{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Hansen AW"],"funding":["National Institute of Neurological Disorders and Stroke","National Institute of Diabetes and Digestive and Kidney Diseases","The Cullen Foundation","NIMH NIH HHS","NHGRI","NIH","National Human Genome Research Institute","NIDDK NIH HHS","NHGRI NIH HHS","NINDS NIH HHS","Baylor College of Medicine President’s Circle Precision Medicine/Population Health Initiative","NHGRI/National Heart, Lung, and Blood Institute","NIGMS NIH HHS"],"pagination":["974-986"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC6849092"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["105(5)"],"pubmed_abstract":["The advent of inexpensive, clinical exome sequencing (ES) has led to the accumulation of genetic data from thousands of samples from individuals affected with a wide range of diseases, but for whom the underlying genetic and molecular etiology of their clinical phenotype remains unknown. In many cases, detailed phenotypes are unavailable or poorly recorded and there is little family history to guide study. To accelerate discovery, we integrated ES data from 18,696 individuals referred for suspected Mendelian disease, together with relatives, in an Apache Hadoop data lake (Hadoop Architecture Lake of Exomes [HARLEE]) and implemented a genocentric analysis that rapidly identified 154 genes harboring variants suspected to cause Mendelian disorders. The approach did not rely on case-specific p"],"journal":["American journal of human genetics"],"pubmed_title":["A Genocentric Approach to Discovery of Mendelian Disorders."],"pmcid":["PMC6849092"],"funding_grant_id":["UM1 HG006542","T32 GM08307-26","P50 MH094268","R01 NS058529","R35 NS105078","K08 HG008986","UM1 HG008898","T32 GM008307","P50 DK096415"],"pubmed_authors":["Bidegain M","Marcus J","Andrews BK","Gibbs RA","Ashley P","Boerwinkle E","McDonald M","Sadeghpour A","Wang L","Khayat MM","Goldberg R","Katsanis S","Katsanis N","Davis EE","Lupski JR","Hill K","Li H","Pizoli C","Cotten CM","Boyd B","Yang Y","Task Force for Neonatal Genomics","Wangler MF","Fisher K","Mikati M","Wiener J","Sabo A","Ellestad S","Sedlazeck FJ","Ashley-Koch AE","Curington T","Chambers E","Jhangiani SN","Kansagra S","Purves T","Posey JE","Liu P","Miller S","Allori A","Cope H","Coban Akdemir ZH","Ross S","Hansen AW","Murugan M","Kurtzberg J","Perilla Y","Muzny DM","Rosenfeld J","Murtha A","Smith E","French A","Sutton VR","Angrist M","Eng CM","Gallentine W"],"additional_accession":[]},"is_claimable":false,"name":"A Genocentric Approach to Discovery of Mendelian Disorders.","description":"The advent of inexpensive, clinical exome sequencing (ES) has led to the accumulation of genetic data from thousands of samples from individuals affected with a wide range of diseases, but for whom the underlying genetic and molecular etiology of their clinical phenotype remains unknown. In many cases, detailed phenotypes are unavailable or poorly recorded and there is little family history to guide study. To accelerate discovery, we integrated ES data from 18,696 individuals referred for suspected Mendelian disease, together with relatives, in an Apache Hadoop data lake (Hadoop Architecture Lake of Exomes [HARLEE]) and implemented a genocentric analysis that rapidly identified 154 genes harboring variants suspected to cause Mendelian disorders. The approach did not rely on case-specific p","dates":{"release":"2019-01-01T00:00:00Z","publication":"2019 Nov","modification":"2026-04-13T07:44:54.651Z","creation":"2024-11-15T23:00:06.886Z"},"accession":"S-EPMC6849092","cross_references":{"pubmed":["31668702"],"doi":["10.1016/j.ajhg.2019.09.027"]}}