<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Smail A</submitter><funding>Medical Research Council</funding><funding>Wellcome Trust</funding><pagination>1414-1421</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC12583576</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>33(11)</volume><pubmed_abstract>Polycomb group (PcG) and Trithorax group (TrxG) complexes represent two major components of the epigenetic machinery. This study aimed to delineate phenotypic similarities and differences across developmental conditions arising from rare variants in PcG and TrxG genes, using data-driven approaches. 462 patients with a PcG or TrxG-associated condition were identified in the DECIPHER dataset. We analysed Human Phenotype Ontology (HPO) data to identify phenotypes enriched in this group, in comparison to other monogenic conditions within DECIPHER. We then assessed phenotypic relationships between single gene diagnoses within the PcG and TrxG group, by applying semantic similarity analysis and hierarchical clustering. Finally, we analysed patient-level phenotypic heterogeneity in this group, ir</pubmed_abstract><journal>European journal of human genetics : EJHG</journal><pubmed_title>Polycomb-associated and Trithorax-associated developmental conditions-phenotypic convergence and heterogeneity.</pubmed_title><pmcid>PMC12583576</pmcid><funding_grant_id>MC_UU_00030/3</funding_grant_id><funding_grant_id>WT223718/Z/21/Z</funding_grant_id><pubmed_authors>Baker K</pubmed_authors><pubmed_authors>Al-Jawahiri R</pubmed_authors><pubmed_authors>Smail A</pubmed_authors></additional><is_claimable>false</is_claimable><name>Polycomb-associated and Trithorax-associated developmental conditions-phenotypic convergence and heterogeneity.</name><description>Polycomb group (PcG) and Trithorax group (TrxG) complexes represent two major components of the epigenetic machinery. This study aimed to delineate phenotypic similarities and differences across developmental conditions arising from rare variants in PcG and TrxG genes, using data-driven approaches. 462 patients with a PcG or TrxG-associated condition were identified in the DECIPHER dataset. We analysed Human Phenotype Ontology (HPO) data to identify phenotypes enriched in this group, in comparison to other monogenic conditions within DECIPHER. We then assessed phenotypic relationships between single gene diagnoses within the PcG and TrxG group, by applying semantic similarity analysis and hierarchical clustering. Finally, we analysed patient-level phenotypic heterogeneity in this group, ir</description><dates><release>2025-01-01T00:00:00Z</release><publication>2025 Nov</publication><modification>2026-06-05T11:46:15.985Z</modification><creation>2026-05-16T03:13:31.548Z</creation></dates><accession>S-EPMC12583576</accession><cross_references><pubmed>39843918</pubmed><doi>10.1038/s41431-025-01784-2</doi></cross_references></HashMap>