Fast multiple-trait genome-wide association analysis for correlated longitudinal measurements.

ABSTRACT: Large-scale longitudinal biobank data can be leveraged to identify genetic variation contributing to human diseases progression and traits trajectories. While methods for genome-wide association studies (GWAS) of multiple correlated traits have been proposed, an efficient multiple-trait approach to model longitudinal phenotypes is not currently available. We developed GAMUT, a genome-wide association approach for multiple longitudinal traits. GAMUT employs a mixed-effects model to fit longitudinal outcomes where a fast algorithm for inversion by recursive partitioning of the random effects submatrix is introduced. To evaluate performance of the algorithms introduced and assess their statistical power and type I error, stochastic simulation was conducted. Consistent with our expectation, po

SUBMITTER: Abdel-Azim G 

PROVIDER: S-EPMC10667366 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature