Predictive capabilities of polygenic scores in an East-Asian population-based cohort: the Singapore Chinese health study
Bibliographic record
Abstract
Polygenic scores (PGS) are derived primarily from European population studies. It is unclear how these perform in risk predictions among East-Asians. We generated 2173 PGSs from 519 traits and assessed their associations with 58 baseline phenotypes in the Singapore Chinese Health Study, a prospective cohort of 23,622 Chinese adults residing in Singapore. PGS performances were evaluated through explained variance (r²) and area under the receiver operating characteristic curve (AUC) in linear and logistic regression models, respectively. Traits with higher heritability exhibited stronger PGS associations, while behavioural traits, like sleep duration, showed weaker associations. Height and type 2 diabetes (T2D) exhibited largest SNP-based heritability with the largest increments in explained variance and AUC. We explored the effect of T2D risk factors on the association between the T2D PGS (PGS003444) and incident T2D. PGS associations were significantly mediated and modified by hypertension (Pindirect = 1.56 × 10−18, Pinteraction = 2.10 × 10−3) and BMI (Pindirect = 1.25 × 10−36, Pinteraction = 1.11 × 10−6). Prediction ability of PGS003444 for incident T2D was stronger among non-overweight individuals without hypertension (AUC = 0.774) than in overweight individuals with hypertension (AUC = 0.709). Our study demonstrates the divergent ability of PGSs in predictions of complex traits. For certain traits, such as T2D, PGSs may have the potential for improving risk prediction and personalized healthcare. The authors analyzed over 2000 polygenic scores (PGSs) to evaluate their associations with 58 baseline traits in the Singapore Chinese Health Study (SCHS) cohort.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".