ArticlePloS one2014
Longitudinal analysis is more powerful than cross-sectional analysis in detecting genetic association with neuroimaging phenotypes.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.
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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
32 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Genetic variants associated with longitudinal changes in brain structure across the lifespan.Nature neuroscience · 2022Pooled it
- Functional Varying-Index Coefficients Model for Dynamic Synergistic Gene-Environment Interactions.Statistics in biosciences · 2026Article
- SPANature communications · 2025Article
- A review of artificial intelligence-based brain age estimation and its applications for related diseases.Briefings in functional genomics · 2025Review
- TOMM40 may mediate GFAP, neurofilament light Protein, pTau181, and brain morphometry in aging.Aging brain · 2025Article
- Article
- Do disfluencies increase with age? Evidence from a sequential corpus study of disfluencies.Psychology and aging · 2023Article
- Minimax Powerful Functional Analysis of Covariance Tests: with Application to Longitudinal Genome-Wide Association Studies.Scandinavian journal of statistics, theory and applications · 2023Article
- Benchmarking tools for detecting longitudinal differential expression in proteomics data allows establishing a robust reproducibility optimization regression approach.Nature communications · 2022Article
- Multivariate partial linear varying coefficients model for gene-environment interactions with multiple longitudinal traits.Statistics in medicine · 2022Article
- Soluble TREM2 in CSF and its association with other biomarkers and cognition in autosomal-dominant Alzheimer's disease: a longitudinal observational study.The Lancet. Neurology · 2022Observational
- Dysmaturational Longitudinal Epigenetic Aging During Transition to Psychosis.Schizophrenia bulletin open · 2022Article
- Multivariate Analysis and Modelling of multiple Brain endOphenotypes: Let's MAMBO!Computational and structural biotechnology journal · 2021Review
- Identifying Imaging Genetics Biomarkers of Alzheimer's Disease by Multi-Task Sparse Canonical Correlation Analysis and Regression.Frontiers in genetics · 2021Article
- A TOMM40/APOE allele encoding APOE-E3 predicts high likelihood of late-onset Alzheimer's disease in autopsy cases.Molecular genetics & genomic medicine · 2020Article
- The emerging landscape of health research based on biobanks linked to electronic health records: Existing resources, statistical challenges, and potential opportunities.Statistics in medicine · 2020Article
- Structured Genome-Wide Association Studies with Bayesian Hierarchical Variable Selection.Genetics · 2019Article
- Leveraging Breeding Values Obtained from Random Regression Models for Genetic Inference of Longitudinal Traits.The plant genome · 2019Article
- Quantitative assessment of field strength, total intracranial volume, sex, and age effects on the goodness of harmonization for volumetric analysis on the ADNI database.Human brain mapping · 2019Article
- Quantifying Neurodegenerative Progression With DeepSymNet, an End-to-End Data-Driven Approach.Frontiers in neuroscience · 2019Article
Corrections and comments
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4 authors.
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Abstract
Most existing genome-wide association analyses are cross-sectional, utilizing only phenotypic data at a single time point, e.g. baseline. On the other hand, longitudinal studies, such as Alzheimer's Disease Neuroimaging Initiative (ADNI), collect phenotypic information at multiple time points. In this article, as a case study, we conducted both longitudinal and cross-sectional analyses of the ADNI data with several brain imaging (not clinical diagnosis) phenotypes, demonstrating the power gains of longitudinal analysis over cross-sectional analysis. Specifically, we scanned genome-wide single nucleotide polymorphisms (SNPs) with 56 brain-wide imaging phenotypes processed by FreeSurfer on 638 subjects. At the genome-wide significance level P < 1.8 x 10(9)) or a less stringent level (e.g. P < 10(7)), longitudinal analysis of the phenotypic data from the baseline to month 48 identified more SNP-phenotype associations than cross-sectional analysis of only the baseline data. In particular, at the genome-wide significance level, both SNP rs429358 in gene APOE and SNP rs2075650 in gene TOMM40 were confirmed to be associated with various imaging phenotypes in multiple regions of interests (ROIs) by both analyses, though longitudinal analysis detected more regional phenotypes associated with the two SNPs and indicated another significant SNP rs439401 in gene APOE. In light of the power advantage of longitudinal analysis, we advocate its use in current and future longitudinal neuroimaging studies.
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