Leveraging longitudinal data from electronic health records to boost statistical power for gene-environment interaction analysis

Gene-environment interactions (G E) play a crucial role in advancing genetic discovery, adressing missing heritability, and facilitating precision medicine. However, most existing G E methods are designed for cross-sectional data, limiting their utility in analyzing the rich longitudinal data in electronic health records. Here, we propose SAGELD, a scalable and accurate genome-wide G E method of longitudinal traits while controlling for sample relatedness in large-scale datasets. SAGELD employs a matrix projection strategy to construct test statistics and adopts the SPAGRM framework for efficient control of sample relatedness, achieving speedups of 10- to 10,000-fold over existing methods while maintaining greater statistical power. We evaluated SAGELD through extensive simulations and real data analyses in the UK Biobank. Using age and body mass index as environmental exposures, we identified 74 genetic loci with significant G age interactions and five with G adiposity interactions, many of which are novel findings. These results highlight the advantages of leveraging longitudinal data in G E analyses.基因-环境交互作用（Gene-environment interactions, G×E）在推动遗传学发现、弥补缺失遗传力以及助力精准医学发展方面发挥着至关重要的作用。然而，当前绝大多数已有的G×E分析方法均针对横截面数据设计，这限制了其在分析电子健康记录中丰富纵向数据时的应用潜力。本文提出SAGELD——一种可扩展且精准的纵向表型全基因组G×E分析方法，可在大规模数据集上校正样本亲缘关系。SAGELD采用矩阵投影策略构建检验统计量，并借助SPAGRM框架实现样本亲缘关系的高效校正，相较于现有方法可实现10至10000倍的运算加速，同时保持更高的统计效力。我们通过大规模模拟实验与英国生物银行（UK Biobank）的真实数据分析对SAGELD进行了全面评估。以年龄与身体质量指数作为环境暴露因素，我们共鉴定出74个存在显著G×年龄交互作用的遗传位点，以及5个存在显著G×体脂交互作用的遗传位点，其中多数为全新发现。上述结果凸显了在G×E分析中利用纵向数据的显著优势。