The integration of expression quantitative trait loci (eQTLs) and genome-wide association study (GWAS) findings to identify causal genes aids in elucidating the biological mechanisms and the discovery of potential drug targets underlying complex traits. This can be achieved by Mendelian randomization (MR), but to date, most MR studies investigating the contribution of genes to brain phenotypes have been conducted on heterogeneous brain tissues and not on specific cell types, thus limiting our knowledge at the cellular level. In this study, we employ a MR framework to infer cell type-specific causal relationships between gene expression and brain-associated complex traits, using eQTL data from eight cell types and large-scale GWASs of 123 imaging-derived phenotypes (IDPs) and 26 brain disorders and behaviors (DBs). Our analysis constructs a cell type-specific causal gene atlas for IDPs and DBs, which include 254 and 217 potential causal cell type-specific eQTL target genes (eGenes) for IDPs and DBs, respectively. The identified results exhibit high cell type specificity, with over 90% of gene-IDP and 80% of gene-DB associations being unique to a single cell type. We highlight shared cell type-specific patterns between IDPs and DBs, characterize the putative causal pathways among cell type-specific causal eGenes, DBs and IDPs, and reveal the spatiotemporal expression patterns of these cell type-specific causal eGenes. We also demonstrate that cell type-specific causal eGenes can characterize the associations between IDPs and DBs. In summary, our study provides novel insights into the genetic foundations at the cellular level that influence brain structures, disorders and behaviors, which reveals important implications for therapeutic targets and brain health management.

The authors have declared no competing interest.

This study was funded by National Natural Science Foundation of China (T2225015, 61932008), Shanghai Science and Technology Commission Program (23JS1410100), Key Science and Technology Project of Hainan Province (ZDYF2024SHFZ058), National Key R&D Program of China (2023YFF1204800, 2020YFA0712403), Lingang Laboratory & National Key Laboratory of Human Factors Engineering Joint Grant (LG-TKN-202203-01) and Lingang Laboratory (LG-GG-202401-ADA010100 and LG-GG-202401-ADA050100).

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The study used ONLY openly available human data that were originally located at the following URLs. The cell type-specific cis-eQTL summary statistics dataset was downloaded from https://zenodo.org/record/6104982#.Y1a2WbZBxPY. The GWAS summary statistics datasets are available from the URLs: CI from http://ssgac.org/documents/CHIC_Summary_Benyamin2014.txt.gz; EA from https://www.dropbox.com/s/ho58e9jmytmpaf8/GWAS_EA_excl23andMe.txt?dl=1; INT from https://ctg.cncr.nl/documents/p1651/SavageJansen_IntMeta_sumstats.zip; NEU from https://ctg.cncr.nl/documents/p1651/sumstats_neuroticism_ctg_format.txt.gz; ASP from https://www.dropbox.com/s/dxfr1uq20wbdj1d/AUTOMOBILE_SPEEDING_PROPENSITY_GWAS.txt?dl=1; DPW from https://conservancy.umn.edu/handle/11299/201564; GRT from https://thessgac.com/papers/2; SC from https://conservancy.umn.edu/handle/11299/201564; INS from https://ctg.cncr.nl/documents/p1651/insomnia_ukb2b_EUR_sumstats_20190311_with_chrX_mac_100.txt.gz; EPI from https://www.epigad.org/gwas_ilae2018_16loci/all_epilepsy_METAL.gz; ICH from https://personal.broadinstitute.org/ryank/3980413.Woo.2014.zip; IS from http://megastroke.org/; AD from https://ctg.cncr.nl/documents/p1651/AD_sumstats_Jansenetal.txt.gz; ALS from http://als.umassmed.edu/images/GWAS_File.zip; MS from https://imsgc.net/data/discovery_metav3.0.meta.gz; PD from https://drive.google.com/open?id=1FZ9UL99LAqyWnyNBxxlx6qOUlfAnublN; OCD from https://doi.org/10.6084/m9.figshare.14672103; TS from https://doi.org/10.6084/m9.figshare.14672232; ANX from https://drive.google.com/drive/folders/1fguHvz7l2G45sbMI9h_veQun4aXNTy1v; MDD from https://doi.org/10.6084/m9.figshare.14672085; ADHD from https://doi.org/10.6084/m9.figshare.14671965; ASD from https://doi.org/10.6084/m9.figshare.14671989; AUD from https://doi.org/10.6084/m9.figshare.14672187; PTSD from https://ipsych.dk/fileadmin/ipsych.dk/Downloads/daner_woautism_ad_sd8-sd6_woautismstress_cleaned.gz; BIP from https://doi.org/10.6084/m9.figshare.14102594; SCZ from https://doi.org/10.6084/m9.figshare.19426775; IDPs from https://www.med.unc.edu/bigs2/data/gwas-summary-statistics/. The prediction weights and covariance used for TWAS analysis are downloaded from PredictDB Data Repository (https://predictdb.org/), and CMC-derived DLPFC prediction models (https://github.com/laurahuckins/CMC_DLPFC_prediXcan). The cis-eQTL summary statistics of the BrainMeta portal are downloaded from https://yanglab.westlake.edu.cn/data/brainmeta/cis_eqtl/. The documented associations between genes and complex traits of the NHGRI-EBI GWAS Catalog are downloaded from https://www.ebi.ac.uk/gwas/api/search/downloads/alternative.

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