Development and Validation of Clinical Whole-Exome and Whole-Genome Sequencing for Detection of Germline Variants in Inherited Disease

Context.— With the decrease in the cost of sequencing, the clinical testing paradigm has shifted from single gene to gene panel and now whole-exome and whole-genome sequencing. Clinical laboratories are rapidly implementing next-generation sequencing–based whole-exome and whole-genome sequencing. Because a large number of targets are covered by whole-exome and whole-genome sequencing, it is critical that a laboratory perform appropriate validation studies, develop a quality assurance and quality control program, and participate in proficiency testing. Objective.— To provide recommendations for whole-exome and whole-genome sequencing assay design, validation, and implementation for the detection of germline variants associated in inherited disorders. Data Sources.— An example of trio sequencing, filtration and annotation of variants, and phenotypic consideration to arrive at clinical diagnosis is discussed. Conclusions.— It is critical that clinical laboratories planning to implement whole-exome and whole-genome sequencing design and validate the assay to specifications and ensure adequate performance prior to implementation. Test design specifications, including variant filtering and annotation, phenotypic consideration, guidance on consenting options, and reporting of incidental findings, are provided. These are important steps a laboratory must take to validate and implement whole-exome and whole-genome sequencing in a clinical setting for germline variants in inherited disorders.

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Genetics of neuropsychiatric disease

Oxford Textbook of Neuropsychiatry ◽

10.1093/med/9780198757139.003.0012 ◽

2020 ◽

pp. 127-140

Author(s):

Stefania Bruno ◽

Nayana Lahiri

Keyword(s):

Whole Genome Sequencing ◽

Genome Sequencing ◽

Genetic Disorders ◽

Single Gene ◽

Diagnostic Techniques ◽

Genetic Influences ◽

Whole Genome ◽

Neuropsychiatric Disease ◽

Genomic Technologies ◽

Whole Exome

To better understand the intricacies of genetic influences on neuropsychiatric disease, it is important first to have a grounding in the models of human inheritance and current diagnostic techniques. This chapter covers the fundamentals of genetic disorders, giving insights into chromosomal, single-gene, and mitochondrial disorders. Moreover, it explores the changing applications of genomic technologies, such as whole exome and whole genome sequencing, through the lens of their implications for neuropsychiatry. Clinical examples are provided to give an idea of the genetic underpinnings of Alzheimer’s disease, Parkinson’s disease, and other familiar disorders.

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Whole-Exome Sequencing and Whole-Genome Sequencing in Critically Ill Neonates Suspected to Have Single-Gene Disorders

Cold Spring Harbor Perspectives in Medicine ◽

10.1101/cshperspect.a023168 ◽

2015 ◽

Vol 6 (2) ◽

pp. a023168 ◽

Cited By ~ 40

Author(s):

Laurie D. Smith ◽

Laurel K. Willig ◽

Stephen F. Kingsmore

Keyword(s):

Whole Genome Sequencing ◽

Exome Sequencing ◽

Critically Ill ◽

Whole Exome Sequencing ◽

Genome Sequencing ◽

Single Gene ◽

Whole Genome ◽

Single Gene Disorders ◽

Whole Exome ◽

Critically Ill Neonates

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Comparison of three variant callers for human whole genome sequencing

10.1101/461798 ◽

2018 ◽

Author(s):

Anna Supernat ◽

Oskar Valdimar Vidarsson ◽

Vidar M. Steen ◽

Tomasz Stokowy

Keyword(s):

Whole Genome Sequencing ◽

Genome Sequencing ◽

Single Gene ◽

Reference Sample ◽

Variant Calling ◽

Whole Genome Sequencing Data ◽

Whole Genome ◽

Sequencing Data ◽

Whole Exome ◽

Indel Calling

ABSTRACTTesting of patients with genetics-related disorders is in progress of shifting from single gene assays to gene panel sequencing, whole-exome sequencing (WES) and whole-genome sequencing (WGS). Since WGS is unquestionably becoming a new foundation for molecular analyses, we decided to compare three currently used tools for variant calling of human whole genome sequencing data. We tested DeepVariant, a new TensorFlow machine learning-based variant caller, and compared this tool to GATK 4.0 and SpeedSeq, using 30×, 15× and 10× WGS data of the well-known NA12878 DNA reference sample.According to our comparison, the performance on SNV calling was almost similar in 30× data, with all three variant callers reaching F-Scores (i.e. harmonic mean of recall and precision) equal to 0.98. In contrast, DeepVariant was more precise in indel calling than GATK and SpeedSeq, as demonstrated by F-Scores of 0.94, 0.90 and 0.84, respectively.We conclude that the DeepVariant tool has great potential and usefulness for analysis of WGS data in medical genetics.

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Micro-costing diagnostics in oncology: from single-gene testing to whole genome sequencing

Expert Review of Pharmacoeconomics & Outcomes Research ◽

10.1080/14737167.2021.1917385 ◽

2021 ◽

Author(s):

Clémence TB Pasmans ◽

Bastiaan BJ Tops ◽

Elisabeth MP Steeghs ◽

Veerle MH Coupé ◽

Katrien Grünberg ◽

...

Keyword(s):

Whole Genome Sequencing ◽

Genome Sequencing ◽

Single Gene ◽

Whole Genome ◽

Gene Testing

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Estimating sequencing error rates using families

BioData Mining ◽

10.1186/s13040-021-00259-6 ◽

2021 ◽

Vol 14 (1) ◽

Author(s):

Kelley Paskov ◽

Jae-Yoon Jung ◽

Brianna Chrisman ◽

Nate T. Stockham ◽

Peter Washington ◽

...

Keyword(s):

Whole Genome Sequencing ◽

Exome Sequencing ◽

Genome Sequencing ◽

Variant Calling ◽

Error Rates ◽

Sequencing Error ◽

Whole Genome ◽

Sequencing Data ◽

Sequencing Platform ◽

Whole Exome

Abstract Background As next-generation sequencing technologies make their way into the clinic, knowledge of their error rates is essential if they are to be used to guide patient care. However, sequencing platforms and variant-calling pipelines are continuously evolving, making it difficult to accurately quantify error rates for the particular combination of assay and software parameters used on each sample. Family data provide a unique opportunity for estimating sequencing error rates since it allows us to observe a fraction of sequencing errors as Mendelian errors in the family, which we can then use to produce genome-wide error estimates for each sample. Results We introduce a method that uses Mendelian errors in sequencing data to make highly granular per-sample estimates of precision and recall for any set of variant calls, regardless of sequencing platform or calling methodology. We validate the accuracy of our estimates using monozygotic twins, and we use a set of monozygotic quadruplets to show that our predictions closely match the consensus method. We demonstrate our method’s versatility by estimating sequencing error rates for whole genome sequencing, whole exome sequencing, and microarray datasets, and we highlight its sensitivity by quantifying performance increases between different versions of the GATK variant-calling pipeline. We then use our method to demonstrate that: 1) Sequencing error rates between samples in the same dataset can vary by over an order of magnitude. 2) Variant calling performance decreases substantially in low-complexity regions of the genome. 3) Variant calling performance in whole exome sequencing data decreases with distance from the nearest target region. 4) Variant calls from lymphoblastoid cell lines can be as accurate as those from whole blood. 5) Whole-genome sequencing can attain microarray-level precision and recall at disease-associated SNV sites. Conclusion Genotype datasets from families are powerful resources that can be used to make fine-grained estimates of sequencing error for any sequencing platform and variant-calling methodology.

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