ArticleCancer communications (London, England)2021
Novel strategy for disease risk prediction incorporating predicted gene expression and DNA methylation data: a multi-phased study of prostate cancer.
Article in Cancer communications (London, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Identification of proteins associated with type 2 diabetes risk in diverse racial and ethnic populations.Diabetologia · 2024Article
- Prostate cancer risk assessment and avoidance of prostate biopsies using fully automatic deep learning in prostate MRI: comparison to PI-RADS and integration with clinical data in nomograms.European radiology · 2024Article
- Methods for the Analysis of Multiple Epigenomic Mediators in Environmental Epidemiology.Current environmental health reports · 2024Review
- Unlocking the future of complex human diseases prediction: multi-omics risk score breakthrough.Frontiers in bioinformatics · 2024Article
- Identification of candidate DNA methylation biomarkers related to Alzheimer's disease risk by integrating genome and blood methylome data.Translational psychiatry · 2023Article
- Identification of blood protein biomarkers associated with prostate cancer risk using genetic prediction models: analysis of over 140,000 subjects.Human molecular genetics · 2023Article
- Asthma Exacerbations: The Genes Behind the Scenes.Journal of investigational allergology & clinical immunology · 2023Review
- Polygenic risk score improves the accuracy of a clinical risk score for coronary artery disease.BMC medicine · 2022Observational
- Novel strategy for disease risk prediction incorporating predicted gene expression and DNA methylation data: a multi-phased study of prostate cancer.Cancer communications (London, England) · 2021Article
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Authors and funding
18 authors.
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Abstract
backgroundDNA 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.
methodsUsing 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.
resultsOur 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.
conclusionsWe 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.
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