GenoBERT: A Language Model for Accurate Genotype Imputation
Genotype imputation enables dense variant coverage for genome-wide association and risk-prediction studies, yet conventional reference-panel methods remain limited by ancestry bias and reduced rare-variant accuracy. We present Genotype Bidirectional Encoder Representations from Transformers (GenoBERT), a transformer-based, reference-free framework that tokenizes phased genotypes and uses a self-attention mechanism to capture both short- and long-range linkage disequilibrium (LD) dependencies. Benchmarking on two independent datasets including the Louisiana Osteoporosis Study (LOS) and the 1000 Genomes Project (1KGP) across ancestry groups and multiple genotype missingness levels (5-50%) s...
| Authors | Lei Huang, Chuan Qiu, Kuan-Jui Su et al. |
|---|---|
| Primary category | q-bio.GN |
| Published | 31 March 2026 |
| Last updated at source | 31 March 2026 |
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