HELICS: Biobank-scale Conditional Synthetic Genome Generation via Latent Flow Matching
Abstract
Individual-level genotype data underpin genetic discovery, disease-risk modeling, and therapeutic development, yet many critical settings remain data-limited, including underrepresented ancestries, rare or imbalanced disease cohorts, and restricted-access biobanks. Conditional synthetic genome generation would support privacy-aware benchmarking, simulation, and data augmentation, but requires learning an ultra-high-dimensional discrete distribution over hundreds of thousands to tens of millions of correlated single-nucleotide polymorphisms (SNPs), or sites of DNA variation. We introduce HELICS: High-fidelity gEneration via Latent Interpolant Conditional Synthesis, a phasing-free latent flow matching framework for genome-wide SNP-level generation from biobank-scale genotype data. HELICS tokenizes chromosomes with convolutional autoencoders, reducing >600K SNPs to 4,096 continuous latent tokens, and trains a transformer parameterized conditional flow matching model over the concatenated multi-chromosome latent representation. Trained on over 417K genomes in the UK Biobank, HELICS generates genome-wide common-variant synthetic cohorts without haplotype phasing and achieves stronger fidelity to real genotypes than existing haplotype-based simulators, including high allele-frequency agreement in external evaluation and superior preservation of local and long-range SNP correlation structure. Conditioning on ancestry and genetic liability summaries enables targeted generation across genetic risk profiles; in perturbation analyses, by increasing the conditioned disease trait liability, HELICS induces SNP-level dosage changes in generated genomes that are highly correlated with published association weights. HELICS provides a scalable generative modeling framework for SNP-level genome data and a path toward conditional synthetic cohorts for data-limited genetic studies.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
HELICS introduces a scalable, phasing-free latent flow matching framework for genome-wide synthetic genome generation that achieves high fidelity to real biobank data and enables controllable generation of genetic liability profiles, representing a significant advancement in computational genomics and generative modeling for high-dimensional discrete biological data.
Methodology
The paper introduces HELICS, a framework for generating synthetic genome-wide SNP data using latent flow matching. The core methodological innovation is the decoupling of the ultra-high-dimensional discrete genotype space (600K+ SNPs) into a manageable continuous latent space via chromosome-specific convolutional autoencoders. This allows the application of a transformer-based flow matching model (USiT) to the concatenated latent tokens of all 22 autosomes. The approach is "phasing-free," meaning it operates directly on unphased dosage data, avoiding the computationally expensive and error-prone step of haplotype phasing required by traditional simulators like HAPGEN2 or HAPNEST. The autoencoder training objective is carefully designed to preserve population genetic structure, including terms for minor allele frequency (MAF) preservation and local linkage disequilibrium (LD) correlation. The flow matching model is conditioned on ancestry principal components and discretized polygenic risk scores (PRS), enabling controllable generation of genetic liability profiles.
Results & evaluation
The evaluation is rigorous and extensive. The model is trained on the UK Biobank (417K genomes) and evaluated on external cohorts (1000 Genomes, CCDG). Key metrics include allele frequency agreement ($R^2=0.992$), LD structure preservation, and population geometry (PCA/UMAP). A particularly strong aspect of the evaluation is the "perturbation analysis," where the authors demonstrate that increasing the conditioned disease liability (e.g., HDL) induces SNP-level dosage changes that correlate highly with published GWAS association weights ($R^2=0.547$ for high-effect variants). This provides strong evidence that the model learns biologically meaningful associations rather than just statistical correlations. The model outperforms existing haplotype-based simulators in fidelity and offers capabilities (conditional generation) that baselines lack.
Reproducibility
The authors provide a GitHub repository with code. However, due to the sensitivity of UK Biobank data, the full model weights and training data are not released. A demo is provided using 1000 Genomes data on chromosome 22, which allows for verification of the pipeline but not the full-scale performance claims. The architectural details, including the dynamic programming for token allocation and specific loss terms, are described in detail in the appendix, aiding reproducibility for those with access to similar-scale data.
Limitations
The model is currently limited to common variants (MAF > 1%) and does not handle rare variants or structural variants. Fidelity is weaker for underrepresented ancestry groups, likely due to data imbalance in the training set. The conditioning is based on coarse PRS deciles, which may limit the granularity of control. The privacy implications of synthetic data generation are discussed but not exhaustively proven; while membership inference attacks show low leakage, the risk of re-identification in specific contexts remains a topic for further study.
Broader impact
This work has significant potential to impact genetic research by enabling the creation of synthetic cohorts for underrepresented populations or rare diseases, where real data is scarce. It facilitates privacy-aware sharing of genetic data and allows for controlled simulation of genetic risk profiles. The ability to generate data conditioned on specific genetic liabilities could accelerate the discovery of new genetic associations and improve the generalizability of polygenic risk scores. HELICS introduces a scalable, phasing-free latent flow matching framework for genome-wide synthetic genome generation that achieves high fidelity to real biobank data and enables controllable generation of genetic liability profiles, representing a significant advancement in computational genomics and generative modeling for high-dimensional discrete biological data.
Author of this paper? Send a correction.