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

Autor: Jong Y. Park, Jirong Long, Timothy R. Rebbeck, Christopher A. Haiman, David V. Conti, Jingjing Zhu, Hong-Wen Deng, Lang Wu, Qing Lu, Chong Wu, Wei Zheng, Liang Wang, Yaohua Yang, Xiaoran Tong, Wei Pan, Austin King, Karen E. Knudsen, Guimin Gao
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Cancer Communications
Cancer Communications, Vol 41, Iss 12, Pp 1387-1397 (2021)
ISSN: 2523-3548
Popis: 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.
An integrative score incorporating genetically predicted gene expression and DNA methylation and other genomic and non‐genomic information advanced our understanding of using genomic information to stratify subjects for prostate cancer.
Databáze: OpenAIRE
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