ArticleGenes, brain, and behavior2024
Principal and independent genomic components of brain structure and function.
Article in Genes, brain, and behavior, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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
8 citing papers in PubMed, 11 citations in OpenAlex.
- Genetic factor analysis for characterizing phenome-wide patterns of genetic pleiotropy.Nature genetics · 2026Article
- Article
- Dissecting the polygenic architecture of psychopathology via singular value decomposition of eight psychiatric genome-wide association studies and evaluation of component-based polygenic scores.General psychiatry · 2026Article
- Distinct impact modes of polygenic disposition to dyslexia in the adult brain.Science advances · 2024Article
- Running in the FAMILY: understanding and predicting the intergenerational transmission of mental illness.European child & adolescent psychiatry · 2024Article
- Joint modeling of human cortical structure: Genetic correlation network and composite-trait genetic correlation.NeuroImage · 2024Article
- Principal and independent genomic components of brain structure and function.Genes, brain, and behavior · 2024Article
- Genomic loci influence patterns of structural covariance in the human brain.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
The highly polygenic and pleiotropic nature of behavioural traits, psychiatric disorders and structural and functional brain phenotypes complicate mechanistic interpretation of related genome-wide association study (GWAS) signals, thereby obscuring underlying causal biological processes. We propose genomic principal and independent component analysis (PCA, ICA) to decompose a large set of univariate GWAS statistics of multimodal brain traits into more interpretable latent genomic components. Here we introduce and evaluate this novel methods various analytic parameters and reproducibility across independent samples. Two UK Biobank GWAS summary statistic releases of 2240 imaging-derived phenotypes (IDPs) were retrieved. Genome-wide beta-values and their corresponding standard-error scaled z-values were decomposed using genomic PCA/ICA. We evaluated variance explained at multiple dimensions up to 200. We tested the inter-sample reproducibility of output of dimensions 5, 10, 25 and 50. Reproducibility statistics of the respective univariate GWAS served as benchmarks. Reproducibility of 10-dimensional PCs and ICs showed the best trade-off between model complexity and robustness and variance explained (PCs: |r
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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.