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Innovative and easy-to-implement strategies are needed to improve the pathogenicity assessment of rare germline missense variants. Somatic cancer driver mutations identified through large-scale tumor sequencing studies often impact genes that are also associated with rare Mendelian disorders. The use of cancer mutation data to aid in the interpretation of germline missense variants, regardless of whether the gene is associated with a hereditary cancer predisposition syndrome or a non-cancer-related developmental disorder, has not been systematically assessed. We extracted putative cancer driver missense mutations from the Cancer Hotspots database and annotated them as germline variants, including presence/absence and classification in ClinVar. We trained two supervised learning models (logistic regression and random forest) to predict variant classifications of germline missense variants in ClinVar using Cancer Hotspot data (training dataset). The performance of each model was evaluated with an independent test dataset generated in part from searching public and private genome-wide sequencing datasets from ~1.5 million individuals. Of the 2,447 cancer mutations, 691 corresponding germline variants had been previously classified in ClinVar: 426 (61.6%) as likely pathogenic/pathogenic, 261 (37.8%) as uncertain significance, and 4 (0.6%) as likely benign/benign. The odds ratio for a likely pathogenic/pathogenic classification in ClinVar was 28.3 (95% confidence interval: 24.2-33.1, p < 0.001), compared with all other germline missense variants in the same 216 genes. Both supervised learning models showed high correlation with pathogenicity assessments in the training dataset. There was high area under precision-recall curve values (0.847 and 0.829) and area under the receiver-operating characteristic curve values (0.821 and 0.774) for logistic regression and random forest models, respectively, when applied to the test dataset. With the use of cancer and germline datasets and supervised learning techniques, our study shows that cancer mutation data can be leveraged to improve the interpretation of germline missense variation potentially causing rare Mendelian disorders.

Abstract Background Intronic variants outside the canonical splice site are challenging to interpret and therefore likely represent an underreported cause of human disease. Autosomal recessive variants in DYNC2H1 are associated with short‐rib thoracic dysplasia 3 with or without polydactyly (SRTD3), a clinically heterogeneous disease generally presenting with short ribs, shortened tubular bones, narrow thorax and acetabular roof anomalies. We describe a case of SRTD3 with compound heterozygous frameshift and intronic variants and highlight the essential role of RNA sequencing (RNA‐Seq) in variant interpretation. Methods Following inconclusive clinical genetic testing identifying a likely pathogenic frameshift variant and an intronic variant of uncertain significance (VUS) in DYNC2H1 in trans, the family enrolled in the Care4Rare Canada research program, where RNA‐Seq studies were performed. Results The proband presented with post‐axial polydactyly of all four limbs, a significantly small chest with a pectus excavatum and anterior flaring of the ribs. RNA‐Seq investigations revealed a novel splice junction as a result of the intronic VUS and significantly decreased DYNC2H1 gene expression in the proband. Conclusion This case demonstrates the diagnostic utility of RNA‐Seq for variant interpretation following inconclusive clinical testing, which can ultimately lead to diagnosis for patients with rare disease.

Summary: Genome-wide sequencing (GWS) is a standard of care for diagnosis of suspected genetic disorders, but the proportion of patients found to have pathogenic or likely pathogenic variants ranges from less than 30% to more than 60% in reported studies. It has been suggested that the diagnostic rate can be improved by interpreting genomic variants in the context of each affected individual’s full clinical picture and by regular follow-up and reinterpretation of GWS laboratory results.Trio exome sequencing was performed in 415 families and trio genome sequencing in 85 families in the CAUSES study. The variants observed were interpreted by a multidisciplinary team including laboratory geneticists, bioinformaticians, clinical geneticists, genetic counselors, pediatric subspecialists, and the referring physician, and independently by a clinical laboratory using standard American College of Medical Genetics and Genomics (ACMG) criteria. Individuals were followed for an average of 5.1 years after testing, with clinical reassessment and reinterpretation of the GWS results as necessary. The multidisciplinary team established a diagnosis of genetic disease in 43.0% of the families at the time of initial GWS interpretation, and longitudinal follow-up and reinterpretation of GWS results produced new diagnoses in 17.2% of families whose initial GWS interpretation was uninformative or uncertain. Reinterpretation also resulted in rescinding a diagnosis in four families (1.9%). Of the families studied, 33.6% had ACMG pathogenic or likely pathogenic variants related to the clinical indication. Close collaboration among clinical geneticists, genetic counselors, laboratory geneticists, bioinformaticians, and individuals’ primary physicians, with ongoing follow-up, reanalysis, and reinterpretation over time, can improve the clinical value of GWS.

Autism spectrum disorder (ASD) is a constellation of neurodevelopmental disorders with high phenotypic and genetic heterogeneity, complicating the discovery of causative genes. Through a forward genetics approach selecting for defective vocalization in mice, we identified Kdm5a as a candidate ASD gene. To validate our discovery, we generated a Kdm5a knockout mouse model (Kdm5a-/-) and confirmed that inactivating Kdm5a disrupts vocalization. In addition, Kdm5a-/- mice displayed repetitive behaviors, sociability deficits, cognitive dysfunction, and abnormal dendritic morphogenesis. Loss of KDM5A also resulted in dysregulation of the hippocampal transcriptome. To determine if KDM5A mutations cause ASD in humans, we screened whole exome sequencing and microarray data from a clinical cohort. We identified pathogenic KDM5A variants in nine patients with ASD and lack of speech. Our findings illustrate the power and efficacy of forward genetics in identifying ASD genes and highlight the importance of KDM5A in normal brain development and function.

Abstract Background Intellectual Disability (ID) is among the most common global disorders, yet etiology is unknown in ~30% of patients despite clinical assessment. Whole genome sequencing (WGS) is able to interrogate the entire genome, providing potential to diagnose idiopathic patients. Methods We conducted WGS on eight children with idiopathic ID and brain structural defects, and their normal parents; carrying out an extensive data analyses, using standard and discovery approaches. Results We verified de novo pathogenic single nucleotide variants (SNV) in ARID1B c.1595delG and PHF6 c.820C > T, potentially causative de novo two base indels in SQSTM1 c.115_116delinsTA and UPF1 c.1576_1577delinsA, and de novo SNVs in CACNB3 c.1289G > A, and SPRY4 c.508 T > A, of uncertain significance. We report results from a large secondary control study of 2081 exomes probing the pathogenicity of the above genes. We analyzed structural variation by four different algorithms including de novo genome assembly. We confirmed a likely contributory 165 kb de novo heterozygous 1q43 microdeletion missed by clinical microarray. The de novo assembly resulted in unmasking hidden genome instability that was missed by standard re-alignment based algorithms. We also interrogated regulatory sequence variation for known and hypothesized ID genes and present useful strategies for WGS data analyses for non-coding variation. Conclusion This study provides an extensive analysis of WGS in the context of ID, providing genetic and structural insights into ID and yielding diagnoses.