Brief Bioinform

A comprehensive survey of deep learning approaches for polygenic risk scores (PRS). Reviews how neural networks can model non-linear relationships between genetic variants and disease risk, going beyond traditional linear PRS methods, and assesses their performance across different traits and architectures. Published in Briefings in Bioinformatics.

Polygenic risk scores (PRS) combine the effects of multiple genetic variants to predict an individual's genetic predisposition to a disease. PRS typically rely on linear models, which assume that all genetic variants act independently. There is growing interest in applying deep learning neural networks to model PRS given their ability to model non-linear relationships. We conducted a survey of the literature to investigate how neural networks are being applied to PRS.

GWANN (Genome-Wide Association Neural Networks) is a novel approach that uses neural networks to perform gene-level association studies. Applied to Alzheimer's disease in UK Biobank, GWANN identifies genes linked to family history of AD by aggregating SNP-level information at the gene level through neural network architectures. Published in Briefings in Bioinformatics.

Augmenting traditional genome-wide association studies (GWAS) with advanced machine learning algorithms can allow the detection of novel signals in available cohorts. We introduce "genome-wide association neural networks (GWANN)", a novel approach that uses neural networks (NNs) to perform a gene-level association study with family history of Alzheimer's disease (AD) in UK Biobank.

MixEHR-SAGE is a PheCode-guided multi-modal topic model that integrates diagnoses, procedures, and medications from EHR to enhance phenotyping for GWAS. By combining expert-informed priors with probabilistic inference, it identifies over 1000 interpretable phenotype topics from UK Biobank data and improves disease incidence prediction and GWAS discovery. Published in Briefings in Bioinformatics.

Phenome-wide association studies rely on disease definitions derived from diagnostic codes, often failing to leverage the full richness of electronic health records (EHR). We present MixEHR-SAGE, a PheCode-guided multi-modal topic model that integrates diagnoses, procedures, and medications to enhance phenotyping from large-scale EHRs. Applied to 350,000 individuals with high-quality genetic data, MixEHR-SAGE-derived risk scores accurately predicted disease incidence and improved GWAS discovery.