ArticleAmerican journal of human genetics2022
GWAS of longitudinal trajectories at biobank scale.
Article in American journal of human genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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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
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Who cites it
32 citing papers in PubMed, 44 citations in OpenAlex.
- Multidimensional GWAS analyses on longitudinal phenotypes reveal candidate genes regulating multi-stage egg production traits in Wannan yellow chicken.Poultry science · 2026Article
- Genetic drivers of protein changes over time: Findings, considerations, and approaches in TOPMed cohorts and UK Biobank.bioRxiv : the preprint server for biology · 2026Article
- Genome-wide association analyses of gestational phenotypes identify context-specific genetic effects.Nature genetics · 2026Article
- FEMA-Long: Modeling unstructured covariances for discovery of time-dependent effects in large-scale longitudinal datasets.PLoS genetics · 2026Article
- Can Psychiatric Genetics Advance Without Incorporating a Life Course Perspective?Biological psychiatry · 2026Review
- A simple approach for multiple observations improves power to detect genetic effects and genomic prediction accuracy.HGG advances · 2026Article
- Distributed quantile regression for big data with missing covariates.Statistics in biosciences · 2026Article
- Current situation and future prospect of biobank.CytoJournal · 2026Review
- Mendelian Randomization With Longitudinal Exposure Data: Simulation Study and Real Data Application.Statistics in medicine · 2026Article
- SPAmix: a scalable, accurate, and universal analysis framework for large-scale genetic association studies in admixed populations.Genome biology · 2025Article
- A simple approach for multiple observations improves power to detect genetic effects and genomic prediction accuracy.medRxiv : the preprint server for health sciences · 2025Article
- Modeling the genomic architecture of adiposity and anthropometrics across the lifespan.Nature communications · 2025Article
- Polygenic and pharmacogenomic contributions to medication dosing: a real-world longitudinal biobank study.Journal of translational medicine · 2025Article
- SPANature communications · 2025Article
- Longitudinal characterization of impulsivity phenotypes boosts signal for genomic correlates and heritability.Molecular psychiatry · 2025Article
- Bias-corrected serum creatinine from UK Biobank electronic medical records generates an important data resource for kidney function trajectories.Scientific reports · 2025Article
- Genetic-by-age interaction analyses on complex traits in UK Biobank and their potential to identify effects on longitudinal trait change.Genome biology · 2024Article
- A framework for conducting GWAS using repeated measures data with an application to childhood BMI.Nature communications · 2024Article
- Analyzing longitudinal trait trajectories using GWAS identifies genetic variants for kidney function decline.Nature communications · 2024Article
- JASPER: Fast, powerful, multitrait association testing in structured samples gives insight on pleiotropy in gene expression.American journal of human genetics · 2024Article
Corrections and comments
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Authors and funding
10 authors at 4 institutions in 1 country.
Funding
Abstract
Biobanks linked to massive, longitudinal electronic health record (EHR) data make numerous new genetic research questions feasible. One among these is the study of biomarker trajectories. For example, high blood pressure measurements over visits strongly predict stroke onset, and consistently high fasting glucose and Hb1Ac levels define diabetes. Recent research reveals that not only the mean level of biomarker trajectories but also their fluctuations, or within-subject (WS) variability, are risk factors for many diseases. Glycemic variation, for instance, is recently considered an important clinical metric in diabetes management. It is crucial to identify the genetic factors that shift the mean or alter the WS variability of a biomarker trajectory. Compared to traditional cross-sectional studies, trajectory analysis utilizes more data points and captures a complete picture of the impact of time-varying factors, including medication history and lifestyle. Currently, there are no efficient tools for genome-wide association studies (GWASs) of biomarker trajectories at the biobank scale, even for just mean effects. We propose TrajGWAS, a linear mixed effect model-based method for testing genetic effects that shift the mean or alter the WS variability of a biomarker trajectory. It is scalable to biobank data with 100,000 to 1,000,000 individuals and many longitudinal measurements and robust to distributional assumptions. Simulation studies corroborate that TrajGWAS controls the type I error rate and is powerful. Analysis of eleven biomarkers measured longitudinally and extracted from UK Biobank primary care data for more than 150,000 participants with 1,800,000 observations reveals loci that significantly alter the mean or WS variability.
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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 Socratic graph.