International Undiagnosed Diseases Programs (UDPs): components and outcomes.

Over the last 15 years, Undiagnosed Diseases Programs have emerged to address the significant number of individuals with suspected but undiagnosed rare genetic diseases, integrating research and clinical care to optimize diagnostic outcomes. This narrative review summarizes the published literature surrounding Undiagnosed Diseases Programs worldwide, including thirteen studies that evaluate outcomes and two commentary papers. Commonalities in the diagnostic and research process of Undiagnosed Diseases Programs are explored through an appraisal of available literature. This exploration allowed for an assessment of the strengths and limitations of each of the six common steps, namely enrollment, comprehensive clinical phenotyping, research diagnostics, data sharing and matchmaking, results, and follow-up. Current literature highlights the potential utility of Undiagnosed Diseases Programs in research diagnostics. Since participants have often had extensive previous genetic studies, research pipelines allow for diagnostic approaches beyond exome or whole genome sequencing, through reanalysis using research-grade bioinformatics tools and multi-omics technologies. The overall diagnostic yield is presented by study, since different selection criteria at enrollment and reporting processes make comparisons challenging and not particularly informative. Nonetheless, diagnostic yield in an undiagnosed cohort reflects the potential of an Undiagnosed Diseases Program. Further comparisons and exploration of the outcomes of Undiagnosed Diseases Programs worldwide will allow for the development and improvement of the diagnostic and research process and in turn improve the value and utility of an Undiagnosed Diseases Program.

Assessment of whole-exome sequencing results in neurogenetic diseases.

Neurogenetic diseases are rare genetic diseases in which neurological findings are prominent. Whole exome sequencing (WES) has led to great advances in the understanding of the causes of neurogenetic diseases. Etiological research ends with the WES method in many patients. This etiological research is called a "diagnostic odyssey" for many families. Here, we present the results of 168 patients who were previously undiagnosed and underwent WES with the suspicion of neurogenetic disease. A total of 168 cases, 94 males and 74 females, with suspected undiagnosed neurogenetic disease were included in the study. We presented the WES results of the patients. The mean age of patients at the time of WES request was 11 years (range 0.25-68 years). Seventy percent (n = 117) of the patients were born from consanguineous marriage. Most of the patients were children (n = 145). Patients were grouped according to age at the time of examination. Patients younger than 18 years of age at the time of examination were classified as children, otherwise adults. Seventy-eight patients had either a pathogenic variant or a likely pathogenic variant so the diagnostic rate for WES in our cohort was %46. Our experience showing the high diagnostic rate of WES, supports its use in undiagnosed neurogenetic diseases. It also affects medical treatment, prognosis and family planning by enabling early diagnosis in patients.

Success and Pitfalls of Genetic Testing in Undiagnosed Diseases: Whole Exome Sequencing and Beyond.

Novel approaches to uncover the molecular etiology of neurodevelopmental disorders (NDD) are highly needed. Even using a powerful tool such as whole exome sequencing (WES), the diagnostic process may still prove long and arduous due to the high clinical and genetic heterogeneity of these conditions. The main strategies to improve the diagnostic rate are based on family segregation, re-evaluation of the clinical features by reverse-phenotyping, re-analysis of unsolved NGS-based cases and epigenetic functional studies. In this article, we described three selected cases from a cohort of patients with NDD in which trio WES was applied, in order to underline the typical challenges encountered during the diagnostic process: (1) an ultra-rare condition caused by a missense variant in MEIS2, identified through the updated Solve-RD re-analysis; (2) a patient with Noonan-like features in which the NGS analysis revealed a novel variant in NIPBL causing Cornelia de Lange syndrome; and (3) a case with de novo variants in genes involved in the chromatin-remodeling complex, for which the study of the epigenetic signature excluded a pathogenic role. In this perspective, we aimed to (i) provide an example of the relevance of the genetic re-analysis of all unsolved cases through network projects on rare diseases; (ii) point out the role and the uncertainties of the reverse phenotyping in the interpretation of the genetic results; and (iii) describe the use of methylation signatures in neurodevelopmental syndromes for the validation of the variants of uncertain significance.

Identifying digenic disease genes via machine learning in the Undiagnosed Diseases Network.

Rare diseases affect millions of people worldwide, and discovering their genetic causes is challenging. More than half of the individuals analyzed by the Undiagnosed Diseases Network (UDN) remain undiagnosed. The central hypothesis of this work is that many of these rare genetic disorders are caused by multiple variants in more than one gene. However, given the large number of variants in each individual genome, experimentally evaluating combinations of variants for potential to cause disease is currently infeasible. To address this challenge, we developed the digenic predictor (DiGePred), a random forest classifier for identifying candidate digenic disease gene pairs by features derived from biological networks, genomics, evolutionary history, and functional annotations. We trained the DiGePred classifier by using DIDA, the largest available database of known digenic-disease-causing gene pairs, and several sets of non-digenic gene pairs, including variant pairs derived from unaffected relatives of UDN individuals. DiGePred achieved high precision and recall in cross-validation and on a held-out test set (PR area under the curve > 77%), and we further demonstrate its utility by using digenic pairs from the recent literature. In contrast to other approaches, DiGePred also appropriately controls the number of false positives when applied in realistic clinical settings. Finally, to enable the rapid screening of variant gene pairs for digenic disease potential, we freely provide the predictions of DiGePred on all human gene pairs. Our work enables the discovery of genetic causes for rare non-monogenic diseases by providing a means to rapidly evaluate variant gene pairs for the potential to cause digenic disease.

Online questionnaire on genetic testing for intractable diseases in Japan: response to and issues associated with the revised medical care act.

In Japan, most genetic testing for intractable diseases has been conducted in research laboratories in the past. However, since the Revised Medical Care Act came into effect on December 1, 2018, genetic testing in compliance with this act has become a major issue. To collect information on this topic, we conducted an online survey of members of the research groups for intractable diseases, which play a central role in medical care and research on intractable diseases with the support of the Ministry of Health, Labor and Welfare, five months after the enactment of the act. We separated the surveyed facilities into those that conducted genetic testing in their own laboratories ("testing facilities") and those that outsourced genetic testing ("outsourcing facilities"). Ninety-five and 66 responses regarding genetic testing were obtained from the testing and outsourcing facilities, respectively. Genetic analysis was the most commonly conducted genetic testing method, accounting for 60% or more of the tests. At the testing facilities that conducted comprehensive analysis with a next-generation sequencer, the number of target diseases for genetic testing was observed to be higher. In these testing facilities, more than 70% were research laboratories. In contrast, at the outsourcing facilities, testing was outsourced to registered clinical laboratories in many cases or to research laboratories. The proportion of genetic testing covered by public medical insurance at the outsourcing facilities was two times higher than that at the testing facilities. The importance of quality control for genetic testing was generally well acknowledged, but there was apprehension regarding the increased cost and burden on staff of quality control assurance, and many testing facilities viewed genetic testing as difficult. The research groups could handle the examination and interpretation of the genetic testing results, and many groups gathered and registered patient information. Within the intractable disease medical support network, there was a relatively large number of collaborations, with studies supported by the Japan Agency for Medical Research and Development (AMED) and Initiative on Rare and Undiagnosed Diseases (IRUD) projects. There were many requests for genetic testing to be covered by public medical insurance. In the future, the implementation of genetic testing using a next-generation sequencer at clinical laboratories with guaranteed quality control and the development of a system for collaboration with research groups will be necessary.

Machine Learning of Patient Characteristics to Predict Admission Outcomes in the Undiagnosed Diseases Network.

Importance: The Undiagnosed Diseases Network (UDN) is a national network that evaluates individual patients whose signs and symptoms have been refractory to diagnosis. Providing reliable estimates of admission outcomes may assist clinical evaluators to distinguish, prioritize, and accelerate admission to the UDN for patients with undiagnosed diseases. Objective: To develop computational models that effectively predict admission outcomes for applicants seeking UDN evaluation and to rank the applications based on the likelihood of patient admission to the UDN. Design, Setting, and Participants: This prognostic study included all applications submitted to the UDN from July 2014 to June 2019, with 1209 applications accepted and 1212 applications not accepted. The main inclusion criterion was an undiagnosed condition despite thorough evaluation by a health care professional; the main exclusion criteria were a diagnosis that explained the objective findings or a review of the records that suggested a diagnosis. A classifier was trained using information extracted from application forms, referral letters from health care professionals, and semantic similarity between referral letters and textual description of known mendelian disorders. The admission labels were provided by the case review committee of the UDN. In addition to retrospective analysis, the classifier was prospectively tested on another 288 applications that were not evaluated at the time of classifier development. Main Outcomes and Measures: The primary outcomes were whether a patient was accepted or not accepted to the UDN and application order ranked based on likelihood of admission. The performance of the classifier was assessed by comparing its predictions against the UDN admission outcomes and by measuring improvement in the mean processing time for accepted applications. Results: The best classifier obtained sensitivity of 0.843, specificity of 0.738, and area under the receiver operating characteristic curve of 0.844 for predicting admission outcomes among 1212 accepted and 1210 not accepted applications. In addition, the classifier can decrease the current mean (SD) UDN processing time for accepted applications from 3.29 (3.17) months to 1.05 (3.82) months (68% improvement) by ordering applications based on their likelihood of acceptance. Conclusions and Relevance: A classification system was developed that may assist clinical evaluators to distinguish, prioritize, and accelerate admission to the UDN for patients with undiagnosed diseases. Accelerating the admission process may improve the diagnostic journeys for these patients and serve as a model for partial automation of triaging or referral for other resource-constrained applications. Such classification models make explicit some of the considerations that currently inform the use of whole-genome sequencing for undiagnosed disease and thereby invite a broader discussion in the clinical genetics community.

Exome and genome sequencing in adults with undiagnosed disease: a prospective cohort study.

BACKGROUND: Exome and genome sequencing have been demonstrated to increase diagnostic yield in paediatric populations, improving treatment options and providing risk information for relatives. There are limited studies examining the clinical utility of these tests in adults, who currently have limited access to this technology. METHODS: Patients from adult and cancer genetics clinics across Toronto, Ontario, Canada were recruited into a prospective cohort study evaluating the diagnostic utility of exome and genome sequencing in adults. Eligible patients were ≥18 years of age and suspected of having a hereditary disorder but had received previous uninformative genetic test results. In total, we examined the diagnostic utility of exome and genome sequencing in 47 probands and 34 of their relatives who consented to participate and underwent exome or genome sequencing. RESULTS: Overall, 17% (8/47) of probands had a pathogenic or likely pathogenic variant identified in a gene associated with their primary indication for testing. The diagnostic yield for patients with a cancer history was similar to the yield for patients with a non-cancer history (4/18 (22%) vs 4/29 (14%)). An additional 24 probands (51%) had an inconclusive result. Secondary findings were identified in 10 patients (21%); three had medically actionable results. CONCLUSIONS: This study lends evidence to the diagnostic utility of exome or genome sequencing in an undiagnosed adult population. The significant increase in diagnostic yield warrants the use of this technology. The identification and communication of secondary findings may provide added value when using this testing modality as a first-line test.

Singapore Undiagnosed Disease Program: Genomic Analysis aids Diagnosis and Clinical Management.

OBJECTIVE: Use next-generation sequencing (NGS) technology to improve our diagnostic yield in patients with suspected genetic disorders in the Asian setting. DESIGN: A diagnostic study conducted between 2014 and 2019 (and ongoing) under the Singapore Undiagnosed Disease Program. Date of last analysis was 1 July 2019. SETTING: Inpatient and outpatient genetics service at two large academic centres in Singapore. PATIENTS: Inclusion criteria: patients suspected of genetic disorders, based on abnormal antenatal ultrasound, multiple congenital anomalies and developmental delay. EXCLUSION CRITERIA: patients with known genetic disorders, either after clinical assessment or investigations (such as karyotype or chromosomal microarray). INTERVENTIONS: Use of NGS technology-whole exome sequencing (WES) or whole genome sequencing (WGS). MAIN OUTCOME MEASURES: (1) Diagnostic yield by sequencing type, (2) diagnostic yield by phenotypical categories, (3) reduction in time to diagnosis and (4) change in clinical outcomes and management. RESULTS: We demonstrate a 37.8% diagnostic yield for WES (n=172) and a 33.3% yield for WGS (n=24). The yield was higher when sequencing was conducted on trios (40.2%), as well as for certain phenotypes (neuromuscular, 54%, and skeletal dysplasia, 50%). In addition to aiding genetic counselling in 100% of the families, a positive result led to a change in treatment in 27% of patients. CONCLUSION: Genomic sequencing is an effective method for diagnosing rare disease or previous 'undiagnosed' disease. The clinical utility of WES/WGS is seen in the shortened time to diagnosis and the discovery of novel variants. Additionally, reaching a diagnosis significantly impacts families and leads to alteration in management of these patients.

Missed diagnoses: Clinically relevant lessons learned through medical mysteries solved by the Undiagnosed Diseases Network.

BACKGROUND: Resources within the Undiagnosed Diseases Network (UDN), such as genome sequencing (GS) and model organisms aid in diagnosis and identification of new disease genes, but are currently difficult to access by clinical providers. While these resources do contribute to diagnoses in many cases, they are not always necessary to reach diagnostic resolution. The UDN experience has been that participants can also receive diagnoses through the thoughtful and customized application of approaches and resources that are readily available in clinical settings. METHODS: The UDN Genetic Counseling and Testing Working Group collected case vignettes that illustrated how clinically available methods resulted in diagnoses. The case vignettes were classified into three themes; phenotypic considerations, selection of genetic testing, and evaluating exome/GS variants and data. RESULTS: We present 12 participants that illustrate how clinical practices such as phenotype-driven genomic investigations, consideration of variable expressivity, selecting the relevant tissue of interest for testing, utilizing updated testing platforms, and recognition of alternate transcript nomenclature resulted in diagnoses. CONCLUSION: These examples demonstrate that when a diagnosis is elusive, an iterative patient-specific approach utilizing assessment options available to clinical providers may solve a portion of cases. However, this does require increased provider time commitment, a particular challenge in the current practice of genomics.

Limitations of exome sequencing in detecting rare and undiagnosed diseases.

While exome sequencing (ES) is commonly the final diagnostic step in clinical genetics, it may miss diagnoses. To clarify the limitations of ES, we investigated the diagnostic yield of genetic tests beyond ES in our Undiagnosed Diseases Network (UDN) participants. We reviewed the yield of additional genetic testing including genome sequencing (GS), copy number variant (CNV), noncoding variant (NCV), repeat expansion (RE), or methylation testing in UDN cases with nondiagnostic ES results. Overall, 36/54 (67%) of total diagnoses were based on clinical findings and coding variants found by ES and 3/54 (6%) were based on clinical findings only. The remaining 15/54 (28%) required testing beyond ES. Of these, 7/15 (47%) had NCV, 6/15 (40%) CNV, and 2/15 (13%) had a RE or a DNA methylation disorder. Thus 18/54 (33%) of diagnoses were not solved exclusively by ES. Several methods were needed to detect and/or confirm the functional effects of the variants missed by ES, and in some cases by GS. These results indicate that tests to detect elusive variants should be considered after nondiagnostic preliminary steps. Further studies are needed to determine the cost-effectiveness of tests beyond ES that provide diagnoses and insights to possible treatment.

Deficiency in the endocytic adaptor proteins PHETA1/2 impairs renal and craniofacial development.

A critical barrier in the treatment of endosomal and lysosomal diseases is the lack of understanding of the in vivo functions of the putative causative genes. We addressed this by investigating a key pair of endocytic adaptor proteins, PH domain-containing endocytic trafficking adaptor 1 and 2 (PHETA1/2; also known as FAM109A/B, Ses1/2, IPIP27A/B), which interact with the protein product of OCRL, the causative gene for Lowe syndrome. Here, we conducted the first study of PHETA1/2 in vivo, utilizing the zebrafish system. We found that impairment of both zebrafish orthologs, pheta1 and pheta2, disrupted endocytosis and ciliogenesis in renal tissues. In addition, pheta1/2 mutant animals exhibited reduced jaw size and delayed chondrocyte differentiation, indicating a role in craniofacial development. Deficiency of pheta1/2 resulted in dysregulation of cathepsin K, which led to an increased abundance of type II collagen in craniofacial cartilages, a marker of immature cartilage extracellular matrix. Cathepsin K inhibition rescued the craniofacial phenotypes in the pheta1/2 double mutants. The abnormal renal and craniofacial phenotypes in the pheta1/2 mutant animals were consistent with the clinical presentation of a patient with a de novo arginine (R) to cysteine (C) variant (R6C) of PHETA1. Expressing the patient-specific variant in zebrafish exacerbated craniofacial deficits, suggesting that the R6C allele acts in a dominant-negative manner. Together, these results provide insights into the in vivo roles of PHETA1/2 and suggest that the R6C variant is contributory to the pathogenesis of disease in the patient.This article has an associated First Person interview with the first author of the paper.

Real-time Metagenomic Analysis of Undiagnosed Fever Cases Unveils a Yellow Fever Outbreak in Edo State, Nigeria.

Fifty patients with unexplained fever and poor outcomes presented at Irrua Specialist Teaching Hospital (ISTH) in Edo State, Nigeria, an area endemic for Lassa fever, between September 2018 - January 2019. After ruling out Lassa fever, plasma samples from these epidemiologically-linked cases were sent to the African Centre of Excellence for Genomics of Infectious Diseases (ACEGID), Redeemer's University, Ede, Osun State, Nigeria, where we carried out metagenomic sequencing which implicated yellow fever virus (YFV) as the etiology of this outbreak. Twenty-nine of the 50 samples were confirmed positive for YFV by reverse transcriptase-quantitative polymerase chain reaction (RT-qPCR), 14 of which resulted in genome assembly. Maximum likelihood phylogenetic analysis revealed that these YFV sequences formed a tightly clustered clade more closely related to sequences from Senegal than sequences from earlier Nigerian isolates, suggesting that the YFV clade responsible for this outbreak in Edo State does not descend directly from the Nigerian YFV outbreaks of the last century, but instead reflects a broader diversity and dynamics of YFV in West Africa. Here we demonstrate the power of metagenomic sequencing for identifying ongoing outbreaks and their etiologies and informing real-time public health responses, resulting in accurate and prompt disease management and control.

A comprehensive, multidisciplinary, precision medicine approach to discover effective therapy for an undiagnosed, progressive, fibroinflammatory disease.

Precision medicine has generated diagnoses for many patients with challenging undiagnosed disorders. Some individuals remain without a diagnosis despite comprehensive testing, and this impedes their treatment. This report addresses the role of personalized medicine in identifying effective therapy for an undiagnosed disease. A 22-year-old woman presented with chronic severe recurrent trismus, facial pain, progressive multicentric inflammatory and fibrotic masses, and high C-reactive protein. Sites of disease included the pterygomaxillary region, masseter muscles, mandible, lung, pericardium, intrabdominal cavity, and retroperitoneum. A diagnosis was not established after an extensive assessment, including multiple biopsies. The patient was subsequently evaluated under the Undiagnosed Diseases Program at the National Institutes of Health. Large scale genotyping, proteomic studies, and in vitro and gene expression analyses of fibroblasts obtained from a major disease locus were performed. Germline genetic testing did not identify strong candidate genes; proteomic studies of the patient's serum and bronchoalveolar lavage fluid and gene expression analyses of her cells were consistent with dysregulation of the tumor necrosis factor-alpha pathway. The patient's cultured fibroblasts were incubated with selected drugs, and cell proliferation was inhibited by hydroxychloroquine. Treatment of the patient with hydroxychloroquine conferred prolonged beneficial clinical effects, including stabilization of trismus and reduction of corticosteroid dose, C-reactive protein, and size of masses. This case represents an example of precision medicine applied to discover effective treatments for individuals with enigmatic undiagnosed disorders.

Diagnostic and clinical utility of whole genome sequencing in a cohort of undiagnosed Chinese families with rare diseases.

Rare diseases are usually chronically debilitating or even life-threatening with diagnostic and therapeutic challenges in current clinical practice. It has been estimated that 80% of rare diseases are genetic in origin, and thus genome sequencing-based diagnosis offers a promising alternative for rare-disease management. In this study, 79 individuals from 16 independent families were performed for whole-genome sequencing (WGS) in an effort to identify the causative mutations for 16 distinct rare diseases that are largely clinically intractable. Comprehensive analysis of variations, including simple nucleotide variants (SNVs), copy-number variations (CNVs), and structural variations (SVs), was implemented using the WGS data. A flexible analysis pipeline that allowed a certain degree of misclassification of disease status was developed to facilitate the identification of causative variants. As a result, disease-causing variants were identified in 10 of the 16 investigated diseases, yielding a diagnostic rate of 62.5%. Additionally, new potentially pathogenic variants were discovered for two disorders, including IGF2/INS-IGF2 in mitochondrial disease and FBN3 in Klippel-Trenaunay-Weber syndrome. Our WGS analysis not only detected a CNV associated with 3p deletion syndrome but also captured a simple sequence repeat (SSR) variation associated with Machado-Joseph disease. To our knowledge, this is the first time the clinical WGS analysis of short-read sequences has been used successfully to identify a causative SSR variation that perfectly segregates with a repeat expansion disorder. After the WGS analysis, we confirmed the initial diagnosis for three of 10 established disorders and modified or corrected the initial diagnosis for the remaining seven disorders. In summary, clinical WGS is a powerful tool for the diagnosis of rare diseases, and its diagnostic clarity at molecular levels offers important benefits for the participating families.

Yield of whole exome sequencing in undiagnosed patients facing insurance coverage barriers to genetic testing.

BACKGROUND: Despite growing evidence of diagnostic yield and clinical utility of whole exome sequencing (WES) in patients with undiagnosed diseases, there remain significant cost and reimbursement barriers limiting access to such testing. The diagnostic yield and resulting clinical actions of WES for patients who previously faced insurance coverage barriers have not yet been explored. METHODS: We performed a retrospective descriptive analysis of clinical WES outcomes for patients facing insurance coverage barriers prior to clinical WES and who subsequently enrolled in the Undiagnosed Diseases Network (UDN). Clinical WES was completed as a result of participation in the UDN. Payer type, molecular diagnostic yield, and resulting clinical actions were evaluated. RESULTS: Sixty-six patients in the UDN faced insurance coverage barriers to WES at the time of enrollment (67% public payer, 26% private payer). Forty-two of 66 (64%) received insurance denial for clinician-ordered WES, 19/66 (29%) had health insurance through a payer known not to cover WES, and 5/66 (8%) had previous payer denial of other genetic tests. Clinical WES results yielded a molecular diagnosis in 23 of 66 patients (35% [78% pediatric, 65% neurologic indication]). Molecular diagnosis resulted in clinical actions in 14 of 23 patients (61%). CONCLUSIONS: These data demonstrate that a substantial proportion of patients who encountered insurance coverage barriers to WES had a clinically actionable molecular diagnosis, supporting the notion that WES has value as a covered benefit for patients who remain undiagnosed despite objective clinical findings.

The genome empowerment scale: An assessment of parental empowerment in families with undiagnosed disease.

While genomic sequencing (ES/GS) has the potential to diagnose children with difficult to diagnose phenotypes, the goal should be not only a diagnosis, but also to empower parents to seek next steps for their children and to emotionally manage the outcome, whether or not a diagnosis is secured. To help achieve this goal, objective measures are needed to assess the process of parental empowerment related to genome sequencing. We present the validity and reliability of the Genome Empowerment Scale (GEmS), developed using a healthcare empowerment theoretical model. To evaluate its psychometric properties, 158 parents of 117 children with an undiagnosed condition undergoing genomic sequencing completed the GEmS, measures for criterion validity and for depression and anxiety. Factor analysis resulted in a four factor solution: (a) meaning of a diagnosis; (b) emotional management of the process; (c) seeking information and support and (d) implications and planning. Reliability and validity analyses show that the GEmS has good psychometric properties. The inter-relationships among the factors revealed a profile that may identify parents at risk for a poorer outcome who may benefit from targeted genetic counseling. The GEmS, an objective measure of parental genomic empowerment, can be utilized for future research and translational applications.

CAGI SickKids challenges: Assessment of phenotype and variant predictions derived from clinical and genomic data of children with undiagnosed diseases.

Whole-genome sequencing (WGS) holds great potential as a diagnostic test. However, the majority of patients currently undergoing WGS lack a molecular diagnosis, largely due to the vast number of undiscovered disease genes and our inability to assess the pathogenicity of most genomic variants. The CAGI SickKids challenges attempted to address this knowledge gap by assessing state-of-the-art methods for clinical phenotype prediction from genomes. CAGI4 and CAGI5 participants were provided with WGS data and clinical descriptions of 25 and 24 undiagnosed patients from the SickKids Genome Clinic Project, respectively. Predictors were asked to identify primary and secondary causal variants. In addition, for CAGI5, groups had to match each genome to one of three disorder categories (neurologic, ophthalmologic, and connective), and separately to each patient. The performance of matching genomes to categories was no better than random but two groups performed significantly better than chance in matching genomes to patients. Two of the ten variants proposed by two groups in CAGI4 were deemed to be diagnostic, and several proposed pathogenic variants in CAGI5 are good candidates for phenotype expansion. We discuss implications for improving in silico assessment of genomic variants and identifying new disease genes.

[Whole exome sequencing and whole genome sequencing in undiagnosed disease: of value for certain patient populations]. / 'Whole exome sequencing' en 'whole genome sequencing' bij ziekte zonder diagnose.

Whole exome sequencing and whole genome sequencing in undiagnosed disease: of value for certain patient populations Whole exome sequencing and whole genome sequencing (WES/WGS) as a diagnostic tool has become more readily available. A recent study on the diagnostic yield in a highly selected patient population with undiagnosed disease has demonstrated the power of a stringent diagnostic process that includes WES/WGS. Up to one third of patients received a diagnosis, following critical clinical review of tests performed previously, additional targeted biochemical or genetic diagnostic tests and/or the application of WES/WGS. In more than 60% of the resolved cases, WES or WGS played a crucial role. The success of the Undiagnosed Disease Network relies strongly on patient selection, review of clinical symptoms and medical records by a team of specialists, and close collaboration with basic scientists and laboratories to study the clinical impact of possible genetic variations and mutations that are discovered through WES/WGS. Although the results are impressive, it remains to be determined whether such a dedicated approach is feasible in a non-research setting.

A toolkit for genetics providers in follow-up of patients with non-diagnostic exome sequencing.

There are approximately 7,000 rare diseases affecting 25-30 million Americans, with 80% estimated to have a genetic basis. This presents a challenge for genetics practitioners to determine appropriate testing, make accurate diagnoses, and conduct up-to-date patient management. Exome sequencing (ES) is a comprehensive diagnostic approach, but only 25%-41% of the patients receive a molecular diagnosis. The remaining three-fifths to three-quarters of patients undergoing ES remain undiagnosed. The Stanford Center for Undiagnosed Diseases (CUD), a clinical site of the Undiagnosed Diseases Network, evaluates patients with undiagnosed and rare diseases using a combination of methods including ES. Frequently these patients have non-diagnostic ES results, but strategic follow-up techniques identify diagnoses in a subset. We present techniques used at the CUD that can be adopted by genetics providers in clinical follow-up of cases where ES is non-diagnostic. Solved case examples illustrate different types of non-diagnostic results and the additional techniques that led to a diagnosis. Frequent approaches include segregation analysis, data reanalysis, genome sequencing, additional variant identification, careful phenotype-disease correlation, confirmatory testing, and case matching. We also discuss prioritization of cases for additional analyses.

Copy-number variants in clinical genome sequencing: deployment and interpretation for rare and undiagnosed disease.

PURPOSE: Current diagnostic testing for genetic disorders involves serial use of specialized assays spanning multiple technologies. In principle, genome sequencing (GS) can detect all genomic pathogenic variant types on a single platform. Here we evaluate copy-number variant (CNV) calling as part of a clinically accredited GS test. METHODS: We performed analytical validation of CNV calling on 17 reference samples, compared the sensitivity of GS-based variants with those from a clinical microarray, and set a bound on precision using orthogonal technologies. We developed a protocol for family-based analysis of GS-based CNV calls, and deployed this across a clinical cohort of 79 rare and undiagnosed cases. RESULTS: We found that CNV calls from GS are at least as sensitive as those from microarrays, while only creating a modest increase in the number of variants interpreted (~10 CNVs per case). We identified clinically significant CNVs in 15% of the first 79 cases analyzed, all of which were confirmed by an orthogonal approach. The pipeline also enabled discovery of a uniparental disomy (UPD) and a 50% mosaic trisomy 14. Directed analysis of select CNVs enabled breakpoint level resolution of genomic rearrangements and phasing of de novo CNVs. CONCLUSION: Robust identification of CNVs by GS is possible within a clinical testing environment.