Time-to-Event Genome-Wide Association Study for Incident Cardiovascular Disease in People With Type 2 Diabetes.

OBJECTIVE: To identify genetic risk factors for incident cardiovascular disease (CVD) among people with type 2 diabetes (T2D). RESEARCH DESIGN AND METHODS: We conducted a multiancestry time-to-event genome-wide association study for incident CVD among people with T2D. We also tested 204 known coronary artery disease (CAD) variants for association with incident CVD. RESULTS: Among 49,230 participants with T2D, 8,956 had incident CVD events (event rate 18.2%). We identified three novel genetic loci for incident CVD: rs147138607 (near CACNA1E/ZNF648, hazard ratio [HR] 1.23, P = 3.6 × 10-9), rs77142250 (near HS3ST1, HR 1.89, P = 9.9 × 10-9), and rs335407 (near TFB1M/NOX3, HR 1.25, P = 1.5 × 10-8). Among 204 known CAD loci, 5 were associated with incident CVD in T2D (multiple comparison-adjusted P < 0.00024, 0.05/204). A standardized polygenic score of these 204 variants was associated with incident CVD with HR 1.14 (P = 1.0 × 10-16). CONCLUSIONS: The data point to novel and known genomic regions associated with incident CVD among individuals with T2D.

Racial and Ethnic Inequities at the Practice and Physician Levels in Timely Next-Generation Sequencing for Patients With Advanced Non-Small-Cell Lung Cancer Treated in the US Community Setting.

PURPOSE: Racial/ethnic inequities in next-generation sequencing (NGS) were examined for patients with advanced non-small-cell lung cancer (aNSCLC) at the practice and physician levels to inform policies to improve equitable quality of care. METHODS: This retrospective study used a nationwide electronic health record-derived deidentified database for patients with aNSCLC diagnosed between April 2018 and March 2022 in the community setting. Timely NGS was an NGS result between initial diagnosis and ≤60 days after advanced diagnosis. We studied how inequities were driven by (1) non-Latinx Black (Black) and Latinx patient under-representation at high testing practices versus (2) Black and Latinx patients being tested at lower rates than non-Latinx White (White) patients, even at the same practice. We defined these two concepts as across inequity and within inequity, respectively, with total inequity as their summation. Mean percentage point inequities were estimated using a Bayesian approach. RESULTS: A total of 12,045 patients (9,981 White; 1,528 Black; 536 Latinx) met study criteria. At the practice level, versus White patients, the mean percentage point difference in NGS testing total inequity was 7.49 for Black and 8.26 for Latinx. Within- and across-practice inequities contributed to total inequity in NGS testing for Black (48% v 52%) and Latinx patients (60% v 40%). At the physician level, versus White patients, the mean percentage point difference in total inequity was 7.73 for Black and 8.81 for Latinx patients. Within- versus across-physician inequities contributed to total inequity for Black and Latinx patients (77% v 23% and 67% v 33%). CONCLUSION: Within-practice, across-practice, and across-physician inequities were main contributors to total inequity in NGS testing, requiring a suite of interventions to effectively address inequities.

Enabling the clinical application of artificial intelligence in genomics: a perspective of the AMIA Genomics and Translational Bioinformatics Workgroup.

OBJECTIVE: Given the importance AI in genomics and its potential impact on human health, the American Medical Informatics Association-Genomics and Translational Biomedical Informatics (GenTBI) Workgroup developed this assessment of factors that can further enable the clinical application of AI in this space. PROCESS: A list of relevant factors was developed through GenTBI workgroup discussions in multiple in-person and online meetings, along with review of pertinent publications. This list was then summarized and reviewed to achieve consensus among the group members. CONCLUSIONS: Substantial informatics research and development are needed to fully realize the clinical potential of such technologies. The development of larger datasets is crucial to emulating the success AI is achieving in other domains. It is important that AI methods do not exacerbate existing socio-economic, racial, and ethnic disparities. Genomic data standards are critical to effectively scale such technologies across institutions. With so much uncertainty, complexity and novelty in genomics and medicine, and with an evolving regulatory environment, the current focus should be on using these technologies in an interface with clinicians that emphasizes the value each brings to clinical decision-making.

Time-to-Event Genome-Wide Association Study for Incident Cardiovascular Disease in People with Type 2 Diabetes Mellitus.

BACKGROUND: Type 2 diabetes mellitus (T2D) confers a two- to three-fold increased risk of cardiovascular disease (CVD). However, the mechanisms underlying increased CVD risk among people with T2D are only partially understood. We hypothesized that a genetic association study among people with T2D at risk for developing incident cardiovascular complications could provide insights into molecular genetic aspects underlying CVD. METHODS: From 16 studies of the Cohorts for Heart & Aging Research in Genomic Epidemiology (CHARGE) Consortium, we conducted a multi-ancestry time-to-event genome-wide association study (GWAS) for incident CVD among people with T2D using Cox proportional hazards models. Incident CVD was defined based on a composite of coronary artery disease (CAD), stroke, and cardiovascular death that occurred at least one year after the diagnosis of T2D. Cohort-level estimated effect sizes were combined using inverse variance weighted fixed effects meta-analysis. We also tested 204 known CAD variants for association with incident CVD among patients with T2D. RESULTS: A total of 49,230 participants with T2D were included in the analyses (31,118 European ancestries and 18,112 non-European ancestries) which consisted of 8,956 incident CVD cases over a range of mean follow-up duration between 3.2 and 33.7 years (event rate 18.2%). We identified three novel, distinct genetic loci for incident CVD among individuals with T2D that reached the threshold for genome-wide significance (P<5.0×10-8): rs147138607 (intergenic variant between CACNA1E and ZNF648) with a hazard ratio (HR) 1.23, 95% confidence interval (CI) 1.15 - 1.32, P=3.6×10-9, rs11444867 (intergenic variant near HS3ST1) with HR 1.89, 95% CI 1.52 - 2.35, P=9.9×10-9, and rs335407 (intergenic variant between TFB1M and NOX3) HR 1.25, 95% CI 1.16 - 1.35, P=1.5×10-8. Among 204 known CAD loci, 32 were associated with incident CVD in people with T2D with P<0.05, and 5 were significant after Bonferroni correction (P<0.00024, 0.05/204). A polygenic score of these 204 variants was significantly associated with incident CVD with HR 1.14 (95% CI 1.12 - 1.16) per 1 standard deviation increase (P=1.0×10-16). CONCLUSIONS: The data point to novel and known genomic regions associated with incident CVD among individuals with T2D.

Data-driven method to enhance craniofacial and oral phenotype vocabularies.

BACKGROUND: A significant amount of clinical information captured as free-text narratives could be better used for several applications, such as clinical decision support, ontology development, evidence-based practice, and research. The Human Phenotype Ontology (HPO) is specifically used for semantic comparisons for diagnostic purposes. All these functions require quality coverage of the domain of interest. The authors used natural language processing to capture craniofacial and oral phenotype signatures from electronic health records and then used these signatures for evaluation of existing oral phenotype ontology coverage. METHODS: The authors applied a text-processing pipeline based on the clinical Text Analysis and Knowledge Extraction System to annotate the clinical notes with Unified Medical Language System codes. The authors extracted the disease or disorder phenotype terms, which were then compared with HPO terms and their synonyms. RESULTS: The authors retrieved 2,153 deidentified clinical notes from 558 patients. Finally, 2,416 unique diseases or disorders phenotype terms were extracted, which included 210 craniofacial or oral phenotype terms. Twenty-six of these phenotypes were not found in the HPO. CONCLUSIONS: The authors demonstrated that natural language processing tools could extract relevant phenotype terms from clinical narratives, which could help identify gaps in existing ontologies and enhance craniofacial and dental phenotyping vocabularies. PRACTICAL IMPLICATIONS: The expansion of terms in the dental, oral, and craniofacial domains in the HPO is particularly important as the dental community moves toward electronic health records.

Expansion of the Human Phenotype Ontology (HPO) knowledge base and resources.

The Human Phenotype Ontology (HPO)-a standardized vocabulary of phenotypic abnormalities associated with 7000+ diseases-is used by thousands of researchers, clinicians, informaticians and electronic health record systems around the world. Its detailed descriptions of clinical abnormalities and computable disease definitions have made HPO the de facto standard for deep phenotyping in the field of rare disease. The HPO's interoperability with other ontologies has enabled it to be used to improve diagnostic accuracy by incorporating model organism data. It also plays a key role in the popular Exomiser tool, which identifies potential disease-causing variants from whole-exome or whole-genome sequencing data. Since the HPO was first introduced in 2008, its users have become both more numerous and more diverse. To meet these emerging needs, the project has added new content, language translations, mappings and computational tooling, as well as integrations with external community data. The HPO continues to collaborate with clinical adopters to improve specific areas of the ontology and extend standardized disease descriptions. The newly redesigned HPO website (www.human-phenotype-ontology.org) simplifies browsing terms and exploring clinical features, diseases, and human genes.

Deep Geodesic Learning for Segmentation and Anatomical Landmarking.

In this paper, we propose a novel deep learning framework for anatomy segmentation and automatic landmarking. Specifically, we focus on the challenging problem of mandible segmentation from cone-beam computed tomography (CBCT) scans and identification of 9 anatomical landmarks of the mandible on the geodesic space. The overall approach employs three inter-related steps. In the first step, we propose a deep neural network architecture with carefully designed regularization, and network hyper-parameters to perform image segmentation without the need for data augmentation and complex post-processing refinement. In the second step, we formulate the landmark localization problem directly on the geodesic space for sparsely-spaced anatomical landmarks. In the third step, we utilize a long short-term memory network to identify the closely-spaced landmarks, which is rather difficult to obtain using other standard networks. The proposed fully automated method showed superior efficacy compared to the state-of-the-art mandible segmentation and landmarking approaches in craniofacial anomalies and diseased states. We used a very challenging CBCT data set of 50 patients with a high-degree of craniomaxillofacial variability that is realistic in clinical practice. The qualitative visual inspection was conducted for distinct CBCT scans from 250 patients with high anatomical variability. We have also shown the state-of-the-art performance in an independent data set from the MICCAI Head-Neck Challenge (2015).

Transcriptional profiling reveals gland-specific differential expression in the three major salivary glands of the adult mouse.

RNA-Seq was used to better understand the molecular nature of the biological differences among the three major exocrine salivary glands in mammals. Transcriptional profiling found that the adult murine parotid, submandibular, and sublingual salivary glands express greater than 14,300 protein-coding genes, and nearly 2,000 of these genes were differentially expressed. Principle component analysis of the differentially expressed genes revealed three distinct clusters according to gland type. The three salivary gland transcriptomes were dominated by a relatively few number of highly expressed genes (6.3%) that accounted for more than 90% of transcriptional output. Of the 912 transcription factors expressed in the major salivary glands, greater than 90% of them were detected in all three glands, while expression for ~2% of them was enriched in an individual gland. Expression of these unique transcription factors correlated with sublingual and parotid specific subsets of both highly expressed and differentially expressed genes. Gene ontology analyses revealed that the highly expressed genes common to all glands were associated with global functions, while many of the genes expressed in a single gland play a major role in the function of that gland. In summary, transcriptional profiling of the three murine major salivary glands identified a limited number of highly expressed genes, differentially expressed genes, and unique transcription factors that represent the transcriptional signatures underlying gland-specific biological properties.

Defining Disease, Diagnosis, and Translational Medicine within a Homeostatic Perturbation Paradigm: The National Institutes of Health Undiagnosed Diseases Program Experience.

Traditionally, the use of genomic information for personalized medical decisions relies on prior discovery and validation of genotype-phenotype associations. This approach constrains care for patients presenting with undescribed problems. The National Institutes of Health (NIH) Undiagnosed Diseases Program (UDP) hypothesized that defining disease as maladaptation to an ecological niche allows delineation of a logical framework to diagnose and evaluate such patients. Herein, we present the philosophical bases, methodologies, and processes implemented by the NIH UDP. The NIH UDP incorporated use of the Human Phenotype Ontology, developed a genomic alignment strategy cognizant of parental genotypes, pursued agnostic biochemical analyses, implemented functional validation, and established virtual villages of global experts. This systematic approach provided a foundation for the diagnostic or non-diagnostic answers provided to patients and serves as a paradigm for scalable translational research.

Distributed Cognition and Process Management Enabling Individualized Translational Research: The NIH Undiagnosed Diseases Program Experience.

The National Institutes of Health Undiagnosed Diseases Program (NIH UDP) applies translational research systematically to diagnose patients with undiagnosed diseases. The challenge is to implement an information system enabling scalable translational research. The authors hypothesized that similar complex problems are resolvable through process management and the distributed cognition of communities. The team, therefore, built the NIH UDP integrated collaboration system (UDPICS) to form virtual collaborative multidisciplinary research networks or communities. UDPICS supports these communities through integrated process management, ontology-based phenotyping, biospecimen management, cloud-based genomic analysis, and an electronic laboratory notebook. UDPICS provided a mechanism for efficient, transparent, and scalable translational research and thereby addressed many of the complex and diverse research and logistical problems of the NIH UDP. Full definition of the strengths and deficiencies of UDPICS will require formal qualitative and quantitative usability and process improvement measurement.

Pharmacogenomic incidental findings in 308 families: The NIH Undiagnosed Diseases Program experience.

PURPOSE: Using single-nucleotide polymorphism (SNP) chip and exome sequence data from individuals participating in the National Institutes of Health (NIH) Undiagnosed Diseases Program (UDP), we evaluated the number and therapeutic informativeness of incidental pharmacogenetic variants. METHODS: Pharmacogenomics Knowledgebase (PharmGKB) annotated sequence variants were identified in 1,101 individuals. Medication records of participants were used to identify individuals prescribed medications with a genetic variant that might alter efficacy. RESULTS: A total of 395 sequence variants, including 19 PharmGKB 1A and 1B variants, were identified in SNP chip sequence data, and 388 variants, including 21 PharmGKB 1A and 1B variants, were identified in the exome sequence data. Nine participants had incidental pharmacogenetic variants associated with altered efficacy of a prescribed medication. CONCLUSIONS: Despite the small size of the NIH UDP patient cohort, we identified pharmacogenetic incidental findings potentially useful for guiding therapy. Consequently, groups conducting clinical genomic studies might consider reporting of pharmacogenetic incidental findings.Genet Med 18 12, 1303-1307.

Computational evaluation of exome sequence data using human and model organism phenotypes improves diagnostic efficiency.

PURPOSE: Medical diagnosis and molecular or biochemical confirmation typically rely on the knowledge of the clinician. Although this is very difficult in extremely rare diseases, we hypothesized that the recording of patient phenotypes in Human Phenotype Ontology (HPO) terms and computationally ranking putative disease-associated sequence variants improves diagnosis, particularly for patients with atypical clinical profiles. METHODS: Using simulated exomes and the National Institutes of Health Undiagnosed Diseases Program (UDP) patient cohort and associated exome sequence, we tested our hypothesis using Exomiser. Exomiser ranks candidate variants based on patient phenotype similarity to (i) known disease-gene phenotypes, (ii) model organism phenotypes of candidate orthologs, and (iii) phenotypes of protein-protein association neighbors. RESULTS: Benchmarking showed Exomiser ranked the causal variant as the top hit in 97% of known disease-gene associations and ranked the correct seeded variant in up to 87% when detectable disease-gene associations were unavailable. Using UDP data, Exomiser ranked the causative variant(s) within the top 10 variants for 11 previously diagnosed variants and achieved a diagnosis for 4 of 23 cases undiagnosed by clinical evaluation. CONCLUSION: Structured phenotyping of patients and computational analysis are effective adjuncts for diagnosing patients with genetic disorders.Genet Med 18 6, 608-617.

The Undiagnosed Diseases Program Integrated Collaboration System (UDPICS): One Program's Experience Developing Custom Software to Support Research for Complex-Disease Families.

The Undiagnosed Diseases Program (UDP) was started in 2008 with the goals of making diagnoses and facilitating related translational research. The individuals and families seen by the UDP are often unique and medically complex. Approximately 40% of UDP cases are pediatric. The Undiagnosed Diseases Program Integrated Collaboration System (UDPICS) was designed to create a collaborative workspace for researchers, clinicians and families. We describe our progress in developing the system to date, focusing on design rationale, challenges and issues that are likely to be common in the development of similar systems in the future.

Developing genomic knowledge bases and databases to support clinical management: current perspectives.

Personalized medicine, the ability to tailor diagnostic and treatment decisions for individual patients, is seen as the evolution of modern medicine. We characterize here the informatics resources available today or envisioned in the near future that can support clinical interpretation of genomic test results. We assume a clinical sequencing scenario (germline whole-exome sequencing) in which a clinical specialist, such as an endocrinologist, needs to tailor patient management decisions within his or her specialty (targeted findings) but relies on a genetic counselor to interpret off-target incidental findings. We characterize the genomic input data and list various types of knowledge bases that provide genomic knowledge for generating clinical decision support. We highlight the need for patient-level databases with detailed lifelong phenotype content in addition to genotype data and provide a list of recommendations for personalized medicine knowledge bases and databases. We conclude that no single knowledge base can currently support all aspects of personalized recommendations and that consolidation of several current resources into larger, more dynamic and collaborative knowledge bases may offer a future path forward.

Three rare diseases in one Sib pair: RAI1, PCK1, GRIN2B mutations associated with Smith-Magenis Syndrome, cytosolic PEPCK deficiency and NMDA receptor glutamate insensitivity.

The National Institutes of Health Undiagnosed Diseases Program evaluates patients for whom no diagnosis has been discovered despite a comprehensive diagnostic workup. Failure to diagnose a condition may arise from the mutation of genes previously unassociated with disease. However, we hypothesized that this could also co-occur with multiple genetic disorders. Demonstrating a complex syndrome caused by multiple disorders, we report two siblings manifesting both similar and disparate signs and symptoms. They shared a history of episodes of hypoglycemia and lactic acidosis, but had differing exam findings and developmental courses. Clinical acumen and exome sequencing combined with biochemical and functional studies identified three genetic conditions. One sibling had Smith-Magenis Syndrome and a nonsense mutation in the RAI1 gene. The second sibling had a de novo mutation in GRIN2B, which resulted in markedly reduced glutamate potency of the encoded receptor. Both siblings had a protein-destabilizing homozygous mutation in PCK1, which encodes the cytosolic isoform of phosphoenolpyruvate carboxykinase (PEPCK-C). In summary, we present the first clinically-characterized mutation of PCK1 and demonstrate that complex medical disorders can represent the co-occurrence of multiple diseases.

The implications of familial incidental findings from exome sequencing: the NIH Undiagnosed Diseases Program experience.

PURPOSE: Using exome sequence data from 159 families participating in the National Institutes of Health Undiagnosed Diseases Program, we evaluated the number and inheritance mode of reportable incidental sequence variants. METHODS: Following the American College of Medical Genetics and Genomics recommendations for reporting of incidental findings from next-generation sequencing, we extracted variants in 56 genes from the exome sequence data of 543 subjects and determined the reportable incidental findings for each participant. We also defined variant status as inherited or de novo for those with available parental sequence data. RESULTS: We identified 14 independent reportable variants in 159 (8.8%) families. For nine families with parental sequence data in our cohort, a parent transmitted the variant to one or more children (nine minor children and four adult children). The remaining five variants occurred in adults for whom parental sequences were unavailable. CONCLUSION: Our results are consistent with the expectation that a small percentage of exomes will result in identification of an incidental finding under the American College of Medical Genetics and Genomics recommendations. Additionally, our analysis of family sequence data highlights that genome and exome sequencing of families has unavoidable implications for immediate family members and therefore requires appropriate counseling for the family.

Novel SNP array analysis and exome sequencing detect a homozygous exon 7 deletion of MEGF10 causing early onset myopathy, areflexia, respiratory distress and dysphagia (EMARDD).

Early-onset myopathy, areflexia, respiratory distress and dysphagia (EMARDD) is a myopathic disorder associated with mutations in MEGF10. By novel analysis of SNP array hybridization and exome sequence coverage, we diagnosed a 10-years old girl with EMARDD following identification of a novel homozygous deletion of exon 7 in MEGF10. In contrast to previously reported EMARDD patients, her weakness was more prominent proximally than distally, and involved her legs more than her arms. MRI of her pelvis and thighs showed muscle atrophy and fatty replacement. Ultrasound of several muscle groups revealed dense homogenous increases in echogenicity. Cloning and sequencing of the deletion breakpoint identified features suggesting the mutation arose by fork stalling and template switching. These findings constitute the first genomic deletion causing EMARDD, expand the clinical phenotype, and provide new insight into the pattern and histology of its muscular pathology.

Recurrent subacute post-viral onset of ataxia associated with a PRF1 mutation.

Inflammation is an important contributor to pediatric and adult neurodegeneration. Understanding the genetic determinants of neuroinflammation provides valuable insight into disease mechanism. We characterize a disorder of recurrent immune-mediated neurodegeneration. We report two sisters who presented with neurodegeneration triggered by infections. The proband, a previously healthy girl, presented at 22.5 months with ataxia and dysarthria following mild gastroenteritis. MRI at onset showed a symmetric signal abnormality of the cerebellar and peritrigonal white matter. Following a progressive course of partial remissions and relapses, she died at 5 years of age. Her older sister had a similar course following varicella infection, she died within 13 months. Both sisters had unremarkable routine laboratory testing, with exception of a transient mild cytopenia in the proband 19 months after presentation. Exome sequencing identified a biallelic perforin1 mutation (PRF1; p.R225W) previously associated with familial hemophagocytic lymphohistiocytosis (FHL). In contrast to FHL, these girls did not have hematopathology or cytokine overproduction. However, 3 years after disease onset, the proband had markedly deficient interleukin-1 beta (IL-1ß) production. These observations extend the spectrum of disease associated with perforin mutations to immune-mediated neurodegeneration triggered by infection and possibly due to primary immunodeficiency.

Characteristics of congenital hepatic fibrosis in a large cohort of patients with autosomal recessive polycystic kidney disease.

BACKGROUND & AIMS: Autosomal recessive polycystic kidney disease (ARPKD), the most common ciliopathy of childhood, is characterized by congenital hepatic fibrosis and progressive cystic degeneration of kidneys. We aimed to describe congenital hepatic fibrosis in patients with ARPKD, confirmed by detection of mutations in PKHD1. METHODS: Patients with ARPKD and congenital hepatic fibrosis were evaluated at the National Institutes of Health from 2003 to 2009. We analyzed clinical, molecular, and imaging data from 73 patients (age, 1-56 years; average, 12.7 ± 13.1 years) with kidney and liver involvement (based on clinical, imaging, or biopsy analyses) and mutations in PKHD1. RESULTS: Initial symptoms were liver related in 26% of patients, and others presented with kidney disease. One patient underwent liver and kidney transplantation, and 10 others received kidney transplants. Four presented with cholangitis and one with variceal bleeding. Sixty-nine percent of patients had enlarged left lobes on magnetic resonance imaging, 92% had increased liver echogenicity on ultrasonography, and 65% had splenomegaly. Splenomegaly started early in life; 60% of children younger than 5 years had enlarged spleens. Spleen volume had an inverse correlation with platelet count and prothrombin time but not with serum albumin level. Platelet count was the best predictor of spleen volume (area under the curve of 0.88905), and spleen length corrected for patient's height correlated inversely with platelet count (R(2) = 0.42, P < .0001). Spleen volume did not correlate with renal function or type of PKHD1 mutation. Twenty-two of 31 patients who underwent endoscopy were found to have varices. Five had variceal bleeding, and 2 had portosystemic shunts. Forty-percent had Caroli syndrome, and 30% had an isolated dilated common bile duct. CONCLUSIONS: Platelet count is the best predictor of the severity of portal hypertension, which has early onset but is underdiagnosed in patients with ARPKD. Seventy percent of patients with ARPKD have biliary abnormalities. Kidney and liver disease are independent, and variability in severity is not explainable by type of PKHD1 mutation; ClinicalTrials.gov number, NCT00068224.

Exome sequencing as a diagnostic tool in a case of undiagnosed juvenile-onset GM1-gangliosidosis.

OBJECTIVE: To utilize high-throughput sequencing to determine the etiology of juvenile-onset neurodegeneration in a 19-year-old woman with progressive motor and cognitive decline. METHODS: Exome sequencing identified an initial list of 133,555 variants in the proband's family, which were filtered using segregation analysis, presence in dbSNP, and an empirically derived gene exclusion list. The filtered list comprised 52 genes: 21 homozygous variants and 31 compound heterozygous variants. These variants were subsequently scrutinized with predicted pathogenicity programs and for association with appropriate clinical syndromes. RESULTS: Exome sequencing data identified 2 GLB1 variants (c.602G>A, p.R201H; c.785G>T, p.G262V). ß-Galactosidase enzyme analysis prior to our evaluation was reported as normal; however, subsequent testing was consistent with juvenile-onset GM1-gangliosidosis. Urine oligosaccharide analysis was positive for multiple oligosaccharides with terminal galactose residues. CONCLUSIONS: We describe a patient with juvenile-onset neurodegeneration that had eluded diagnosis for over a decade. GM1-gangliosidosis had previously been excluded from consideration, but was subsequently identified as the correct diagnosis using exome sequencing. Exome sequencing can evaluate genes not previously associated with neurodegeneration, as well as most known neurodegeneration-associated genes. Our results demonstrate the utility of "agnostic" exome sequencing to evaluate patients with undiagnosed disorders, without prejudice from prior testing results.