Potentially causal associations between placental DNA methylation and schizophrenia and other neuropsychiatric disorders

Ariadna Cilleros-Portet, Corina Lesseur, Sergi Marí, Marta Cosin-Tomas, Manuel Lozano, Amaia Irizar, Amber Burt, Iraia García-Santisteban, Diego Garrido Martín, Geòrgia Escaramís, Alba Hernangomez-Laderas, Raquel Soler-Blasco, Charles E Breeze, Bárbara P Gonzalez-Garcia, Loreto Santa-Marina, Jia Chen, Sabrina Llop, Mariana F Fernández, Martine Vrijhed, Jesús Ibarluzea, Mònica Guxens, Carmen Marsit, Mariona Bustamante, Jose Ramon Bilbao, Nora Fernandez-Jimenez

Department of Genetics, Physical Anthropology and Animal Physiology, Biocruces-Bizkaia Health Research Institute and University of the Basque Country (UPV/EHU), Leioa, Spain.

Increasing evidence supports the role of the placenta in neurodevelopment and in the onset of neuropsychiatric disorders. Recently, mQTL and iQTL maps have proven useful in understanding relationships between SNPs and GWAS that are not captured by eQTL. In this context, we propose that part of the genetic predisposition to complex neuropsychiatric disorders acts through placental DNA methylation. We construct a public placental cis-mQTL database including 214,830 CpG sites calculated in 368 fetal placenta DNA samples from the INMA project, and run cell type-, gestational age- and sex-imQTL models. We combine these data with summary statistics of GWAS on ten neuropsychiatric disorders using summary-based Mendelian randomization and colocalization. We also evaluate the influence of identified DNA methylation sites on placental gene expression in the RICHS cohort. We find that placental cis-mQTLs are enriched in placenta-specific active chromatin regions, and establish that part of the genetic burden for schizophrenia, bipolar disorder, and major depressive disorder confers risk through placental DNA methylation. The potential causality of several of the observed associations is reinforced by secondary association signals identified in conditional analyses, the involvement of cell type-imQTLs, and the correlation of identified DNA methylation sites with the expression levels of relevant genes in the placenta.

Placental mQTLs browser

If you use the data or code from this study, please cite the following publication:
Cilleros-Portet, A., Lesseur, C., Marí, S. et al. & Fernandez-Jimenez, N. (2025). Potentially causal associations between placental DNA methylation and schizophrenia and other neuropsychiatric disorders. Nature Communications 16, 2431. https://doi.org/10.1038/s41467-025-57760-3

ReadMe

This repository contains the coded used for the A. Cilleros-Portet et al. 2025, and the scripts are described as follows.

1. Map of cis-mQTLs with TensorQTL nominal approach (tensorqtl_nominal.py).
2. Map of cis-mQTLs with TensorQTL permuted and conditional approach (tensorqtl_permuted_conditional.py).
3. Map of interacting cis-mQTLs with TensorQTL interaction approach (tensorqtl_interaction.py).
4. Get the final mQTL database, only applicable for nominal and interaction analysis (get_final_database.sh).
5. Gene set enrichment analysis with Disease Onthology database (GSEA_analysis.R).
6. Over-representation analysis with Gene Onthology and Kyoto Encyclopedia of Genes and Genomes (missMethyl_GSE.R).
7. Illumina annotation enrichment chi-square tests (enrichment.R).
8. Select CpG list for eFORGE analysis (CpGlist_eFORGE.R).
9. Formatting GWAS summary-statistics for SMR (checkalleles_maformat.R).
10. Formatting mQTL database for SMR (getFastQTL_format.R).
11. Run mutli-SNP based SMR test (runSMR.sh).
12. Get the CpG list overlapping GWAS loci for colocalization analysis (coloc_step1_overlap_CpGs_gr.R)
13. Get the BED file for colocalization analysis (coloc_step2_get_bed_file.R)
14. Run TensorQTL for colocalization analysis (coloc_step3_run_tensorqtl.py)
15. Format TensorQTL results for colocalization analysis (coloc_step4_concat_tensor_results.sh)
16. Run colocalization test (coloc_step5_run_coloc.R)
17. Classify cell-type interacting-mQTLs according to Kim-Hellmuth et al.2020 interpretation (classify_imQTLs.R).
18. Condition analysis of the GWAS summary-statistics (conditional_GWAS.r).
19. Condition analysis of the mQTLs summary-statistics (conditional_mQTLs.r).
20. Run multi-SNP based SMR test using the conditional GWAS and mQTL summary-statistics (conditional_SMR.R).