{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Mosley JD"],"funding":["NICHD NIH HHS","NCATS NIH HHS","NCRR NIH HHS","American Heart Association-American Stroke Association","NHLBI NIH HHS","NHGRI NIH HHS","NINDS NIH HHS","NLM NIH HHS","American Heart Association (American Heart Association, Inc.)","NIGMS NIH HHS"],"pagination":["3522"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC6117367"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["9(1)"],"pubmed_abstract":["Defining the full spectrum of human disease associated with a biomarker is necessary to advance the biomarker into clinical practice. We hypothesize that associating biomarker measurements with electronic health record (EHR) populations based on shared genetic architectures would establish the clinical epidemiology of the biomarker. We use Bayesian sparse linear mixed modeling to calculate SNP weightings for 53 biomarkers from the Atherosclerosis Risk in Communities study. We use the SNP weightings to computed predicted biomarker values in an EHR population and test associations with 1139 diagnoses. Here we report 116 associations meeting a Bonferroni level of significance. A false discovery rate (FDR)-based significance threshold reveals more known and undescribed associations across a broad range of biomarkers, including biometric measures, plasma proteins and metabolites, functional assays, and behaviors. We confirm an inverse association between LDL-cholesterol level and septicemia risk in an independent epidemiological cohort. This approach efficiently discovers biomarker-disease associations."],"journal":["Nature communications"],"pubmed_title":["A study paradigm integrating prospective epidemiologic cohorts and electronic health records to identify disease biomarkers."],"pmcid":["PMC6117367"],"funding_grant_id":["U01 HG004424","U01 HG004402","HHSN268201100011I","RC2 GM092618","U01 HG006389","KL2 TR000446","U01 HG006388","15MCPRP25620006","U01 HG006385","T32 GM007569","U01 HG006382","HHSN268201100009I","HHSN268201100005C","U01 HG008685","UL1 RR024975","HHSN268201100007C","HHSN268201100009C","16FTF30130005","S10 RR025141","HHSN268201100011C","HHSN268201100005I","R01 LM010685","HHSN268201100007I","HHSN268201100005G","UL1 TR002243","U01 HG004438","R01 HL133786","U01 HG004798","U01 HG006379","U01 HG006830","U01 HG006378","U01 HG006375","UL1 TR000445","R01 NS032830","U01 HG006380","U01 HG008672","HHSN268201100006C","HHSN268201100008C","P50 GM115305","R01 GM120523","U01 HG008657","R01 HD074711","HHSN268201100010C","HHSN268201100008I","HHSN268201100012C","U19 HL065962"],"pubmed_authors":["McCarty CA","Roden DM","Peissig PL","Crosslin DR","Williams MS","Bastarache L","Shaffer CM","Chute CG","Gordon AS","Wells QS","Wang TJ","Weiss ST","Carrell DS","Jarvik GP","Van Driest SL","Namjou B","Mosley JD","Larson EB","Kullo IJ","Brilliant MH","Karnes JH","Pacheco JA","Verma SS","Linneman JG","Stein CM","Wei WQ","Edwards TL","Ritchie MD","Borthwick KM","Davis LK","Thompson W","Denny JC","Feng Q","Palmer MR"],"additional_accession":[]},"is_claimable":false,"name":"A study paradigm integrating prospective epidemiologic cohorts and electronic health records to identify disease biomarkers.","description":"Defining the full spectrum of human disease associated with a biomarker is necessary to advance the biomarker into clinical practice. We hypothesize that associating biomarker measurements with electronic health record (EHR) populations based on shared genetic architectures would establish the clinical epidemiology of the biomarker. We use Bayesian sparse linear mixed modeling to calculate SNP weightings for 53 biomarkers from the Atherosclerosis Risk in Communities study. We use the SNP weightings to computed predicted biomarker values in an EHR population and test associations with 1139 diagnoses. Here we report 116 associations meeting a Bonferroni level of significance. A false discovery rate (FDR)-based significance threshold reveals more known and undescribed associations across a broad range of biomarkers, including biometric measures, plasma proteins and metabolites, functional assays, and behaviors. We confirm an inverse association between LDL-cholesterol level and septicemia risk in an independent epidemiological cohort. This approach efficiently discovers biomarker-disease associations.","dates":{"release":"2018-01-01T00:00:00Z","publication":"2018 Aug","modification":"2024-11-05T18:42:16.133Z","creation":"2019-03-26T23:54:21Z"},"accession":"S-EPMC6117367","cross_references":{"pubmed":["30166544"],"doi":["10.1038/s41467-018-05624-4"]}}