Genome-Wide Polygenic Risk Scores and Prediction of Gestational Diabetes in South Asian Women
Bibliographic record
Abstract
ABSTRACT Gestational diabetes Mellitus (GDM) affects 1 in 7 births and is associated with numerous adverse health outcomes for both mother and child. GDM is suspected to share a large common genetic background with type 2 diabetes (T2D). The first aim of this study, was to characterize different GDM polygenic risk scores (PRSs) using data from the South Asian Birth Cohort (START). The second aim of this study was to estimate the heritability of GDM. PRSs were derived for 832 South Asian women from START using the pruning and thresholding (P+T), LDpred, and GraBLD methods. Weights were derived from multi-ethnic (Mahajan et al ., 2014) and white Caucasian (Scott et al ., 2017) studies of the DIAGRAM consortium. Association with GDM was tested using logistic regression. Heritability of GDM was estimated using the GRMEL approach. Results were replicated in samples from the UK Biobank (UKB) study. The top P+T, LDpred and GraBLD PRSs were all based on Mahajan et al . The best PRS was highly associated with GDM in START (AUC=0.62, OR=1.60 [95% CI=1.44–1.69]), and in South Asian (AUC=0.65) and white British (AUC=0.58) women from UKB. Heritability of GDM approximated 0.55±0.83 in START and 0.18±0.22 in white British women from UKB. Our results highlight the importance of combining genome-wide genotypes and summary statistics from large multi-ethnic studies to optimize PRSs in South Asians.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.002 | 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".