Genomic profiling in the placenta : toward a greater understanding of genetic variation contributing to placental insufficiency and fetal growth restriction
Bibliographic record
Abstract
Fetal growth restriction (FGR) is a common pregnancy complication in which the fetus does not grow to its genetic potential due to a pathological cause, which puts it at greater risk for morbidity and mortality in the perinatal period and poor health outcomes in childhood and adulthood. Although the etiology of FGR is diverse, insufficient function of the placenta underlies many cases, as the placenta is a crucial organ to support fetal growth and development and a healthy pregnancy. One of the few established genetic contributors to placental insufficiency and non-syndromic FGR is trisomy confined to the placenta. Beyond this, the contribution of smaller genomic imbalances (copy number variants) or common single nucleotide variants and their impact on gene regulation in the placenta, for example through DNA methylation, remains largely unexplored. In this thesis, I hypothesized that placental genomic imbalances, including aneuploidy and copy number variants (CNVs), and candidate single nucleotide variants in a gene relevant to DNA methylation (DNAme) are associated with poor fetal growth and/or altered placental DNAme. Using molecular-cytogenetic and microarray techniques, I assessed aneuploidy and CNVs in placentas from infants born small-for-gestational age (SGA) and adequately-grown controls. I found that confined placental mosaicism of autosomal aneuploidies or rare candidate CNVs involving genes related to placental function or growth were present in about 18% of SGA cases, and that CNV load was not associated with SGA. I also characterized a novel case of eight 2-4 Mb duplications confined to the placenta of an infant with FGR, in which the CNVs arose de novo in a cell in the trophoblast lineage. Finally, I studied two candidate single nucleotide polymorphisms in MTHFR, involved in the metabolic pathway that produces one-carbon units for methylation reactions and purine synthesis. I found that these variants were not associated with altered placental DNAme, and that there was only a trend for increased risk of placental insufficiency complications of FGR and/or preeclampsia. Through these studies, I contributed to our understanding of genetic variation in the placenta and its association with FGR and placental insufficiency, and provided a foundation from which future studies can build.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".