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Exploratory Genome and Transcriptome-Wide Association Analyses of Addiction-Related Phenotypes in a Twin Cohort.

Code ↔ Paper

5 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 5 matches
  1. [1] § 3. Results ↔ gwas.r, lines 1–68 · score 0.67 · alcohol consumption, illicit drug, TOP6BL, behavioral disinhibition, alcohol dependence, ADAM32
  2. [2] § 2. Materials and Methods ↔ gwas.r, lines 1–68 · score 0.63 · alcohol consumption, illicit drug, behavioral disinhibition, alcohol dependence, discovery, Composite
  3. [3] § 2. Materials and Methods ↔ twas.sh, lines 14–23 · score 0.60 · alcohol consumption, illicit drug, behavioral disinhibition, alcohol dependence, Composite, nicotine
  4. [4] § 3. Results ↔ twas.sh, lines 14–23 · score 0.56 · alcohol consumption, illicit drug, behavioral disinhibition, alcohol dependence, nicotine, TWAS
  5. [5] § 2. Materials and Methods ↔ gwas.r, lines 70–150 · score 0.50 · Gene symbols, position, numeric, chromosomal, SNP, thresholds

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

R · 159 lines · 7.5 KB · no license · 3 matches

  1. # Title: Publication Composite Figure 2 (GWAS)
  2. # Description: Generates the 5 plots with CORRECTED phenotype mappings.
  3. # Uses Blue/Yellow alternating chromosome colors.
  5. library(data.table)
  6. library(ggplot2)
  7. library(dplyr)
  8. library(readr)
  9. library(ggrepel)
  10. library(stringr)
  11. library(patchwork)
  13. ASSOC_ROOT <- "/Users/an/Desktop/Discovery data/"
  14. FILE_PATTERN <- "chr{chr}_pheno{pheno}_lmm.assoc.txt"
  15. OUTPUT_DIR <- "/Users/an/Desktop/PLOTS_PAPERSTYLE/"
  17. if (!dir.exists(OUTPUT_DIR)) dir.create(OUTPUT_DIR, recursive = TRUE)
  19. # ==============================================================================
  20. # THE CORRECTED PHENOTYPE MAPPING
  21. # ==============================================================================
  22. PHENO_MAP <- c(
  23. "1" = "Nicotine Composite Score",
  24. "2" = "Alcohol Consumption Composite Score",
  25. "3" = "Alcohol Dependence Composite Score",
  26. "4" = "Illicit Drug Composite Score",
  27. "5" = "Behavioral Disinhibition Composite Score"
  28. )
  30. # CHANGED: Alternating Blue and Yellow hex codes
  31. PLOT_COLORS <- c("#1f78b4", "#ffc125")
  32. SIGNIFICANCE_THRESHOLD <- 6
  34. # ==============================================================================
  35. # THE MASTER DICTIONARY (Re-aligned to match the correct traits)
  36. # ==============================================================================
  37. annotation_mapping <- tribble(
  38. ~PhenotypeID, ~CoordinateString, ~GeneSymbol,
  40. # Pheno 1 (Nicotine)
  41. "1", "3:29195780", "EOMES",
  43. # Pheno 2 (Alcohol Consumption)
  44. "2", "7:153433844", "DPP6",
  45. "2", "22:50725237", "TOP6BL",
  46. "2", "22:50724593", "ADAM32",
  48. # Pheno 3 (Alcohol Dependence)
  49. "3", "2:180554440", "TNS1",
  50. "3", "3:139986727", "NMNAT3",
  51. "3", "5:148871502", "HTR4",
  52. "3", "13:48927587", "RCBTB2",
  53. "3", "22:50725237", "ADAM32",
  54. "3", "22:50724593", "TOP6BL",
  56. # Pheno 4 (Illicit Drug)
  57. "4", "2:169756930", "LRP1B",
  58. "4", "3:54112290", "ERC2",
  59. "4", "7:153433844", "DPP6",
  60. "4", "13:48927587", "RCBTB2",
  61. "4", "17:10702703", "ABCA8",
  63. # Pheno 5 (Behavioral Disinhibition)
  64. "5", "2:180554440", "TNS1",
  65. "5", "3:139986727", "NMNAT3",
  66. "5", "5:148871502", "HTR4",
  67. "5", "13:48927587", "RCBTB2"
  68. )
  70. create_manhattan <- function(pheno_id, title_text, show_x_axis = TRUE) {
  71. message(sprintf(" -> Processing Phenotype %s: %s...", pheno_id, title_text))
  72. gwas_raw_all <- data.frame()
  74. for (c in 1:22) {
  75. filename <- str_glue(FILE_PATTERN, chr = c, pheno = pheno_id)
  76. full_path <- file.path(ASSOC_ROOT, filename)
  78. if (file.exists(full_path)) {
  79. this_chr <- fread(full_path, data.table = FALSE)
  80. actual_cols <- colnames(this_chr)
  82. if ("chr" %in% actual_cols) names(this_chr)[names(this_chr) == "chr"] <- "CHR"
  83. if ("ps" %in% actual_cols) names(this_chr)[names(this_chr) == "ps"] <- "BP"
  84. if ("pos" %in% actual_cols) names(this_chr)[names(this_chr) == "pos"] <- "BP"
  86. if ("rs" %in% actual_cols) names(this_chr)[names(this_chr) == "rs"] <- "SNP"
  87. else if (!"SNP" %in% actual_cols) this_chr$SNP <- NA
  89. if ("p_lrt" %in% actual_cols) names(this_chr)[names(this_chr) == "p_lrt"] <- "P"
  90. else if ("p_wald" %in% actual_cols) names(this_chr)[names(this_chr) == "p_wald"] <- "P"
  92. this_chr <- this_chr %>%
  93. mutate(CHR = as.integer(CHR), BP = as.numeric(BP), P = as.numeric(P), SNP = as.character(SNP)) %>%
  94. mutate(CoordKey = paste0(CHR, ":", sprintf("%d", as.integer(BP)))) %>%
  95. mutate(DefaultLabel = ifelse(is.na(SNP) | SNP == "" | SNP == ".", CoordKey, SNP)) %>%
  96. select(CHR, BP, P, CoordKey, DefaultLabel)
  98. gwas_raw_all <- bind_rows(gwas_raw_all, this_chr)
  99. }
  100. }
  102. gwas_raw_all <- gwas_raw_all %>% filter(!is.na(P) & P > 0)
  103. gwas_dat <- gwas_raw_all %>% mutate(logP = -log10(P))
  104. nCHR <- length(unique(gwas_dat$CHR))
  106. gwas_dat_cumulative <- gwas_dat %>%
  107. group_by(CHR) %>%
  108. summarise(chr_len = max(BP)) %>%
  109. mutate(tot = cumsum(as.numeric(chr_len)) - chr_len) %>%
  110. select(-chr_len) %>%
  111. left_join(gwas_dat, ., by = "CHR") %>%
  112. arrange(CHR, BP) %>%
  113. mutate(BPcum = BP + tot)
  115. axis_set <- gwas_dat_cumulative %>% group_by(CHR) %>% summarize(center = mean(range(BPcum)))
  117. current_dict <- annotation_mapping %>% filter(PhenotypeID == pheno_id)
  118. sig_snps <- gwas_dat_cumulative %>% filter(logP > SIGNIFICANCE_THRESHOLD) %>% left_join(current_dict, by = c("CoordKey" = "CoordinateString"))
  120. dict_matches <- sig_snps %>% filter(!is.na(GeneSymbol)) %>% mutate(FinalLabel = GeneSymbol)
  121. chrs_with_dict <- unique(dict_matches$CHR)
  122. fallback_snps <- sig_snps %>% filter(! CHR %in% chrs_with_dict) %>% group_by(CHR) %>% slice_max(order_by = logP, n = 1, with_ties = FALSE) %>% ungroup() %>% mutate(FinalLabel = DefaultLabel)
  124. top_snps <- bind_rows(dict_matches, fallback_snps)
  126. # Brute force label cleanup (Universal)
  127. top_snps <- top_snps %>% mutate(FinalLabel = case_when(
  128. FinalLabel == "22:50725237" ~ "ADAM32", FinalLabel == "22:50724593" ~ "TOP6BL",
  129. FinalLabel == "7:153433844" ~ "DPP6", FinalLabel == "3:29195780" ~ "EOMES",
  130. FinalLabel == "2:180554440" ~ "TNS1", FinalLabel == "3:139986727" ~ "NMNAT3",
  131. FinalLabel == "5:148871502" ~ "HTR4", FinalLabel == "13:48927587" ~ "RCBTB2",
  132. FinalLabel == "2:169756930" ~ "LRP1B", FinalLabel == "3:54112290" ~ "ERC2",
  133. FinalLabel == "17:10702703" ~ "ABCA8", TRUE ~ FinalLabel
  134. ))
  136. man_plot <- ggplot(gwas_dat_cumulative, aes(x = BPcum, y = logP)) +
  137. theme_minimal(base_size = 14) +
  138. theme(panel.grid.major.x = element_blank(), panel.grid.minor.x = element_blank(), panel.grid.major.y = element_line(color = "lightgrey", linetype = "solid"), panel.grid.minor.y = element_blank(), axis.text.x = element_text(size = 10, vjust = 0.5), plot.title = element_text(hjust = 0.5, face = "bold", size = 16), axis.title.y = element_text(face="bold")) +
  139. labs(title = title_text, x = "Chromosome", y = expression(bold(paste("-log"[10], "(P)")))) +
  140. geom_point(aes(color = as.factor(CHR)), alpha = 0.8, size = 1.3) +
  141. scale_color_manual(values = rep(PLOT_COLORS, nCHR)) +
  142. scale_x_continuous(label = axis_set$CHR, breaks = axis_set$center, expand = c(0.02, 0)) +
  143. scale_y_continuous(expand = c(0, 0), limits = c(0, max(gwas_dat_cumulative$logP) + 1.5)) +
  144. geom_hline(yintercept = SIGNIFICANCE_THRESHOLD, color = "red", linetype = "dashed", size = 0.8) +
  145. theme(legend.position = "none") +
  146. geom_label_repel(data = top_snps, aes(label = FinalLabel), fontface = "bold", color = "black", fill = "white", box.padding = unit(0.35, "lines"), point.padding = unit(0.5, "lines"), segment.color = "grey50", size = 4, label.padding = unit(0.2, "lines"), label.r = unit(0.1, "lines"), label.size = 0.25, min.segment.length = 0, max.overlaps = Inf)
  148. if (!show_x_axis) man_plot <- man_plot + theme(axis.title.x = element_blank())
  149. return(man_plot)
  150. }
  152. message("\nGenerating CORRECTED GWAS Composite (Blue/Yellow)...")
  153. plot_list <- list()
  154. for (i in 1:5) { pheno_id <- as.character(i); plot_list[[i]] <- create_manhattan(pheno_id, PHENO_MAP[pheno_id], show_x_axis = (i == 5)) }
  156. composite_figure <- (plot_list[[1]] / plot_list[[2]] / plot_list[[3]] / plot_list[[4]] / plot_list[[5]]) + plot_annotation(tag_levels = 'A') & theme(plot.tag = element_text(size = 20, face = "bold"))
  157. output_file <- file.path(OUTPUT_DIR, "Figure_2_Composite_Manhattan_CORRECTED_MAPPING.png")
  158. ggsave(filename = output_file, plot = composite_figure, width = 14, height = 25, units = "in", dpi = 300, limitsize = FALSE)
  159. message(sprintf("Success! Saved to: %s", output_file))

gwas.r at commit b8dc0e2, no license · at the source

Overview

Authors: Jiahua Zhou1, An Phuc Ta1, Catherine Yang2, Ahmed El Shamy2
  1. College of Medicine, California Northstate University, Elk Grove, CA 95757, USA; (J.Z.); (A.P.T.)
  2. College of Graduate Studies, California Northstate University, Elk Grove, CA 95757, USA
Institutions: California Northstate University (United States)
Journal: Biomedicines, volume 14, issue 8, article 1677
Dates: received 15 May 2026; accepted 15 July 2026; published online 26 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3390/biomedicines14081677 · PMID 42652060 · PMCID PMC13510053 · OpenAlex W7171287186
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), other condition (population), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Preprocessing, Connectivity
Keywords: assortive mating, behavioral disinhibition, functional genomics, mesolimbic system, polygenic risk, pleiotropy, externalizing spectrum, transcriptome-wide association study
Topic: Genetic Associations and Epidemiology (Genetics, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Citations: not cited yet (Europe PMC); 39 references in the paper

Abstract

Background/Objectives: Substance use behaviors share a complex, overlapping polygenic architecture, yet translating genome-wide association study (GWAS) findings into actionable biological mechanisms remains challenging. This study aimed to characterize the genetic architecture of five substance use traits (alcohol consumption, alcohol dependence, nicotine use, illicit drug use, and behavioral disinhibition) and identify shared and distinct gene expression signatures within the neural circuits governing addiction. Methods: We reanalyzed 7188 individuals from the Minnesota Center for Twin and Family Research (MCTFR) cohort utilizing longitudinal composite phenotypes spanning five substance-use domains and general behavioral disinhibition. Post-QC, 6874 individuals were retained for downstream analysis. Following genomic imputation and linear mixed model GWAS (GEMMA), we utilized the SNipar framework to partition polygenic risk scores (PRS) into direct and indirect genetic effects, investigating intergenerational shifts in genetic penetrance and effects of assortative mating. Finally, we integrated our summary statistics with brain tissue reference panels to perform a transcriptome-wide association study (TWAS) modeling genetically regulated gene expression within neural circuits relevant to addiction. Results: Partitioning of polygenic risk revealed that while surface-level parental DNA correlations were modest (r = 0.08), underlying latent genetic correlations approached unity (Rδ ≈ 0.99), indicating that addiction risk clustering in families is driven by intense assortive mating and concentrated biological inheritance. Multi-phenotype TWAS identified several significant gene–phenotype associations—notably ADAM32 and SLC9A3, which demonstrated pleiotropic effects across multiple substance use categories. Crucially, these significant TWAS signals were enriched in striatal structures (caudate, putamen, substantia nigra) and frontal cortical regions. Conclusions: Our findings support a model of shared genetic liability across diverse substance use behaviors, mediated by specific gene expression patterns in the mesolimbic dopamine system and frontal cortex. By integrating multi-phenotype GWAS and TWAS, this study highlights pleiotropic candidate genes and provides critical insights into the tissue-specific neurobiological pathways underlying addiction vulnerability.

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 5 matches between paragraphs and lines of code.

jared8844/code-for-addiction-paper

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: b8dc0e26babd6bc7370fbcd08e29835d5acb587d, 26 June 2026
Languages: R (1), Shell (1)
Size: 10 files, 2 scripts
Software Heritage: not archived
Found in: “Data Availability Statement”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: data.table (1 file), ggplot2 (1 file), patchwork (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
3 files
At the source: github.com/jared8844/code-for-addiction-paper/tree/main

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

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 2 scripts, each with its path and the digest of its content;
  • 5 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

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 R, Bash, and Python scripts used for genotype quality control, GWAS execution, LD clumping, PRS construction, TWAS analysis, post-processing, and figure generation are publicly available at: https://github.com/jared8844/code-for-addiction-paper/tree/main (accessed on 14 July 2026). The repository includes the analysis workflow, relevant command-line calls, software versions, package dependencies, and available computational logs. Final tables were regenerated from the final analysis of outputs and manually checked for consistency with the manuscript.

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

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 8 keywords, 36 references.

Cite

This paper

Zhou, J., Ta, A. P., Yang, C., & El Shamy, A. (2026). Exploratory Genome and Transcriptome-Wide Association Analyses of Addiction-Related Phenotypes in a Twin Cohort. Biomedicines, 14(8), 1677. https://doi.org/10.3390/biomedicines14081677

BibTeX

@article{zhou2026exploratory,
author = {Zhou, Jiahua and Ta, An Phuc and Yang, Catherine and El Shamy, Ahmed},
title = {{Exploratory Genome and Transcriptome-Wide Association Analyses of Addiction-Related Phenotypes in a Twin Cohort}},
journal = {Biomedicines},
year = {2026},
month = jul,
volume = {14},
number = {8},
pages = {1677},
publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},
issn = {2227-9059},
doi = {10.3390/biomedicines14081677},
url = {https://doi.org/10.3390/biomedicines14081677},
pmid = {42652060},
pmcid = {PMC13510053}
}

RIS

TY - JOUR
AU - Zhou, Jiahua
AU - Ta, An Phuc
AU - Yang, Catherine
AU - El Shamy, Ahmed
TI - Exploratory Genome and Transcriptome-Wide Association Analyses of Addiction-Related Phenotypes in a Twin Cohort
T2 - Biomedicines
J2 - Biomedicines
PY - 2026
DA - 2026/07/26
VL - 14
IS - 8
SP - 1677
SN - 2227-9059
PB - Multidisciplinary Digital Publishing Institute (MDPI)
DO - 10.3390/biomedicines14081677
UR - https://doi.org/10.3390/biomedicines14081677
LA - en
ER -

CSL-JSON

{
"id": "10.3390/biomedicines14081677",
"type": "article-journal",
"title": "Exploratory Genome and Transcriptome-Wide Association Analyses of Addiction-Related Phenotypes in a Twin Cohort",
"container-title": "Biomedicines",
"author": [
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"family": "Zhou",
"given": "Jiahua"
},
{
"family": "Ta",
"given": "An Phuc"
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{
"family": "Yang",
"given": "Catherine"
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{
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"given": "Ahmed"
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"container-title-short": "Biomedicines",
"volume": "14",
"issue": "8",
"page": "1677",
"DOI": "10.3390/biomedicines14081677",
"PMID": "42652060",
"PMCID": "PMC13510053",
"ISSN": "2227-9059",
"publisher": "Multidisciplinary Digital Publishing Institute (MDPI)",
"URL": "https://doi.org/10.3390/biomedicines14081677",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
26
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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