Leveraging longitudinal data to boost statistical power for gene-environment interaction analysis.

doi:10.1038/s43588-026-01002-z

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Article

Leveraging longitudinal data to boost statistical power for gene-environment interaction analysis.

Nature computational science - 1 Jun 2026

Xu He, Ma Yuzhuo, Liu Yufei, Li Yin, Wan Lin, Zhang Ji-Feng, Zhao Yanlong, Yue Weihua, Zhang Peipei, Bi Wenjian

Abstract excerpt

Gene-environment interaction (G×E) analyses play a crucial role in advancing genetic discovery, addressing missing heritability, and facilitating precision medicine. However, existing G×E methods are mostly designed for cross-sectional data, limiting the utility of longitudinal data. Here we propose SAGELD, a scalable and accurate genome-wide G×E method for longitudinal traits that controls for sample relatedness...

Topics

Gene-Environment Interaction
Humans
Genome-Wide Association Study
Longitudinal Studies
UK Biobank
Polymorphism, Single Nucleotide
Cross-Sectional Studies
Body Mass Index
Models, Genetic
Computer Simulation

Source

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Identifiers

PMID: 42304093
DOI: 10.1038/s43588-026-01002-z

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PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 31 Aug 2026.

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