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Integrative multi-omics identifies <i>DOC2A</i> as a novel pharmacological target for bipolar disorder.

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  1. [1] § Results › Linking DOC2A-associated co-expression networks to synaptic pathways ↔ Pipeline.r, lines 85–165 · score 0.75 · intramodular connectivity, hub genes, co expression, turquoise, GS, MM

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The authors' code

R · 165 lines · 7.9 KB · no license · 1 match

  1. # ==============================================================================
  2. # This script integrates PWAS, SMR, COLOC, DEA, WGCNA, and Enrichment analysis.
  3. # ==============================================================================
  5. # [Global Settings & Dependencies]
  6. options(stringsAsFactors = FALSE)
  7. library(tidyverse)
  8. library(data.table)
  9. library(coloc)
  10. library(lmerTest)
  11. library(car)
  12. library(WGCNA)
  13. library(clusterProfiler)
  14. library(org.Hs.eg.db)
  15. library(DOSE)
  17. # ==============================================================================
  18. #### PART 1: PWAS (FUSION Pipeline) ####
  19. # ==============================================================================
  21. # Step 1: Weight computation (Precomputed as per FUSION protocol)
  22. # Models: top1, blup, lasso, enet, bslmm; hsq_p 0.01
  24. # Step 2: Chromosome-wise Association Test
  25. chr_list <- 1:22
  26. gwas_sumstats <- "bd_gwas_summary.ma"
  27. weight_index <- "brain_pqtl_weights.pos"
  28. weight_dir <- "brain_pqtl_weights/"
  29. ld_ref_prefix <- "LDREF/1000G.EUR."
  30. output_prefix <- "pwas_chr"
  32. for (chr in chr_list) {
  33. system(paste0(
  34. "Rscript FUSION.assoc_test.R ",
  35. "--sumstats ", gwas_sumstats, " --weights ", weight_index, " ",
  36. "--weights_dir ", weight_dir, " --ref_ld_chr ", ld_ref_prefix, " ",
  37. "--chr ", chr, " --force_model enet --max_impute 0.5 --min_r2pred 0.7 ",
  38. "--out ", output_prefix, chr, ".out"
  39. ))
  40. }
  42. # Step 3: Merging & FDR Correction (PFDR < 0.05)
  43. pwas_all <- map_df(chr_list, ~read_tsv(paste0(output_prefix, .x, ".out"), show_col_types = FALSE))
  44. pwas_all$FDR <- p.adjust(pwas_all$P, method = "fdr")
  45. write_tsv(pwas_all, "pwas_genomewide_fdr_corrected.out")
  47. pwas_significant <- pwas_all %>% filter(FDR < 0.05)
  48. write_tsv(pwas_significant, "pwas_significant_fdr05.out")
  50. # ==============================================================================
  51. #### PART 2: SMR & HEIDI Test ####
  52. # ==============================================================================
  54. # Step 1: BESD Conversion
  55. system("./smr --make-besd --bfile reference_genotype --gwas-summary pqtl_summary.ma --maf 0.01 --diff-freq 0.2 --out pqtl_besd")
  57. # Step 2: SMR Analysis (HEIDI threshold > 0.01)
  58. system("./smr --bfile reference_genotype --gwas-summary bd_gwas_summary.ma --beqtl-summary pqtl_besd --out smr_bd_result --thread-num 4 --maf 0.01 --diff-freq 0.2 --heidi-thres 0.01 --smr-multi")
  60. # Step 3: Extraction
  61. smr_results <- read.table("smr_bd_result.smr", header = TRUE)
  62. smr_heidi_passed <- smr_results %>% filter(p_HEIDI > 0.01)
  63. write.table(smr_heidi_passed, "smr_heidi_validated_results.txt", row.names = FALSE, quote = FALSE)
  65. # ==============================================================================
  66. #### PART 3: Bayesian Colocalization (COLOC) ####
  67. # ==============================================================================
  69. gwas_sum <- read_tsv("bd_gwas_summary.ma", show_col_types = FALSE)
  70. pqtl_sum <- read_tsv("pqtl_summary.ma", show_col_types = FALSE)
  71. matched_snps <- intersect(gwas_sum$SNP, pqtl_sum$SNP)
  73. # Prepare Datasets
  74. gwas_matched <- gwas_sum %>% filter(SNP %in% matched_snps)
  75. pqtl_matched <- pqtl_sum %>% filter(SNP %in% matched_snps)
  77. coloc_result <- coloc.abf(
  78. dataset1 = list(beta = gwas_matched$beta, varbeta = (gwas_matched$se)^2, type = "cc", s = 0.5, N = 158036, MAF = gwas_matched$MAF, snp = gwas_matched$SNP),
  79. dataset2 = list(beta = pqtl_matched$beta, varbeta = (pqtl_matched$se)^2, type = "quant", N = 376, sdY = 1, MAF = pqtl_matched$MAF, snp = pqtl_matched$SNP),
  80. p1 = 1e-4, p2 = 1e-4, p12 = 1e-6
  81. )
  82. write.table(coloc_result$summary, "coloc_posterior_probabilities.txt", row.names = TRUE, quote = FALSE)
  83. sensitivity(coloc_result, rule = "H4 > 0.8")
  85. # ==============================================================================
  86. #### PART 4: Differential Expression (LMM) ####
  87. # ==============================================================================
  89. # Data Preparation
  90. neuron_dat <- read.csv("neuron_expression_normalized.csv") %>% mutate(Disease = factor(Disease, levels = c("Control", "BD")), Donor = factor(Donor))
  91. astro_dat <- read.csv("astro_expression_normalized.csv") %>% mutate(Disease = factor(Disease, levels = c("Control", "BD")), Donor = factor(Donor), log_DOC2A = log(DOC2A))
  93. # Fit Models
  94. summary_neuron <- summary(lmer(DOC2A ~ Disease + (1 | Donor), data = neuron_dat))
  95. summary_astro <- summary(lmer(log_DOC2A ~ Disease + (1 | Donor), data = astro_dat))
  97. # ==============================================================================
  98. #### PART 5: WGCNA (Co-expression Network) ####
  99. # ==============================================================================
  101. expression_matrix <- read.csv("normalized_expression_matrix.csv", row.names = 1)
  102. pheno_dat <- read.csv("phenotype_data.csv", row.names = 1)
  104. # Step 1: MAD Filtering
  105. mad_values <- apply(expression_matrix, 1, mad)
  106. dens <- density(mad_values)
  107. cutoff_mad <- dens$x[which(diff(sign(diff(dens$y))) == 2)[1]]
  108. datExpr <- t(expression_matrix[mad_values >= cutoff_mad, ])
  110. # Step 2: Quality Control
  111. gsg <- goodSamplesGenes(datExpr, verbose = 3)
  112. if (!gsg$allOK) datExpr <- datExpr[gsg$goodSamples, gsg$goodGenes]
  113. pheno_dat <- pheno_dat[rownames(datExpr), ]
  115. # Step 3: Trait Matrix Preparation
  116. datTraits <- data.frame(
  117. BD = as.numeric(pheno_dat$Disease.state %in% c("Bipolar Disorder", "Bipolar Disorder, Lithium Non-responsive", "Bipolar Disorder, Lithium Responsive")),
  118. Control = as.numeric(pheno_dat$Disease.state == "Control"),
  119. BD_nonres = as.numeric(pheno_dat$Disease.state == "Bipolar Disorder, Lithium Non-responsive"),
  120. BD_res = as.numeric(pheno_dat$Disease.state == "Bipolar Disorder, Lithium Responsive")
  121. )
  122. datTraits <- datTraits[, colSums(datTraits) > 0]
  124. # Step 4-5: Power Selection & Network Construction
  125. sft <- pickSoftThreshold(datExpr, powerVector = c(1:10, seq(12, 30, by = 2)), verbose = 5)
  126. net <- blockwiseModules(datExpr, power = sft$powerEstimate, TOMType = "unsigned",
  127. minModuleSize = 30, reassignThreshold = 0, mergeCutHeight = 0.25,
  128. deepSplit = 2, numericLabels = TRUE, pamRespectsDendro = FALSE,
  129. saveTOMs = TRUE, saveTOMFileBase = "TOM", verbose = 3)
  131. moduleColors <- labels2colors(net$colors)
  132. write.csv(data.frame(Gene = colnames(datExpr), Module = moduleColors), "module_gene_assignment.csv", row.names = FALSE)
  134. # Step 6-8: Correlation, Membership (MM), Significance (GS) & Hubs
  135. MEs <- moduleEigengenes(datExpr, moduleColors)$eigengenes
  136. moduleTraitCor <- cor(MEs, datTraits, use = "p")
  138. # Hub Identification (Top 10)
  139. adj <- adjacency(datExpr, power = sft$powerEstimate, type = "unsigned")
  140. kIM <- intramodularConnectivity(adj, moduleColors)
  141. write.csv(kIM %>% arrange(desc(kTotal)) %>% head(10), "hub_genes_top10.csv")
  143. # Step 9: Module Stability (Subsampling)
  144. stab <- sampledBlockwiseModules(datExpr = datExpr, nRuns = 10, fraction = 0.8,
  145. power = sft$powerEstimate, networkType = "unsigned",
  146. minModuleSize = 30, mergeCutHeight = 0.25, verbose = 0)
  148. # ==============================================================================
  149. #### PART 6: Functional Enrichment (Default Universe) ####
  150. # ==============================================================================
  152. target_genes <- colnames(datExpr)[moduleColors == "turquoise"]
  153. entrez_ids <- bitr(target_genes, fromType = "SYMBOL", toType = "ENTREZID", OrgDb = org.Hs.eg.db)
  155. # KEGG Enrichment
  156. kegg_enrich <- enrichKEGG(gene = entrez_ids$ENTREZID, organism = "hsa", pvalueCutoff = 0.05, qvalueCutoff = 0.05)
  157. kegg_result <- setReadable(kegg_enrich, OrgDb = org.Hs.eg.db, keyType = "ENTREZID")@result %>% filter(p.adjust <= 0.05)
  158. write.csv(kegg_result, "kegg_enrichment_results.csv", row.names = FALSE)
  160. # GO Enrichment (Simplified)
  161. go_enrich <- enrichGO(gene = entrez_ids$ENTREZID, OrgDb = org.Hs.eg.db, ont = "ALL", pvalueCutoff = 0.05, qvalueCutoff = 0.05)
  162. go_simplified <- simplify(setReadable(go_enrich, OrgDb = org.Hs.eg.db, keyType = "ENTREZID"), cutoff = 0.7, by = "p.adjust")
  163. write.csv(go_simplified@result %>% filter(p.adjust <= 0.05), "go_enrichment_results.csv", row.names = FALSE)
  165. # End of Pipeline

Pipeline.r at commit 12846a9, no license · at the source

Overview

Authors: Chengsong Yuan1, Bo Zhang1, Yuanyuan Liang2, Junze Ran1, Shiyi Hu1, Andi Liu1, Yuran Liu1, Fengqin Qin3, Lingqi Jian1, Yongji He4, Feng Han5, Chengcheng Zhang1
ORCID iDs: Chengcheng Zhang
  1. Mental Health Center and Psychiatric Laboratory, the State Key Laboratory of Biotherapy, Sichuan University West China Hospital Mental Health Center, China
  2. Department of Nephrology, Sixth People’s Hospital of Chengdu, China
  3. Department of Neurology, The 3rd Affiliated Hospital of Chengdu Medical College, China
  4. Clinical Trial Center, National Medical Products Administration Key Laboratory for Clinical Research and Evaluation of Innovative Drugs, West China Hospital of Sichuan University, China
  5. Department of Emergency Medicine, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, China
Institutions: Sichuan University (China); West China Medical Center of Sichuan University (China); West China Hospital of Sichuan University (China); Chengdu Medical College (China); Hainan General Hospital (China)
Journal: Psychological medicine, volume 56, article e162
Dates: received 28 October 2025; accepted 12 April 2026; published online 28 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1017/s0033291726104565 · PMID 42206661 · PMCID PMC13234528 · OpenAlex W7162638119
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), bipolar (population)
Keywords: bipolar disorder, differential expression analysis, DOC2A, drug target, Mendelian randomization, PWAS
MeSH: Bipolar Disorder*, Calcium-Binding Proteins*, Nerve Tissue Proteins*, Genome-Wide Association Study, Hippocampus, Humans, Molecular Docking Simulation, Multiomics, Prefrontal Cortex, Proteomics (* major topic)
Topic: Genetic Associations and Epidemiology (Genetics, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Key R&D Projects of Science and Technology Department of Sichuan Province (2021YFS0248); Postdoctoral Foundation of West China Hospital (2020HXBH163); China Postdoctoral Science Foundation (2020M673247); Sichuan Science and Technology Program (2026NSFSC0591)
Citations: not cited yet (Europe PMC); 104 references in the paper

Abstract

Background: Current bipolar disorder (BD) therapies suffer from limited efficacy and adverse effects, necessitating mechanistically grounded targets.

Methods: We integrated BD genome-wide association study data (158,036 cases; 2,796,499 controls) with brain proteomics (ROSMAP and Banner dorsolateral prefrontal cortex, n = 376 and 152) to perform proteome-wide association studies (PWAS). Bayesian colocalization and summary-data-based Mendelian randomization (SMR) prioritized causal genes. Cell-type-specific transcriptomics validated dysregulation in iPSC-derived neurons, astrocytes, and postmortem hippocampus/prefrontal cortex. Weighted gene co-expression networks (WGCNAs), functional enrichment, and molecular docking assessed functional pathways and druggability.

Results: PWAS identified eight BD-associated genes (false discovery rate < 0.05), with DOC2A emerging as the top candidate. Colocalization (H4 > 0.8) and SMR supported a causal association of DOC2A with BD, with no pleiotropy (heterogeneity in dependent instruments P > 0.01); DOC2A expression decreased in BD across neurons (P = 4.26 × 10−2), astrocytes (P = 2.09 × 10−2), hippocampus (P = 9.80 × 10−3, t = −2.738), and prefrontal cortex (P = 1.44 × 10−2, t = −2.580); WGCNA positioned DOC2A as a key regulator (module membership/gene significance P < 0.05) of co-expression networks enriched for BD-associated processes including neurotransmitter secretion and postsynaptic actin cytoskeleton organization (P < 0.05); molecular docking revealed favorable-affinity binding (ΔG < −4 kcal/mol) between DOC2A and BD-related drugs and neuroprotective compounds.

Conclusions: Our convergent multi-omics framework highlights DOC2A dysregulation as a key contributor to synaptic dysfunction in BD and nominates it as a promising therapeutic target. The demonstrated interaction with existing neuroactive compounds provides immediate translational avenues.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 1 match between paragraphs and lines of code.

CY1479/BD_Project

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 12846a9db7ad3f3909758d114d621c1337cfe24b, 14 March 2026
Languages: R (1)
Size: 1 file, 1 script
Software Heritage: not archived
Found in: “Data availability statement”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: car (1 file), clusterProfiler (1 file), data.table (1 file), lmerTest (1 file), tidyverse (1 file), WGCNA (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
1 file
At the source: github.com/CY1479/BD_Project

The paper's code and data availability statement is in the Data section.

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  • 1 script, each with its path and the digest of its content;
  • 1 match between paragraphs of the paper and lines of the code (method lexical-v1);
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Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data availability statement

The data used in the study can be accessed and downloaded from original studies (Glausier, Kimoto, Fish, & Lewis, 2015; Lanz et al., 2019; Maycox et al., 2009; O’Connell et al., 2025; Santos et al., 2021; Vadodaria et al., 2021; Voineagu et al., 2011; Wingo et al., 2021). The computational scripts and analytical workflows of this study can be found in a publicly accessible repository: https://github.com/CY1479/BD_Project

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 12 authors, 6 keywords, 10 MeSH terms, 4 funders, 103 references.

Cite

This paper

Yuan, C., Zhang, B., Liang, Y., Ran, J., Hu, S., Liu, A., Liu, Y., Qin, F., Jian, L., He, Y., Han, F., & Zhang, C. (2026). Integrative multi-omics identifies &lt;i&gt;DOC2A&lt;/i&gt; as a novel pharmacological target for bipolar disorder. Psychological medicine, 56, e162. https://doi.org/10.1017/s0033291726104565

BibTeX

@article{yuan2026integrative,
author = {Yuan, Chengsong and Zhang, Bo and Liang, Yuanyuan and Ran, Junze and Hu, Shiyi and Liu, Andi and Liu, Yuran and Qin, Fengqin and Jian, Lingqi and He, Yongji and Han, Feng and Zhang, Chengcheng},
title = {{Integrative multi-omics identifies \&lt;i\&gt;DOC2A\&lt;/i\&gt; as a novel pharmacological target for bipolar disorder}},
journal = {Psychological medicine},
year = {2026},
month = may,
volume = {56},
pages = {e162},
publisher = {Cambridge University Press},
issn = {0033-2917},
doi = {10.1017/s0033291726104565},
url = {https://doi.org/10.1017/s0033291726104565},
pmid = {42206661},
pmcid = {PMC13234528}
}

RIS

TY - JOUR
AU - Yuan, Chengsong
AU - Zhang, Bo
AU - Liang, Yuanyuan
AU - Ran, Junze
AU - Hu, Shiyi
AU - Liu, Andi
AU - Liu, Yuran
AU - Qin, Fengqin
AU - Jian, Lingqi
AU - He, Yongji
AU - Han, Feng
AU - Zhang, Chengcheng
TI - Integrative multi-omics identifies &lt;i&gt;DOC2A&lt;/i&gt; as a novel pharmacological target for bipolar disorder
T2 - Psychological medicine
J2 - Psychol Med
PY - 2026
DA - 2026/05/28
VL - 56
SP - e162
SN - 0033-2917
PB - Cambridge University Press
DO - 10.1017/s0033291726104565
UR - https://doi.org/10.1017/s0033291726104565
LA - en
ER -

CSL-JSON

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"type": "article-journal",
"title": "Integrative multi-omics identifies &lt;i&gt;DOC2A&lt;/i&gt; as a novel pharmacological target for bipolar disorder",
"container-title": "Psychological medicine",
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"family": "Yuan",
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{
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"container-title-short": "Psychol Med",
"volume": "56",
"page": "e162",
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"PMID": "42206661",
"PMCID": "PMC13234528",
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"language": "en",
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"date-parts": [
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