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

## TL;DR

This paper introduces PNL, a new software tool that builds better polygenic risk scores using a neural network approach, improving disease prediction in specific populations.

## Contribution

PNL combines multiple PRS methods using a novel Convolutional Neural Network called PairNet to build optimized polygenic risk scores.

## Key findings

- PNL achieved or improved best AUC results for asthma, type 2 diabetes, and vertigo using only Taiwan Biobank data.
- Incorporating UK Biobank data improved PNL performance for asthma and type 2 diabetes but not for vertigo.
- Adding more candidate models does not always increase AUC, reducing overfitting concerns.

## Abstract

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.

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.