Towards efficient population-level genome-wide association studies
Bibliographic record
Abstract
Big genomic resources such as UK Biobank involve hundreds of thousands of subjects and are being established for prospective epidemiological cohort studies with the goal of improving the screening and treatment of disease.Genome-wide association studies (GWAS) on these resources experience time and space efficiency issues which are ampliĄed at the population level.We show two new methods for mitigation of these issues.Firstly, we present a new compressed Ąle format and associated software which exploits properties of the statistical distribution of population genetics Ąles and enables computationally faster and smaller GWAS, which results in reduced costs for GWAS research.We benchmark this new method on Thousand Genomes Project data against the current state-of-the-art and Ąnd a signiĄcant space efficiency increase.Secondly, software implementing an efficient clustering method for discovered associations from such studies is also presented.The method is applied on GWAS of nearly 4,000 brain imaging phenotypes from UK Biobank, with results associated with pathways involved in various diseases.
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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 | 0.007 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".