PNL: a software to build polygenic risk scores using a super learner approach based on PairNet, a Convolutional Neural Network

• Published in: Bioinformatics (Oxford, England) Vol. 41; no. 2
• Main Authors: Chen, Ting-Huei, Lee, Chia-Jung, Chen, Syue-Pu, Wu, Shang-Jung, Fann, Cathy S J
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 04.02.2025
• Subjects: Algorithms; Applications Note; Asthma - genetics; Convolutional Neural Networks; Diabetes Mellitus, Type 2 - genetics; Genetic Predisposition to Disease; Genetic Risk Score; Humans; Multifactorial Inheritance; Neural Networks, Computer; Software
• ISSN: 1367-4811; 1367-4803; 1367-4811
• DOI: 10.1093/bioinformatics/btaf071

Summary Polygenic risk scores (PRSs) hold promise for early disease diagnosis and personalized treatment, but their overall discriminative power remains limited for many diseases in the general population. As a result, numerous novel PRS modeling techniques have been developed to improve predictive performance, but determining the most effective method for a specific application remains uncertain until tested. Hence, we introduce a novel, versatile tool for building an optimized PRS model by integrating candidate models from multiple existing PRS building methods that use target population data and/or incorporating information from other populations through a trans-ethnic approach. Our tool, PNL is based on PairNet algorithm, a Convolutional Neural Network with low computation complexity through simple paring operation. In the case studies for asthma, type 2 diabetes, and vertigo, the optimal PRS model generated with PNL using only Taiwan biobank (TWB) data achieved Area Under the Curves (AUCs) that matched or improved the best results using other methods individually. Incorporating the UK Biobank data (UKBB) data further improved performance of PNL for asthma and type 2 diabetes. For vertigo, unlike the other diseases, individual method analysis showed that UKBB data alone generally produced lower AUCs compared to TWB data alone. As a result, incorporating UKBB data did not improve AUC with PNL, suggesting that increasing the number of candidate models does not necessarily result in higher AUC values, alleviating concerns about overfitting. Availability and implementation The python code for PairNet algorithm incorporated in PNL is freely available on: https://github.com/FannLab/pairnet. An archived, citable version is stored on: https://doi.org/10.5281/zenodo.14838227.