Curation and expansion of the Human Phenotype Ontology for systemic autoinflammatory diseases improves phenotype-driven disease-matching

Introduction

Accurate and standardized phenotypic descriptions are essential in diagnosing rare diseases and discovering new diseases, and the Human Phenotype Ontology (HPO) system was developed to provide a rich collection of hierarchical phenotypic descriptions. However, although the HPO terms for inborn errors of immunity have been improved and curated, it has not been investigated whether this curation improves the diagnosis of systemic autoinflammatory disease (SAID) patients. Here, we aimed to study if improved HPO annotation for SAIDs enhanced SAID identification and to demonstrate the potential of phenotype-driven genome diagnostics using curated HPO terms for SAIDs.

Methods

We collected HPO terms from 98 genetically confirmed SAID patients across eight different European SAID expertise centers and used the LIRICAL (Likelihood Ratio Interpretation of Clinical Abnormalities) computational algorithm to estimate the effect of HPO curation on the prioritization of the correct SAID for each patient.

Results

Our results show that the percentage of correct diagnoses increased from 66% to 86% and that the number of diagnoses with the highest ranking increased from 38 to 45. In a further pilot study, curation also improved HPO-based whole-exome sequencing (WES) analysis, diagnosing 10/12 patients before and 12/12 after curation. In addition, the average number of candidate diseases that needed to be interpreted decreased from 35 to 2.

Discussion

This study demonstrates that curation of HPO terms can increase identification of the correct diagnosis, emphasizing the high potential of HPO-based genome diagnostics for SAIDs.

A pipeline-friendly software tool for genome diagnostics to prioritize genes by matching patient symptoms to literature

Despite an explosive growth of next-generation sequencing data, genome diagnostics only provides a molecular diagnosis to a minority of patients. Software tools that prioritize genes based on patient symptoms using known gene-disease associations may complement variant filtering and interpretation to increase chances of success. However, many of these tools cannot be used in practice because they are embedded within variant prioritization algorithms, or exist as remote services that cannot be relied upon or are unacceptable because of legal/ethical barriers. In addition, many tools are not designed for command-line usage, closed-source, abandoned, or unavailable. We present Variant Interpretation using Biomedical literature Evidence (VIBE), a tool to prioritize disease genes based on Human Phenotype Ontology codes. VIBE is a locally installed executable that ensures operational availability and is built upon DisGeNET-RDF, a comprehensive knowledge platform containing gene-disease associations mostly from literature and variant-disease associations mostly from curated source databases. VIBE's command-line interface and output are designed for easy incorporation into bioinformatic pipelines that annotate and prioritize variants for further clinical interpretation. We evaluate VIBE in a benchmark based on 305 patient cases alongside seven other tools. Our results demonstrate that VIBE offers consistent performance with few cases missed, but we also find high complementarity among all tested tools. VIBE is a powerful, free, open source and locally installable solution for prioritizing genes based on patient symptoms. Project source code, documentation, benchmark and executables are available at https://github.com/molgenis/vibe.

A post-hoc comparison of the utility of sanger sequencing and exome sequencing for the diagnosis of heterogeneous diseases

The advent of massive parallel sequencing is rapidly changing the strategies employed for the genetic diagnosis and research of rare diseases that involve a large number of genes. So far it is not clear whether these approaches perform significantly better than conventional single gene testing as requested by clinicians. The current yield of this traditional diagnostic approach depends on a complex of factors that include gene-specific phenotype traits, and the relative frequency of the involvement of specific genes. To gauge the impact of the paradigm shift that is occurring in molecular diagnostics, we assessed traditional Sanger-based sequencing (in 2011) and exome sequencing followed by targeted bioinformatics analysis (in 2012) for five different conditions that are highly heterogeneous, and for which our center provides molecular diagnosis. We find that exome sequencing has a much higher diagnostic yield than Sanger sequencing for deafness, blindness, mitochondrial disease, and movement disorders. For microsatellite-stable colorectal cancer, this was low under both strategies. Even if all genes that could have been ordered by physicians had been tested, the larger number of genes captured by the exome would still have led to a clearly superior diagnostic yield at a fraction of the cost.

Challenges for implementing next-generation sequencing-based genome diagnostics: it's also the people, not just the machines

The scope of next-generation DNA sequencing (NGS) is transitioning from research to diagnostics (and beyond), but the conditions for routine clinical application have not been clearly defined. Technological limitations for sequencing a patient's DNA fast and affordably are rapidly disappearing. At the same time, more and more is known about the role of DNA variation in disease susceptibility, disease development and response to treatment. Consequently, more and more pediatricians, cardiologists and other medical specialists would like to apply NGS-based diagnostics. The standard, comprehensive and easy-to-handle genetic test these specialists are looking for, however, is not yet available. Molecular diagnostic laboratories have started to implement NGS into their routine workflows, but are also becoming increasingly aware that the context in which they operate is changing. It becomes apparent that the major challenges are not in the technology, but rather in anticipating the changing scope and scale. Developing the infrastructure to sustainably perform NGS-based diagnostics in a changing technological, clinical and societal context is therefore more relevant than defining minimal performance criteria or standard analysis pipelines. Implementing NGS-based diagnostics comes with novel applications, emerging service models and reconfiguration of professional roles, and should thus be considered in the context of future healthcare. Here, we present the key elements for transition of NGS from research to diagnostics.