Abstract
Genome-wide association studies (GWASs) have identified numerous risk genes for depression. Nevertheless, genes crucial for understanding the molecular mechanisms of depression and effective antidepressant drug targets are largely unknown. Addressing this, we aimed to highlight potentially causal genes by systematically integrating the brain and blood protein and expression quantitative trait loci (QTL) data with a depression GWAS dataset via a statistical framework including Mendelian randomization (MR), Bayesian colocalization, and Steiger filtering analysis. In summary, we identified three candidate genes (TMEM106B, RAB27B, and GMPPB) based on brain data and two genes (TMEM106B and NEGR1) based on blood data with consistent robust evidence at both the protein and transcriptional levels. Furthermore, the protein-protein interaction (PPI) network provided new insights into the interaction between brain and blood in depression. Collectively, four genes (TMEM106B, RAB27B, GMPPB, and NEGR1) affect depression by influencing protein and gene expression level, which could guide future researches on candidate genes investigations in animal studies as well as prioritize antidepressant drug targets.
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Data availability
Data of brain pQTL from the ROS/MAP study are available through https://doi.org/10.7303/syn23627957. The smaller pQTL data from the 144 cognitively healthy participants of the ROS/MAP study are available though https://doi.org/10.7303/syn24172458. Data are available for general research use according to the following requirements for data access and data attribution (https://adknowledgeportal.org/DataAccess/Instructions). Data of brain eQTL from the PsychENCODE Consortium are accessible in BESD format through https://cnsgenomics.com/software/smr/#eQTLsummarydata. Data from AGES Reykjavik study can be accessed at www.sciencemag.org/cgi/content/full/science.aaq1327/DC1. Data from the AGES Reykjavik study are available through collaboration (AGES_data_request@hjarta.is) under a data usage agreement with the IHA. GTEx can be accessed at https://gtexportal.org/home/datasets (GTEx Analysis V6) or in BESD format through https://cnsgenomics.com/software/smr/#eQTLsummarydata. Data of eQTLGen are available through https://www.eqtlgen.org/cis-eqtls.html. Summary statistics for the Howard’s meta-analysis of depression GWAS from UK Biobank and PGC_139k are available from https://doi.org/10.7488/ds/2458.
Materials availability
Correspondence and requests for materials should be addressed to JTY.
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Acknowledgements
This work was made possible by the generous sharing of statistics from the public databases. The authors acknowledge the important contributions of the many publicly available datasets used in this report’s analysis, including the ROS and MAP, the PsychENCODE Consortium, the AGES Reykjavik study, the GTEx project and the eQTLGen Consortium for their kind dedication. We thank the Howard’s GWAS meta-analysis of depression. Analyses were made possible by their generous sharing of GWAS summary statistics. How to access the data is shown below.
Funding
This study was supported by grants from the National Natural Science Foundation of China (82071201, 81971032), Shanghai Municipal Science and Technology Major Project (No.2018SHZDZX01), Research Start-up Fund of Huashan Hospital (2022QD002), Excellence 2025 Talent Cultivation Program at Fudan University (3030277001), ZHANGJIANG LAB, Tianqiao and Chrissy Chen Institute, and the State Key Laboratory of Neurobiology and Frontiers Center for Brain Science of Ministry of Education, Fudan University.
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JTY and FL conceptualized the study and revised the manuscript. YTD, YNO, BSW, YYX, YYH, and YL analyzed and interpreted the data. YTD and YNO prepared all the figures and tables. YTD, YNO, YJ, and JS drafted the manuscript. All authors contributed to the writing and revisions of the paper and approved the final version.
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Deng, YT., Ou, YN., Wu, BS. et al. Identifying causal genes for depression via integration of the proteome and transcriptome from brain and blood. Mol Psychiatry 27, 2849–2857 (2022). https://doi.org/10.1038/s41380-022-01507-9
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DOI: https://doi.org/10.1038/s41380-022-01507-9
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