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arXiv AI By Lei Huang, Chuan Qiu, Kuan-Jui Su, Anqi Liu, Yun Gong, Weiqiang Lin, Lindong Jiang, Chen Zhao, Meng Song, Jeffrey Deng, Qing Tian, Zhe Luo, Ping Gong, Hui Shen, Chaoyang Zhang, Hong-Wen Deng

GenoBERT: A Language Model for Accurate Genotype Imputation

Read the original on arXiv AI →

GenoBERT is a transformer-based, reference‑free model that tokenizes phased genotypes and uses self‑attention to capture short‑ and long‑range linkage disequilibrium. Benchmarking on the Louisiana Osteoporosis Study and the 1000 Genomes Project shows GenoBERT outperforms four baseline methods, achieving an overall imputation accuracy of about 0.98 at up to 25% missing data and maintaining r² > 0.90 even at 50% missingness across multiple ancestries. The model’s 128‑SNP context window is sufficient to capture local correlation structures, and it remains robust to small sample sizes and weak LD, offering a scalable solution for genotype imputation without relying on reference panels.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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