arXiv cs.AI· 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·· 14 小时前AI 评分31
GenoBERT：面向精准基因型填充的语言模型
GenoBERT: A Language Model for Accurate Genotype Imputation
AI 导读
GenoBERT 是一个基于 Transformer、无需参考面板的基因型填充框架，将定相基因型 token 化并用自注意力捕捉短程与长程连锁不平衡依赖。
正文 · 原文
Authors: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
Abstract: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%) shows that GenoBERT achieves the highest overall accuracy compared to four baseline methods (Beagle5.4, SCDA, BiU-Net, and STICI). At practical sparsity levels (up to 25% missing), GenoBERT attains high overall imputation accuracy ($r^2 approx 0.98$) across datasets, and maintains robust performance ($r^2 > 0.90$) even at 50% missingness. Experimental results across different ancestries confirm consistent gains across datasets, with resilience to small sample sizes and weak LD. A 128-SNP (single-nucleotide polymorphism) context window (approximately 100 Kb) is validated through LD-decay analyses as sufficient to capture local correlation structures. By eliminating reference-panel dependence while preserving high accuracy, GenoBERT provides a scalable and robust solution for genotype imputation and a foundation for downstream genomic modeling.
| Subjects: | Genomics (q-bio.GN); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2604.00058 [q-bio.GN] |
| (or arXiv:2604.00058v2 [q-bio.GN] for this version) | |
| https://doi.org/10.48550/arXiv.2604.00058 arXiv-issued DOI via DataCite |
Submission history
From: Lei Huang [view email]
[v1]
Tue, 31 Mar 2026 04:16:55 UTC (4,453 KB)
[v2]
Thu, 29 Oct 2026 02:49:25 UTC (4,592 KB)
来源：arXiv cs.AI · arxiv.org