Testing for associations between systolic blood pressure and single-nucleotide polymorphism profiles obtained from sparse principal component analysis
Bibliographic record
Abstract
BACKGROUND: Hypertension is a prevalent condition linked to major cardiovascular conditions and multiple other comorbidities. Genetic information can offer a deeper understanding about susceptibility and the underlying disease mechanisms. The Genetic Analysis Workshop 18 (GAW18) provides abundant genotype data to determine genetic associations for being hypertensive and for the underlying trait of systolic blood pressure (SBP). The high-dimensional nature of this data promotes dimension reduction techniques to remove excess noise and also synthesize genetic information for complex, polygenic traits. METHODS: For both measured and simulated phenotype data from GAW18, we use sparse principal component analysis to obtain sparse genetic profiles that represent the underlying data structures. We then detect associations between the obtained sparse principal components (PCs) and SBP, a major indicator of hypertension, following up by investigating the sparse PCs for genetic structure to gain insight into new patterns. RESULTS: After adjusting for multiple testing, 27 of 122 PCs were significantly associated with measured SBP, offering a large number of components to investigate. Considering the top 3 PCs, linked genetic regions have been identified; these may act in unison while associated with SBP. Simulated data offered similar results. CONCLUSIONS: Sparse PCs can offer a new data-driven approach to structuring genotype data and understanding the genetic mechanics behind complex, polygenic traits such as hypertension.
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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 | 0.005 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".