American journal of human genetics
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Rare diseases affect millions of people worldwide, and discovering their genetic causes is challenging. More than half of the individuals analyzed by the Undiagnosed Diseases Network (UDN) remain undiagnosed. The central hypothesis of this work is that many of these rare genetic disorders are caused by multiple variants in more than one gene. ⋯ In contrast to other approaches, DiGePred also appropriately controls the number of false positives when applied in realistic clinical settings. Finally, to enable the rapid screening of variant gene pairs for digenic disease potential, we freely provide the predictions of DiGePred on all human gene pairs. Our work enables the discovery of genetic causes for rare non-monogenic diseases by providing a means to rapidly evaluate variant gene pairs for the potential to cause digenic disease.
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Hereditary hemorrhagic telangiectasia (HHT) is a Mendelian disease characterized by vascular malformations (VMs) including visceral arteriovenous malformations and mucosal telangiectasia. HHT is caused by loss-of-function (LoF) mutations in one of three genes, ENG, ACVRL1, or SMAD4, and is inherited as an autosomal-dominant condition. Intriguingly, the constitutional mutation causing HHT is present throughout the body, yet the multiple VMs in individuals with HHT occur focally, rather than manifesting as a systemic vascular defect. ⋯ We identified low-frequency somatic mutations in 9/19 telangiectasia through the use of next-generation sequencing. We established phase for seven of nine samples, which confirms that the germline and somatic mutations in all seven samples exist in trans configuration; this is consistent with a genetic two-hit mechanism. These combined data suggest that bi-allelic loss of ENG or ACVRL1 may be a required event in the development of telangiectasia, and that rather than haploinsufficiency, VMs in HHT are caused by a Knudsonian two-hit mechanism.
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Sequencing-based studies have identified novel risk genes associated with severe epilepsies and revealed an excess of rare deleterious variation in less-severe forms of epilepsy. To identify the shared and distinct ultra-rare genetic risk factors for different types of epilepsies, we performed a whole-exome sequencing (WES) analysis of 9,170 epilepsy-affected individuals and 8,436 controls of European ancestry. We focused on three phenotypic groups: severe developmental and epileptic encephalopathies (DEEs), genetic generalized epilepsy (GGE), and non-acquired focal epilepsy (NAFE). ⋯ The larger gene groups for the GABAergic pathway or cation channels also showed a significant mutational burden in DEEs and GGE. Although no single gene surpassed exome-wide significance among individuals with GGE or NAFE, highly constrained genes and genes encoding ion channels were among the lead associations; such genes included CACNA1G, EEF1A2, and GABRG2 for GGE and LGI1, TRIM3, and GABRG2 for NAFE. Our study, the largest epilepsy WES study to date, confirms a convergence in the genetics of severe and less-severe epilepsies associated with ultra-rare coding variation, and it highlights a ubiquitous role for GABAergic inhibition in epilepsy etiology.
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Spinal muscular atrophy (SMA) is a neuromuscular disease causing the most frequent genetic childhood lethality. Recently, nusinersen, an antisense oligonucleotide (ASO) that corrects SMN2 splicing and thereby increases full-length SMN protein, has been approved by the FDA and EMA for SMA therapy. However, the administration of nusinersen in severe and/or post-symptomatic SMA-affected individuals is insufficient to counteract the disease. ⋯ While low-dose SMN-ASO rescues multiorgan impairment, additional NCALD reduction significantly ameliorated SMA pathology including electrophysiological and histological properties of neuromuscular junctions and muscle at P21 and motoric deficits at 3 months. The present study shows the additional benefit of a combinatorial SMN-dependent and SMN-independent ASO-based therapy for SMA. This work illustrates how a modifying gene, identified in some asymptomatic individuals, helps to develop a therapy for all SMA-affected individuals.
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Integration of detailed phenotype information with genetic data is well established to facilitate accurate diagnosis of hereditary disorders. As a rich source of phenotype information, electronic health records (EHRs) promise to empower diagnostic variant interpretation. However, how to accurately and efficiently extract phenotypes from heterogeneous EHR narratives remains a challenge. ⋯ We then assessed the broader utility by examining two additional EHR datasets, including 31 individuals who were suspected of having a Mendelian disease and underwent different types of genetic testing and 20 individuals with positive diagnoses of specific Mendelian etiologies of chronic kidney disease from exome sequencing. Finally, through several retrospective case studies, we demonstrated how combined analyses of genotype data and deep phenotype data from EHRs can expedite genetic diagnoses. In summary, EHR-Phenolyzer leverages EHR narratives to automate phenotype-driven analysis of clinical exomes or genomes, facilitating the broader implementation of genomic medicine.