Placental DNA methylation signatures of maternal smoking during pregnancy and potential impacts on fetal growth

Everson, Todd M.; Vives-Usano, Marta; Seyve, Emie; Lacasaña, Marina; Craig, Jeffrey M.; Lesseur, Corina; Baker, Emily; Fernández-Jiménez, Nora; Heude, Barbara; Perron, Patrice; Gónzalez-Alzaga, Beatriz; Halliday, Jane; Deyssenroth, Maya A.; Íñiguez, Carmen; Bouchard, Luigi; Carmona-Sáez, Pedro; Loke, Yuk Jing; Hao, Ke; Belmonte, Thalía; Munier, Charles; Martorell-Marugán, Jordi; Muggli, Evelyne; Chen, Jia; Fernández, Mariana F.; Tost, Jörg; Gómez-Martín, Antonio; London, Stephanie J.; Sunyer, Jordi; Marsit, Carmen J.; Lepeule, Johanna; Hivert, Marie‐France

doi:10.1101/663567

Cited by 7 publications

(4 citation statements)

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“…(Morales et al, 2019) carried out a Sobel analysis of mediation between smoking and birth weight, found the test significant for cg27402634. In the list of 51 CpGs with high effect sizes, several additional statistical associations between placental methylation and maternal smoking have been reported in previous studies, including cg21992501 in the gene TTC27 , cg25585967 and cg17823829, respectively in the TRIO and KDM5B genes (Morales et al, 2019;Everson et al, 2019). Turning to the rest of the methylome, we found additional associations that may adversely affect mother-child health.…”

Section: Gwas Of Flowering Time Insupporting

confidence: 73%

Sparse latent factor regression models for genome-wide and epigenome-wide association studies

Jumentier

Caye

Heude

et al. 2022

Statistical Applications in Genetics and Molecular Biology

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Association of phenotypes or exposures with genomic and epigenomic data faces important statistical challenges. One of these challenges is to account for variation due to unobserved confounding factors, such as individual ancestry or cell-type composition in tissues. This issue can be addressed with penalized latent factor regression models, where penalties are introduced to cope with high dimension in the data. If a relatively small proportion of genomic or epigenomic markers correlate with the variable of interest, sparsity penalties may help to capture the relevant associations, but the improvement over non-sparse approaches has not been fully evaluated yet. Here, we present least-squares algorithms that jointly estimate effect sizes and confounding factors in sparse latent factor regression models. In simulated data, sparse latent factor regression models generally achieved higher statistical performance than other sparse methods, including the least absolute shrinkage and selection operator and a Bayesian sparse linear mixed model. In generative model simulations, statistical performance was slightly lower (while being comparable) to non-sparse methods, but in simulations based on empirical data, sparse latent factor regression models were more robust to departure from the model than the non-sparse approaches. We applied sparse latent factor regression models to a genome-wide association study of a flowering trait for the plant Arabidopsis thaliana and to an epigenome-wide association study of smoking status in pregnant women. For both applications, sparse latent factor regression models facilitated the estimation of non-null effect sizes while overcoming multiple testing issues. The results were not only consistent with previous discoveries, but they also pinpointed new genes with functional annotations relevant to each application.

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Section: Gwas Of Flowering Time Insupporting

confidence: 73%

Sparse latent factor regression models for genome-wide and epigenome-wide association studies

Jumentier

Caye

Heude

et al. 2022

Statistical Applications in Genetics and Molecular Biology

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“…Interestingly, in our unstratified analyses, we identified some DMRs that have been previously involved in mental health issues. The emotional symptoms subscale was significantly positively associated with DMRs located within EVX1, which is involved in central nervous system development, and THSD7A, which has been shown to be highly expressed in the placenta and could play an important role in the appropriate vascularization of the placenta [69]. Placental DNA methylation of CALCB, involved in hormone and neuropeptide hormone activity, and CLIP4, a biomarker of suicidal ideation [70], were also associated with the emotional symptoms subscale in our unstratified analyses as well as in girls for CLIP4.…”

Section: Discussionmentioning

confidence: 99%

Epigenome-Wide Associations of Placental DNA Methylation and Behavioral and Emotional Difficulties in Children at 3 Years of Age

Nakamura

Broséus

Tost

et al. 2023

IJMS

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The placenta is a key organ for fetal and brain development. Its epigenome can be regarded as a biochemical record of the prenatal environment and a potential mechanism of its association with the future health of the fetus. We investigated associations between placental DNA methylation levels and child behavioral and emotional difficulties, assessed at 3 years of age using the Strengths and Difficulties Questionnaire (SDQ) in 441 mother–child dyads from the EDEN cohort. Hypothesis-driven and exploratory analyses (on differentially methylated probes (EWAS) and regions (DMR)) were adjusted for confounders, technical factors, and cell composition estimates, corrected for multiple comparisons, and stratified by child sex. Hypothesis-driven analyses showed an association of cg26703534 (AHRR) with emotional symptoms, and exploratory analyses identified two probes, cg09126090 (intergenic region) and cg10305789 (PPP1R16B), as negatively associated with peer relationship problems, as well as 33 DMRs, mostly positively associated with at least one of the SDQ subscales. Among girls, most associations were seen with emotional difficulties, whereas in boys, DMRs were as much associated with emotional than behavioral difficulties. This study provides the first evidence of associations between placental DNA methylation and child behavioral and emotional difficulties. Our results suggest sex-specific associations and might provide new insights into the mechanisms of neurodevelopment.

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“…(Morales et al ., 2019) carried out a Sobel analysis of mediation between smoking and birth weight, found the test significant for cg27402634. In the list of 51 CpGs with high effect sizes, several additional statistical associations between placental methylation and maternal smoking have been reported in previous studies, including cg21992501 in the gene TTC27 (Cardenas et al ., 2019), cg25585967 and cg17823829, respectively in the TRIO and KDM5B genes (Morales et al ., 2019; Everson et al ., 2019). Turning to the rest of the methylome, we found additional associations that may adversely affect mother-child health.…”

Section: Discussionmentioning

confidence: 99%

Sparse latent factor regression models for genome-wide and epigenome-wide association studies

Jumentier

Caye

Heude

et al. 2020

Preprint

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1Association of phenotypes or exposures with genomic and epigenomic data faces 2 important statistical challenges. One of these challenges is to remove variation due to 3 unobserved confounding factors, such as individual ancestry or cell-type composition 4 in tissues. This issue can be addressed with penalized latent factor regression models, 5 where penalties are introduced to cope with high dimension in the data. If a rela-6 tively small proportion of genomic or epigenomic markers correlate with the variable 7 of interest, sparsity penalties may help to capture the relevant associations, but the 8 improvement over non-sparse approaches has not been fully evaluated yet. In this 9 study, we introduced least-squares algorithms that jointly estimate effect sizes and 10 confounding factors in sparse latent factor regression models. Computer simulations 11 provided evidence that sparse latent factor regression models achieve higher statistical 12 performance than other sparse methods, including the least absolute shrinkage and 13 selection operator (LASSO) and a Bayesian sparse linear mixed model (BSLMM). 14 Additional simulations based on real data showed that sparse latent factor regression 15 models were more robust to departure from the generative model than non-sparse 16 approaches, such as surrogate variable analysis (SVA) and other methods. We ap-17 plied sparse latent factor regression models to a genome-wide association study of 18 a flowering trait for the plant Arabidopsis thaliana and to an epigenome-wide asso-19 ciation study of smoking status in pregnant women. For both applications, sparse 20 latent factor regression models facilitated the estimation of non-null effect sizes while 21 avoiding multiple testing problems. The results were not only consistent with pre-22 vious discoveries, but they also pinpointed new genes with functional annotations 23 relevant to each application. 24 2 ables, including batch effects, individual ancestry or tissue cell-type composition are 49 integrated in the regression model by using latent factors. In these models, effect sizes 50 and latent factors are estimated jointly. The latent factor regression framework en-51 compasses several methods which include surrogate variable analysis (SVA, Leek and 52 Storey (2007)), latent factor mixed models (LFMM, Frichot et al. (2013)), residual 53 principal component analysis (Kalaitzis and Lawrence, 2012), and confounder ad-54 justed testing and estimation (CATE, Wang et al. (2017)). Each method has specific 55 merits relative to some category of association study, and the performances of the 56 methods have been extensively debated in recent surveys (for example, see Kaushal 57 et al. (2017)). 58A property of many latent factor regression models is to use regularization pa-59 rameters inducing constraints on effect size estimates. Among those methods, sparse 60 regression models suppose that a relatively small proportion of all genomic variables 61 correlate with the variable of interest or affect the phenotype, and ev...

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Placental DNA methylation signatures of maternal smoking during pregnancy and potential impacts on fetal growth

Abstract: 109

Cited by 7 publications

References 70 publications

Sparse latent factor regression models for genome-wide and epigenome-wide association studies

Sparse latent factor regression models for genome-wide and epigenome-wide association studies

Epigenome-Wide Associations of Placental DNA Methylation and Behavioral and Emotional Difficulties in Children at 3 Years of Age

Sparse latent factor regression models for genome-wide and epigenome-wide association studies

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