Background:DNA methylation and gene expression are known to play important roles in the etiology of human diseases such as prostate cancer(PCa).However,it has not yet been possible to incorporate information of DNA me...Background:DNA methylation and gene expression are known to play important roles in the etiology of human diseases such as prostate cancer(PCa).However,it has not yet been possible to incorporate information of DNA methylation and gene expression into polygenic risk scores(PRSs).Here,we aimed to develop and validate an improved PRS for PCa risk by incorporating genetically predicted gene expression and DNA methylation,and other genomic information using an integrative method.Methods:Using data from the PRACTICAL consortium,we derived multiple sets of genetic scores,including those based on available single-nucleotide polymorphisms through widely used methods of pruning and thresholding,LDpred,LDpred-funt,AnnoPred,and EBPRS,as well as PRS constructed using the genetically predicted gene expression and DNA methylation through a revised pruning and thresholding strategy.In the tuning step,using the UK Biobank data(1458 prevalent cases and 1467 controls),we selected PRSs with the best performance.Using an independent set of data from the UK Biobank,we developed an integrative PRS combining information from individual scores.Furthermore,in the testing step,we tested the performance of the integrative PRS in another independent set of UK Biobank data of incident cases and controls.Results:Our constructed PRS had improved performance(C statistics:76.1%)over PRSs constructed by individual benchmark methods(from 69.6%to 74.7%).Furthermore,our new PRS had much higher risk assessment power than family history.The overall net reclassification improvement was 69.0%by adding PRS to the baseline model compared with 12.5%by adding family history.Conclusions:We developed and validated a new PRS which may improve the utility in predicting the risk of developing PCa.Our innovative method can also be applied to other human diseases to improve risk prediction across multiple outcomes.展开更多
基金NIH,Grant/Award Number:R03 AG070669Canadian Institutes of Health Research,European Commission’s Seventh Framework Programme grant agreement,Grant/Award Number:HEALTH-F2-2009-223175+11 种基金Cancer Research UK,Grant/Award Numbers:C5047/A7357,C1287/A10118,C1287/A16563,C5047/A3354,C5047/A10692,C16913/A6135The National Institute of Health(NIH)Cancer Post-Cancer GWAS,Grant/Award Number:1 U19 CA 148537-01The National Health and Medical Research Council,Australia,Grant/Award Numbers:126402,209057,251533,396414,450104,504700,504702,504715,623204,940394,614296US National Institutes of Health(NIH),Grant/Award Number:U19 CA 148537Prostate cancer SuscEptibility(ELLIPSE),Grant/Award Number:X01HG007492Center for Inherited Disease Research(CIDR),Grant/Award Number:HHSN268201200008INIH NCI,Grant/Award Number:U01 CA188392European Community’s Seventh Framework Programme,Grant/Award Number:223175Post-Cancer GWAS initiative,Grant/Award Numbers:1U19 CA148537,1U19 CA148065,1U19 CA148112U.S.National Institutes of Health,National Cancer Institute,Grant/Award Numbers:U01-CA98233,U01-CA98710,U01-CA98216,U01-CA98758Swedish Cancer Foundation,Grant/Award Numbers:09-0677,11-484,12-823Swedish Research Council,Swedish Research Council,Grant/Award Numbers:K2010-70X-20430-04-3,2014-2269。
文摘Background:DNA methylation and gene expression are known to play important roles in the etiology of human diseases such as prostate cancer(PCa).However,it has not yet been possible to incorporate information of DNA methylation and gene expression into polygenic risk scores(PRSs).Here,we aimed to develop and validate an improved PRS for PCa risk by incorporating genetically predicted gene expression and DNA methylation,and other genomic information using an integrative method.Methods:Using data from the PRACTICAL consortium,we derived multiple sets of genetic scores,including those based on available single-nucleotide polymorphisms through widely used methods of pruning and thresholding,LDpred,LDpred-funt,AnnoPred,and EBPRS,as well as PRS constructed using the genetically predicted gene expression and DNA methylation through a revised pruning and thresholding strategy.In the tuning step,using the UK Biobank data(1458 prevalent cases and 1467 controls),we selected PRSs with the best performance.Using an independent set of data from the UK Biobank,we developed an integrative PRS combining information from individual scores.Furthermore,in the testing step,we tested the performance of the integrative PRS in another independent set of UK Biobank data of incident cases and controls.Results:Our constructed PRS had improved performance(C statistics:76.1%)over PRSs constructed by individual benchmark methods(from 69.6%to 74.7%).Furthermore,our new PRS had much higher risk assessment power than family history.The overall net reclassification improvement was 69.0%by adding PRS to the baseline model compared with 12.5%by adding family history.Conclusions:We developed and validated a new PRS which may improve the utility in predicting the risk of developing PCa.Our innovative method can also be applied to other human diseases to improve risk prediction across multiple outcomes.