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Deep neural network improves the estimation of polygenic risk scores for breast cancer

Journal of Human Genetics volume 66, pages 359–369 (2021)Cite this article

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Abstract

Polygenic risk scores (PRS) estimate the genetic risk of an individual for a complex disease based on many genetic variants across the whole genome. In this study, we compared a series of computational models for estimation of breast cancer PRS. A deep neural network (DNN) was found to outperform alternative machine learning techniques and established statistical algorithms, including BLUP, BayesA, and LDpred. In the test cohort with 50% prevalence, the Area Under the receiver operating characteristic Curve (AUC) were 67.4% for DNN, 64.2% for BLUP, 64.5% for BayesA, and 62.4% for LDpred. BLUP, BayesA, and LPpred all generated PRS that followed a normal distribution in the case population. However, the PRS generated by DNN in the case population followed a bimodal distribution composed of two normal distributions with distinctly different means. This suggests that DNN was able to separate the case population into a high-genetic-risk case subpopulation with an average PRS significantly higher than the control population and a normal-genetic-risk case subpopulation with an average PRS similar to the control population. This allowed DNN to achieve 18.8% recall at 90% precision in the test cohort with 50% prevalence, which can be extrapolated to 65.4% recall at 20% precision in a general population with 12% prevalence. Interpretation of the DNN model identified salient variants that were assigned insignificant p values by association studies, but were important for DNN prediction. These variants may be associated with the phenotype through nonlinear relationships.

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Acknowledgements

We would like to thank the OU Supercomputing Center for Education & Research (OSCER) for supercomputing technical support, the DRIVE project for the GWAS data, NIH dbGap for data access authorization, and Dr. Xu Chao for helpful discussions. The study was funded by Dr. Pan’s startup funding from the University of Oklahoma and by the Oak Ridge National Laboratory (ORNL)’ Directed Research Development (LDRD) Funding. Oak Ridge National Laboratory is managed by UT-Battelle, LLC for the U.S. Department of Energy under Contract Number DE-AC05-00OR22725.

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Authors and Affiliations

  1. School of Computer Science, University of Oklahoma, Norman, OK, USA

    Adrien Badré, Justin C. Reynolds & Chongle Pan

  2. Department of Microbiology and Plant Biology, University of Oklahoma, Norman, OK, USA

    Li Zhang & Chongle Pan

  3. Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA

    Wellington Muchero

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Badré, A., Zhang, L., Muchero, W. et al. Deep neural network improves the estimation of polygenic risk scores for breast cancer. J Hum Genet 66, 359–369 (2021). https://doi.org/10.1038/s10038-020-00832-7

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