2026年9月9日，美国加州大学伯克利分校Yun S. Song等科学家在《自然》发表研究，提出了GPN-Star模型，用于预测全基因组功能约束。

基因组语言模型已成为直接从DNA序列学习全基因组功能约束的一种强大方法。然而，从自然语言处理改编而来的标准基因组语言模型通常需要较大的模型规模与计算资源，但在预测任务中仍不及经典演化模型。在此，研究人员提出了具有物种树和比对表示的基因组预训练网络（GPN-Star），这是一种有生物学基础的基因组语言模型，具有系统发育感知架构，利用全基因组比对和物种树来显式建模演化关系。GPN-Star在涵盖脊椎动物、哺乳动物和灵长类演化时间尺度的比对上训练，在人类基因组编码区和非编码区的广泛变异效应预测任务中均达到最先进性能。跨时间尺度分析显示，建模较近期演化与更深演化具有任务依赖性优势。为展示其推动人类遗传学的潜力，研究人员表明，GPN-Star在优先排序致病性和精细定位的全基因组关联研究变异方面显著优于先前方法，产生复杂性状遗传力的强富集，并提高罕见变异关联检验的效力。除人类之外，研究人员为五种模式生物——小鼠、原鸡、黑腹果蝇、秀丽隐杆线虫和拟南芥——训练了GPN-Star，证明了该框架的稳健性和普适性。总之，这些结果将GPN-Star定位为一种可扩展、强大且灵活的基因组解读工具，非常适合利用日益丰富的比较基因组学数据。

附：英文原文

Title: Predicting genome-wide functional constraints with GPN-Star

Author: Ye, Chengzhong, Benegas, Gonzalo, Albors, Carlos, Li, Jianan Canal, Prillo, Sebastian, Fields, Peter D., Clarke, Brian, Song, Yun S.

Issue&Volume: 2026-09-09

Abstract: Genomic language models have emerged as a powerful approach for learning genome-wide functional constraints directly from DNA sequences1. However, standard genomic language models adapted from natural language processing often require large model sizes and computational resources, yet still fall short of classical evolutionary models in predictive tasks2,3,4. Here we introduce a genomic pretrained network with species tree and alignment representations (GPN-Star), which is a biologically grounded genomic language model featuring a phylogeny-aware architecture that leverages whole-genome alignments and species trees to model evolutionary relationships explicitly. Trained on alignments spanning vertebrate, mammal and primate evolutionary timescales, GPN-Star achieves state-of-the-art performance across a wide range of variant effect prediction tasks in both coding and non-coding regions of the human genome. Analyses across timescales show task-dependent advantages of modelling more recent versus deeper evolution. To demonstrate its potential to advance human genetics, we show that GPN-Star substantially outperforms previous methods in prioritizing pathogenic and fine-mapped genome-wide association study variants, yields strong enrichments of complex trait heritability and improves power in rare variant association testing5. Extending beyond humans, we train GPN-Star for five model organisms—Mus musculus, Gallus gallus, Drosophila melanogaster, Caenorhabditis elegans and Arabidopsis thaliana—demonstrating the robustness and generalizability of the framework. Taken together, these results position GPN-Star as a scalable, powerful and flexible tool for genome interpretation, well suited to leverage the growing abundance of comparative genomics data.

DOI: 10.1038/s41586-026-11005-5

Source: https://www.nature.com/articles/s41586-026-11005-5