Generating synthetic genotypes using diffusion models.

ABSTRACT:

Summary

In this paper, we introduce the first diffusion model designed to generate complete synthetic human genotypes, which, by standard protocols, one can straightforwardly expand into full-length, DNA-level genomes. The synthetic genotypes mimic real human genotypes without just reproducing known genotypes, in terms of approved metrics. When training biomedically relevant classifiers with synthetic genotypes, accuracy is near-identical to the accuracy achieved when training classifiers with real data. We further demonstrate that augmenting small amounts of real with synthetically generated genotypes drastically improves performance rates. This addresses a significant challenge in translational human genetics: real human genotypes, although emerging in large volumes from genome w

SUBMITTER: Kenneweg P 

PROVIDER: S-EPMC12261458 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature