Solving patients with rare diseases through programmatic reanalysis of genome-phenome data (original) (raw)
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Human Mutation, 2022
Rare disease patients are more likely to receive a rapid molecular diagnosis nowadays thanks to the wide adoption of next generation sequencing. However, many cases remain undiagnosed even after exome or genome analysis, because the methods used missed the molecular cause in a known gene, or a novel causative gene could not be identified and/or confirmed. To address these challenges, the RD-Connect Genome-Phenome Analysis Platform (GPAP) facilitates the collation, discovery, sharing and analysis of standardised genome-phenome data within a collaborative environment. Authorised clinicians and researchers submit pseudonymised phenotypic profiles encoded using the Human Phenotype Ontology, and raw genomic data which is processed through a standardised pipeline. After an optional embargo period, the data is shared with other platform users, with the objective that similar cases in the system and queries from peers may help diagnose the case. Additionally, the platform enables bidirectional discovery of similar cases in other databases from the Matchmaker Exchange network. To facilitate genome-phenome analysis and interpretation by clinical researchers, the RD-Connect GPAP provides a powerful user-friendly interface and leverages tens of information sources. As a result, the resource has already helped diagnose hundreds of rare disease patients and discover new disease causing genes. This article is protected by copyright. All rights reserved.
GenIO: a phenotype-genotype analysis web server for clinical genomics of rare diseases
BMC Bioinformatics, 2018
Background: GenIO is a novel web-server, designed to assist clinical genomics researchers and medical doctors in the diagnostic process of rare genetic diseases. The tool identifies the most probable variants causing a rare disease, using the genomic and clinical information provided by a medical practitioner. Variants identified in a whole-genome, whole-exome or target sequencing studies are annotated, classified and filtered by clinical significance. Candidate genes associated with the patient's symptoms, suspected disease and complementary findings are identified to obtain a small manageable number of the most probable recessive and dominant candidate gene variants associated with the rare disease case. Additionally, following the American College of Medical Genetics and Genomics and the Association of Molecular Pathology (ACMG-AMP) guidelines and recommendations, all potentially pathogenic variants that might be contributing to disease and secondary findings are identified. Results: A retrospective study was performed on 40 patients with a diagnostic rate of 40%. All the known genes that were previously considered as disease causing were correctly identified in the final inherit model output lists. In previously undiagnosed cases, we had no additional yield. Conclusion: This unique, intuitive and user-friendly tool to assists medical doctors in the clinical genomics diagnostic process is openly available at https://bioinformatics.ibioba-mpsp-conicet.gov.ar/GenIO/.
Solve-RD: systematic pan-European data sharing and collaborative analysis to solve rare diseases
European Journal of Human Genetics
For the first time in Europe hundreds of rare disease (RD) experts team up to actively share and jointly analyse existing patient’s data. Solve-RD is a Horizon 2020-supported EU flagship project bringing together >300 clinicians, scientists, and patient representatives of 51 sites from 15 countries. Solve-RD is built upon a core group of four European Reference Networks (ERNs; ERN-ITHACA, ERN-RND, ERN-Euro NMD, ERN-GENTURIS) which annually see more than 270,000 RD patients with respective pathologies. The main ambition is to solve unsolved rare diseases for which a molecular cause is not yet known. This is achieved through an innovative clinical research environment that introduces novel ways to organise expertise and data. Two major approaches are being pursued (i) massive data re-analysis of >19,000 unsolved rare disease patients and (ii) novel combined -omics approaches. The minimum requirement to be eligible for the analysis activities is an inconclusive exome that can be ...
A flexible computational pipeline for research analyses of unsolved clinical exome cases
npj Genomic Medicine, 2020
Exome sequencing has enabled molecular diagnoses for rare disease patients but often with initial diagnostic rates of ~25−30%. Here we develop a robust computational pipeline to rank variants for reassessment of unsolved rare disease patients. A comprehensive web-based patient report is generated in which all deleterious variants can be filtered by gene, variant characteristics, OMIM disease and Phenolyzer scores, and all are annotated with an ACMG classification and links to ClinVar. The pipeline ranked 21/34 previously diagnosed variants as top, with 26 in total ranked ≤7th, 3 ranked ≥13th; 5 failed the pipeline filters. Pathogenic/likely pathogenic variants by ACMG criteria were identified for 22/145 unsolved cases, and a previously undefined candidate disease variant for 27/145. This open access pipeline supports the partnership between clinical and research laboratories to improve the diagnosis of unsolved exomes. It provides a flexible framework for iterative developments to f...
Genome Medicine
Background We report the findings from 4437 individuals (3219 patients and 1218 relatives) who have been analyzed by whole genome sequencing (WGS) at the Genomic Medicine Center Karolinska-Rare Diseases (GMCK-RD) since mid-2015. GMCK-RD represents a long-term collaborative initiative between Karolinska University Hospital and Science for Life Laboratory to establish advanced, genomics-based diagnostics in the Stockholm healthcare setting. Methods Our analysis covers detection and interpretation of SNVs, INDELs, uniparental disomy, CNVs, balanced structural variants, and short tandem repeat expansions. Visualization of results for clinical interpretation is carried out in Scout—a custom-developed decision support system. Results from both singleton (84%) and trio/family (16%) analyses are reported. Variant interpretation is done by 15 expert teams at the hospital involving staff from three clinics. For patients with complex phenotypes, data is shared between the teams. Results Overal...
Genomic answers for children: Dynamic analyses of >1000 pediatric rare disease genomes
Genetics in Medicine, 2022
PURPOSE: To provide comprehensive diagnostic and candidate analyses in a pediatric rare disease cohort through the Genomic Answers for Kids (GA4K) program. METHODS: Extensive analyses of 960 families with suspected genetic disorders including short-read exome (ES) and genome sequencing (srGS); PacBio HiFi long-read GS (HiFi-GS); variant calling for smallnucleotide (SNV), structural (SV) and repeat variants; and machine-learning variant prioritization. Structured phenotypes, prioritized variants and pedigrees are stored in PhenoTips database, with data sharing through controlled access (dbGAP). RESULTS: Diagnostic rates ranged from 11% for cases with prior negative genetic tests to 34.5% in naïve patients. Incorporating SVs from GS added up to 13% of new diagnoses in previously unsolved cases. HiFi-GS yielded increased discovery rate with >4-fold more rare coding SVs than srGS. Variants and genes of unknown significance (VUS/GUS) remain the most common finding (58% of non-diagnostic cases). CONCLUSION: Computational prioritization is efficient for diagnostic SNVs. Thorough identification of non-SNVs remains challenging and is partly mitigated by HiFi-GS sequencing. Importantly, community research is supported by sharing real-time data to accelerate gene validation, and by providing HiFi variant (SNV/SV) resources from >1,000 human alleles to facilitate implementation of new sequencing platforms for rare disease diagnoses.
Genome-wide rare variant analysis for thousands of phenotypes in 54,000 exomes
2019
Defining the effects that rare variants can have on human phenotypes is essential to advancing our understanding of human health and disease. Large-scale human genetic analyses have thus far focused on common variants, but the development of large cohorts of deeply phenotyped individuals with exome sequence data has now made comprehensive analyses of rare variants possible. We analyzed the effects of rare (MAF<0.1%) variants on 3,166 phenotypes in 40,468 exome-sequenced individuals from the UK Biobank and performed replication as well as meta-analyses with 1,067 phenotypes in 13,470 members of the Healthy Nevada Project (HNP) cohort who underwent Exome+ sequencing at Helix. Our analyses of non-benign coding and loss of function (LoF) variants identified 78 gene-based associations that passed our statistical significance threshold (p<5×10-9). These are associations in which carrying any rare coding or LoF variant in the gene is associated with an enrichment for a specific pheno...
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. ...
Whole-genome sequencing of patients with rare diseases in a national health system
Nature
Most patients with rare diseases do not receive a molecular diagnosis and the aetiological variants and mediating genes for more than half such disorders remain to be discovered 1. We implemented whole-genome sequencing (WGS) in a national health system to streamline diagnosis and to discover unknown aetiological variants, in the coding and non-coding regions of the genome. In a pilot study for the 100,000 Genomes Project, we generated WGS data for 13,037 participants, of whom 9,802 had a rare disease, and provided a genetic diagnosis to 1,138 of the 7,065 patients with detailed phenotypic data. We identified 95 Mendelian associations between genes and rare diseases, of which 11 have been discovered since 2015 and at least 79 are confirmed aetiological. Using WGS of UK Biobank 2 , we showed that rare alleles can explain the presence of some individuals in the tails of a quantitative red blood cell (RBC) trait. Finally, we identified 4 novel non-coding variants which cause disease through the disruption of transcription of ARPC1B, GATA1, LRBA and MPL. Our study demonstrates a synergy by using WGS for diagnosis and aetiological discovery in routine healthcare. Rare diseases affect approximately 1 in 20 people, but only a minority of patients receive a genetic diagnosis 3. Approximately 10,000 rare diseases are known, but fewer than half have a resolved genetic aetiology 1. Even when the aetiology is known, the prospects for diagnosis are severely diminished by fragmentary phenotyping and the restriction of testing to disease-specific panels of genes. On average, a molecular cause is determined after three misdiagnoses and 16 physician visits over several years 4. Recent development of WGS technology allows systematic, comprehensive genetic testing in integrated health systems concurrent with aetiological discovery in the coding and non-coding genome.
European Journal of Human Genetics
Clinical exome sequencing (CES) has become the preferred diagnostic platform for complex pediatric disorders with suspected monogenic etiologies. Despite rapid advancements, the major challenge still resides in identifying the casual variants among the thousands of variants detected during CES testing, and thus establishing a molecular diagnosis. To improve the clinical exome diagnostic efficiency, we developed Phenoxome, a robust phenotype-driven model that adopts a network-based approach to facilitate automated variant prioritization. Phenoxome dissects the phenotypic manifestation of a patient in concert with their genomic profile to filter and then prioritize variants that are likely to affect the function of the gene (potentially pathogenic variants). To validate our method, we have compiled a clinical cohort of 105 positive patient samples that represent a wide range of genetic heterogeneity. Phenoxome identifies the causative variants within the top 5, 10, or 25 candidates in more than 50%, 71%, or 88% of these exomes, respectively. Furthermore, we show that our method is optimized for clinical testing by outperforming the current state-of-art method. We have demonstrated the performance of Phenoxome using a clinical cohort and showed that it enables rapid and accurate interpretation of clinical exomes. Phenoxome is available at https://phenoxome.chop.edu/.