A pipeline for multivariate genome-wide associations studies with morphological brain features
Bibliographic record
Abstract
Medical imaging datasets, such as magnetic resonance, are increasingly being used to investigate the genetic architecture of the brain. These images are commonly used as imaging-specific or –derived phenotypes when conducting genotype-phenotype association studies. When using this type of phenotype, multivariate genome-wide association study (GWAS) designs are considered better suited than univariate methods due to the ability to account for the inherent correlations between the phenotypes related to brain structures as determined from medical images. The main objective of this work is to establish and evaluate a comprehensive pipeline for investigating genotype-phenotype associations of the human brain using canonical component analysis. The proposed pipeline was tested to investigate genotype-phenotype associations between cortical brain region volumes in subjects with attention-deficit hyperactivity disorder as a proof-of-principle. Canonical component analysis, a form of multivariate GWAS and machine learning, was utilized to determine genotype-phenotype associations between cortical brain region volumes in subjects with attention-deficit hyperactivity disorder. Using the developed pipeline, several significant (p-value < 5E−04) single nucleotide polymorphisms were found that reside in or near several genes like DSCAM or DPYSL2 that are known to be associated with neurological and mental disorders or substance addiction, a common comorbidity for subjects with attention-deficit hyperactivity disorder. These clinically meaningful results show that the proposed pipeline using canonical component analysis can be used to investigate the genetic architecture of the brain.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.010 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".