Details

Autor(en) / Beteiligte

• Galer, Peter D.
• Ganesan, Shiva
• Lewis-Smith, David
• McKeown, Sarah E.
• Pendziwiat, Manuela
• Helbig, Katherine L.
• Ellis, Colin A.
• Rademacher, Annika
• Smith, Lacey
• Poduri, Annapurna
• Seiffert, Simone
• von Spiczak, Sarah
• Muhle, Hiltrud
• van Baalen, Andreas
• Thomas, Rhys H.
• Krause, Roland
• Weber, Yvonne
• Helbig, Ingo

Titel

Semantic Similarity Analysis Reveals Robust Gene-Disease Relationships in Developmental and Epileptic Encephalopathies

Ist Teil von

• American journal of human genetics, 2020-10, Vol.107 (4), p.683-697

Ort / Verlag

United States: Elsevier Inc

Erscheinungsjahr

2020

Quelle

Elsevier ScienceDirect Journals

Beschreibungen/Notizen

• More than 100 genetic etiologies have been identified in developmental and epileptic encephalopathies (DEEs), but correlating genetic findings with clinical features at scale has remained a hurdle because of a lack of frameworks for analyzing heterogenous clinical data. Here, we analyzed 31,742 Human Phenotype Ontology (HPO) terms in 846 individuals with existing whole-exome trio data and assessed associated clinical features and phenotypic relatedness by using HPO-based semantic similarity analysis for individuals with de novo variants in the same gene. Gene-specific phenotypic signatures included associations of SCN1A with “complex febrile seizures” (HP: 0011172; p = 2.1 × 10−5) and “focal clonic seizures” (HP: 0002266; p = 8.9 × 10−6), STXBP1 with “absent speech” (HP: 0001344; p = 1.3 × 10−11), and SLC6A1 with “EEG with generalized slow activity” (HP: 0010845; p = 0.018). Of 41 genes with de novo variants in two or more individuals, 11 genes showed significant phenotypic similarity, including SCN1A (n = 16, p < 0.0001), STXBP1 (n = 14, p = 0.0021), and KCNB1 (n = 6, p = 0.011). Including genetic and phenotypic data of control subjects increased phenotypic similarity for all genetic etiologies, whereas the probability of observing de novo variants decreased, emphasizing the conceptual differences between semantic similarity analysis and approaches based on the expected number of de novo events. We demonstrate that HPO-based phenotype analysis captures unique profiles for distinct genetic etiologies, reflecting the breadth of the phenotypic spectrum in genetic epilepsies. Semantic similarity can be used to generate statistical evidence for disease causation analogous to the traditional approach of primarily defining disease entities through similar clinical features.