Genomic architecture of autism from comprehensive whole-genome sequence annotation.

Fully understanding autism spectrum disorder (ASD) genetics requires whole-genome sequencing (WGS). We present the latest release of the Autism Speaks MSSNG resource, which includes WGS data from 5,100 individuals with ASD and 6,212 non-ASD parents and siblings (total n = 11,312). Examining a wide variety of genetic variants in MSSNG and the Simons Simplex Collection (SSC; n = 9,205), we identified ASD-associated rare variants in 718/5,100 individuals with ASD from MSSNG (14.1%) and 350/2,419 from SSC (14.5%). Considering genomic architecture, 52% were nuclear sequence-level variants, 46% were nuclear structural variants (including copy-number variants, inversions, large insertions, uniparental isodisomies, and tandem repeat expansions), and 2% were mitochondrial variants. Our study provides a guidebook for exploring genotype-phenotype correlations in families who carry ASD-associated rare variants and serves as an entry point to the expanded studies required to dissect the etiology in the ∼85% of the ASD population that remain idiopathic.

SCIP: software for efficient clinical interpretation of copy number variants detected by whole-genome sequencing.

Copy number variants (CNVs) represent major etiologic factors in rare genetic diseases. Current clinical CNV interpretation workflows require extensive back-and-forth with multiple tools and databases. This increases complexity and time burden, potentially resulting in missed genetic diagnoses. We present the Suite for CNV Interpretation and Prioritization (SCIP), a software package for the clinical interpretation of CNVs detected by whole-genome sequencing (WGS). The SCIP Visualization Module near-instantaneously displays all information necessary for CNV interpretation (variant quality, population frequency, inheritance pattern, and clinical relevance) on a single page-supported by modules providing variant filtration and prioritization. SCIP was comprehensively evaluated using WGS data from 1027 families with congenital cardiac disease and/or autism spectrum disorder, containing 187 pathogenic or likely pathogenic (P/LP) CNVs identified in previous curations. SCIP was efficient in filtration and prioritization: a median of just two CNVs per case were selected for review, yet it captured all P/LP findings (92.5% of which ranked 1st). SCIP was also able to identify one pathogenic CNV previously missed. SCIP was benchmarked against AnnotSV and a spreadsheet-based manual workflow and performed superiorly than both. In conclusion, SCIP is a novel software package for efficient clinical CNV interpretation, substantially faster and more accurate than previous tools (available at https://github.com/qd29/SCIP , a video tutorial series is available at https://bit.ly/SCIPVideos ).

Expanding the phenotypic spectrum of NOTCH1 variants: clinical manifestations in families with congenital heart disease.

Pathogenic variants in NOTCH1 are associated with non-syndromic congenital heart disease (CHD) and Adams-Oliver syndrome (AOS). The clinical presentation of individuals with damaging NOTCH1 variants is characterized by variable expressivity and incomplete penetrance; however, data on systematic phenotypic characterization are limited. We report the genotype and phenotype of a cohort of 33 individuals (20 females, 13 males; median age 23.4 years, range 2.5-68.3 years) from 11 families with causative NOTCH1 variants (9 inherited, 2 de novo; 9 novel), ascertained from a proband with CHD. We describe the cardiac and extracardiac anomalies identified in these 33 individuals, only four of whom met criteria for AOS. The most common CHD identified was tetralogy of Fallot, though various left- and right-sided lesions and septal defects were also present. Extracardiac anomalies identified include cutis aplasia (5/33), cutaneous vascular anomalies (7/33), vascular anomalies of the central nervous system (2/10), Poland anomaly (1/33), pulmonary hypertension (2/33), and structural brain anomalies (3/14). Identification of these findings in a cardiac proband cohort supports NOTCH1-associated CHD and NOTCH1-associated AOS lying on a phenotypic continuum. Our findings also support (1) Broad indications for NOTCH1 molecular testing (any familial CHD, simplex tetralogy of Fallot or hypoplastic left heart); (2) Cascade testing in all at-risk relatives; and (3) A thorough physical exam, in addition to cardiac, brain (structural and vascular), abdominal, and ophthalmologic imaging, in all gene-positive individuals. This information is important for guiding the medical management of these individuals, particularly given the high prevalence of NOTCH1 variants in the CHD population.

Using whole genome sequence findings to assess gene-disease causality in cardiomyopathy and arrhythmia patients.

Aim: The genetic etiologies of cardiomyopathies and arrhythmias have not been fully elucidated. Materials & methods: Research findings from genome analyses in a cardiomyopathy and arrhythmia cohort were gathered. Gene-disease relationships from two databases were compared with patient phenotypes. A literature review was conducted for genes with limited evidence. Results: Of 43 genes with candidate findings from 18 cases, 23.3% of genes had never been curated, 15.0% were curated for cardiomyopathies, 16.7% for arrhythmias and 31.3% for other conditions. 25.5% of candidate findings were curated for the patient's specific phenotype with 11.8% having definitive evidence. MYH6 and TPCN1 were flagged for recuration. Conclusion: Findings from genome sequencing in disease cohorts may be useful to guide gene-curation efforts.

GeneTerpret: a customizable multilayer approach to genomic variant prioritization and interpretation.

BACKGROUND: Variant interpretation is the main bottleneck in medical genomic sequencing efforts. This usually involves genome analysts manually searching through a multitude of independent databases, often with the aid of several, mostly independent, computational tools. To streamline variant interpretation, we developed the GeneTerpret platform which collates data from current interpretation tools and databases, and applies a phenotype-driven query to categorize the variants identified in the genome(s). The platform assigns quantitative validity scores to genes by query and assembly of the genotype-phenotype data, sequence homology, molecular interactions, expression data, and animal models. It also uses the American College of Medical Genetics and Genomics (ACMG) criteria to categorize variants into five tiers of pathogenicity. The final output is a prioritized list of potentially causal variants/genes. RESULTS: We tested GeneTerpret by comparing its performance to expert-curated genes (ClinGen's gene-validity database) and variant pathogenicity reports (DECIPHER database). Output from GeneTerpret was 97.2% and 83.5% concordant with the expert-curated sources, respectively. Additionally, similar concordance was observed when GeneTerpret's performance was compared with our internal expert-interpreted clinical datasets. CONCLUSIONS: GeneTerpret is a flexible platform designed to streamline the genome interpretation process, through a unique interface, with improved ease, speed and accuracy. This modular and customizable system allows the user to tailor the component-programs in the analysis process to their preference. GeneTerpret is available online at https://geneterpret.com .