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.

通过比较计算模型，本研究发现深度神经网络(DNN)在估计乳腺癌多基因风险评分(PRS)方面胜过其他机器学习技术和统计算法，包括BLUP、BayesA和LDpred。DNN能够将案例人群分为高基因风险和正常基因风险两个亚群，并准确预测疾病风险，其中DNN在50%患病率测试样本中实现了18.8%的召回率和90%的精确度，可推广至12%患病率的一般人群中的20%精确度下达到65.4%的召回率。此外，通过DNN模型的解释还发现了在关联研究中被赋予不显著p值的重要变异体，这些变异体可能通过非线性关系与表型相关联。

深度神经网络改进乳腺癌多基因风险评分的估计