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Next-generation sequencing (NGS) technologies are now established in clinical laboratories as a primary testing modality in genomic medicine. These technologies have reduced the cost of large-scale sequencing by several orders of magnitude. It is now cost-effective to analyze an individual with disease-targeted gene panels, exome sequencing, or genome sequencing to assist in the diagnosis of a wide array of clinical scenarios. While clinical validation and use of NGS in many settings is established, there are continuing challenges as technologies and the associated informatics evolve. To assist clinical laboratories with the validation of NGS methods and platforms, the ongoing monitoring of NGS testing to ensure quality results, and the interpretation and reporting of variants found using these technologies, the American College of Medical Genetics and Genomics (ACMG) has developed the following technical standards.

Next-generation sequencing-based assays are increasingly used in clinical molecular laboratories to detect somatic variants in solid tumors and hematologic malignancies and to detect constitutional variants. Proficiency testing data are potential sources of information about challenges in performing these assays. To examine the most common sources of unacceptable results from the College of American Pathologists Next-Generation Sequencing Bioinformatics, Hematological Malignancies, Solid Tumor, and Germline surveys and provide recommendations on how to avoid these pitfalls and improve performance. The College of American Pathologists next-generation sequencing somatic and germline proficiency testing survey results from 2016 to 2019 were analyzed to identify the most common causes of unacceptable results. On somatic and germline proficiency testing surveys, 95.9% (18 815/19 623) and 97.8% (33 890/34 641) of all variants were correctly identified, respectively. The most common causes of unacceptable results related to sequencing were false-negative errors in genomic regions that were difficult to sequence because of high GC content. False-positive errors occurred in the context of homopolymers and pseudogenes. Recurrent errors in variant annotation were seen for dinucleotide and duplication variants and included unacceptable transcript selection and outdated variant nomenclature. A small percentage of preanalytic or postanalytic errors were attributed to specimen swaps and transcription errors. Laboratories demonstrate overall excellent performance for detecting variants in both somatic and germline proficiency testing surveys. Proficiency testing survey results highlight infrequent, but recurrent, analytic and nonanalytic challenges in performing next- generation sequencing-based assays and point to remedies to help laboratories improve performance.

We have previously examined characteristics of maternal chromosomes 21 that exhibited a single recombination on 21q and proposed that certain recombination configurations are risk factors for either meiosis I (MI) or meiosis II (MII) nondisjunction. The primary goal of this analysis was to examine characteristics of maternal chromosomes 21 that exhibited multiple recombinant events on 21q to determine whether additional risk factors or mechanisms are suggested. In order to identify the origin (maternal or paternal) and stage (MI or MII) of the meiotic errors, as well as placement of recombination, we genotyped over 1,500 SNPs on 21q. Our analyses included 785 maternal MI errors, 87 of which exhibited two recombinations on 21q, and 283 maternal MII errors, 81 of which exhibited two recombinations on 21q. Among MI cases, the average location of the distal recombination was proximal to that of normally segregating chromosomes 21 (35.28 vs. 38.86 Mb), a different pattern than that seen for single events and one that suggests an association with genomic features. For MII errors, the most proximal recombination was closer to the centromere than that on normally segregating chromosomes 21 and this proximity was associated with increasing maternal age. This pattern is same as that seen among MII errors that exhibit only one recombination. These findings are important as they help us better understand mechanisms that may underlie both age-related and nonage-related meiotic chromosome mal-segregation.

Advanced maternal age and altered recombination are known risk factors for Down syndrome cases due to maternal nondisjunction of chromosome 21, whereas the impact of other environmental and genetic factors is unclear. The aim of this study was to investigate an association between low maternal socioeconomic status and chromosome 21 nondisjunction. Data from 714 case and 977 control families were used to assess chromosome 21 meiosis I and meiosis II nondisjunction errors in the presence of three low socioeconomic status factors: (i) both parents had not completed high school, (ii) both maternal grandparents had not completed high school, and (iii) an annual household income of <$25,000. We applied logistic regression models and adjusted for covariates, including maternal age and race/ethnicity. As compared with mothers of controls (n = 977), mothers with meiosis II chromosome 21 nondisjunction (n = 182) were more likely to have a history of one low socioeconomic status factor (odds ratio = 1.81; 95% confidence interval = 1.07–3.05) and ≥2 low socioeconomic status factors (odds ratio = 2.17; 95% confidence interval = 1.02–4.63). This association was driven primarily by having a low household income (odds ratio = 1.79; 95% confidence interval = 1.14–2.73). The same statistically significant association was not detected among maternal meiosis I errors (odds ratio = 1.31; 95% confidence interval = 0.81–2.10), in spite of having a larger sample size (n = 532). We detected a significant association between low maternal socioeconomic status and meiosis II chromosome 21 nondisjunction. Further studies are warranted to explore which aspects of low maternal socioeconomic status, such as environmental exposures or poor nutrition, may account for these results. Genet Med15 9, 698–705.

Purpose: The goal of this study was to identify the contribution of large copy-number variants to Down syndrome–associated atrioventricular septal defects, the risk for which in the trisomic population is 2,000-fold more as compared with that of the general disomic population. Methods: Genome-wide copy-number variant analysis was performed on 452 individuals with Down syndrome (210 cases with complete atrioventricular septal defects; 242 controls with structurally normal hearts) using Affymetrix SNP 6.0 arrays, making this the largest heart study conducted to date on a trisomic background. Results: Large, common copy-number variants with substantial effect sizes (OR > 2.0) do not account for the increased risk observed in Down syndrome–associated atrioventricular septal defects. By contrast, cases had a greater burden of large, rare deletions ( P < 0.01) and intersected more genes ( P < 0.007) as compared with controls. We also observed a suggestive enrichment of deletions intersecting ciliome genes in cases as compared with controls. Conclusion: Our data provide strong evidence that large, rare deletions increase the risk of Down syndrome–associated atrioventricular septal defects, whereas large, common copy-number variants do not appear to increase the risk of Down syndrome–associated atrioventricular septal defects. The genetic architecture of atrioventricular septal defects is complex and multifactorial in nature. Genet Med 17 7, 554–560.

Gene sequencing panels are a powerful diagnostic tool for many clinical presentations associated with genetic disorders. Advances in DNA sequencing technology have made gene panels more economical, flexible, and efficient. Because the genes included on gene panels vary widely between laboratories in gene content (e.g., number, reason for inclusion, evidence level for gene–disease association) and technical completeness (e.g., depth of coverage), standards that address technical and clinical aspects of gene panels are needed. This document serves as a technical standard for laboratories designing, offering, and reporting gene panel testing. Although these principles can apply to multiple indications for genetic testing, the primary focus is on diagnostic gene panels (as opposed to carrier screening or predictive testing) with emphasis on technical considerations for the specific genes being tested. This technical standard specifically addresses the impact of gene panel content on clinical sensitivity, specificity, and validity—in the context of gene evidence for contribution to and strength of evidence for gene–disease association—as well as technical considerations such as sequencing limitations, presence of pseudogenes/gene families, mosaicism, transcript choice, detection of copy-number variants, reporting, and disclosure of assay limitations.

The ability of a single technology, next-generation sequencing, to provide both sequence and copy number variant (CNV) results has driven the merger of clinical cytogenetics and molecular genetics. Consequently, the distinction between the definition of a sequence variant and a CNV is blurry. As the 2015 American College of Medical Genetics and Genomics/Association for Molecular Pathology (ACMG/AMP) standards and guidelines for interpretation of sequence variants address CNV classification only sparingly, this study focused on adapting ACMG/AMP criteria for single-gene CNV interpretation. CNV-specific modifications of the 2015 ACMG/AMP criteria were developed and their utility was independently tested by three diagnostic laboratories. Each laboratory team interpreted the same 12 single-gene CNVs using three systems: (1) without ACMG/AMP guidance, (2) with ACMG/AMP criteria,and (3) with new modifications. A replication study of 12 different CNVs validated the modified criteria. The adapted criteria system presented here showed improved concordance and usability for single-gene CNVs compared with using the ACMG/AMP interpretation guidelines focused on sequence variants. These single-gene CNV criteria modifications could be used as a supplement to the ACMG/AMP guidelines for sequence variants, allowing for a streamlined workflow and a step toward a uniform classification system for both sequence and copy number alterations.

Clinical genetics laboratories have recently adopted guidelines for the interpretation of sequence variants set by the American College of Medical Genetics (ACMG) and Association for Molecular Pathology (AMP). The use of in silico algorithms to predict whether amino acid substitutions result in human disease is inconsistent across clinical laboratories. The clinical genetics community must carefully consider how in silico predictions can be incorporated into variant interpretation in clinical practice. Please see related Research article: https://doi.org/10.1186/s13059-017-1353-5

An amendment to this paper has been published and can be accessed via a link at the top of the paper.An amendment to this paper has been published and can be accessed via a link at the top of the paper.