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

• URL: http://arxiv.org/abs/2307.13010v1
• Date: Mon, 24 Jul 2023 13:35:36 GMT
• Title: Deep neural network improves the estimation of polygenic risk scores for breast cancer
• Authors: Adrien Badr\'e, Li Zhang, Wellington Muchero, Justin C. Reynolds, Chongle Pan
• Abstract summary: Polygenic risk scores (PRS) estimate the genetic risk of an individual for a complex disease based on many genetic variants across the whole genome. A deep neural network (DNN) was found to outperform alternative machine learning techniques and established statistical algorithms.
• Score: 3.9918594409417576
• License: http://creativecommons.org/licenses/by/4.0/
• 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 bi-modal 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 sub-population with an average PRS significantly higher than the control population and a normal-genetic-risk case sub-population 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 non-linear relationships.

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