A Deep Ensemble Encoder Network Method for Improved Polygenic Risk Score Prediction
Ozdemir, O. B.; Chen, R.; Li, R.
Show abstract
Genome-wide association studies of various heritable human traits and diseases have identified numerous associated single nucleotide polymorphisms (SNPs), most of which have small or modest effects. Polygenic risk scores aim to better estimate individuals genetic predisposition by aggregating the effects of multiple SNPs from GWAS. However, current PRS is designed to capture only simple linear genetic effects across the genome, limiting their ability to fully account for the complex polygenic architecture. To address this, we propose DeepEnsembleEncodeNet (DEEN), a new method that ensembles autoencoders and fully connected neural networks to better identify and model linear and non-linear SNP effects across different genomic regions, improving its ability to predict disease risks. To demonstrate DEENs performance, we optimized the model across binary and continuous traits from the UK Biobank. Model evaluation on the held-out UK Biobank testing dataset, as well as the independent All of Us dataset, showed improved prediction and risk stratification, consistently outperforming other methods.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Incorporating family disease history and controlling case-control imbalance for population based genetic association studies 95%
- Exploiting deep transfer learning for the prediction of functional noncoding variants using genomic sequence 94%
- Uncovering genetic associations in the human diseasome using an endophenotype-augmented disease network 94%
Similar papers in this journal
- Deep transfer learning provides a Pareto improvement for multi-ancestral clinico-genomic prediction of diseases 96%
- MetaRNN: Differentiating Rare Pathogenic and Rare Benign Missense SNVs and InDels Using Deep Learning 94%
- Finding associations in a heterogeneous setting: Statistical test for aberration enrichment 93%
Similar papers in this journal
- Variational Autoencoder-based Model Improves Polygenic Prediction in Blood Cell Traits 96%
- Leveraging TOPMed Imputation Server and Constructing a Cohort-Specific Imputation Reference Panel to Enhance Genotype Imputation among Cystic Fibrosis Patients 94%
- Scalable Bayesian functional GWAS method accounting for multivariate quantitative functional annotations with applications to studying Alzheimer’s disease 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.