Genetic variants influence on the placenta regulatory landscape

Abstract

From genomic association studies, quantitative trait loci analysis, and epigenomic mapping, it is evident that significant efforts are necessary to define genetic-epigenetic interactions and understand their role in disease susceptibility and progression. For this reason, an analysis of the effects of genetic variation on gene expression and DNA methylation in human placentas at high resolution and whole-genome coverage will have multiple mechanistic and practical implications. By producing and analyzing DNA sequence variation (n = 303), DNA methylation (n = 303) and mRNA expression data (n = 80) from placentas from healthy women, we investigate the regulatory landscape of the human placenta and offer analytical approaches to integrate different types of genomic data and address some potential limitations of current platforms. We distinguish two profiles of interaction between expression and DNA methylation, revealing linear or bimodal effects, reflecting differences in genomic context, transcription factor recruitment, and possibly cell subpopulations. These findings help to clarify the interactions of genetic, epigenetic, and transcriptional regulatory mechanisms in normal human placentas. They also provide strong evidence for genotype-driven modifications of transcription and DNA methylation in normal placentas. In addition to these mechanistic implications, the data and analytical methods presented here will improve the interpretability of genome-wide and epigenome-wide association studies for human traits and diseases that involve placental functions.

Parent-of-origin-specific allelic expression in the human placenta is limited to established imprinted loci and it is stably maintained across pregnancy

“…Several studies have shown that candidate loci associated with placenta-specific maternal methylation are associated with actual parental allelic transcriptional bias at only half the loci [6, 13, 14]. Additionally, allelic imbalances in DNA methylation may reflect the underlying differences in primary DNA sequence [47, 48]. Concerning RNA-Seq-based studies, spurious claims of parental monoallelic expression may arise from modest informative sample sets, random sampling errors of transcript pools entering library preparation and RNA-sequencing, insufficient read coverage and limited QC (e.g., RNA-Seq mapping or genotyping errors), and loose statistical criteria in defining imprinted genes (reviewed in [7]).…”

Effect for Human Genomic Variation During the BMP4-Induced Conversion From Pluripotent Stem Cells to Trophoblast

“…Recently, studies have demonstrated how genetic variation affects gene expression (Banovich et al, 2018;Delahaye et al, 2018). Kilpinen et al (2013) suggest that genetic variation appears to be the primary driver of gene expression variation.…”

“…An integrative analysis using combinations of genomic, epigenomic, and transcriptomic data will provide a basis for biomarker discovery and help to provide insight of disease etiology. Genomic sequence variation, epigenetic factor, and gene expression are interdependent and jointly contribute to the normal functioning or dysfunction of tissue (Delahaye et al, 2018). For example, the sequencing variations can alter the TF binding strength to regulate gene expression directly (Karczewski et al, 2011;Madsen et al, 2018;Johnston et al, 2019).…”

Accurate ethnicity prediction from placental DNA methylation data

“…b PlaNET utilizes a subset of ethnicity-predictive sites from the HM450K. To investigate whether genetic signal is present in the measurement for these sites, we cross-referenced ethnicity-predictive sites to an existing placental mQTL database [42] and determined whether any sites had SNPs present in either the probe body, CpG site of interrogation, or single base extension sites, based on dbSNP137…”