Genome‐Wide Association Study of Pre‐Eclampsia Detects Novel Maternal Single Nucleotide Polymorphisms and Copy‐Number Variants in Subsets of the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) Study Cohort
Bibliographic record
Abstract
A genome-wide association study was undertaken to identify maternal single nucleotide polymorphisms (SNPs) and copy-number variants (CNVs) associated with pre-eclampsia. Case-control analysis was performed on 1070 Afro-Caribbean (n = 21 cases and 1049 controls) and 723 Hispanic (n = 62 cases and 661 controls) mothers and 1257 mothers of European ancestry (n = 50 cases and 1207 controls) from the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) study. European ancestry subjects were genotyped on Illumina Human610-Quad and Afro-Caribbean and Hispanic subjects were genotyped on Illumina Human1M-Duo BeadChip microarrays. Genome-wide SNP data were analyzed using PLINK. CNVs were called using three detection algorithms (GNOSIS, PennCNV, and QuantiSNP), merged using CNVision, and then screened using stringent criteria. SNP and CNV findings were compared to those of the Study of Pregnancy Hypertension in Iowa (SOPHIA), an independent pre-eclampsia case-control dataset of Caucasian mothers (n = 177 cases and 116 controls). A list of top SNPs were identified for each of the HAPO ethnic groups, but none reached Bonferroni-corrected significance. Novel candidate CNVs showing enrichment among pre-eclampsia cases were also identified in each of the three ethnic groups. Several variants were suggestively replicated in SOPHIA. The discovered SNPs and copy-number variable regions present interesting candidate genetic variants for pre-eclampsia that warrant further replication and investigation.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".