SNPgen: Phenotype-Supervised Genotype Representation and Synthetic Data Generation via Latent Diffusion

Abstract

Polygenic risk scores and other genomic analyses require large individual-level genotype datasets, yet strict data access restrictions impede sharing. Synthetic genotype generation offers a privacy-preserving alternative, but most existing methods operate unconditionally, producing samples without phenotype alignment, or rely on unsupervised compression, creating a gap between statistical fidelity and downstream task utility. We present SNPgen, a two-stage conditional latent diffusion framework for generating phenotype-supervised synthetic genotypes. SNPgen combines GWAS-guided variant selection (1,024-2,048 trait-associated SNPs) with a variational autoencoder for genotype compression and a latent diffusion model conditioned on binary disease labels via classifier-free guidance. Evaluated on 458,724 UK Biobank individuals across four complex diseases (coronary artery disease, breast cancer, type 1 and type 2 diabetes), models trained on synthetic data matched real-data predictive performance in a train-on-synthetic, test-on-real protocol, approaching genome-wide PRS methods that use $2$-$6\times$ more variants. Privacy analysis confirmed zero identical matches, near-random membership inference (AUC $\approx 0.50$), preserved linkage disequilibrium structure, and high allele frequency correlation ($r \geq 0.95$) with source data. A controlled simulation with known causal effects verified faithful recovery of the imposed genetic association structure.

Figures (11)

• Figure 1: SNPgen pipeline. UK Biobank genotypes and external GWAS summary statistics are combined for GWAS-guided SNP selection, producing a compact set of trait-associated variants. In Stage 1, a 1D VAE encoder compresses the selected SNPs into a continuous latent space $\mathbf{z}$. In Stage 2, a latent diffusion model (1D UNet DDPM) generates novel latent representations conditioned on binary disease labels via cross-attention, which are then decoded into synthetic trait-conditioned genotypes.
• Figure 2: Downstream risk prediction (ROC-AUC) across four UK Biobank traits. Bars show three models (XGBoost, XGBoost Balanced, PRS Univariate) under four data conditions: Real, Reconstructed, Synthetic, and Synthetic Augmented. Error bars: 95% CI from 5-fold CV. Dashed lines: External PRS baseline. Cohort sizes: CAD $n{=}458{,}724$ (35,306 cases); BC $n{=}248{,}987$ (19,634 cases, female only); T1D $n{=}458{,}724$ (4,593 cases); T2D $n{=}458{,}724$ (38,541 cases).
• Figure 3: Pairwise LD ($r^2$) for T2D (first 200 SNPs). Left: original. Centre: reconstructed. Right: synthetic. Block-diagonal structure is preserved.
• Figure S1: Pairwise LD ($r^2$) for CAD (2,048 SNPs). Left: original. Centre: VAE-reconstructed. Right: LDM-generated synthetic.
• Figure S2: Pairwise LD ($r^2$) for BC (1,024 SNPs). Left: original. Centre: VAE-reconstructed. Right: LDM-generated synthetic.