Repetier server defined limits5/16/2023 ![]() I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. JJA is funded by an NIHR advanced fellowship (NIHR302478). DB was generously supported by a National Institute of Health Research (NIHR) Research Professorship RP-1. Funding StatementĮGS was supported by the Kerkut Charitable Trust, a Foulkes Fellowship from the Foulkes Foundation, and the University of Southampton's Presidential Scholarship Award HLR and AO'D-L and sequencing were supported by the National Human Genome Research Institute (NHGRI) grant U01HG011755 as part of the GREGoR consortium and HR by NHGRI R01HG009141. The authors have declared no competing interest. Applying GenePy at scale has identified potential diagnoses for 456/3183 (14%) of undiagnosed participants who had a top-5 ranked GenePy score in a recessive disease gene, whilst adding only 1.2 additional variants (per individual) for assessment. A further 334/669 (50%) of cases have a possible missed diagnosis but require functional validation. After removing 13 withdrawn participants, in 122/669 (18%) of the phenotype-matched cases, we identified a putative missed diagnosis in a top-ranked gene supported by phasing, ClinVar and ACMG classification. Where phenotypes overlapped, we extracted rare variants in the gene of interest and applied phase, ClinVar and ACMG classification looking for putative causal biallelic variants.ģ184 affected individuals without a molecular diagnosis had a top-5 ranked GenePy gene score and 682/3184 (21%) had phenotypes overlapping with one of the top-ranking genes. We assessed these participants’ phenotypes for overlap with the disease gene associated phenotype for which they were highly ranked. ![]() For each gene, we ranked participant GenePy scores for that gene, and scrutinised affected individuals without a diagnosis whose scores ranked amongst the top-5 for each gene. We calculated GenePy scores for 2862 recessive disease genes in 78,216 individuals in 100KGP. GenePy then combines all variant scores for individual genes, generating an aggregate score per gene, per participant. GenePy scores all variants called in a given individual, incorporating allele frequency, zygosity, and a user-defined deleterious metric (CADD v1.6 applied herein). We sought to identify potential missed biallelic diagnoses independent of the gene panel applied using GenePy - a whole gene pathogenicity metric. ![]() However, assessing biallelic variants without a gene panel is challenging, due to the number of variants requiring scrutiny. The 100,000 Genomes Project (100KGP) diagnosed a quarter of recruited affected participants, but 26% of diagnoses were in genes not on the chosen gene panel(s) with many being de novo variants of high impact. ![]()
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