GenIO: a phenotype-genotype analysis web server for clinical genomics of rare diseases

Decoding complex inherited phenotypes in rare disorders: the DECIPHERD initiative for rare undiagnosed diseases in Chile

Rare diseases affect millions of people worldwide, and most have a genetic etiology. The incorporation of next-generation sequencing into clinical settings, particularly exome and genome sequencing, has resulted in an unprecedented improvement in diagnosis and discovery in the past decade. Nevertheless, these tools are unavailable in many countries, increasing health care gaps between high- and low-and-middle-income countries and prolonging the "diagnostic odyssey" for patients. To advance genomic diagnoses in a setting of limited genomic resources, we developed DECIPHERD, an undiagnosed diseases program in Chile. DECIPHERD was implemented in two phases: training and local development. The training phase relied on international collaboration with Baylor College of Medicine, and the local development was structured as a hybrid model, where clinical and bioinformatics analysis were performed in-house and sequencing outsourced abroad, due to lack of high-throughput equipment in Chile. We describe the implementation process and findings of the first 103 patients. They had heterogeneous phenotypes, including congenital anomalies, intellectual disabilities and/or immune system dysfunction. Patients underwent clinical exome or research exome sequencing, as solo cases or with parents using a trio design. We identified pathogenic, likely pathogenic or variants of unknown significance in genes related to the patients´ phenotypes in 47 (45.6%) of them. Half were de novo informative variants, and half of the identified variants have not been previously reported in public databases. DECIPHERD ended the diagnostic odyssey for many participants. This hybrid strategy may be useful for settings of similarly limited genomic resources and lead to discoveries in understudied populations.

EasySMPC: a simple but powerful no-code tool for practical secure multiparty computation

BACKGROUND

Modern biomedical research is data-driven and relies heavily on the re-use and sharing of data. Biomedical data, however, is subject to strict data protection requirements. Due to the complexity of the data required and the scale of data use, obtaining informed consent is often infeasible. Other methods, such as anonymization or federation, in turn have their own limitations. Secure multi-party computation (SMPC) is a cryptographic technology for distributed calculations, which brings formally provable security and privacy guarantees and can be used to implement a wide-range of analytical approaches. As a relatively new technology, SMPC is still rarely used in real-world biomedical data sharing activities due to several barriers, including its technical complexity and lack of usability.

RESULTS

To overcome these barriers, we have developed the tool EasySMPC, which is implemented in Java as a cross-platform, stand-alone desktop application provided as open-source software. The tool makes use of the SMPC method Arithmetic Secret Sharing, which allows to securely sum up pre-defined sets of variables among different parties in two rounds of communication (input sharing and output reconstruction) and integrates this method into a graphical user interface. No additional software services need to be set up or configured, as EasySMPC uses the most widespread digital communication channel available: e-mails. No cryptographic keys need to be exchanged between the parties and e-mails are exchanged automatically by the software. To demonstrate the practicability of our solution, we evaluated its performance in a wide range of data sharing scenarios. The results of our evaluation show that our approach is scalable (summing up 10,000 variables between 20 parties takes less than 300 s) and that the number of participants is the essential factor.

CONCLUSIONS

We have developed an easy-to-use "no-code solution" for performing secure joint calculations on biomedical data using SMPC protocols, which is suitable for use by scientists without IT expertise and which has no special infrastructure requirements. We believe that innovative approaches to data sharing with SMPC are needed to foster the translation of complex protocols into practice.

Phenotype-aware prioritisation of rare Mendelian disease variants

A molecular diagnosis from the analysis of sequencing data in rare Mendelian diseases has a huge impact on the management of patients and their families. Numerous patient phenotype-aware variant prioritisation (VP) tools have been developed to help automate this process, and shorten the diagnostic odyssey, but performance statistics on real patient data are limited. Here we identify, assess, and compare the performance of all up-to-date, freely available, and programmatically accessible tools using a whole-exome, retinal disease dataset from 134 individuals with a molecular diagnosis. All tools were able to identify around two-thirds of the genetic diagnoses as the top-ranked candidate, with LIRICAL performing best overall. Finally, we discuss the challenges to overcome most cases remaining undiagnosed after current, state-of-the-art practices.

Phenotype-driven approaches to enhance variant prioritization and diagnosis of rare disease

Rare disease diagnostics and disease gene discovery have been revolutionized by whole-exome and genome sequencing but identifying the causative variant(s) from the millions in each individual remains challenging. The use of deep phenotyping of patients and reference genotype-phenotype knowledge, alongside variant data such as allele frequency, segregation, and predicted pathogenicity, has proved an effective strategy to tackle this issue. Here we review the numerous tools that have been developed to automate this approach and demonstrate the power of such an approach on several thousand diagnosed cases from the 100,000 Genomes Project. Finally, we discuss the challenges that need to be overcome if we are going to improve detection rates and help the majority of patients that still remain without a molecular diagnosis after state-of-the-art genomic interpretation.

Genome sequencing data analysis for rare disease gene discovery

Rare diseases occur in a smaller proportion of the general population, which is variedly defined as less than 200 000 individuals (US) or in less than 1 in 2000 individuals (Europe). Although rare, they collectively make up to approximately 7000 different disorders, with majority having a genetic origin, and affect roughly 300 million people globally. Most of the patients and their families undergo a long and frustrating diagnostic odyssey. However, advances in the field of genomics have started to facilitate the process of diagnosis, though it is hindered by the difficulty in genome data analysis and interpretation. A major impediment in diagnosis is in the understanding of the diverse approaches, tools and datasets available for variant prioritization, the most important step in the analysis of millions of variants to select a few potential variants. Here we present a review of the latest methodological developments and spectrum of tools available for rare disease genetic variant discovery and recommend appropriate data interpretation methods for variant prioritization. We have categorized the resources based on various steps of the variant interpretation workflow, starting from data processing, variant calling, annotation, filtration and finally prioritization, with a special emphasis on the last two steps. The methods discussed here pertain to elucidating the genetic basis of disease in individual patient cases via trio- or family-based analysis of the genome data. We advocate the use of a combination of tools and datasets and to follow multiple iterative approaches to elucidate the potential causative variant.

Whole-exome sequencing reveals a novel homozygous mutation in the COQ8B gene associated with nephrotic syndrome

Nephrotic syndrome arising from monogenic mutations differs substantially from acquired ones in their clinical prognosis, progression, and disease management. Several pathogenic mutations in the COQ8B gene are known to cause nephrotic syndrome. Here, we used the whole-exome sequencing (WES) technology to decipher the genetic cause of nephrotic syndrome (CKD stage-V) in a large affected consanguineous family. Our study exposed a novel missense homozygous mutation NC_000019.9:g.41209497C > T; NM_024876.4:c.748G > A; NP_079152.3:p.(Asp250Asn) in the 9th exon of the COQ8B gene, co-segregated well with the disease phenotype. Our study provides the first insight into this homozygous condition, which has not been previously reported in 1000Genome, ClinVar, ExAC, and genomAD databases. In addition to the pathogenic COQ8B variant, the WES data also revealed some novel and recurrent mutations in the GLA, NUP107, COQ2, COQ6, COQ7 and COQ9 genes. The novel variants observed in this study have been submitted to the ClinVar database and are publicly available online with the accessions: SCV001451361.1, SCV001451725.1 and SCV001451724.1. Based on the patient's clinical history and genomic data with in silico validation, we conclude that pathogenic mutation in the COQ8B gene was causing kidney failure in an autosomal recessive manner. We recommend WES technology for genetic testing in such a consanguineous family to not only prevent the future generation, but early detection can help in disease management and therapeutic interventions.

Whole-Exome Sequencing Reveals a Rare Variant of OTOF Gene Causing Congenital Non-syndromic Hearing Loss Among Large Muslim Families Favoring Consanguinity

Non-syndromic hearing loss (NSHL) is one of the most frequent auditory deficits in humans characterized by high clinical and genetic heterogeneity. Very few studies have reported the relationship between OTOF (Locus: DFNB9) and hereditary hearing loss in India. We aimed to decipher the genetic cause of prelingual NSHL in a large affected Muslim consanguineous families using whole-exome sequencing (WES). The study was performed following the guidelines and regulations of the Indian Council of Medical Research (ICMR), New Delhi. The population was identified from Jammu and Kashmir, the Northernmost part of India. Near about 100 individuals were born deaf-mute in the village of 3,000 inhabitants. A total of 103 individuals (with 52 cases and 51 controls) agreed to participate in this study. Our study revealed a rare non-sense homozygous mutation NC_000002.11:g.2:26702224G>A; NM_001287489.2:c.2122C>T; NP_001274418.1:p.(Arg708^∗) in the 18th exon of the OTOF gene. Our study provides the first insight into this homozygous condition, which has not been previously reported in ExAC, 1,000 Genome and genomAD databases. Furthermore, the variant was confirmed in the population cohort (n = 103) using Sanger sequencing. In addition to the pathogenic OTOF variant, the WES data also revealed novel and recurrent mutations in CDH23, GJB2, MYO15A, OTOG, and SLC26A4 genes. The rare pathogenic and the novel variants observed in this study have been submitted to the ClinVar database and are publicly available online with the accessions SCV001448680.1, SCV001448682.1 and SCV001448681.1. We conclude that OTOF-related NSHL hearing loss is prevalent in the region due to successive inbreeding in its generations. We recommend premarital genetic testing and genetic counseling strategies to minimize and control the disease risk in future generations.

Diagnosis of Rare Diseases: a scoping review of clinical decision support systems

BACKGROUND

Rare Diseases (RDs), which are defined as diseases affecting no more than 5 out of 10,000 people, are often severe, chronic and life-threatening. A main problem is the delay in diagnosing RDs. Clinical decision support systems (CDSSs) for RDs are software systems to support clinicians in the diagnosis of patients with RDs. Due to their clinical importance, we conducted a scoping review to determine which CDSSs are available to support the diagnosis of RDs patients, whether the CDSSs are available to be used by clinicians and which functionalities and data are used to provide decision support.

METHODS

We searched PubMed for CDSSs in RDs published between December 16, 2008 and December 16, 2018. Only English articles, original peer reviewed journals and conference papers describing a clinical prototype or a routine use of CDSSs were included. For data charting, we used the data items "Objective and background of the publication/project", "System or project name", "Functionality", "Type of clinical data", "Rare Diseases covered", "Development status", "System availability", "Data entry and integration", "Last software update" and "Clinical usage".

RESULTS

The search identified 636 articles. After title and abstracting screening, as well as assessing the eligibility criteria for full-text screening, 22 articles describing 19 different CDSSs were identified. Three types of CDSSs were classified: "Analysis or comparison of genetic and phenotypic data," "machine learning" and "information retrieval". Twelve of nineteen CDSSs use phenotypic and genetic data, followed by clinical data, literature databases and patient questionnaires. Fourteen of nineteen CDSSs are fully developed systems and therefore publicly available. Data can be entered or uploaded manually in six CDSSs, whereas for four CDSSs no information for data integration was available. Only seven CDSSs allow further ways of data integration. thirteen CDSS do not provide information about clinical usage.

CONCLUSIONS

Different CDSS for various purposes are available, yet clinicians have to determine which is best for their patient. To allow a more precise usage, future research has to focus on CDSSs RDs data integration, clinical usage and updating clinical knowledge. It remains interesting which of the CDSSs will be used and maintained in the future.

An Improved Phenotype-Driven Tool for Rare Mendelian Variant Prioritization: Benchmarking Exomiser on Real Patient Whole-Exome Data

Next-generation sequencing has revolutionized rare disease diagnostics, but many patients remain without a molecular diagnosis, particularly because many candidate variants usually survive despite strict filtering. Exomiser was launched in 2014 as a Java tool that performs an integrative analysis of patients' sequencing data and their phenotypes encoded with Human Phenotype Ontology (HPO) terms. It prioritizes variants by leveraging information on variant frequency, predicted pathogenicity, and gene-phenotype associations derived from human diseases, model organisms, and protein-protein interactions. Early published releases of Exomiser were able to prioritize disease-causative variants as top candidates in up to 97% of simulated whole-exomes. The size of the tested real patient datasets published so far are very limited. Here, we present the latest Exomiser version 12.0.1 with many new features. We assessed the performance using a set of 134 whole-exomes from patients with a range of rare retinal diseases and known molecular diagnosis. Using default settings, Exomiser ranked the correct diagnosed variants as the top candidate in 74% of the dataset and top 5 in 94%; not using the patients' HPO profiles (i.e., variant-only analysis) decreased the performance to 3% and 27%, respectively. In conclusion, Exomiser is an effective support tool for rare Mendelian phenotype-driven variant prioritization.

The utility of phenomics in diagnosis of inherited metabolic disorders

Inherited metabolic disorders (IMDs) are debilitating inherited diseases, with phenotypic, biochemical and genetic heterogeneity, frequently leading to prolonged diagnostic odysseys. Mitochondrial disorders represent one of the most severe classes of IMDs, wherein defects in >350 genes lead to multi-system disease. Diagnostic rates have improved considerably following the adoption of next-generation sequencing (NGS) technologies, but are still far from perfect. Phenomic annotation is an emerging concept which is being utilised to enhance interpretation of NGS results. To test whether phenomic correlations have utility in mitochondrial disease and IMDs, we created a gene-to-phenotype interaction network with searchable elements, for Leigh syndrome, a frequently observed paediatric mitochondrial disorder. The Leigh Map comprises data on 92 genes and 275 phenotypes standardised in human phenotype ontology terms, with 80% predictive accuracy. This commentary highlights the usefulness of the Leigh Map and similar resources and the challenges associated with integrating phenomic technologies into clinical practice.