Evidence map›Paper›PMID 31779682›Full record

ArticleClinical epigenetics2019

DNA methylation associated with postpartum depressive symptoms overlaps findings from a genome-wide association meta-analysis of depression.

Dana M Lapato, Roxann Roberson-Nay, Robert M Kirkpatrick, Bradley T Webb, Timothy P York, Patricia A Kinser

Open access · goldAbstract read
In one paragraph

Article in Clinical epigenetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
1.1field-weighted citation impact, top 20% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed, 18 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 1 institution in 1 country.

Dana M LapatoDepartment of Human and Molecular Genetics, Virginia Commonwealth University, Richmond, VA, USA. lapatodm@vcu.edu.ORCID 0000-0001-8169-9754
Roxann Roberson-NayVirginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA.
Robert M KirkpatrickVirginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA.
Bradley T WebbVirginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA.
Timothy P YorkDepartment of Human and Molecular Genetics, Virginia Commonwealth University, Richmond, VA, USA.
Patricia A KinserSchool of Nursing, Virginia Commonwealth University, Richmond, VA, USA.
Virginia Commonwealth University · US

Funding

Research Supplement to Promote Diversity in Health Related ResearchUL1TR002649 · NCATS · VIRGINIA COMMONWEALTH UNIVERSITY · PI MOELLER, FREDERICK GERARD · 2018 to 2022
$19.1M
VCU NIMHD Comprehensive Center of Excellence (Project 1; PI: Strauss)P60MD002256 · NIMHD · VIRGINIA COMMONWEALTH UNIVERSITY · PI BRADLEY, CATHY J · 2007 to 2016
$13.1M
Center for Clinical and Translational ResearchUL1TR000058 · NCATS · VIRGINIA COMMONWEALTH UNIVERSITY · PI MOELLER, FREDERICK GERARD · 2012 to 2015
$12.5M
Research Training: Psychiatric &Statistical GeneticsT32MH020030 · NIMH · VIRGINIA COMMONWEALTH UNIVERSITY · PI HERMINE H MAES, MICHAEL CHURTON NEALE · 1999 to 2026
$6.7M
NCATS NIH HHS UL1 TR000058NCATS NIH HHS UL1 TR002649NIMHD NIH HHS P60 MD002256NIMH NIH HHS T32 MH020030
6 · The paper itself

Abstract

backgroundPerinatal depressive symptoms have been linked to adverse maternal and infant health outcomes. The etiology associated with perinatal depressive psychopathology is poorly understood, but accumulating evidence suggests that understanding inter-individual differences in DNA methylation (DNAm) patterning may provide insight regarding the genomic regions salient to the risk liability of perinatal depressive psychopathology.

resultsGenome-wide DNAm was measured in maternal peripheral blood using the Infinium MethylationEPIC microarray. Ninety-two participants (46% African-American) had DNAm samples that passed all quality control metrics, and all participants were within 7 months of delivery. Linear models were constructed to identify differentially methylated sites and regions, and permutation testing was utilized to assess significance. Differentially methylated regions (DMRs) were defined as genomic regions of consistent DNAm change with at least two probes within 1 kb of each other. Maternal age, current smoking status, estimated cell-type proportions, ancestry-relevant principal components, days since delivery, and chip position served as covariates to adjust for technical and biological factors. Current postpartum depressive symptoms were measured using the Edinburgh Postnatal Depression Scale. Ninety-eight DMRs were significant (false discovery rate < 5%) and overlapped 92 genes. Three of the regions overlap loci from the latest Psychiatric Genomics Consortium meta-analysis of depression.

conclusionsMany of the genes identified in this analysis corroborate previous allelic, transcriptomic, and DNAm association results related to depressive phenotypes. Future work should integrate data from multi-omic platforms to understand the functional relevance of these DMRs and refine DNAm association results by limiting phenotypic heterogeneity and clarifying if DNAm differences relate to the timing of onset, severity, duration of perinatal mental health outcomes of the current pregnancy or to previous history of depressive psychopathology.

Indexed as

DNA MethylationAdultBlack or African AmericanDepression, PostpartumEpigenesis, GeneticFemaleGenetic Association StudiesGenome-Wide Association StudyHumansLinear ModelsMaternal AgePerinatal CarePregnancySmokingDifferentially methylated regionsDNA methylationEpigenome-wide association studyMajor depressionPerinatal depressive psychopathology

Identifiers

PMID31779682
PMCPMC6883636
OpenAlexW2990892731

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.