ArticleScientific reports2018
Genome-wide Analysis of Large-scale Longitudinal Outcomes using Penalization -GALLOP algorithm.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Genome-Wide Meta-Analysis of Cerebrospinal Fluid Biomarkers in Alzheimer's Disease and Parkinson's Disease Cohorts.Movement disorders : official journal of the Movement Disorder Society · 2023Pooled it
- FEMA-Long: Modeling unstructured covariances for discovery of time-dependent effects in large-scale longitudinal datasets.PLoS genetics · 2026Article
- Mendelian Randomization With Longitudinal Exposure Data: Simulation Study and Real Data Application.Statistics in medicine · 2026Article
- A framework for conducting GWAS using repeated measures data with an application to childhood BMI.Nature communications · 2024Article
- A framework for conducting time-varying genome-wide association studies: An application to body mass index across childhood in six multiethnic cohorts.medRxiv : the preprint server for health sciences · 2024Article
- Fast multiple-trait genome-wide association analysis for correlated longitudinal measurements.Scientific reports · 2023Article
- GWAS of longitudinal trajectories at biobank scale.American journal of human genetics · 2022Article
- A genome-wide association study of the longitudinal course of executive functions.Translational psychiatry · 2021Article
- Interaction analyses based on growth parameters of GWAS between Escherichia coli and Staphylococcus aureus.AMB Express · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genome-wide association studies (GWAS) with longitudinal phenotypes provide opportunities to identify genetic variations associated with changes in human traits over time. Mixed models are used to correct for the correlated nature of longitudinal data. GWA studies are notorious for their computational challenges, which are considerable when mixed models for thousands of individuals are fitted to millions of SNPs. We present a new algorithm that speeds up a genome-wide analysis of longitudinal data by several orders of magnitude. It solves the equivalent penalized least squares problem efficiently, computing variances in an initial step. Factorizations and transformations are used to avoid inversion of large matrices. Because the system of equations is bordered, we can re-use components, which can be precomputed for the mixed model without a SNP. Two SNP effects (main and its interaction with time) are obtained. Our method completes the analysis a thousand times faster than the R package lme4, providing an almost identical solution for the coefficients and p-values. We provide an R implementation of our algorithm.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.