A Year in Review: 2023 Park, Jason Y; Young, Ian S; Baudhuin, Linnea M
Clinical chemistry (Baltimore, Md.),
12/2023, Letnik:
69, Številka:
12
Journal Article
Furthermore, consumers likely apply subjective interpretations of genetic risk data and may not fully understand the clinical validity or utility or limitations of their test results. ...medical ...providers (the majority of them hardly any more prepared than the general consumer to understand these reports) then need to step in to help the consumers understand their genetic data and provide relevant follow-up such as confirmatory testing or other measures. Furthermore, 23andMe has failed to respond to the FDA's requests for proof of analytical and clinical validation. ...the FDA was leftwith no choice but to require that if 23andMe wants to sell a health-related medical device to consumers, it needs to demonstrate that its product is safe and effective or cease offering the test.
Evolutions in Clinical Chemistry Park, Jason Y; Baudhuin, Linnea M; Young, Ian S
Clinical chemistry (Baltimore, Md.),
01/2023, Letnik:
69, Številka:
1
Journal Article
Current clinical laboratory practice guidelines for next-generation sequencing (NGS) do not provide definitive guidance on confirming NGS variants. Sanger confirmation of NGS results can be ...inefficient, redundant, and expensive. We evaluated the accuracy of NGS-detected single-nucleotide variants (SNVs) and insertion/deletion variants (indels) and the necessity of NGS variant confirmation using four NGS target-capture gene panels covering 117 genes, 568 Kbp, and 77 patient DNA samples. Unique NGS-detected variants (1080 SNVs and 124 indels) underwent Sanger confirmation and/or were compared to data from the 1000 Genomes Project (1000G). Recurrent variants in unrelated samples resulted in 919 comparisons between NGS and Sanger, with 100% concordance. In a second comparison, 762 unique NGS results (736 SNVs, 26 indels) from seven 1000G samples were found to have 97.1% concordance with 1000G phase 1 data. Sanger sequencing and 1000G phase 3 data confirmed the accuracy of the NGS results for all 1000G phase 1 discrepancies. In all samples, the depth of coverage exceeded 100× in >99.7% of bases in the target regions. In conclusion, confirmatory analysis by Sanger sequencing of SNVs detected via capture-based NGS testing that meets appropriate quality thresholds is unnecessarily redundant. In contrast, Sanger sequencing for indels may be required for defining the correct genomic location, and Sanger may be used for quality-assurance purposes.
Marfan syndrome is a systemic disorder that typically involves FBN1 mutations and cardiovascular manifestations. We investigated FBN1 genotype-phenotype correlations with aortic events (aortic ...dissection and prophylactic aortic surgery) in patients with Marfan syndrome.
Genotype and phenotype information from probands (n = 179) with an FBN1 pathogenic or likely pathogenic variant were assessed.
A higher frequency of truncating or splicing FBN1 variants was observed in Ghent criteria-positive patients with an aortic event (n = 34) as compared with all other probands (n = 145) without a reported aortic event (79 vs. 39%; P < 0.0001), as well as Ghent criteria-positive probands (n = 54) without an aortic event (79 vs. 48%; P = 0.0039). Most probands with an early aortic event had a truncating or splicing variant (100% (n = 12) and 95% (n = 21) of patients younger than 30 and 40 years old, respectively). Aortic events occurred at a younger median age in patients with truncating/splicing variants (29 years) as compared with those with missense variants (51 years). A trend toward a higher frequency of truncating/splicing variants in patients with aortic dissection (n = 21) versus prophylactic surgery (n = 13) (85.7 vs. 69.3%; not significant) was observed.
These aortic event- and age-associated findings may have important implications for the management of Marfan syndrome patients with FBN1 truncating and splicing variants.Genet Med 17 3, 177-187.
For the purposes of this study, the authors received FASTQraw sequencing data from one laboratory and BAM files from the other 2 laboratories. ...they had to convert the FASTQ file from the one ...laboratory to BAM using their own bioinformatics pipeline, and these steps may have been performed differently compared with the pipeline used at the laboratory that provided the FASTQ data. ...the utilization of the authors' own laboratory bioinformatics pipeline may have contributed to some of the differences observed in this study. ...false-positive de novo variants may be reported from the exome when the technical quality of coverage of the region is poor in a parental sample, resulting in inaccurate assessment of the genomic location. Because de novo variants have many downstream impacts to the patient and family in terms of genetic counseling, follow-up testing, and family planning, the importance of performing targeted confirmatory testing of potential de novo variants in parental DNA before reporting out such variants from exome analyses cannot be overstated. Misclassified variants can have serious clinical consequences that can affect individual patients and their family members. ...ordering providers should also be aware that they may receive variable results based on how different laboratories interpret the same variant.
Significant barriers, such as lack of professional guidelines, specialized training for interpretation of pharmacogenomics (PGx) data, and insufficient evidence to support clinical utility, prevent ...preemptive PGx testing from being widely clinically implemented. The current study, as a pilot project for the Right Drug, Right Dose, Right Time-Using Genomic Data to Individualize Treatment Protocol, was designed to evaluate the impact of preemptive PGx and to optimize the workflow in the clinic setting. We used an 84-gene next-generation sequencing panel that included SLCO1B1, CYP2C19, CYP2C9, and VKORC1 together with a custom-designed CYP2D6 testing cascade to genotype the 1013 subjects in laboratories approved by the Clinical Laboratory Improvement Act. Actionable PGx variants were placed in patient's electronic medical records where integrated clinical decision support rules alert providers when a relevant medication is ordered. The fraction of this cohort carrying actionable PGx variant(s) in individual genes ranged from 30% (SLCO1B1) to 79% (CYP2D6). When considering all five genes together, 99% of the subjects carried an actionable PGx variant(s) in at least one gene. Our study provides evidence in favor of preemptive PGx testing by identifying the risk of a variant being present in the population we studied.
Gene-specific knowledge can enhance genetic variant classification, but may not be routinely incorporated into clinical laboratory practice. For example, FBN1 variants associated with Marfan syndrome ...may be variably classified depending on knowledge of FBN1-specific critical regions. In order to assess variability in classification of FBN1 variants, 674 FBN1 missense variants from 18 ClinVar submitters were compared and reanalyzed using FBN1-specific criteria and ACMG/AMP 2015 guidelines for variant interpretation. Conflicting variant classifications occurred in 30.7% of the missense variants that had multiple submitters. There were 451 classifications of 361 critical residue missense variants, with 80.0% (361/451) classified as likely pathogenic or pathogenic (L)P. Non-cysteine critical residue variants were less likely to be classified as (L)P 55.3% (78/141) than cysteine variants 91.3% (283/310) and were more likely to lack evidence citing the functional significance of the amino acid impacted. Application of FBN1-specific knowledge allowed for reclassification or discrepancy resolution in 65/361 (18.0%) critical residue variants. There were 522 classifications of 313 unique missense variants not known to impact a critical residue. Of these, 31.6% (165/522) were likely overclassified as either (L)P or uncertain significance (VUS), especially when minor allele frequency (MAF) was taken into account, and we reclassified or resolved classification discrepancies in 128/313 (40.9%) of these variants. Our results provide a refined framework and resource for FBN1 variant classification, and further supports the more global implications of combining gene-based knowledge with ACMG/AMP criteria and appropriate MAF cutoffs for variant classification that extend beyond FBN1.
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