Genomic diagnostics in polycystic kidney disease: an assessment of real-world use of whole-genome sequencing.

Autosomal Dominant Polycystic Kidney Disease (ADPKD) is common, with a prevalence of 1/1000 and predominantly caused by disease-causing variants in PKD1 or PKD2. Clinical diagnosis is usually by age-dependent imaging criteria, which is challenging in patients with atypical clinical features, without family history, or younger age. However, there is increasing need for definitive diagnosis of ADPKD with new treatments available. Sequencing is complicated by six pseudogenes that share 97% homology to PKD1 and by recently identified phenocopy genes. Whole-genome sequencing can definitively diagnose ADPKD, but requires validation for clinical use. We initially performed a validation study, in which 42 ADPKD patients underwent sequencing of PKD1 and PKD2 by both whole-genome and Sanger sequencing, using a blinded, cross-over method. Whole-genome sequencing identified all PKD1 and PKD2 germline pathogenic variants in the validation study (sensitivity and specificity 100%). Two mosaic variants outside pipeline thresholds were not detected. We then examined the first 144 samples referred to a clinically-accredited diagnostic laboratory for clinical whole-genome sequencing, with targeted-analysis to a polycystic kidney disease gene-panel. In this unselected, diagnostic cohort (71 males :73 females), the diagnostic rate was 70%, including a diagnostic rate of 81% in patients with typical ADPKD (98% with PKD1/PKD2 variants) and 60% in those with atypical features (56% PKD1/PKD2; 44% PKHD1/HNF1B/GANAB/ DNAJB11/PRKCSH/TSC2). Most patients with atypical disease did not have clinical features that predicted likelihood of a genetic diagnosis. These results suggest clinicians should consider diagnostic genomics as part of their assessment in polycystic kidney disease, particularly in atypical disease.

Utility of NIST Whole-Genome Reference Materials for the Technical Validation of a Multigene Next-Generation Sequencing Test.

The sensitivity and specificity of next-generation sequencing laboratory developed tests (LDTs) are typically determined by an analyte-specific approach. Analyte-specific validations use disease-specific controls to assess an LDT's ability to detect known pathogenic variants. Alternatively, a methods-based approach can be used for LDT technical validations. Methods-focused validations do not use disease-specific controls but use benchmark reference DNA that contains known variants (benign, variants of unknown significance, and pathogenic) to assess variant calling accuracy of a next-generation sequencing workflow. Recently, four whole-genome reference materials (RMs) from the National Institute of Standards and Technology (NIST) were released to standardize methods-based validations of next-generation sequencing panels across laboratories. We provide a practical method for using NIST RMs to validate multigene panels. We analyzed the utility of RMs in validating a novel newborn screening test that targets 70 genes, called NEO1. Despite the NIST RM variant truth set originating from multiple sequencing platforms, replicates, and library types, we discovered a 5.2% false-negative variant detection rate in the RM truth set genes that were assessed in our validation. We developed a strategy using complementary non-RM controls to demonstrate 99.6% sensitivity of the NEO1 test in detecting variants. Our findings have implications for laboratories or proficiency testing organizations using whole-genome NIST RMs for testing.

Identifying Glucokinase Monogenic Diabetes in a Multiethnic Gestational Diabetes Mellitus Cohort: New Pregnancy Screening Criteria and Utility of HbA1c.

OBJECTIVE: Glucokinase monogenic diabetes (GCK-maturity-onset diabetes of the young [MODY]) should be differentiated from gestational diabetes mellitus (GDM) because management differs. New pregnancy-specific screening criteria (NSC) have been proposed to identify women who warrant GCK genetic testing. We tested NSC and HbA1c in a multiethnic GDM cohort and examined projected referrals for GCK testing. RESEARCH DESIGN AND METHODS: Using a GDM database, 63 of 776 women had a postpartum oral glucose tolerance test suggestive of GCK-MODY. Of these 63 women, 31 agreed to undergo GCK testing. NSC accuracy and HbA1c were examined. Projected referrals were calculated by applying the NSC to a larger GDM database (n = 4,415). RESULTS: Four of 31 women were confirmed as having GCK-MODY (prevalence ∼0.5-1/100 with GDM). The NSC identified all Anglo-Celtic women but did not identify one Indian woman. The NSC will refer 6.1% of GDM cases for GCK testing, with more Asian/Indian women referred despite lower disease prevalence. Antepartum HbA1c was not higher in those with GCK-MODY. CONCLUSIONS: The NSC performed well in Anglo-Celtic women. Ethnic-specific criteria should be explored.

Managing incidental findings in exome sequencing for research.

Exome sequencing for research has become available for broadly based genomic studies as well as smaller targeted investigations. New exome research projects being considered will intentionally process a large amount of common and rare DNA variation for the purpose of finding specific links between genotype and phenotype. However, the risks of uncovering a clinically relevant incidental finding are not uniform across projects but are highly dependent on the question being asked and exactly how it is intended to be answered.Factors that influence the possibility of revealing a clinically relevant incidental DNA variation include the following: The overall design of the study and the number of participants involved, the mode of inheritance of the phenotype including whether the phenotype is likely to have a monogenic or a complex inheritance, whether the study is assessing a known list of genes or not, and whether the causative DNA variation is likely to be rare or common. Importantly, differing bioinformatics DNA variant filtering strategies strongly influence the odds of discovering an incidental finding. This chapter provides a framework for understanding and assessing the likelihood of discovering clinically relevant, incidental DNA variations that are not directly related to the question being addressed in a particular exome research project. It also outlines DNA variant filtering and functional informatics approaches that can investigate specific genomic questions while minimizing the risks of uncovering an incidental finding.

Diagnostic validation of a familial hypercholesterolaemia cohort provides a model for using targeted next generation DNA sequencing in the clinical setting.

Our aim was to assess the sensitivity and specificity of a next generation DNA sequencing (NGS) platform using a capture based DNA library preparation method. Data and experience gained from this diagnostic validation can be used to progress the applications of NGS in the wider molecular diagnostic setting. A technical cross-validation comparing the current molecular diagnostic gold standard methods of Sanger DNA sequencing and multiplex ligation-dependant probe amplification (MLPA) versus a customised capture based targeted re-sequencing method on a SOLiD 5500 sequencing platform was carried out using a cohort of 96 familial hypercholesterolaemia (FH) samples. We compared a total of 595 DNA variations (488 common single nucleotide polymorphisms, 73 missense mutations, 9 nonsense mutations, 3 splice site point mutations, 13 small indels, 2 multi-exonic duplications and 7 multi-exonic deletions) found previously in the 96 FH samples. DNA variation detection sensitivity and specificity were both 100% for the SOLiD 5500 NGS platform compared with Sanger sequencing and MLPA only when both LifeScope and Integrative Genomics Viewer softwares were utilised. The methods described here offer a high-quality strategy for the detection of a wide range of DNA mutations in diseases with a moderate number of well described causative genes. However, there are important issues related to the bioinformatic algorithms employed to detect small indels.

In silico analysis of the exome for gene discovery.

Here we describe a bioinformatic strategy for extracting and analyzing the list of variants revealed from an exome sequencing project to identify potential disease genes. This in silico method filters out the majority of common SNPs and extracts a list of potential candidate protein-coding and non-coding RNA (ncRNA) genes. The workflow employs Galaxy, a publically available Web-based software, to filter and sort sequence variants identified by capture-based target enrichment and sequencing from exomes including selected ncRNAs.

Computer-assisted reading of DNA sequences.

DNA sequencing is increasingly used in a range of medical activities involving DNA diagnostics and research. This is the result of improving technology and cheaper costs. Paradoxically, a greater demand for DNA sequencing has placed additional work on the laboratory because sequencing profiles must be checked visually despite the availability of informatics-based tools in interpreting DNA sequence traces. In this environment it is essential to have more sophisticated software that will allow the sites of known and unknown DNA variants to be quickly identified, as well as providing an objective assessment of quality for the DNA sequence generated. This chapter describes the Applied Biosystems SeqScape software program (version 2.5) and how it has assisted in the interpretation of DNA sequencing in a DNA diagnostic laboratory.

Complex phenotypes in the haemoglobinopathies: recommendations on screening and DNA testing.

This document considers a number of scenarios involving complex haemoglobinopathies and provides 28 recommendations at both the clinical and laboratory levels on how these should be managed.