Scalable Newborn Screening Solutions: Bioinformatics and Next-Generation Sequencing.

Expansion of the newborn disorder panel requires the incorporation of new testing modalities. This is especially true for disorders lacking robust biomarkers for detection in primary screening methods and for disorders requiring genotyping or sequencing as a second-tier and/or diagnostic test. In this commentary, we discuss how next-generation sequencing (NGS) methods can be used as a secondary testing method in NBS. Additionally, we elaborate on the importance of genomic variant repositories for the annotation and interpretation of variants. Barriers to the incorporation of NGS and bioinformatics within NBS are discussed, and ideas for a regional bioinformatics model and shared variant repository are presented as potential solutions.

Methods and feasibility study for exome sequencing as a universal second-tier test in newborn screening.

PURPOSE: Newborn screening disorders increasingly require genetic variant analysis as part of second-tier or confirmatory testing. Sanger sequencing and gene-specific next-generation sequencing (NGS)-based tests, the current methods of choice, are costly and lack scalability when expanding to new conditions. We describe a scalable, exome sequencing-based NGS pipeline with a priori analysis restriction that can be universally applied to any NBS disorder. METHODS: De-identified abnormal newborn screening specimens representing severe combined immune deficiency (SCID), cystic fibrosis (CF), VLCAD deficiency, metachromatic leukodystrophy (MLD), and in silico sequence read data sets were used to validate the pipeline. To support interpretation and clinical decision-making within the bioinformatics pipeline, variants from multiple databases were curated and validated. RESULTS: CFTR variant panel analysis correctly identified all variants. Concordance compared with diagnostic testing results for targeted gene analysis was between 78.6% and 100%. Validation of the bioinformatics pipeline with in silico data sets revealed a 100% detection rate. Varying degrees of overlap were observed between ClinVar and other databases ranging from 3% to 65%. Data normalization revealed that 11% of variants across the databases required manual curation. CONCLUSION: This pipeline allows for restriction of analysis to variants within a single gene or multiple genes, and can be readily expanded to full exome analysis if clinically indicated and parental consent is granted.

Toward newborn screening of metachromatic leukodystrophy: results from analysis of over 27,000 newborn dried blood spots.

PURPOSE: Metachromatic leukodystrophy (MLD) is a lysosomal storage disorder caused by the deficiency of arylsulfatase A (ARSA), which results in the accumulation of sulfatides. Newborn screening for MLD may be considered in the future as innovative treatments are advancing. We carried out a research study to assess the feasibility of screening MLD using dried blood spots (DBS) from de-identified newborns. METHODS: To minimize the false-positive rate, a two-tier screening algorithm was designed. The primary test was to quantify C16:0-sulfatide in DBS by ultraperformance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). The screening cutoff was established based on the results from 15 MLD newborns to achieve 100% sensitivity. The secondary test was to measure the ARSA activity in DBS from newborns with abnormal C16:0-sulfatide levels. Only newborns that displayed both abnormal C16:0-sulfatide abundance and ARSA activity were considered screen positives. RESULTS: A total of 27,335 newborns were screened using this two-tier algorithm, and 2 high-risk cases were identified. ARSA gene sequencing identified these two high-risk subjects to be a MLD-affected patient and a heterozygote. CONCLUSION: Our study demonstrates that newborn screening for MLD is highly feasible in a real-world scenario with near 100% assay specificity.

ClinVar Miner: Demonstrating utility of a Web-based tool for viewing and filtering ClinVar data.

ClinVar Miner is a Web-based suite that utilizes the data held in the National Center for Biotechnology Information's ClinVar archive. The goal is to render the data more accessible to processes pertaining to conflict resolution of variant interpretation as well as tracking details of data submission and data management for detailed variant curation. Here, we establish the use of these tools to address three separate use cases and to perform analyses across submissions. We demonstrate that the ClinVar Miner tools are an effective means to browse and consolidate data for variant submitters, curation groups, and general oversight. These tools are also relevant to the variant interpretation community in general.

Improving the Sequence Ontology terminology for genomic variant annotation.

BACKGROUND: The Genome Variant Format (GVF) uses the Sequence Ontology (SO) to enable detailed annotation of sequence variation. The annotation includes SO terms for the type of sequence alteration, the genomic features that are changed and the effect of the alteration. The SO maintains and updates the specification and provides the underlying ontologicial structure. METHODS: A requirements analysis was undertaken to gather terms missing in the SO release at the time, but needed to adequately describe the effects of sequence alteration on a set of variant genomic annotations. We have extended and remodeled the SO to include and define all terms that describe the effect of variation upon reference genomic features in the Ensembl variation databases. RESULTS: The new terminology was used to annotate the human reference genome with a set of variants from both COSMIC and dbSNP. A GVF file containing 170,853 sequence alterations was generated using the SO terminology to annotate the kinds of alteration, the effect of the alteration and the reference feature changed. There are four kinds of alteration and 24 kinds of effect seen in this dataset. (Ensembl Variation annotates 34 different SO consequence terms: http://www.ensembl.org/info/docs/variation/predicted_data.html). CONCLUSIONS: We explain the updates to the Sequence Ontology to describe the effect of variation on existing reference features. We have provided a set of annotations using this terminology, and the well defined GVF specification. We have also provided a provisional exploration of this large annotation dataset.