medRxivGenetic & Genomic MedicinePreprint
Genetic Architecture of Placental Efficiency for Term Infants: Evidence from Monoaminergic Pathways and Placental Tissue Expression in the Norwegian Mother, Father and Child Cohort Study (MoBa)
Jonas Oesterhaug AnderseniD, Stener NerlandiD, Piotr Pawel JaholkowskiiD, Gianluca UrsiniiD, Srdjan DjuroviciD, Anne Cathrine StaffiD, Anders Martin DaleiD, Ole A. AndreasseniD, Ingrid AgartziD, Alexey A. ShadriniD, Laura Anne WortingeriD
Abstract
The placenta plays a central role in supporting fetal growth. The birthweight-to-placental weight ratio is a key index of placental efficiency (PLE) and its capacity to adapt to fetal demands. We conducted the first genome-wide association study (GWAS) of PLE in 63,875 term singleton births from the Norwegian Mother, Father and Child cohort (MoBa), with complementary maternal (N = 60,472) and paternal (N = 40,116) analyses. Across offspring and maternal genomes, we identified multiple genome-wide significant loci, with TSNAX-DISC1 consistently implicated. Comparative genetic analyses showed overlap between PLE and placental weight, but minimal overlap with birth weight, indicating that PLE captures aspects of placental adaptation beyond overall growth. Gene-set enrichment highlighted monoaminergic pathways, particularly norepinephrine uptake and transport, and gene-property analyses demonstrated enrichment in placental tissue, unlike birth weight or placental weight. Global and local genetic correlation analyses revealed predominantly strong negative correlations between PLE and offspring, maternal, and paternal placental weight, strongest for paternal genomes. Notably, mapped genes including SLC6A2, SLC22A2, and SLC22A3 connect PLE to monoamine signaling, consistent with a placental contribution to neurodevelopment. These findings reveal a distinct genetic architecture of PLE and shared pathways linking placental function and offspring brain development.
- organism: human
- assay: bulk RNA-seq
- research focus: GWAS / association mapping
- approach: statistical model
- Citations
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