Faculty Opinions recommendation of Variants in ADCY5 and near CCNL1 are associated with fetal growth and birth weight.
Bibliographic record
Abstract
To identify genetic variants associated with birth weight, we meta-analyzed six genome-wide association (GWA) studies (n = 10,623 Europeans from pregnancy/birth cohorts) and followed up two lead signals in 13 replication studies (n = 27,591). rs900400 near LEKR1 and CCNL1 (P = 2 x 10(-35)) and rs9883204 in ADCY5 (P = 7 x 10(-15)) were robustly associated with birth weight. Correlated SNPs in ADCY5 were recently implicated in regulation of glucose levels and susceptibility to type 2 diabetes, providing evidence that the well-described association between lower birth weight and subsequent type 2 diabetes has a genetic component, distinct from the proposed role of programming by maternal nutrition. Using data from both SNPs, we found that the 9% of Europeans carrying four birth weight-lowering alleles were, on average, 113 g (95% CI 89-137 g) lighter at birth than the 24% with zero or one alleles (P(trend) = 7 x 10(-30)). The impact on birth weight is similar to that of a mother smoking 4-5 cigarettes per day in the third trimester of pregnancy. PMID: 20372150 Funding information This work was supported by: Medical Research Council, United Kingdom Grant ID: G0600705 Medical Research Council, United Kingdom Grant ID: G9815508 Medical Research Council, United Kingdom Grant ID: G0601261 NIDDK NIH HHS, United States Grant ID: R01 DK078150 NICHD NIH HHS, United States Grant ID: R01 HD034568 Wellcome Trust, United Kingdom Grant ID: 89061/Z/09/Z Medical Research Council, United Kingdom Grant ID: G0000934 Wellcome Trust, United Kingdom Grant ID: 076113/B/04/Z NHLBI NIH HHS, United States Grant ID: HL0876792 NICHD NIH HHS, United States Grant ID: R24 HD050924 NIMH NIH HHS, United States Grant ID: MH63706 NIMH NIH HHS, United States Grant ID: RL1 MH083268 NICHD NIH HHS, United States Grant ID: HD05450 Wellcome Trust, United Kingdom Grant ID: 068545/Z/02 NIDDK NIH HHS, United States Grant ID: P30 DK056350 NHLBI NIH HHS, United States Grant ID: R01 HL085144 NIDDK NIH HHS, United States Grant ID: 1R01DK075787 Medical Research Council, United Kingdom Grant ID: G0400546 NHLBI NIH HHS, United States Grant ID: K24 HL068041 Wellcome Trust, United Kingdom Grant ID: 090532 NICHD NIH HHS, United States Grant ID: R01 HD056465 Department of Health, United Kingdom Grant ID: PHCS/C4/4/016 NHLBI NIH HHS, United States Grant ID: HL085144 NIMH NIH HHS, United States Grant ID: MH083268 NCRR NIH HHS, United States Grant ID: RR20649 NICHD NIH HHS, United States Grant ID: HD056465 NIDDK NIH HHS, United States Grant ID: DK56350 Canadian Institutes of Health Research, Canada Grant ID: MOP 82893 NHLBI NIH HHS, United States Grant ID: HL068041 FIC NIH HHS, United States Grant ID: TW05596 Medical Research Council, United Kingdom Grant ID: G0500539 FIC NIH HHS, United States Grant ID: R01 TW005596 NCRR NIH HHS, United States Grant ID: P20 RR020649 NIEHS NIH HHS, United States Grant ID: ES10126 Wellcome Trust, United Kingdom Grant ID: 085541 NIDDK NIH HHS, United States Grant ID: R01 DK075787 Medical Research Council, United Kingdom Grant ID: G0601653 NICHD NIH HHS, United States Grant ID: HD034568 NICHD NIH HHS, United States Grant ID: R37 HD034568 Wellcome Trust, United Kingdom Grant ID: 085301 Medical Research Council, United Kingdom Grant ID: G0800582 NHLBI NIH HHS, United States Grant ID: R01 HL087679 Medical Research Council, United Kingdom Grant ID: G0600331 Medical Research Council, United Kingdom Grant ID: G0500070 NIEHS NIH HHS, United States Grant ID: P30 ES010126 NIDDK NIH HHS, United States Grant ID: DK075787 British Heart Foundation, United Kingdom Medical Research Council, United Kingdom Grant ID: G0801056 NIMH NIH HHS, United States Grant ID: R01 MH063706 Chief Scientist Office, United Kingdom Grant ID: CZB/4/710 NIDDK NIH HHS, United States Grant ID: DK078150 More Less keyboard_arrow_down
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.516 | 0.267 |
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".