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Whole exome and whole genome sequencing
Published in Moshe Hod, Vincenzo Berghella, Mary E. D'Alton, Gian Carlo Di Renzo, Eduard Gratacós, Vassilios Fanos, New Technologies and Perinatal Medicine, 2019
Variant annotation is the process of determining the potential effects of a variant on the function of one or more genes and an assessment of the likelihood that the phenotype is due to the affected gene or genes. NGS generates thousands of sequence variants, and these must be filtered and prioritized for clinical interpretation. The annotation process enriches for rare variants, which are more likely to be pathogenic, and eliminates common variants, which are more likely to be benign, and also predicts functional effect. Annotation tools include information about genetic variants, such as the presence of the variant in population databases, evolutionary conservation of the variant among different species, and the genomic structure where the variant is located. Large-scale genomic sequencing databases, such as the Genome Aggregation Database (gnomAD), can be used to distinguish common and rare variants in the population. Databases of previously assessed variants, such as ClinVar, have been established to collect and distribute information about previously interpreted variants (24).
The regulatory landscape
Published in Priya Hays, Advancing Healthcare Through Personalized Medicine, 2017
The FDA is planning a new approach: “new regulatory approaches will be needed to enable the Agency to provide appropriate oversight, in a way that is more suitable to the complexity and data-richness of this new technology,” according to the Jen Madsen of Arnold & Porter LLC. To ensure their analytical performance, sequencing technologies must rely on quality-based standards for NGS test performance that would be created in collaboration with experts in genomics and used to ensure test accuracy and reliability. To ensure the clinical validity of NGS, technologies would use high-quality curated genetic databases that provide information on genetic variants and their association with disease, for example, ClinVar and ClinGen. A precedent existed in the 23andMe test for Bloom syndrome where, in February 2015, the FDA used current regulatory authorities to down-classify the test to class II to approve carrier screening tests through the 510(K) process, signaling flexibility.
Carrier Screening for Single-Gene Disorders
Published in Carlos Simón, Carmen Rubio, Handbook of Genetic Diagnostic Technologies in Reproductive Medicine, 2022
Julio Martin, Arantxa Hervas, Ana Bover, Laura Santa, Ana Cervero
Regarding variant interpretation, most providers today use their own criteria to determine the clinical impact of findings from sequencing, adapted from or following agreed standards and guidelines for variant categorization [23]. One possible scenario is to first choose pathogenic or likely pathogenic variants annotated in databases (i.e., ClinVar [24] and/or HGMD [25]). A curated list of these already known pathogenic variants should allow for automation of variant classification of a significant number of findings. These mutations typically correspond to variants reported in patients with solid medical evidence. A second step or filter could be evaluating allele frequency, implemented to classify detected variants as common or rare. Typically, variants with an allele frequency > 1% in dbSNP (www.ncbi.nlm.nih.gov/SNP) in the 1000 Genomes project (www.1000genomes.org) or in an in-house database are defined as common variants and are usually categorized as likely polymorphisms. Exceptions are made for well-described annotated pathogenic variants with allele frequency > 1%. Variants with a frequency < 1% are considered to be rare variants. Further filtering steps take into consideration the type of mutation and its functional impact, zygosity, disease prevalence, detection in patients vs. controls, etc. Regarding mutation types, rare missense single-nucleotide variants (SNVs) and in-frame small insertion or deletions (indels) coding sequences with an allele frequency lower than the estimated prevalence of the corresponding disease with no homozygous status ever detected in controls, but not reported in patients or reported but without clear evidence of being a disease-causing change, could or are normally classified as variants of unknown significance (VUS). Finally, rare variants—typically below 1%—with severe functional impact (frameshift deletions, nonsense SNVs, and splice site variants) and with allele frequency below the corresponding disease prevalence with homozygous status ever detected in controls are classified as likely pathogenic. The previous examples for variant interpretation must be taken with caution since they were enumerated here or represent general examples. Each analysis of variant interpretation will require more in-depth understanding before a final classification can be awarded.
Detection of outlying proportions
Published in Journal of Applied Statistics, 2018
Finally, we consider the pseudo-real data illustrated in the introduction, with an excerpt in Table 1. We selected the genomic region corresponding to several genes and generated pseudo-real datasets as follows. We computationally introduced several mutations, randomly selected among those with clinical relevance as described in ClinVar database. ClinVar is a public resource that aggregates information about genomic variation and its relationship to human health, see Landrum et al. [11]. For each of the two datasets (the un-mutated ‘reference’ dataset and the ‘mutated’ dataset) we generated 100,000 random sequences simulating NGS sequences by using ART simulation software [4]. As stated by Huang et al. [4]: ‘ART generates simulated sequencing reads by emulating the sequencing process with built-in, technology-specific read error models and base quality value profiles parameterized empirically in large sequencing datasets.’ Moreover ART has been developed ‘for testing and benchmarking tools for next generation sequencing data analysis including read alignment, de novo assembly and genetic variation discovery.’
Evaluating gap junction variants for a role in pediatric cataract: an overview of the genetic landscape and clinical classification of variants in the GJA3 and GJA8 genes
Published in Expert Review of Ophthalmology, 2023
Johanna L Jones, Kathryn P Burdon
ClinVar is a public archive of human genetic variants with an accompanying interpretation of their association to a disease or phenotype [138]. Submissions can be made from both clinical and research facilities following registration with the site. ClinVar was officially launched in 2013, and there has been increased reporting to this resource from both the research and clinical sectors following the release and uptake of the ACMG-AMP standards and guidelines for the interpretation of sequence variants [139] by the American College of Medical Genetics and Association for Molecular Pathology (ACMG-AMP). The ACMG-AMP guidelines provide a series of criteria against which to assess a variant detected in a known disease-causing gene in a patient with the appropriate phenotype. Each criterion is assigned a strength level dependent on its capacity to distinguish benign from pathogenic evidence. The combination of criteria applied, along with the strength level, results in a classification of benign (B), likely benign (LB), variant of uncertain significance (VUS), likely pathogenic (LP), or pathogenic (P). A diagnostic benefit from genetic testing is achieved for patients when collective evidence enables a classification of LP or P. When variants are classified as VUS, they leave doubts over causality and can impede patients receiving a timely molecular diagnosis, if at all. There are clearly recognized benefits of a definitive molecular diagnosis including the ability to access accurate genetic counseling and recurrence risk prediction as well as determining if the cataract is isolated or part of a more complex syndrome [140]. Similarly, obtaining sufficient evidence to classify variants as LB or B means variants can be discharged as candidates and focus can be directed to alternate genetic causes.