ATLAS: Population-Level Disease Locus Discovery via Differential Attention in Genomic Language Models

作者：Yuqi Liu, Kaiwen Deng, Yuhua Ye, Zilin Wang, Jiajie Zhan, Shicheng Chen, Xinyue Hu, An Chang, Huijun Shen, Zhaorong Li, Shiping Liu, Xin Jin, Kui Chen, Xianzhi Qi, Xiangmin Xu, Haiqiang Zhang · 年份：2026 · DOI：10.1145/3770855.3819004 · 研究领域：Computer science、Artificial intelligence、Computational biology、Machine learning、Natural language processing、Biology、Genetics

Identifying disease-associated genetic variants remains a key challenge in genomics, especially in small cohorts or for rare and complex mutation types where genome-wide association studies (GWAS) often fall short. We introduce ATLAS, a population-level framework that leverages attention signals from pretrained genomic language models (gLMs) to detect disease-associated genes and loci directly from raw sequences—without requiring explicit variant calls or supervised training. ATLAS first performs gene-level differential attention analysis to prioritize candidate genes, followed by base-level analysis to localize disease-associated regions at single-haplotype resolution. We validate ATLAS on synthetic and ?-thalassemia datasets, demonstrating robust performance across diverse allele frequencies (down to 10%), cohort sizes (below 200 individuals per group), and genomic scales. Compared to GWAS, ATLAS achieves higher recall of known loci and captures haplotype-specific signals missed by traditional methods. Cross-model benchmarking further shows that precise localization depends on both model size and pretraining on diverse human genomes. In summary, ATLAS offers a scalable, sequence-native alternative to traditional statistical genetics.