NetPRS: SNP interaction aware network-based polygenic risk score for Alzheimer’s disease

Sunghong Park, Dong-gi Lee, Juhyeon Kim, Doyoon Kim, Chang Hyung Hong, Sang Joon Son, Hyun Woong Roh, Kanghee Park, Dokyoon Kim, Hyunjung Shin, Hyun Goo Woo

Keywords: Polygenic risk score, Alzheimer’s disease, SNP interaction network, semi-supervised learning (SSL)

Abstract: Alzheimer’s disease (AD) is underscored by its polygenic nature, attributable to variants across multiple genetic loci. This has led to the development of the polygenic risk score (PRS) model, which estimates individual risk by aggregating risk alleles weighted from their effect sizes. While early models were limited to utilizing only independent effects of single nucleotide polymorphisms (SNPs), recent models have been advanced to consider epistatic interactions between SNPs. However, SNPs interact through various channels, and typically, they are associated with each other through SNP-gene relations and gene-gene interactions. Moreover, SNPs interact synergetically, exhibiting diverse joint effects of genetic variations. Given these properties of SNP interactions, the PRS models need improvement to account for the interactive effects between SNPs in a polygenic manner, especially for genetically complex diseases such as AD. In this study, we propose a two-stage approach for AD risk assessment, called network-based PRS (NetPRS). First, the phenotypic and genomic interactions are quantified and integrated into networks. Second, the independent effects of SNPs are propagated on the integrated SNP networks using graph-based machine learning model. Through this procedure, NetPRS extracts the globally interactive effects between SNPs and integrates these effects to predict the risk of AD. The proposed method was applied to two cohort datasets: the Alzheimer's Disease Neuroimaging Initiative dataset with 1,175 participants, and a South Korean dataset with 724 participants. Experimental results showed that the integrated effects of NetPRS more clearly distinguished between AD and control groups, outperforming the six existing methods by 16.4% on average.

Track: 4. AI-based clinical decision support systems

Registration Id: YYN54QFL859

Submission Number: 18