ArticleGenome biology2024
Genetic-by-age interaction analyses on complex traits in UK Biobank and their potential to identify effects on longitudinal trait change.
Article in Genome biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Design and model choices shape inference of age-varying genetic effects on complex traits.Nature aging · 2026Article
- Genetic determinants of childhood blood pressure and heart rate in relation to adult health outcomes: the consortium of childhood blood pressure.European heart journal · 2026Article
- Partitioned blood pressure polygenic risk reveals differential genetic effects of tissue-specific enhancers and their interactions on cardiovascular disease.Research square · 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
- Modelling time-varying genetic effects on binary disease risk via functional Mendelian randomization.Bioinformatics (Oxford, England) · 2026Article
- Using Negative Control Outcomes to Detect Selection Bias in Mendelian Randomization Studies.Statistics in medicine · 2026Article
- An evaluation of age-varying genetic effects underlying body-mass index and blood pressure in the UK Biobank.PLoS genetics · 2026Article
- Causal effect heterogeneity estimation using summary statistics.Research square · 2026Article
- TAS2R38 taster variants-linked MGAM expression in Alzheimer's disease: a novel target for precision drug repurposing.Frontiers in aging neuroscience · 2026Article
- The Interplay Between Sleep and Mental Health: A Genetic Perspective.Biological psychiatry · 2025Review
- The influence of CHRNA5 and D398N missense variation on social and emotional behaviors in rodents and humans.Translational psychiatry · 2025Article
- Exposure accumulation drives age-dependent disease architectures and polygenic risk scores.medRxiv : the preprint server for health sciences · 2025Article
- Hypothesis test of specific parametric structure in a generalized additive model.medRxiv : the preprint server for health sciences · 2025Article
- Polygenic and pharmacogenomic contributions to medication dosing: a real-world longitudinal biobank study.Journal of translational medicine · 2025Article
- Heritability of Alzheimer's disease-related plasma biomarkers in the Amish population.Alzheimer's & dementia (Amsterdam, Netherlands)Article
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Authors and funding
6 authors.
Funding
Abstract
backgroundGenome-wide association studies (GWAS) have identified thousands of loci for disease-related human traits in cross-sectional data. However, the impact of age on genetic effects is underacknowledged. Also, identifying genetic effects on longitudinal trait change has been hampered by small sample sizes for longitudinal data. Such effects on deteriorating trait levels over time or disease progression can be clinically relevant.
resultsUnder certain assumptions, we demonstrate analytically that genetic-by-age interaction observed in cross-sectional data can be indicative of genetic association on longitudinal trait change. We propose a 2-stage approach with genome-wide pre-screening for genetic-by-age interaction in cross-sectional data and testing identified variants for longitudinal change in independent longitudinal data. Within UK Biobank cross-sectional data, we analyze 8 complex traits (up to 370,000 individuals). We identify 44 genetic-by-age interactions (7 loci for obesity traits, 26 for pulse pressure, few to none for lipids). Our cross-trait view reveals trait-specificity regarding the proportion of loci with age-modulated effects, which is particularly high for pulse pressure. Testing the 44 variants in longitudinal data (up to 50,000 individuals), we observe significant effects on change for obesity traits (near APOE, TMEM18, TFAP2B) and pulse pressure (near FBN1, IGFBP3; known for implication in arterial stiffness processes).
conclusionsWe provide analytical and empirical evidence that cross-sectional genetic-by-age interaction can help pinpoint longitudinal-change effects, when cross-sectional data surpasses longitudinal sample size. Our findings shed light on the distinction between traits that are impacted by age-dependent genetic effects and those that are not.
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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.