Biology subjects

Bernstein, J. A.

Publications and source records attributed to Bernstein, J. A..

6 recordsLinked to original sources

bioRxiv2018

Identification of rare-disease genes in diverse undiagnosed cases using whole blood transcriptome sequencing and large control cohorts

RNA sequencing (RNA-seq) is a complementary approach for Mendelian disease diagnosis for patients in whom exome-sequencing is not informative. For both rare neuromuscular and mitochondrial disorders, its application has improved diagnostic rates. However, the generalizability of this approach to diverse Mendelian diseases has yet to be evaluated. We sequenced whole blood RNA from 56 cases with undiagnosed rare diseases spanning 11 diverse disease categories to evaluate the general application of RNA-seq to Mendelian disease diagnosis. We developed a robust approach to compare rare disease cases to existing large sets of RNA-seq controls (N=1,594 external and N=31 family-based controls) and demonstrated the substantial impacts of gene and variant filtering strategies on disease gene identification when combined with RNA-seq. Across our cohort, we observed that RNA-seq yields a 8.5% diagnostic rate. These diagnoses included diseases where blood would not intuitively reflect evidence of disease. We identified RARS2 as an under-expression outlier containing compound heterozygous pathogenic variants for an individual exhibiting profound global developmental delay, seizures, microcephaly, hypotonia, and progressive scoliosis. We also identified a new splicing junction in KCTD7 for an individual with global developmental delay, loss of milestones, tremors and seizures. Our study provides a broad evaluation of blood RNA-seq for the diagnosis of rare disease.

genomics↗

bioRxiv2018

ClinPhen extracts and prioritizes patientphenotypes directly from medical records to accelerate genetic disease diagnosis

PurposeSevere genetic diseases affect 7 million births per year, worldwide. Diagnosing these diseases is necessary for optimal care, but it can involve the manual evaluation of hundreds of genetic variants per case, with many variants taking an hour to evaluate. Automatic gene-ranking approaches shorten this process by reporting which of the genes containing variants are most likely to be causing the patients symptoms. To use these tools, busy clinicians must manually encode patient phenotypes, which is a cumbersome and imprecise process. With 60 million patients expected to be sequenced in the next 7 years, a fast alternative to manual phenotype extraction from the clinical notes in patients medical records will become necessary.\n\nMethodsWe introduce ClinPhen: a fast, high-accuracy tool that automatically converts the clinical notes into a prioritized list of patient symptoms using HPO terms.\n\nResultsClinPhen shows superior accuracy to existing phenotype extractors, and when paired with a gene-ranking tool it significantly improve the latters performance.\n\nConclusionCompared to manual phenotype extraction, ClinPhen saves more than 5 hours per case in Mendelian diagnosis alone. Summing over millions of forthcoming cases whose medical notes await phenotype encoding, ClinPhen makes a substantial contribution towards ending all patients diagnostic odyssey.

genomics↗

bioRxiv2018

S-CAP extends clinical-grade pathogenicity prediction to genetic variants that affect RNA splicing

There are over 15,000 known variants that cause human inherited disease by disrupting RNA splicing. While several in silico methods such as CADD, EIGEN and LINSIGHT are commonly used to predict the pathogenicity of noncoding variants, we introduce S-CAP, a tool developed specially for splicing which is better able to effectively distinguish pathogenic splicing-relevant variants from benign variants. S-CAP is a novel splicing pathogenicity predictor that reduces the number of splicing-relevant variants of uncertain significance in patient exomes by 41%, a nearly 3-fold improvement over existing noncoding pathogenicity measures while correctly classifying known pathogenic splicing-relevant variants with a clinical-grade 95% sensitivity.

genomics↗

bioRxiv2018

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

PurposeExome 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.\n\nMethodsWe 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.\n\nResultsPhrank outperforms existing methods at ranking the causative disease or gene when applied to 169 real patient exomes with Mendelian diagnoses. Phranks 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.\n\nConclusionUsing 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.

genomics↗

bioRxiv2017

AMELIE accelerates Mendelian patient diagnosis directly from the primary literature

The diagnosis of Mendelian disorders requires labor-intensive literature research. Our software system AMELIE (Automatic Mendelian Literature Evaluation) greatly automates this process. AMELIE parses hundreds of thousands of full text articles to find an underlying diagnosis to explain a patients phenotypes given the patients exome. AMELIE prioritizes patient candidate genes for their likelihood of causing the patients phenotypes. Diagnosis of singleton patients (without relatives exomes) is the most time-consuming scenario. AMELIEs gene ranking method was tested on 215 singleton Mendelian patients with a clinical diagnosis. AMELIE ranked the causal gene among the top 2 in the majority (63%) of cases. Examining AMELIEs top 10 genes, amounting to 8% of 124 candidate genes with rare functional variants per patient, results in diagnosis for 95% of cases. Strikingly, training only on gene pathogenicity knowledge from 2011 leads to identical performance compared to training on current data. An accompanying analysis web portal has launched at AMELIE.stanford.edu.

genetics↗

bioRxiv2017

High Frequency Actionable Pathogenic Exome Mutations in an Average-Risk Cohort

Whole exome sequencing (WES) is increasingly utilized in both clinical and non-clinical settings, but little is known about the utility of WES in healthy individuals. In order to determine the frequency of both medically actionable and non-actionable but medically relevant exome findings in the general population we assessed the exomes of 70 participants who have been extensively characterized over the past several years as part of a longitudinal integrated multi-omics profiling study at Stanford University. We assessed exomes for rare likely pathogenic and pathogenic variants in genes associated with Mendelian disease in the Online Mendelian Inheritance in Man (OMIM) database. We used American College of Medical Genetics (ACMG) guidelines were used for the classification of rare sequence variants, and additionally we assessed pharmacogenetic variants. Twelve out of 70 (17%) participants had medically actionable findings in Mendelian disease genes, including 6 (9%) with mutations in genes not currently included in the ACMGs list of 59 actionable genes. This number is higher than that reported in previous studies and suggests added benefit from utilizing expanded gene lists and manual curation to assess actionable findings. A total of 60 participants (89%) had non-actionable findings identified including 57 who were found to be mutation carriers for recessive diseases and 21 who have increased Alzheimers disease risk due to heterozyg ous or homozygous APOE e4 alleles (18 participants had both). These results suggest that exome sequencing may have considerably more utility for health management in the general population than previously thought.

genomics↗