Leveraging a machine learning derived surrogate phenotype to improve power for genome-wide association studies of partially missing phenotypes in population biobanks
Bibliographic record
Abstract
Abstract Within population biobanks, genetic discovery for specialized phenotypes is often limited by incomplete ascertainment. Machine learning (ML) is increasingly used to impute missing phenotypes from surrogate information. However, imputing missing phenotypes can invalidate statistical inference when the imputation model is misspecified, and proxy analysis of the ML-phenotype can introduce spurious associations. To overcome these limitations, we introduce SynSurr, an approach that jointly analyzes a partially missing target phenotype with a “synthetic surrogate”, its predicted value from an ML-model. SynSurr estimates the same genetic effect as standard genome-wide association studies (GWAS) of the target phenotype, but improves power provided the synthetic surrogate is correlated with the target. Unlike imputation or proxy analysis, SynSurr does not require that the synthetic surrogate is obtained from a correctly specified generative model. We perform extensive simulations and an ablation analysis to compare SynSurr with existing methods. We also apply SynSurr to empower GWAS of dual-energy x-ray absorptiometry traits within the UK Biobank, leveraging a synthetic surrogate composed of bioelectrical impedance and anthropometric traits.
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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 | 0.031 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".