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. 2024 May 21;14(1):11632.
doi: 10.1038/s41598-024-62513-1.

Stacked neural network for predicting polygenic risk score

Sun Bin Kim  1 , Joon Ho Kang  1 , MyeongJae Cheon  1 , Dong Jun Kim  1 , Byung-Chul Lee  2
Affiliations

Stacked neural network for predicting polygenic risk score

Sun Bin Kim et al. Sci Rep. .

Abstract

In recent years, the utility of polygenic risk scores (PRS) in forecasting disease susceptibility from genome-wide association studies (GWAS) results has been widely recognised. Yet, these models face limitations due to overfitting and the potential overestimation of effect sizes in correlated variants. To surmount these obstacles, we devised the Stacked Neural Network Polygenic Risk Score (SNPRS). This novel approach synthesises outputs from multiple neural network models, each calibrated using genetic variants chosen based on diverse p-value thresholds. By doing so, SNPRS captures a broader array of genetic variants, enabling a more nuanced interpretation of the combined effects of these variants. We assessed the efficacy of SNPRS using the UK Biobank data, focusing on the genetic risks associated with breast and prostate cancers, as well as quantitative traits like height and BMI. We also extended our analysis to the Korea Genome and Epidemiology Study (KoGES) dataset. Impressively, our results indicate that SNPRS surpasses traditional PRS models and an isolated deep neural network in terms of accuracy, highlighting its promise in refining the efficacy and relevance of PRS in genetic studies.

Keywords: Deep learning; Ensemble learning; Polygenic risk score.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
The overall architecture of SNPRS model.
Figure 2
Figure 2
Nagelkerke R-square and R-square plot comparison for UKBB dataset with single models and stacked one.
Figure 3
Figure 3
Nagelkerke R-square and R-square plot comparison for UKBB dataset with other models.
Figure 4
Figure 4
Nagelkerke R-square and R-square plot comparison for KoGES dataset with single models and stacked one.
Figure 5
Figure 5
Nagelkerke R-square and R-square plot comparison for KoGES dataset with other models.
Algorithm 1
Algorithm 1
Stacked neural network.
See this image and copyright information in PMC

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