<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Seaby EG</submitter><funding>NIMH NIH HHS</funding><funding>Gerald Kerkut Charitable Trust</funding><funding>National Institute for Health Research (NIHR)</funding><funding>NHGRI NIH HHS</funding><funding>National Institute of Mental Health</funding><funding>National Human Genome Research Institute</funding><funding>Wellcome Trust</funding><pagination>351-362</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC9950176</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>142(3)</volume><pubmed_abstract>&lt;h4>Background&lt;/h4>Genome sequencing was first offered clinically in the UK through the 100,000 Genomes Project (100KGP). Analysis was restricted to predefined gene panels associated with the patient's phenotype. However, panels rely on clearly characterised phenotypes and risk missing diagnoses outside of the panel(s) applied. We propose a complementary method to rapidly identify pathogenic variants, including those missed by 100KGP methods.&lt;h4>Methods&lt;/h4>The Loss-of-function Observed/Expected Upper-bound Fraction (LOEUF) score quantifies gene constraint, with low scores correlated with haploinsufficiency. We applied DeNovoLOEUF, a filtering strategy to sequencing data from 13,949 rare disease trios in the 100KGP, by filtering for rare, de novo, loss-of-function variants in disease genes</pubmed_abstract><journal>Human genetics</journal><pubmed_title>Targeting de novo loss-of-function variants in constrained disease genes improves diagnostic rates in the 100,000 Genomes Project.</pubmed_title><pmcid>PMC9950176</pmcid><funding_grant_id>U41 HG006834</funding_grant_id><funding_grant_id>U24 HG011450</funding_grant_id><funding_grant_id>RP-2016-07-011</funding_grant_id><funding_grant_id>U01 MH119689</funding_grant_id><funding_grant_id>UM1 HG008900</funding_grant_id><pubmed_authors>Savage K</pubmed_authors><pubmed_authors>Taylor Tavares AL</pubmed_authors><pubmed_authors>Bevers R</pubmed_authors><pubmed_authors>Fowler T</pubmed_authors><pubmed_authors>Rehm HL</pubmed_authors><pubmed_authors>Chan GC</pubmed_authors><pubmed_authors>Williams E</pubmed_authors><pubmed_authors>Thomas NS</pubmed_authors><pubmed_authors>Pullinger J</pubmed_authors><pubmed_authors>Lopez FJ</pubmed_authors><pubmed_authors>Maleady-Crowe F</pubmed_authors><pubmed_authors>Baralle D</pubmed_authors><pubmed_authors>Lahnstein L</pubmed_authors><pubmed_authors>Hubbard TJP</pubmed_authors><pubmed_authors>Ennis S</pubmed_authors><pubmed_authors>Welland MJ</pubmed_authors><pubmed_authors>Minneci F</pubmed_authors><pubmed_authors>Stuckey A</pubmed_authors><pubmed_authors>Sieghart A</pubmed_authors><pubmed_authors>Genomics England Consortium</pubmed_authors><pubmed_authors>Caulfield MJ</pubmed_authors><pubmed_authors>Hamblin A</pubmed_authors><pubmed_authors>Mueller M</pubmed_authors><pubmed_authors>O'Donovan P</pubmed_authors><pubmed_authors>Ambrose JC</pubmed_authors><pubmed_authors>Scott RH</pubmed_authors><pubmed_authors>Tucci A</pubmed_authors><pubmed_authors>Moutsianas L</pubmed_authors><pubmed_authors>Bleda M</pubmed_authors><pubmed_authors>Leigh SEA</pubmed_authors><pubmed_authors>Patch C</pubmed_authors><pubmed_authors>Thomas ERA</pubmed_authors><pubmed_authors>Brittain H</pubmed_authors><pubmed_authors>Jones LJ</pubmed_authors><pubmed_authors>Seaby EG</pubmed_authors><pubmed_authors>Arumugam P</pubmed_authors><pubmed_authors>Kasperaviciute D</pubmed_authors><pubmed_authors>Henderson S</pubmed_authors><pubmed_authors>Pereira MB</pubmed_authors><pubmed_authors>Rogers T</pubmed_authors><pubmed_authors>Boardman-Pretty F</pubmed_authors><pubmed_authors>McEntagart M</pubmed_authors><pubmed_authors>Kayikci M</pubmed_authors><pubmed_authors>Thompson SR</pubmed_authors><pubmed_authors>Murugaesu N</pubmed_authors><pubmed_authors>Leong IUS</pubmed_authors><pubmed_authors>Kousathanas A</pubmed_authors><pubmed_authors>Witkowska K</pubmed_authors><pubmed_authors>Tanguy M</pubmed_authors><pubmed_authors>Smith SC</pubmed_authors><pubmed_authors>Jackson R</pubmed_authors><pubmed_authors>Giess A</pubmed_authors><pubmed_authors>Rendon A</pubmed_authors><pubmed_authors>Sosinsky A</pubmed_authors><pubmed_authors>O'Donnell-Luria A</pubmed_authors><pubmed_authors>Perez-Gil D</pubmed_authors><pubmed_authors>Need AC</pubmed_authors><pubmed_authors>Sawant K</pubmed_authors><pubmed_authors>Boustred CR</pubmed_authors><pubmed_authors>Siddiq A</pubmed_authors><pubmed_authors>Odhams CA</pubmed_authors><pubmed_authors>Webb A</pubmed_authors><pubmed_authors>Rahim T</pubmed_authors><pubmed_authors>Wood SM</pubmed_authors></additional><is_claimable>false</is_claimable><name>Targeting de novo loss-of-function variants in constrained disease genes improves diagnostic rates in the 100,000 Genomes Project.</name><description>&lt;h4>Background&lt;/h4>Genome sequencing was first offered clinically in the UK through the 100,000 Genomes Project (100KGP). Analysis was restricted to predefined gene panels associated with the patient's phenotype. However, panels rely on clearly characterised phenotypes and risk missing diagnoses outside of the panel(s) applied. We propose a complementary method to rapidly identify pathogenic variants, including those missed by 100KGP methods.&lt;h4>Methods&lt;/h4>The Loss-of-function Observed/Expected Upper-bound Fraction (LOEUF) score quantifies gene constraint, with low scores correlated with haploinsufficiency. We applied DeNovoLOEUF, a filtering strategy to sequencing data from 13,949 rare disease trios in the 100KGP, by filtering for rare, de novo, loss-of-function variants in disease genes</description><dates><release>2023-01-01T00:00:00Z</release><publication>2023 Mar</publication><modification>2026-05-16T03:14:55.148Z</modification><creation>2025-02-18T23:44:18.07Z</creation></dates><accession>S-EPMC9950176</accession><cross_references><pubmed>36477409</pubmed><doi>10.1007/s00439-022-02509-x</doi></cross_references></HashMap>