Novel strategy for disease risk prediction incorporating predicted gene expression and DNA methylation data: a multi-phased study of prostate cancer

Chong Wu, Jingjing Zhu, Austin King, Xiaoran Tong, Qing Lu, Jong Y. Park, Liang Wang, Guimin Gao, Hong Wen Deng, Yaohua Yang, Karen E. Knudsen, Timothy R. Rebbeck, Jirong Long, Wei Zheng, Wei Pan, David V. Conti, Christopher A. Haiman, Lang Wu

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


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.

Original languageEnglish
Pages (from-to)1387-1397
Number of pages11
JournalCancer Communications
Issue number12
StatePublished - Dec 2021

Bibliographical note

Publisher Copyright:
© 2021 The Authors. Cancer Communications published by John Wiley & Sons Australia, Ltd. on behalf of Sun Yat-sen University Cancer Center


  • integrative models
  • polygenic risk scores
  • predicted DNA methylation
  • predicted gene expression
  • prostate cancer
  • risk prediction

ASJC Scopus subject areas

  • Oncology
  • Cancer Research


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