PT  - JOURNAL ARTICLE
AU  - Darina Czamara
AU  - Gökçen Eraslan
AU  - Jari Lahti
AU  - Christian M. Page
AU  - Marius Lahti-Pulkkinen
AU  - Esa Hämäläinen
AU  - Eero Kajantie
AU  - Hannele Laivuori
AU  - Pia M Villa
AU  - Rebecca M. Reynolds
AU  - Wenche Nystad
AU  - Siri E Håberg
AU  - Stephanie J London
AU  - Kieran J O’Donnell
AU  - Elika Garg
AU  - Michael J Meaney
AU  - Sonja Entringer
AU  - Pathik D Wadhwa
AU  - Claudia Buss
AU  - Meaghan J Jones
AU  - David TS Lin
AU  - Julie L MacIsaac
AU  - Michael S Kobor
AU  - Nastassja Koen
AU  - Heather J Zar
AU  - Karestan C Koenen
AU  - Shareefa Dalvie
AU  - Dan J Stein
AU  - Ivan Kondofersky
AU  - Nikola S Müller
AU  - Fabian J Theis
AU  - Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium
AU  - Katri Räikkönen
AU  - Elisabeth B Binder*
TI  - Variably methylated regions in the newborn epigenome: environmental, genetic and combined influences
AID  - 10.1101/436113
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 436113
4099  - http://biorxiv.org/content/early/2018/10/17/436113.short
4100  - http://biorxiv.org/content/early/2018/10/17/436113.full
AB  - Background Epigenetic processes, including DNA methylation (DNAm), are among the mechanisms allowing integration of genetic and environmental factors to shape cellular function. While many studies have investigated either environmental or genetic contributions to DNAm, few have assessed their integrated effects. We examined the relative contributions of prenatal environmental factors and genotype on DNA methylation in neonatal blood at variably methylated regions (VMRs), defined as consecutive CpGs showing the highest variability of DNAm in 4 independent cohorts (PREDO, DCHS, UCI, MoBa, N=2,934).Results We used Akaike’s information criterion to test which factors best explained variability of methylation in the cohort-specific VMRs: several prenatal environmental factors (E) including maternal demographic, psychosocial and metabolism related phenotypes, genotypes in cis (G), or their additive (G+E) or interaction (GxE) effects. G+E and GxE models consistently best explained variability in DNAm of VMRs across the cohorts, with G explaining the remaining sites best. VMRs best explained by G, GxE or G+E, as well as their associated functional genetic variants (predicted using deep learning algorithms), were located in distinct genomic regions, with different enrichments for transcription and enhancer marks. Genetic variants of not only G and G+E models, but also of variants in GxE models were significantly enriched in genome wide association studies (GWAS) for complex disorders.Conclusion Genetic and environmental factors in combination best explain DNAm at VMRs. The CpGs best explained by G, G+E or GxE are functionally distinct. The enrichment of GxE variants in GWAS for complex disorders supports their importance for disease risk.