InpherNet accelerates monogenic disease diagnosis using patients' candidate genes' neighbors.

PURPOSE: Roughly 70% of suspected Mendelian disease patients remain undiagnosed after genome sequencing, partly because knowledge about pathogenic genes is incomplete and constantly growing. Generating a novel pathogenic gene hypothesis from patient data can be time-consuming especially where cohort-based analysis is not available. METHODS: Each patient genome contains dozens to hundreds of candidate variants. Many sources of indirect evidence about each candidate may be considered. We introduce InpherNet, a network-based machine learning approach leveraging Monarch Initiative data to accelerate this process. RESULTS: InpherNet ranks candidate genes based on orthologs, paralogs, functional pathway members, and colocalized interaction partner gene neighbors. It can propose novel pathogenic genes and reveal known pathogenic genes whose diagnosed patient-based annotation is missing or partial. InpherNet is applied to patient cases where the causative gene is incorrectly ranked low by clinical gene-ranking methods that use only patient-derived evidence. InpherNet correctly ranks the causative gene top 1 or top 1-5 in roughly twice as many cases as seven comparable tools, including in cases where no clinical evidence for the diagnostic gene is in our knowledgebase. CONCLUSION: InpherNet improves the state of the art in considering candidate gene neighbors to accelerate monogenic diagnosis.

Transcription factor expression defines subclasses of developing projection neurons highly similar to single-cell RNA-seq subtypes.

We are only just beginning to catalog the vast diversity of cell types in the cerebral cortex. Such categorization is a first step toward understanding how diversification relates to function. All cortical projection neurons arise from a uniform pool of progenitor cells that lines the ventricles of the forebrain. It is still unclear how these progenitor cells generate the more than 50 unique types of mature cortical projection neurons defined by their distinct gene-expression profiles. Moreover, exactly how and when neurons diversify their function during development is unknown. Here we relate gene expression and chromatin accessibility of two subclasses of projection neurons with divergent morphological and functional features as they develop in the mouse brain between embryonic day 13 and postnatal day 5 in order to identify transcriptional networks that diversify neuron cell fate. We compare these gene-expression profiles with published profiles of single cells isolated from similar populations and establish that layer-defined cell classes encompass cell subtypes and developmental trajectories identified using single-cell sequencing. Given the depth of our sequencing, we identify groups of transcription factors with particularly dense subclass-specific regulation and subclass-enriched transcription factor binding motifs. We also describe transcription factor-adjacent long noncoding RNAs that define each subclass and validate the function of Myt1l in balancing the ratio of the two subclasses in vitro. Our multidimensional approach supports an evolving model of progressive restriction of cell fate competence through inherited transcriptional identities.

AMELIE speeds Mendelian diagnosis by matching patient phenotype and genotype to primary literature.

The diagnosis of Mendelian disorders requires labor-intensive literature research. Trained clinicians can spend hours looking for the right publication(s) supporting a single gene that best explains a patient's disease. AMELIE (Automatic Mendelian Literature Evaluation) greatly accelerates this process. AMELIE parses all 29 million PubMed abstracts and downloads and further parses hundreds of thousands of full-text articles in search of information supporting the causality and associated phenotypes of most published genetic variants. AMELIE then prioritizes patient candidate variants for their likelihood of explaining any patient's given set of phenotypes. Diagnosis of singleton patients (without relatives' exomes) is the most time-consuming scenario, and AMELIE ranked the causative gene at the very top for 66% of 215 diagnosed singleton Mendelian patients from the Deciphering Developmental Disorders project. Evaluating only the top 11 AMELIE-scored genes of 127 (median) candidate genes per patient resulted in a rapid diagnosis in more than 90% of cases. AMELIE-based evaluation of all cases was 3 to 19 times more efficient than hand-curated database-based approaches. We replicated these results on a retrospective cohort of clinical cases from Stanford Children's Health and the Manton Center for Orphan Disease Research. An analysis web portal with our most recent update, programmatic interface, and code is available at AMELIE.stanford.edu.

AVADA: toward automated pathogenic variant evidence retrieval directly from the full-text literature.

PURPOSE: Both monogenic pathogenic variant cataloging and clinical patient diagnosis start with variant-level evidence retrieval followed by expert evidence integration in search of diagnostic variants and genes. Here, we try to accelerate pathogenic variant evidence retrieval by an automatic approach. METHODS: Automatic VAriant evidence DAtabase (AVADA) is a novel machine learning tool that uses natural language processing to automatically identify pathogenic genetic variant evidence in full-text primary literature about monogenic disease and convert it to genomic coordinates. RESULTS: AVADA automatically retrieved almost 60% of likely disease-causing variants deposited in the Human Gene Mutation Database (HGMD), a 4.4-fold improvement over the current best open source automated variant extractor. AVADA contains over 60,000 likely disease-causing variants that are in HGMD but not in ClinVar. AVADA also highlights the challenges of automated variant mapping and pathogenicity curation. However, when combined with manual validation, on 245 diagnosed patients, AVADA provides valuable evidence for an additional 18 diagnostic variants, on top of ClinVar's 21, versus only 2 using the best current automated approach. CONCLUSION: AVADA advances automated retrieval of pathogenic monogenic variant evidence from full-text literature. Far from perfect, but much faster than PubMed/Google Scholar search, careful curation of AVADA-retrieved evidence can aid both database curation and patient diagnosis.

Emergent high fatality lung disease in systemic juvenile arthritis.

OBJECTIVE: To investigate the characteristics and risk factors of a novel parenchymal lung disease (LD), increasingly detected in systemic juvenile idiopathic arthritis (sJIA). METHODS: In a multicentre retrospective study, 61 cases were investigated using physician-reported clinical information and centralised analyses of radiological, pathological and genetic data. RESULTS: LD was associated with distinctive features, including acute erythematous clubbing and a high frequency of anaphylactic reactions to the interleukin (IL)-6 inhibitor, tocilizumab. Serum ferritin elevation and/or significant lymphopaenia preceded LD detection. The most prevalent chest CT pattern was septal thickening, involving the periphery of multiple lobes ± ground-glass opacities. The predominant pathology (23 of 36) was pulmonary alveolar proteinosis and/or endogenous lipoid pneumonia (PAP/ELP), with atypical features including regional involvement and concomitant vascular changes. Apparent severe delayed drug hypersensitivity occurred in some cases. The 5-year survival was 42%. Whole exome sequencing (20 of 61) did not identify a novel monogenic defect or likely causal PAP-related or macrophage activation syndrome (MAS)-related mutations. Trisomy 21 and young sJIA onset increased LD risk. Exposure to IL-1 and IL-6 inhibitors (46 of 61) was associated with multiple LD features. By several indicators, severity of sJIA was comparable in drug-exposed subjects and published sJIA cohorts. MAS at sJIA onset was increased in the drug-exposed, but was not associated with LD features. CONCLUSIONS: A rare, life-threatening lung disease in sJIA is defined by a constellation of unusual clinical characteristics. The pathology, a PAP/ELP variant, suggests macrophage dysfunction. Inhibitor exposure may promote LD, independent of sJIA severity, in a small subset of treated patients. Treatment/prevention strategies are needed.

CRISPR/Cas9 Genome Engineering in Engraftable Human Brain-Derived Neural Stem Cells.

Human neural stem cells (NSCs) offer therapeutic potential for neurodegenerative diseases, such as inherited monogenic nervous system disorders, and neural injuries. Gene editing in NSCs (GE-NSCs) could enhance their therapeutic potential. We show that NSCs are amenable to gene targeting at multiple loci using Cas9 mRNA with synthetic chemically modified guide RNAs along with DNA donor templates. Transplantation of GE-NSC into oligodendrocyte mutant shiverer-immunodeficient mice showed that GE-NSCs migrate and differentiate into astrocytes, neurons, and myelin-producing oligodendrocytes, highlighting the fact that GE-NSCs retain their NSC characteristics of self-renewal and site-specific global migration and differentiation. To show the therapeutic potential of GE-NSCs, we generated GALC lysosomal enzyme overexpressing GE-NSCs that are able to cross-correct GALC enzyme activity through the mannose-6-phosphate receptor pathway. These GE-NSCs have the potential to be an investigational cell and gene therapy for a range of neurodegenerative disorders and injuries of the central nervous system, including lysosomal storage disorders.

Phrank measures phenotype sets similarity to greatly improve Mendelian diagnostic disease prioritization.

PURPOSE: Exome sequencing and diagnosis is beginning to spread across the medical establishment. The most time-consuming part of genome-based diagnosis is the manual step of matching the potentially long list of patient candidate genes to patient phenotypes to identify the causative disease. METHODS: We introduce Phrank (for phenotype ranking), an information theory-inspired method that utilizes a Bayesian network to prioritize candidate diseases or genes, as a stand-alone module that can be run with any underlying knowledgebase and any variant filtering scheme. RESULTS: Phrank outperforms existing methods at ranking the causative disease or gene when applied to 169 real patient exomes with Mendelian diagnoses. Phrank's greatest improvement is in disease space, where across all 169 patients it ranks only 3 diseases on average ahead of the true diagnosis, whereas Phenomizer ranks 32 diseases ahead of the causal one. CONCLUSIONS: Using Phrank to rank all patient candidate genes or diseases, as they start working through a new case, will save the busy clinician much time in deriving a genetic diagnosis.

ClinPhen extracts and prioritizes patient phenotypes directly from medical records to expedite genetic disease diagnosis.

PURPOSE: Diagnosing monogenic diseases facilitates optimal care, but can involve the manual evaluation of hundreds of genetic variants per case. Computational tools like Phrank expedite this process by ranking all candidate genes by their ability to explain the patient's phenotypes. To use these tools, busy clinicians must manually encode patient phenotypes from lengthy clinical notes. With 100 million human genomes estimated to be sequenced by 2025, a fast alternative to manual phenotype extraction from clinical notes will become necessary. METHODS: We introduce ClinPhen, a fast, high-accuracy tool that automatically converts clinical notes into a prioritized list of patient phenotypes using Human Phenotype Ontology (HPO) terms. RESULTS: ClinPhen shows superior accuracy and 20× speedup over existing phenotype extractors, and its novel phenotype prioritization scheme improves the performance of gene-ranking tools. CONCLUSION: While a dedicated clinician can process 200 patient records in a 40-hour workweek, ClinPhen does the same in 10 minutes. Compared with manual phenotype extraction, ClinPhen saves an additional 3-5 hours per Mendelian disease diagnosis. Providers can now add ClinPhen's output to each summary note attached to a filled testing laboratory request form. ClinPhen makes a substantial contribution to improvements in efficiency critically needed to meet the surging demand for clinical diagnostic sequencing.

An MTF1 binding site disrupted by a homozygous variant in the promoter of ATP7B likely causes Wilson Disease.

Approximately 2% of the human genome accounts for protein-coding genes, yet most known Mendelian disease-causing variants lie in exons or splice sites. Individuals who symptomatically present with monogenic disorders but do not possess function-altering variants in the protein-coding regions of causative genes may harbor variants in the surrounding gene regulatory domains. We present such a case: a male of Afghani descent was clinically diagnosed with Wilson Disease-a disorder of systemic copper buildup-but was found to have no function-altering coding variants in ATP7B (ENST00000242839.4), the typically causative gene. Our analysis revealed the homozygous variant chr13:g.52,586,149T>C (NC_000013.10, hg19) 676 bp into the ATP7B promoter, which disrupts a metal regulatory transcription factor 1 (MTF1) binding site and diminishes expression of ATP7B in response to copper intake, likely resulting in Wilson Disease. Our approach to identify the causative variant can be generalized to systematically discover function-altering non-coding variants underlying disease and motivates evaluation of gene regulatory variants.

Biallelic loss-of-function WNT5A mutations in an infant with severe and atypical manifestations of Robinow syndrome.

Robinow syndrome (RS) is a well-recognized Mendelian disorder known to demonstrate both autosomal dominant and autosomal recessive inheritance. Typical manifestations include short stature, characteristic facies, and skeletal anomalies. Recessive inheritance has been associated with mutations in ROR2 while dominant inheritance has been observed for mutations in WNT5A, DVL1, and DVL3. Through trio whole genome sequencing, we identified a homozygous frameshifting single nucleotide deletion in WNT5A in a previously reported, deceased infant with a unique constellation of features comprising a 46,XY disorder of sex development with multiple congenital malformations including congenital diaphragmatic hernia, ambiguous genitalia, dysmorphic facies, shortened long bones, adactyly, and ventricular septal defect. The parents, who are both heterozygous for the deletion, appear clinically unaffected. In conjunction with published observations of Wnt5a double knockout mice, we provide evidence for the possibility of autosomal recessive inheritance in association with WNT5A loss-of-function mutations in RS.

Deriving genomic diagnoses without revealing patient genomes.

Patient genomes are interpretable only in the context of other genomes; however, genome sharing enables discrimination. Thousands of monogenic diseases have yielded definitive genomic diagnoses and potential gene therapy targets. Here we show how to provide such diagnoses while preserving participant privacy through the use of secure multiparty computation. In multiple real scenarios (small patient cohorts, trio analysis, two-hospital collaboration), we used our methods to identify the causal variant and discover previously unrecognized disease genes and variants while keeping up to 99.7% of all participants' most sensitive genomic information private.