{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Karunamuni RA"],"funding":["Cancer Research UK","NIBIB NIH HHS","Medical Research Council","National Institute for Health Research (NIHR)","NCI NIH HHS"],"pagination":["532-541"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8157993"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["24(2)"],"pubmed_abstract":["Background
Polygenic hazard scores (PHS) can identify individuals with increased risk of prostate cancer. We estimated the benefit of additional SNPs on performance of a previously validated PHS (PHS46).Materials and method
180 SNPs, shown to be previously associated with prostate cancer, were used to develop a PHS model in men with European ancestry. A machine-learning approach, LASSO-regularized Cox regression, was used to select SNPs and to estimate their coefficients in the training set (75,596 men). Performance of the resulting model was evaluated in the testing/validation set (6,411 men) with two metrics: (1) hazard ratios (HRs) and (2) positive predictive value (PPV) of prostate-specific antigen (PSA) testing. HRs were estimated between individuals with PHS in the to"],"journal":["Prostate cancer and prostatic diseases"],"pubmed_title":["Additional SNPs improve risk stratification of a polygenic hazard score for prostate cancer."],"pmcid":["PMC8157993"],"funding_grant_id":["NIHR202411","19169","15064","NIHR201620","G0500966","24432","29019","K08 EB026503","29017","UG1 CA189974","MR/M012190/1","U01 CA182883","U10 CA037429"],"pubmed_authors":["Gamulin M","Berndt SI","Teixeira MR","Rosenstein BS","Lophatananon A","Huynh-Le MP","Grindedal EM","Koutros S","UKGPCS collaborators","Nordestgaard BG","Andreassen OA","Pashayan N","Lane A","Neal DE","Wiklund F","Wolk A","Kote-Jarai Z","MacInnis RJ","Eeles RA","Ingles SA","Karunamuni RA","Turner EL","Donovan JL","Claessens F","Zheng W","Razack A","Stanford JL","Nielsen SF","Mucci LA","Brenner H","Neuhausen SL","APCB BioResource (Australian Prostate Cancer BioResource)","Martin RM","Roobol MJ","Travis RC","Mills IG","Seibert TM","Usmani N","Cussenot O","Tangen CM","Kibel AS","Sorensen KD","Vega A","Fan CC","Park JY","Newcomb LF","West CML","Profile Study Steering Committee","Muir K","Haiman CA","John EM","Walsh EI","Schleutker J","Townsend PA","Logothetis CJ","Kogevinas M","Gago-Dominguez M","Gronberg H","Albanes D","IMPACT Study Steering Committee and Collaborators","Batra J","Maier C","Dale AM","Hamilton RJ","Penney KL","Kaneva R","Canary PASS Investigators","Thompson W","Hamdy FC","PRACTICAL Consortium","Cybulski C"],"additional_accession":[]},"is_claimable":false,"name":"Additional SNPs improve risk stratification of a polygenic hazard score for prostate cancer.","description":"Background
Polygenic hazard scores (PHS) can identify individuals with increased risk of prostate cancer. We estimated the benefit of additional SNPs on performance of a previously validated PHS (PHS46).Materials and method
180 SNPs, shown to be previously associated with prostate cancer, were used to develop a PHS model in men with European ancestry. A machine-learning approach, LASSO-regularized Cox regression, was used to select SNPs and to estimate their coefficients in the training set (75,596 men). Performance of the resulting model was evaluated in the testing/validation set (6,411 men) with two metrics: (1) hazard ratios (HRs) and (2) positive predictive value (PPV) of prostate-specific antigen (PSA) testing. HRs were estimated between individuals with PHS in the to","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021 Jun","modification":"2026-05-09T04:14:57.06Z","creation":"2025-04-05T00:23:41.734Z"},"accession":"S-EPMC8157993","cross_references":{"pubmed":["33420416"],"doi":["10.1038/s41391-020-00311-2"]}}