Genetic determinants of glucose levels in pregnancy: genetic risk scores analysis and GWAS in the Norwegian STORK cohort

Moen, Gunn-Helen; LeBlanc, Marissa; Sommer, Christine; Prasad, Rashmi B.; Lekva, Tove; Normann, Kjersti Ringvoll; Qvigstad, Elisabeth; Groop, Leif; Birkeland, Kåre I.; Evans, David M.; Frøslie, Kathrine Frey

doi:10.1530/eje-18-0478

Cited by 16 publications

(15 citation statements)

References 36 publications

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“…So far, only two of the identified placental miR-eQTLs have been reported in the GWAS catalog: rs10853101 (miR-10a-5p) associated with the risk to diverticular disease and rs1424569 (hsa-miR-490-3p) with cardiac PR interval. In perspective, the phenotype effects of placental miR-eQTLs individually or as a component in polygenic risk scores for pregnancy-related traits ( Moen et al, 2018 ; Ursini et al, 2018 ; Lamri et al, 2020 ; Steinthorsdottir et al, 2020 ) are still to be clarified. The pilot association testing of miR-eQTLs with newborn traits in the current study may have suffered from inadequate statistical power due to highly polygenic contribution (each variant with small effect), as well as combined effect of maternal and fetal genetics in modulating anthropometric parameters at birth ( Beaumont et al, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

Coordinated Expressional Landscape of the Human Placental miRNome and Transcriptome

Inno

Kikas

Lillepea

et al. 2021

Front. Cell Dev. Biol.

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Placenta is a unique organ that serves its own function, and contributes to maternal gestational adaptation and fetal development. Coordination of its transcriptome to satisfy all the maternal-fetal needs across gestation is not fully understood. MicroRNAs are powerful transcriptome modulators capable to adjust rapidly the expression level and dynamics of large gene sets. This MiR-Seq based study presents a multi-omics investigation of the human placental miRNome and its synergy with the transcriptome. The analysis included 52 placentas representing three trimesters of normal pregnancy, and term cases of late-onset preeclampsia (LO-PE), gestational diabetes and affected fetal growth. Gestational-age dependent differential expression (FDR < 0.05) was detected for 319 of 417 tested miRNAs (76.5%). A shared list of target genes of dynamic miRNAs suggested their coordinated action. The most abundant miR-143-3p revealed as a marker for pregnancy progression. The data suggested critical, but distinct roles of placenta-specific imprinted C19MC and C14MC miRNA clusters. Paternally encoded primate-specific C19MC was highly transcribed during first trimester, potentially fine-tuning the early placental transcriptome in dosage-sensitive manner. Maternally encoded eutherian C14MC showed high expression until term, underlining its key contribution across gestation. A major shift in placental miRNome (16% miRNAs) was observed in LO-PE, but not in other term pregnancy complications. Notably, 13/38 upregulated miRNAs were transcribed from C19MC and only one from C14MC, whereas 11/28 downregulated miRNAs represented C14MC and none C19MC. miR-210-3p, miR-512-5p, miR-32-5p, miR-19a-3p, miR-590-3p, miR-379-5p were differentially expressed in LO-PE and cases of small-for-gestational-age newborns, supporting a shared etiology. Expression correlation analysis with the RNA-Seq data (16,567 genes) of the same samples clustered PE-linked miRNAs into five groups. Large notable clusters of miRNA–gene pairs showing directly and inversely correlated expression dynamics suggested potential functional relationships in both scenarios. The first genome-wide study of placental miR-eQTLs identified 66 placental SNVs associated with the expression of neighboring miRNAs, including PE-linked miRNAs miR-30a-5p, miR-210-3p, miR-490-3p and miR-518-5p. This study provided a rich catalog of miRNAs for further in-depth investigations of their individual and joint effect on placental transcriptome. Several highlighted miRNAs may serve as potential biomarkers for pregnancy monitoring and targets to prevent or treat gestational disorders.

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Section: Discussionmentioning

confidence: 99%

Coordinated Expressional Landscape of the Human Placental miRNome and Transcriptome

Inno

Kikas

Lillepea

et al. 2021

Front. Cell Dev. Biol.

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“…For example, there is preliminary evidence to suggest that genetic variants associated with cardiometabolic phenotypes in the general population (i.e. men and women who are not pregnant) are also associated with the same traits in mothers during pregnancy, meaning that SNPs that proxy these traits also proxy the exposures in pregnant women 2 , 31 …”

Section: Using Two-sample Mr Studies To Analyse the Causal Effect Of mentioning

confidence: 99%

Elucidating the role of maternal environmental exposures on offspring health and disease using two-sample Mendelian randomization

Evans

Moen

Hwang

et al. 2019

International Journal of Epidemiology

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Background There is considerable interest in estimating the causal effect of a range of maternal environmental exposures on offspring health-related outcomes. Previous attempts to do this using Mendelian randomization methodologies have been hampered by the paucity of epidemiological cohorts with large numbers of genotyped mother–offspring pairs. Methods We describe a new statistical model that we have created which can be used to estimate the effect of maternal genotypes on offspring outcomes conditional on offspring genotype, using both individual-level and summary-results data, even when the extent of sample overlap is unknown. Results We describe how the estimates obtained from our method can subsequently be used in large-scale two-sample Mendelian randomization studies to investigate the causal effect of maternal environmental exposures on offspring outcomes. This includes studies that aim to assess the causal effect of in utero exposures related to fetal growth restriction on future risk of disease in offspring. We illustrate our framework using examples related to offspring birthweight and cardiometabolic disease, although the general principles we espouse are relevant for many other offspring phenotypes. Conclusions We advocate for the establishment of large-scale international genetics consortia that are focused on the identification of maternal genetic effects and committed to the public sharing of genome-wide summary-results data from such efforts. This information will facilitate the application of powerful two-sample Mendelian randomization studies of maternal exposures and offspring outcomes.

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“…In a previous study in Gen3G and HAPO-EU, we found that trait-based polygenic scores (with component variants selected on the basis of effects outside pregnancy) for fasting glucose, fasting insulin, insulin secretion, and insulin sensitivity were associated with GDM (4). Similarly, Moen et al (37) found associations between polygenic scores for fasting glucose, BMI, and T2D and glucose levels in pregnancy. The current study builds upon this literature by testing variants grouped by physiologic pathway (rather than by effects on a single trait) for associations with pregnancy glycemic traits and GDM; this allows us to identify physiologic pathways that play a role in GDM pathogenesis.…”

Section: Discussionmentioning

confidence: 88%

“…Previous studies have tested T2D-associated variants for association with GDM, but few have probed variants grouped on the basis of more specific physiology (2,4,(37)(38)(39). In a previous study in Gen3G and HAPO-EU, we found that trait-based polygenic scores (with component variants selected on the basis of effects outside pregnancy) for fasting glucose, fasting insulin, insulin secretion, and insulin sensitivity were associated with GDM (4).…”

Section: Discussionmentioning

confidence: 99%

Genetic Loci and Physiologic Pathways Involved in Gestational Diabetes Mellitus Implicated Through Clustering

et al. 2020

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Hundreds of common genetic variants acting through distinguishable physiologic pathways influence the risk of type 2 diabetes (T2D). It is unknown to what extent the physiology underlying gestational diabetes mellitus (GDM) is distinct from that underlying T2D. In this study of >5,000 pregnant women from three cohorts, we aimed to identify physiologically related groups of maternal variants associated with GDM using two complementary approaches that were based on Bayesian nonnegative matrix factorization (bNMF) clustering. First, we tested five bNMF clusters of maternal T2D-associated variants grouped on the basis of physiology outside of pregnancy for association with GDM. We found that cluster polygenic scores representing genetic determinants of reduced β-cell function and abnormal hepatic lipid metabolism were associated with GDM; these clusters were not associated with infant birth weight. Second, we derived bNMF clusters of maternal variants on the basis of pregnancy physiology and tested these clusters for association with GDM. We identified a cluster that was strongly associated with GDM as well as associated with higher infant birth weight. The effect size for this cluster’s association with GDM appeared greater than that for T2D. Our findings imply that the genetic and physiologic pathways that lead to GDM differ, at least in part, from those that lead to T2D.

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Genetic determinants of glucose levels in pregnancy: genetic risk scores analysis and GWAS in the Norwegian STORK cohort

Cited by 16 publications

References 36 publications

Coordinated Expressional Landscape of the Human Placental miRNome and Transcriptome

Coordinated Expressional Landscape of the Human Placental miRNome and Transcriptome

Elucidating the role of maternal environmental exposures on offspring health and disease using two-sample Mendelian randomization

Genetic Loci and Physiologic Pathways Involved in Gestational Diabetes Mellitus Implicated Through Clustering

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