biostudies-literatureApicella CEuropean Union’s Horizon 2020 research and innovation program578https://www.ebi.ac.uk/biostudies/studies/S-EPMC9954093biostudies-literatureUnknown12(4)In this study, we attempted to find genetic variants affecting gene expression (eQTL = expression Quantitative Trait Loci) in the human placenta in normal and pathological situations. The analysis of gene expression in placental diseases (Pre-eclampsia and Intra-Uterine Growth Restriction) is hindered by the fact that diseased placental tissue samples are generally taken at earlier gestations compared to control samples. The difference in gestational age is considered a major confounding factor in the transcriptome regulation of the placenta. To alleviate this significant problem, we propose here a novel approach to pinpoint disease-specific cis-eQTLs. By statistical correction for gestational age at sampling as well as other confounding/surrogate variables systematically searched and idenCellsPan-Genomic Regulation of Gene Expression in Normal and Pathological Human Placentas.PMC9954093765274Khalil AMehats CGascoin GJacques SGiorgione VMiralles FRuano CSMGaspar CApicella CMurdoch CEThilaganathan BVaiman DMarcellin LfalsePan-Genomic Regulation of Gene Expression in Normal and Pathological Human Placentas.In this study, we attempted to find genetic variants affecting gene expression (eQTL = expression Quantitative Trait Loci) in the human placenta in normal and pathological situations. The analysis of gene expression in placental diseases (Pre-eclampsia and Intra-Uterine Growth Restriction) is hindered by the fact that diseased placental tissue samples are generally taken at earlier gestations compared to control samples. The difference in gestational age is considered a major confounding factor in the transcriptome regulation of the placenta. To alleviate this significant problem, we propose here a novel approach to pinpoint disease-specific cis-eQTLs. By statistical correction for gestational age at sampling as well as other confounding/surrogate variables systematically searched and iden2023-01-01T00:00:00Z2023 Feb2026-04-08T13:24:22.376Z2025-02-19T00:13:56.627ZS-EPMC99540933683124410.3390/cells12040578