OSCR

Deciphering cell type-specific causal genetic effects on brain imaging-derived phenotypes and disorders with single-cell Mendelian randomization.

Code ↔ Paper

9 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 9 matches
  1. [1] § Methods › Mendelian randomization between brain IDPs and DBs ↔ Part-3_Follow_up_analysis/6.1.putative causal DBs.DBToIDP.R, lines 81–142 · score 0.92 · Weighted median, Weighted mode, MR Egger, TwoSampleMR, putatively causal, ALS
  2. [2] § Methods › Mendelian randomization between brain IDPs and DBs ↔ Part-3_Follow_up_analysis/6.2.putative causal IDPs.IDPToDB.R, lines 82–143 · score 0.91 · Weighted median, Weighted mode, MR Egger, TwoSampleMR, putatively causal, ALS
  3. [3] § Methods › Primary Mendelian randomization analysis ↔ Part-3_Follow_up_analysis/4.1.replication rate in BrainMeta.R, lines 91–160 · score 0.72 · inverse variance weighted, Wald ratio, TwoSampleMR, IVW, exposure, pleiotropy
  4. [4] § Methods › Transcriptome-wide association study ↔ Part-1_TWAS/1.MetaXcan.R, lines 61–132 · score 0.64 · MetaXcan, brain tissue, V8, covariance, models, TWAS
  5. [5] § Methods › Gene set enrichment analysis ↔ Part-3_Follow_up_analysis/3.5.gene enrichment analysis.R, lines 61–113 · score 0.63 · clusterProfiler, enrichGO, enrichment, gene
  6. [6] § Results › Pervasive phenotype pleiotropy of causal eGenes ↔ Part-3_Follow_up_analysis/3.4.pLI score.R, lines 1–49 · score 0.59 · pLI scores, Behavioral cognitive phenotype, Brain regional volume, Neurological disorder, White matter microstructure, Psychiatric disorder
  7. [7] § Results › Pervasive phenotype pleiotropy of causal eGenes ↔ Part-3_Follow_up_analysis/3.4.pLI score.R, lines 1–49 · score 0.54 · pLI scores, brain regional volume, neurological disorder, behavioral cognitive, white matter microstructure, psychiatric
  8. [8] § Results › Pervasive phenotype pleiotropy of causal eGenes ↔ Part-3_Follow_up_analysis/3.4.pLI score.R, lines 51–106 · score 0.53 · pLI scores, brain regional volume, neurological disorder, white matter microstructure, psychiatric disorder
  9. [9] § Results › Shared cell type specificity of causal eGenes between different phenotype classes ↔ Part-3_Follow_up_analysis/6.3.putative routes.cell_type_eGene-DB-IDP.sankey plot.R, lines 108–185 · score 0.52 · DB IDP, inhibitory neuron, excitatory neurons, ASP, DPW, ADHD

Paper

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The authors' code

R · 224 lines · 12 KB · no license · 3 matches

  1. rm(list=ls())
  2. setwd('F:/类脑/1_实操文件/genexpMR/')
  3. library(data.table)
  4. library(ggplot2)
  7. #pli score
  8. pli_score = fread('data/gnomad.v2.1.1.lof_metrics.by_gene.txt', header = T, data.table = F)
  9. pli_score = pli_score[,c('gene', 'pLI')]
  12. f1 = fread('results/signif_genes.CellToIDP.fdr.txt', data.table = F)
  13. f2 = fread('results/signif_genes.CellToDisorder.fdr.txt', data.table = F)
  14. f1$tissue_gene = paste(f1$tissue, f1$gene, sep='.')
  15. f2$tissue_gene = paste(f2$tissue, f2$gene, sep='.')
  16. trait_mapping = read.csv('gwas_meta_all.csv', header = T)
  17. trait_mapping = trait_mapping[, c('TraitCategory1', 'TraitAbbr')]
  18. PD = trait_mapping[trait_mapping$TraitCategory1 == 'Psychiatric disorder', 'TraitAbbr']
  19. ND = trait_mapping[trait_mapping$TraitCategory1 == 'Neurological disorder', 'TraitAbbr']
  20. BP = trait_mapping[trait_mapping$TraitCategory1 == 'Behavioral-cognitive phenotype', 'TraitAbbr']
  22. set1 = f1[!grepl('FA', f1$pheno), ] #Brain regional volume
  23. score1 = subset(pli_score, pli_score$gene %in% set1$gene_name)
  24. score1$group = 'Brain regional volume'
  25. set2 = f1[grepl('FA', f1$pheno), ] #White matter microstructure
  26. score2 = subset(pli_score, pli_score$gene %in% set2$gene_name)
  27. score2$group = 'White matter microstructure'
  28. set3 = subset(f2, f2$pheno %in% PD) #Psychiatric disorder
  29. score3 = subset(pli_score, pli_score$gene %in% set3$gene_name)
  30. score3$group = 'Psychiatric disorder'
  31. set4 = subset(f2, f2$pheno %in% ND) #Neurological disorder
  32. score4 = subset(pli_score, pli_score$gene %in% set4$gene_name)
  33. score4$group = 'Neurological disorder'
  34. set5 = subset(f2, f2$pheno %in% BP) #Behavioral-cognitive disorder
  35. score5 = subset(pli_score, pli_score$gene %in% set5$gene_name)
  36. score5$group = 'Behavioral-cognitive disorder'
  39. p = list()
  40. p <- list(#`set1` = set1$tissue_gene,
  41. #`set2` = set2$tissue_gene,
  42. #`set3` = set3$tissue_gene,
  43. #`set4` = set4$tissue_gene,
  44. #`set5` = set5$tissue_gene,
  45. `set1 & set1` = names(table(set1$tissue_gene))[which(table(set1$tissue_gene) > 1)], #shared in at least two traits in BRV
  46. `set2 & set2` = names(table(set2$tissue_gene))[which(table(set2$tissue_gene) > 1)],
  47. `set3 & set3` = names(table(set3$tissue_gene))[which(table(set3$tissue_gene) > 1)],
  48. `set4 & set4` = names(table(set4$tissue_gene))[which(table(set4$tissue_gene) > 1)],
  49. `set5 & set5` = names(table(set5$tissue_gene))[which(table(set5$tissue_gene) > 1)],
  51. `set1 & set2` = Reduce(intersect, list(set1$tissue_gene, set2$tissue_gene)),
  52. `set1 & set3` = Reduce(intersect, list(set1$tissue_gene, set3$tissue_gene)),
  53. `set1 & set4` = Reduce(intersect, list(set1$tissue_gene, set4$tissue_gene)),
  54. `set1 & set5` = Reduce(intersect, list(set1$tissue_gene, set5$tissue_gene)),
  55. `set2 & set3` = Reduce(intersect, list(set2$tissue_gene, set3$tissue_gene)),
  56. `set2 & set4` = Reduce(intersect, list(set2$tissue_gene, set4$tissue_gene)),
  57. `set2 & set5` = Reduce(intersect, list(set2$tissue_gene, set5$tissue_gene)),
  58. `set3 & set4` = Reduce(intersect, list(set3$tissue_gene, set4$tissue_gene)),
  59. `set3 & set5` = Reduce(intersect, list(set3$tissue_gene, set5$tissue_gene)),
  60. `set4 & set5` = Reduce(intersect, list(set4$tissue_gene, set5$tissue_gene)),
  61. `set1 & set2 & set3` = Reduce(intersect, list(set1$tissue_gene,set2$tissue_gene,set3$tissue_gene)),
  62. `set1 & set2 & set4` = Reduce(intersect, list(set1$tissue_gene,set2$tissue_gene,set4$tissue_gene)),
  63. `set1 & set2 & set5` = Reduce(intersect, list(set1$tissue_gene,set2$tissue_gene,set5$tissue_gene)),
  64. `set2 & set3 & set4` = Reduce(intersect, list(set2$tissue_gene,set3$tissue_gene,set4$tissue_gene)),
  65. `set2 & set3 & set5` = Reduce(intersect, list(set2$tissue_gene,set3$tissue_gene,set5$tissue_gene)),
  66. `set3 & set4 & set5` = Reduce(intersect, list(set3$tissue_gene,set4$tissue_gene,set5$tissue_gene)),
  67. `set1 & set2 & set3 & set4` = Reduce(intersect, list(set1$tissue_gene,set2$tissue_gene,set3$tissue_gene,set4$tissue_gene)),
  68. `set1 & set2 & set3 & set5` = Reduce(intersect, list(set1$tissue_gene,set2$tissue_gene,set3$tissue_gene,set5$tissue_gene)),
  69. `set2 & set3 & set4 & set5` = Reduce(intersect, list(set2$tissue_gene,set3$tissue_gene,set4$tissue_gene,set5$tissue_gene)),
  70. `set1 & set2 & set3 & set4 & set5` = Reduce(intersect, list(set1$tissue_gene,set2$tissue_gene,set3$tissue_gene,set4$tissue_gene,set5$tissue_gene))
  71. )
  74. gene_set1 = unique(pli_score[which(pli_score$pLI >=0.95), 'gene'])
  77. mapping = rbind(f1[,c("gene_name",'tissue_gene')], f2[, c("gene_name",'tissue_gene')])
  78. mapping = mapping[!(duplicated(mapping$tissue_gene)), ]
  80. result = data.frame(stringsAsFactors = F)
  81. for (i in 1:length(p)){
  82. p[[i]] = subset(mapping, mapping$tissue_gene %in% p[[i]]) $ gene_name
  84. gene_set2 = p[[i]][which(p[[i]] %in% gene_set1)]
  85. q = length(which(gene_set1 %in% gene_set2))
  86. m = length(gene_set1)
  87. n = nrow(pli_score) - m
  88. k = length(gene_set2)
  90. p_hyper = phyper(q-1, m, n, k, lower.tail=F)
  91. result.tmp = data.frame(group = names(p)[i], p = p_hyper)
  92. result = rbind(result, result.tmp)
  93. }
  94. result$group = gsub('set1', 'BRV', result$group) #Brain regional volume
  95. result$group = gsub('set2', 'WMM',result$group) #White matter microstructure
  96. result$group = gsub('set3', 'PD', result$group) #Psychiatric disorder
  97. result$group = gsub('set4', 'ND', result$group) #Neurological disorder
  98. result$group = gsub('set5', 'BCP', result$group) #Behavioral-cognitive disorder
  100. group_high = result[which(result$p < 0.05), 'group']
  101. group_low = result[which(result$p >= 0.05), 'group']
  104. for (i in 1:length(p)){
  105. p[[i]] = subset(pli_score, pli_score$gene %in% p[[i]]) $ pLI
  106. }
  108. data = data.frame(stringsAsFactors = F)
  109. for (i in 1:length(p)){
  110. data.tmp = data.frame(pLI = p[[i]])
  111. if(nrow(data.tmp) == 0) next
  112. data.tmp$group = names(p)[i]
  113. data = rbind(data.tmp, data)
  114. }
  116. data$group = gsub('set1', 'BRV', data$group) #Brain regional volume
  117. data$group = gsub('set2', 'WMM',data$group) #White matter microstructure
  118. data$group = gsub('set3', 'PD', data$group) #Psychiatric disorder
  119. data$group = gsub('set4', 'ND', data$group) #Neurological disorder
  120. data$group = gsub('set5', 'BCP', data$group) #Behavioral-cognitive Phenotypes
  125. plot = aggregate(data$pLI, by=list(data$group), mean)
  126. names(plot)[2] = 'mean'
  127. plot$var = aggregate(data$pLI, by=list(data$group), var)$x
  128. plot = plot[-which(is.na(plot$var)), ]
  129. look = aggregate(data$pLI, by=list(data$group), min)
  130. look$max = aggregate(data$pLI, by=list(data$group), max)$x
  132. plot = plot[order(plot$mean, decreasing = T), ]
  133. plot$Group.1 = factor(plot$Group.1, levels = unique(plot$Group.1))
  136. color = rep('#F09F96', nrow(plot))
  137. ggplot(plot, aes(x = Group.1, y = mean)) +
  138. geom_segment(aes(x = Group.1, xend = Group.1, y = mean-var, yend = mean+var), linewidth = 1.2, color = color) +
  139. geom_segment(aes(x = 1:nrow(plot)-0.1, xend = 1:nrow(plot)+0.1, y = mean-var, yend = mean-var), linewidth = 1.2, color = color) +
  140. geom_segment(aes(x = 1:nrow(plot)-0.1, xend = 1:nrow(plot)+0.1, y = mean+var, yend = mean+var), linewidth = 1.2, color = color) +
  141. geom_point(size = 5, color = color) +
  142. scale_shape_manual(values = c(19,17)) + #指定点的性状
  143. labs(y = "pLI score") +
  144. theme(plot.title = element_text(hjust = 0.5, size = 7.5), #标题居中, 字体设置
  145. panel.background = element_rect(fill = 'white', color = 'black', linewidth = 1.4), #图片周围加框
  146. panel.grid = element_blank(), #不要网格线
  147. legend.position = 'none', ##不要图例
  148. axis.title.x = element_blank(), #不要x轴标题和标签
  149. axis.title.y = element_text(size=16),
  150. axis.ticks.x=element_blank(),
  151. axis.text.y = element_text(size = 12,color = 'black'),
  152. axis.text.x = element_text(angle = 45, hjust = 1, size = 12, color = 'black'))
  154. ###Between class
  155. p = list()
  156. plot1 = subset(plot, plot$Group.1 %in% c('BRV & ND', 'WMM & ND','BRV & PD',
  157. 'BRV & BCP','WMM & PD', 'WMM & BCP')) #pairwise
  159. color = '#675E88'
  160. p[[1]] = ggplot(plot1, aes(x = Group.1, y = mean)) +
  161. geom_segment(aes(x = Group.1, xend = Group.1, y = mean-var, yend = mean+var), linewidth = 1.2, color = color) +
  162. geom_segment(aes(x = 1:nrow(plot1)-0.1, xend = 1:nrow(plot1)+0.1, y = mean-var, yend = mean-var), linewidth = 1.2, color = color) +
  163. geom_segment(aes(x = 1:nrow(plot1)-0.1, xend = 1:nrow(plot1)+0.1, y = mean+var, yend = mean+var), linewidth = 1.2, color = color) +
  164. geom_point(size = 5, color = color) +
  165. scale_shape_manual(values = c(19,17)) +
  166. labs(y = "pLI score") +
  167. scale_y_continuous(limits = c(0, 0.7), breaks = seq(0, 0.7, by = 0.1)) +
  168. theme(plot.title = element_text(hjust = 0.5, size = 7.5),
  169. panel.background = element_rect(fill = 'white', color = 'black', linewidth = 1.4),
  170. panel.grid = element_blank(),
  171. legend.position = 'none',
  172. axis.title.x = element_blank(),
  173. axis.title.y = element_text(size=16),
  174. axis.ticks.x=element_blank(),
  175. axis.text.y = element_text(size = 12,color = 'black'),
  176. axis.text.x = element_text(angle = 45, hjust = 1, size = 12, color = 'black'))
  179. ###Within class
  180. plot2 = subset(plot, plot$Group.1 %in% c('PD & ND','ND & BCP', 'PD & BCP', 'BRV & WMM')) #pairwise
  181. color = '#F09F96'
  182. p[[2]] = ggplot(plot2, aes(x = Group.1, y = mean)) +
  183. geom_segment(aes(x = Group.1, xend = Group.1, y = mean-var, yend = mean+var), linewidth = 1.2, color = color) +
  184. geom_segment(aes(x = 1:nrow(plot2)-0.1, xend = 1:nrow(plot2)+0.1, y = mean-var, yend = mean-var), linewidth = 1.2, color = color) +
  185. geom_segment(aes(x = 1:nrow(plot2)-0.1, xend = 1:nrow(plot2)+0.1, y = mean+var, yend = mean+var), linewidth = 1.2, color = color) +
  186. geom_point(size = 5, color = color) +
  187. scale_shape_manual(values = c(19,17)) +
  188. labs(y = "pLI score") +
  189. scale_y_continuous(limits = c(0, 0.7), breaks = seq(0, 0.7, by = 0.1)) +
  190. theme(plot.title = element_text(hjust = 0.5, size = 7.5),
  191. panel.background = element_rect(fill = 'white', color = 'black', linewidth = 1.4),
  192. panel.grid = element_blank(),
  193. legend.position = 'none',
  194. axis.title.x = element_blank(),
  195. axis.title.y = element_text(size=16),
  196. axis.ticks.x=element_blank(),
  197. axis.text.y = element_text(size = 12,color = 'black'),
  198. axis.text.x = element_text(angle = 45, hjust = 1, size = 12, color = 'black'))
  201. #within group
  202. plot3 = subset(plot, plot$Group.1 %in% c( "BRV & BRV","WMM & WMM","PD & PD","BCP & BCP"))
  203. color = c('#C58CA9', '#4AB74C', '#0069E9', '#FEB500')
  204. p[[3]] = ggplot(plot3, aes(x = Group.1, y = mean)) +
  205. geom_segment(aes(x = Group.1, xend = Group.1, y = mean-var, yend = mean+var), linewidth = 1.2, color = color) +
  206. geom_segment(aes(x = 1:nrow(plot3)-0.1, xend = 1:nrow(plot3)+0.1, y = mean-var, yend = mean-var), linewidth = 1.2, color = color) +
  207. geom_segment(aes(x = 1:nrow(plot3)-0.1, xend = 1:nrow(plot3)+0.1, y = mean+var, yend = mean+var), linewidth = 1.2, color = color) +
  208. geom_point(size = 5, color = color) +
  209. scale_shape_manual(values = c(19,17)) +
  210. labs(y = "pLI score") +
  211. scale_y_continuous(limits = c(0, 0.7), breaks = seq(0, 0.7, by = 0.1)) +
  212. theme(plot.title = element_text(hjust = 0.5, size = 7.5),
  213. panel.background = element_rect(fill = 'white', color = 'black', linewidth = 1.4),
  214. panel.grid = element_blank(),
  215. legend.position = 'none',
  216. axis.title.x = element_blank(),
  217. axis.title.y = element_text(size=16),
  218. axis.ticks.x=element_blank(),
  219. axis.text.y = element_text(size = 12,color = 'black'),
  220. axis.text.x = element_text(angle = 45, hjust = 1, size = 12, color = 'black'))
  222. library(gridExtra)
  223. text = 'grid.arrange(p[[3]], p[[2]], p[[1]], ncol=3, nrow=1)'
  224. eval(parse(text = text))

3.4.pLI score.R at commit 9fe9e93, no license · at the source

Overview

Authors: Anyi Yang1, Xingzhong Zhao1, Xing-Ming Zhao1,2,3,4,5,6, Yucheng T. Yang2,3,4,6
ORCID iDs: Anyi Yang, Xingzhong Zhao, Xing-Ming Zhao, Yucheng T. Yang
  1. Department of Neurology, Zhongshan Hospital and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
  2. College of Biomedical Engineering, Fudan University, Shanghai, China
  3. Department of Cardiovascular Medicine, RuiJin Hospital Lu Wan Branch, Shanghai Jiaotong University School of Medicine, Shanghai, China
  4. Huzhou Central Hospital, Affiliated Central Hospital Huzhou University, Huzhou, Zhejiang, China
  5. State Key Laboratory of Brain Function and Disorders, Institutes of Brain Science, Fudan University, Shanghai, China
  6. MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
Institutions: Fudan University (China); Zhongshan Hospital (China); Shanghai Jiao Tong University (China); Ruijin Hospital (China); Huzhou Normal University (China); Huzhou Central Hospital (China); Frontiers Center for Brain Science of the Ministry of Education (China)
Journal: PLoS computational biology, volume 22, issue 6, article e1014422
Dates: received 16 May 2025; accepted 7 June 2026; published online 17 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1371/journal.pcbi.1014422 · PMID 42308238 · PMCID PMC13289931 · OpenAlex W7165018918
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, Connectivity, fMRI & imaging
MeSH: Brain*, Brain Diseases*, Mendelian Randomization Analysis*, Neuroimaging*, Single-Cell Analysis*, Computational Biology, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Phenotype, Polymorphism, Single Nucleotide, Quantitative Trait Loci (* major topic)
Journal subjects: Biology and Life Sciences, Genetics, Phenotypes, Cell Biology, Cellular Types, Animal Cells, Neurons, Neuroscience, Cellular Neuroscience, Anatomy, Nervous System, Central Nervous System, Medicine and Health Sciences, Physical Sciences, Materials Science, Materials Physics, Microstructure, Physics, Computational Biology, Genome Analysis, Genome-Wide Association Studies, Genomics, Human Genetics, Gene Expression, Heredity, Psychology, Behavior, Social Sciences
Topic: Single-cell and spatial transcriptomics (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: National Natural Science Foundation of China (National Science Foundation of China) (T2225015, 62433008); Shanghai Science and Technology Commission Program (23JS1410100, 24JS2810100); Major Project of Guangzhou National Laboratory (GZNL2024A01003); National Key R&D Program of China (2023YFF1204800, 2025YFA1309200, 2025YFC3409300); Shanghai Municipal Education Commission (24KXZNA11)
Citations: not cited yet (Europe PMC); 169 references in the paper

Abstract

Reconstructing causality routes from genetic effects to complex phenotypes in particular cell types is crucial for understanding biological mechanisms underlying the brain-associated phenotypes including imaging-derived phenotypes (IDPs), and brain disorders and behaviors (DBs). Here, we develop a single-cell Mendelian randomization framework to infer cell type-specific causal relationships between gene expression and diverse brain-associated complex phenotypes by integrating single-cell expression quantitative trait loci (cis-eQTLs) and genome-wide association study findings. We identifiy a set of 254 and 217 cis-eQTL target genes (eGenes) that may have causal effects on 112 IDPs and 26 DBs in eight cell types, respectively. These causal eGenes exhibit strong cell type specificity and varied pleiotropy among different types of brain-associated phenotypes. Further integrative analysis reveals putative causality routes among cell type-specific causal eGenes and brain-associated complex phenotypes. Finally, we characterize the spatiotemporal expression patterns of these causal eGenes, and highlight the coordinated associations of the brain-associated phenotypes based on the expression of their causal eGenes. Overall, our study presents a large-scale analysis of the genetic effects of brain structures, disorders and behaviors, providing a catalog of cell type-specific causal eGenes.

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 9 matches between paragraphs and lines of code.

sldrcyang/ExpMR

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 9fe9e93936b4d08a14b07105fb43392e0765958e, 7 February 2026
Languages: R (88)
Size: 89 files, 88 scripts
Software Heritage: not archived
Found in: “Data Availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: data.table (87 files), tidyverse (43 files), ggplot2 (11 files), pheatmap (3 files), clusterProfiler (1 file), reshape2 (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
89 files
At the source: github.com/sldrcyang/ExpMR

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

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Data

Datasets cited

Data Availability

The scripts in the study are publicly available via GitHub at https://github.com/sldrcyang/ExpMR. The predicted cell type-specific causal eGenes, and their associated IDPs and DBs can be freely downloaded in our supplementary website (https://mai.fudan.edu.cn/expmr). 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 https://www.nhlbi.nih.gov/grasp/downloads/ResultsFebruary2017/2014/2014_Benyamin_CHIC; EA from https://zenodo.org/records/18584527/files/GWAS_EA_excl23andMe.zip?download=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://zenodo.org/records/18584527/files/AUTOMOBILE_SPEEDING_PROPENSITY_GWAS.zip?download=1; DPW from https://conservancy.umn.edu/handle/11299/201564; GRT from https://zenodo.org/records/18584527/files/RISK_behavior.RISK_GWAS_MA_UKB+replication.zip?download=1; 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 https://zenodo.org/records/18584527/files/alsMetaSummaryStats_march21st2018.tab.zip?download=1; MS from https://imsgc.net/data/discovery_metav3.0.meta.gz; PD from https://zenodo.org/records/18584527/files/nallsEtAl2019_excluding23andMe_allVariants.tab.zip?download=1; OCD from https://doi.org/10.6084/m9.figshare.14672103; TS from https://doi.org/10.6084/m9.figshare.14672232; ANX from https://zenodo.org/records/18584527/files/TotAnx_OR_sumstats.zip?download=1; 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 pLI scores are downloaded from ExAc (https://genome.ucsc.edu/cgi-bin/hgTrackUi?db=hg19&g=gnomadPLI; version v2.1.1). 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 phenotypes of the NHGRI-EBI GWAS Catalog are downloaded from https://www.ebi.ac.uk/gwas/api/search/downloads/associations/v1.0.2?split=false. The cell type-specific eQTL summary statistics from the brainSCOPE resource are downloaded from https://brainscope.gersteinlab.org/integrative_files.html. The differentially expressed genes associated with AD from Mathys et al. 2023 are downloaded from https://github.com/mathyslab7/ROSMAP_snRNAseq_PFC/tree/main/Results/Differential_gene_expression_analysis.

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 12 MeSH terms, 5 funders, 167 references.

Cite

This paper

Yang, A., Zhao, X., Zhao, X.-M., & Yang, Y. T. (2026). Deciphering cell type-specific causal genetic effects on brain imaging-derived phenotypes and disorders with single-cell Mendelian randomization. PLoS computational biology, 22(6), e1014422. https://doi.org/10.1371/journal.pcbi.1014422

BibTeX

@article{yang2026deciphering,
author = {Yang, Anyi and Zhao, Xingzhong and Zhao, Xing-Ming and Yang, Yucheng T.},
title = {{Deciphering cell type-specific causal genetic effects on brain imaging-derived phenotypes and disorders with single-cell Mendelian randomization}},
journal = {PLoS computational biology},
year = {2026},
month = jun,
volume = {22},
number = {6},
pages = {e1014422},
publisher = {PLOS},
issn = {1553-734X},
doi = {10.1371/journal.pcbi.1014422},
url = {https://doi.org/10.1371/journal.pcbi.1014422},
pmid = {42308238},
pmcid = {PMC13289931}
}

RIS

TY - JOUR
AU - Yang, Anyi
AU - Zhao, Xingzhong
AU - Zhao, Xing-Ming
AU - Yang, Yucheng T.
TI - Deciphering cell type-specific causal genetic effects on brain imaging-derived phenotypes and disorders with single-cell Mendelian randomization
T2 - PLoS computational biology
J2 - PLoS Comput Biol
PY - 2026
DA - 2026/06/17
VL - 22
IS - 6
SP - e1014422
SN - 1553-734X
PB - PLOS
DO - 10.1371/journal.pcbi.1014422
UR - https://doi.org/10.1371/journal.pcbi.1014422
LA - en
ER -

CSL-JSON

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"author": [
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"given": "Anyi"
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{
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"given": "Yucheng T."
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"container-title-short": "PLoS Comput Biol",
"volume": "22",
"issue": "6",
"page": "e1014422",
"DOI": "10.1371/journal.pcbi.1014422",
"PMID": "42308238",
"PMCID": "PMC13289931",
"ISSN": "1553-734X",
"publisher": "PLOS",
"URL": "https://doi.org/10.1371/journal.pcbi.1014422",
"language": "en",
"issued": {
"date-parts": [
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