用基因语言模型从全基因组序列预测疾病相关表达变化。
Multi-megabase scale genome interpretation with genetic language models
- 构建多尺度基因语言模型Phenformer，直接从8800万碱基序列生成机制假说。
- 在15万+个体数据上验证，预测疾病相关细胞/组织类型更符合文献。
- 无需实验数据即可提升疾病风险预测性能与跨人群泛化能力。
解析遗传变异如何驱动疾病风险对理解疾病机制至关重要。然而，由于人类基因组规模庞大，且其影响跨越从分子到整体生物的多种细胞、组织和尺度，基因组解读极具挑战。本文提出Phenformer，一种可生成机制假说的多尺度基因语言模型，能直接从长达8800万碱基的DNA序列中推断基因序列差异如何导致不同细胞和组织中的疾病相关表达变化。基于超过15万例个体的全基因组测序数据，结果显示Phenformer生成的疾病相关细胞和组织假说比现有最先进方法更符合文献报道；同时，经Phenformer增强的疾病风险预测模型表现出更优的预测性能与跨人群泛化能力。无需额外实验数据即可实现多兆碱基尺度的全基因组精准解读，有助于深入理解疾病分子机制，并实现个体化疾病风险预测。
原文摘要 · Abstract (English)
Understanding how molecular changes caused by genetic variation drive disease risk is crucial for deciphering disease mechanisms. However, interpreting genome sequences is challenging because of the vast size of the human genome, and because its consequences manifest across a wide range of cells, tissues and scales -- spanning from molecular to whole organism level. Here, we present Phenformer, a multi-scale genetic language model that learns to generate mechanistic hypotheses as to how differences in genome sequence lead to disease-relevant changes in expression across cell types and tissues directly from DNA sequences of up to 88 million base pairs. Using whole genome sequencing data from more than 150 000 individuals, we show that Phenformer generates mechanistic hypotheses about disease-relevant cell and tissue types that match literature better than existing state-of-the-art methods, while using only sequence data. Furthermore, disease risk predictors enriched by Phenformer show improved prediction performance and generalisation to diverse populations. Accurate multi-megabase scale interpretation of whole genomes without additional experimental data enables both a deeper understanding of molecular mechanisms involved in disease and improved disease risk prediction at the level of individuals.
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