Uncovering the burden of hidden ciliopathies in the 100 000 Genomes Project: a reverse phenotyping approach. Issue 12 (28th June 2022) - Best, Sunayna; Yu, Jing; Lord, Jenny; Roche, Matthew; Watson, Christopher Mark; Bevers, Roel P J; Stuckey, Alex; Madhusudhan, Savita; Jewell, Rosalyn; Sisodiya, Sanjay M; Lin, Siying; Turner, Stephen; et al.
Abstract : Background: The 100 000 Genomes Project (100K) recruited National Health Service patients with eligible rare diseases and cancer between 2016 and 2018. PanelApp virtual gene panels were applied to whole genome sequencing data according to Human Phenotyping Ontology (HPO) terms entered by recruiting clinicians to guide focused analysis. Methods: We developed a reverse phenotyping strategy to identify 100K participants with pathogenic variants in nine prioritised disease genes ( BBS1, BBS10, ALMS1, OFD1, DYNC2H1, WDR34, NPHP1, TMEM67, CEP290 ), representative of the full phenotypic spectrum of multisystemic primary ciliopathies. We mapped genotype data 'backwards' onto available clinical data to assess potential matches against phenotypes. Participants with novel molecular diagnoses and key clinical features compatible with the identified disease gene were reported to recruiting clinicians. Results: We identified 62 reportable molecular diagnoses with variants in these nine ciliopathy genes. Forty-four have been reported by 100K, 5 were previously unreported and 13 are new diagnoses. We identified 11 participants with unreportable, novel molecular diagnoses, who lacked key clinical features to justify reporting to recruiting clinicians. Two participants had likely pathogenic structural variants and one a deep intronic predicted splice variant. These variants would not be prioritised for review by standard 100K diagnostic pipelines. Conclusion: Reverse phenotyping improves the rate of successful molecular diagnosis for unsolved 100K participants with primary ciliopathies. Previous analyses likely missed these diagnoses because incomplete HPO term entry led to incorrect gene panel choice, meaning that pathogenic variants were not prioritised. Better phenotyping data are therefore essential for accurate variant interpretation and improved patient benefit.