene deletion identified by long-read sequencing technology. Methods. Enrolled in this study was a family from the Quanzhou region of Southeast China. Routine blood analysis and hemoglobin (Hb) capillary electrophoresis were used for hematological screening. Genetic testing for common α- and β-thalassemia was carried out using the reverse dot blot hybridization technique. Long-read sequencing was performed to detect rare globin gene variants. Specific gap-polymerase chain reaction (gap-PCR) and/or Sanger sequencing were further used to verify the detected variants. Results. None of the common α- and β-thalassemia mutations or deletions were observed in the family. However, decreased levels of MCV, MCH, and abnormal Hb bands were observed in the family members, who were suspected as rare thalassemia carriers. Further, long-read sequencing demonstrated a large novel 7.414 kb deletion NG_000007.3:g.63511_70924del partially cover HBB and HBD globin genes causing delta-beta fusion gene in the proband. Parental verification indicated that the deletion was inherited from the proband’s father, while none of the globin gene variants were observed in the proband’s mother. In addition, the novel δ/β-globin gene deletion was further verified by gap-PCR and Sanger sequencing. Conclusion. In this study, we first present a large novel δ/β-globin gene deletion in a Chinese family using long-read sequencing, which may cause δβ-thalassemia. This study further enhances that long-read sequencing would be applied as a sharp tool for detecting rare and novel globin gene variants.

A large number of variants identified through clinical genetic testing in disease susceptibility genes are of uncertain significance (VUS). Following the recommendations of the American College of Medical Genetics and Genomics (ACMG) and Association for Molecular Pathology (AMP), the frequency in case-control datasets (PS4 criterion) can inform their interpretation. We present a novel case-control likelihood ratio-based method that incorporates gene-specific age-related penetrance. We demonstrate the utility of this method in the analysis of simulated and real datasets. In the analysis of simulated data, the likelihood ratio method was more powerful compared to other methods. Likelihood ratios were calculated for a case-control dataset of BRCA1 and BRCA2 variants from the Breast Cancer Association Consortium (BCAC) and compared with logistic regression results. A larger number of variants reached evidence in favor of pathogenicity, and a substantial number of variants had evidence against pathogenicity—findings that would not have been reached using other case-control analysis methods. Our novel method provides greater power to classify rare variants compared with classical case-control methods. As an initiative from the ENIGMA Analytical Working Group, we provide user-friendly scripts and preformatted Excel calculators for implementation of the method for rare variants in BRCA1, BRCA2, and other high-risk genes with known penetrance.