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Related Experiment Video

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

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

He Xu1, Yuzhuo Ma1, Yufei Liu1

  • 1Department of Medical Genetics, School of Basic Medical Sciences, Peking University, Beijing, China.

Nature Computational Science
|June 16, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

We developed SAGELD, a new method for analyzing gene-environment interactions in longitudinal studies. This approach enhances genetic discovery and precision medicine by utilizing longitudinal data more effectively than previous methods.

Related Experiment Videos

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical genomics

Background:

  • Gene-environment interaction (G×E) analyses are vital for genetic discovery, heritability, and precision medicine.
  • Current G×E methods primarily use cross-sectional data, limiting the use of valuable longitudinal data.
  • There is a need for efficient G×E methods that can leverage longitudinal data in large-scale studies.

Purpose of the Study:

  • To introduce SAGELD, a scalable and accurate genome-wide G×E method designed for longitudinal traits.
  • To enable G×E analyses in large datasets while controlling for sample relatedness.
  • To demonstrate the advantages of using longitudinal data for G×E studies.

Main Methods:

  • SAGELD employs matrix projection for test statistics construction.
  • The SPAGRM framework is utilized for efficient control of sample relatedness.
  • The method was validated through simulations and UK Biobank data analysis.

    • Main Results:

    • SAGELD achieves significant speedups (10- to 10,000-fold) compared to existing methods.
    • The method demonstrates greater statistical power than cross-sectional G×E analyses.
    • Identified 74 loci for genetic × age interactions and 5 loci for genetic × adiposity interactions using UK Biobank data.

    Conclusions:

    • SAGELD offers a scalable and powerful approach for genome-wide G×E analyses with longitudinal data.
    • Leveraging longitudinal data in G×E analyses significantly enhances genetic discovery.
    • The findings support the utility of SAGELD for advancing precision medicine through comprehensive genetic insights.