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.
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.
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.
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.
Cancer communications (London, England). 2021 Sep 14 [Epub ahead of print]
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
Department of Statistics, Florida State University, Tallahassee, FL, 32304, USA., Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI, 96813, USA., Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, 48824, USA., Department of Biostatistics, University of Florida, Gainesville, FL, 32603, USA., Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA., Department of Tumor Biology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA., Department of Public Health Sciences, University of Chicago, Chicago, IL, 60637, USA., Center of Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, Tulane University, New Orleans, LA, 70112, USA., Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, 37203, USA., Department of Cancer Biology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, 19107, USA., Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA., Division of Biostatistics, University of Minnesota, Minneapolis, MN, 55455, USA., Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA, 90033, USA.