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Determinants of functional burden pleiotropy and gene dosage responses across human traits.

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The 21 matches
  1. [1] § Results › Between-trait CNV burden correlations and comparison to other variant classes ↔ Figure_Generation/Figure5.Rmd, lines 7–89 · score 0.98 · forced vital capacity, visuospatial working memory, squares regression, single nucleotide polymorphism, edge thickness, function SNV burden
  2. [2] § Results › Functional burden pleiotropy is higher for genes assigned to brain tissue ↔ Figure_Generation/Figure5.Rmd, lines 7–89 · score 0.89 · forced vital capacity, visuospatial working memory, single nucleotide polymorphism, Townsend deprivation, educational attainment, fluid intelligence
  3. [3] § Results › Dissecting functional burden pleiotropy, gene function, and genetic constraint ↔ Figure_Generation/Figure4.Rmd, lines 7–28 · score 0.84 · top decile LOEUF, Functional pleiotropy normalized, Constraint gene percentages, intolerant genes, body cell, pleiotropy compared
  4. [4] § Results › Dissecting functional burden pleiotropy, gene function, and genetic constraint ↔ Fig3.Rmd, lines 10–43 · score 0.84 · top decile LOEUF, Functional pleiotropy normalized, Constraint gene percentages, intolerant genes, pleiotropy compared, body cell
  5. [5] § Results › Functional burden pleiotropy is higher for genes assigned to brain tissue ↔ Fig2.Rmd, lines 41–161 · score 0.76 · medulla oblongata, locus coeruleus, substantia nigra, activity, pons, cortex
  6. [6] § Methods › Genetic constraint metrics ↔ Figure_Generation/Figure4.Rmd, lines 168–211 · score 0.76 · pHaplo, pTriplo, gene length, CDS, het, missense
  7. [7] § Methods › Genetic constraint metrics ↔ Fig3.Rmd, lines 160–227 · score 0.75 · pHaplo, pTriplo, gene length, CDS, het, missense
  8. [8] § Results › Gene dosage effects across whole-body and whole-brain cell types ↔ Figure_Generation/Figure5.Rmd, lines 239–293 · score 0.72 · HbA1c, Standing Height, Heart Rate, trait categories, BMD, HDL
  9. [9] § Results › Functional burden pleiotropy is higher for genes assigned to brain tissue ↔ Fig2.Rmd, lines 41–161 · score 0.69 · HbA1c, standing height, heart rate, BMD, HDL, FVC
  10. [10] § Methods › CNV- burden correlations ↔ Functional_Burden_Association_Analysis/FunBurd_Multitrait_EffectSizes_Computation_Script_On_ToyDatasets.ipynb, lines 1–75 · score 0.66 · logistic regression, binary trait, continuous trait, covariates, burden, CNV
  11. [11] § Results › Monotonic versus non-monotonic gene dosage responses across traits ↔ Figure_Generation/Figure6.Rmd, lines 231–248 · score 0.62 · Wilcoxon rank sum, gene dosage responses, brain traits, monotonic, Box, Figure 6
  12. [12] § Methods › Replication in the All of Us cohort ↔ Figure_Generation/Figure2.Rmd, lines 600–696 · score 0.62 · Standing Height, Heart Rate, AoU, cohorts, Platelet, UKBB
  13. [13] § Results › Functional burden pleiotropy is higher for genes assigned to brain tissue ↔ Figure_Generation/Figure2.Rmd, lines 52–93 · score 0.59 · locus coeruleus, substantia nigra, pons, cortex, brain, tissue
  14. [14] § Methods › Replication in the All of Us cohort ↔ Figure_Generation/Figure3.Rmd, lines 514–572 · score 0.58 · Standing Height, Heart Rate, AoU, Platelet, UKBB, BMI
  15. [15] § Methods › Normative constraint modeling ↔ Figure_Generation/Figure4.Rmd, lines 231–251 · score 0.57 · normative model, constraint fraction, functional pleiotropy, genetic constraint
  16. [16] § Results › Functional burden pleiotropy is higher for genes assigned to brain tissue ↔ Figure_Generation/Figure3.Rmd, lines 52–96 · score 0.57 · blood assays, mental health, physical, reproductive, cognitive, tissues
  17. [17] § Results › Dissecting functional burden pleiotropy, gene function, and genetic constraint ↔ Fig3.Rmd, lines 511–565 · score 0.53 · Wilcox Ranksum, brain gene, normative, modeling, pleiotropy, FDR
  18. [18] § Results › Dissecting functional burden pleiotropy, gene function, and genetic constraint ↔ Fig3.Rmd, lines 10–43 · score 0.52 · LOEUF top decile, constraint metric, genetic constraint, constrained genes, FDR corrected, brain gene
  19. [19] § Methods › CNV- burden correlations ↔ Functional_Burden_Association_Analysis/FunBurd_Multitrait_EffectSizes_Computation_Script_On_ToyDatasets.ipynb, lines 1–75 · score 0.52 · logistic regression, binary trait, burden, CNV
  20. [20] § Results › Dissecting functional burden pleiotropy, gene function, and genetic constraint ↔ Figure_Generation/Figure4.Rmd, lines 7–28 · score 0.52 · LOEUF top decile, constraint metric, genetic constraint, FDR corrected, brain gene, constrained genes
  21. [21] § Methods › Permutation preserving Jaccard distance (P-Jaccard) ↔ Permutation_PreservingJaccardDistance_P_Jaccard/PJaccard_Computation.ipynb, lines 104–144 · score 0.50 · Jaccard distance, permutations, matrix, correlations

Paper

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The authors' code

R Markdown · 377 lines · 18 KB · MIT · 4 matches

  1. ---
  2. title: "Fig4"
  3. author: "Kuldeep Kumar"
  4. output: github_document
  5. ---
  7. ## Fig. 4: Dissecting pleiotropy, gene function, and genetic constraint.
  9. #### -- Figure legend -- ####
  10. **Legend:** (A) Correlation between the fraction of constraint genes (LOEUF top-decile) within a gene set and the number of traits showing significant associations with that gene set for deletions and duplications. Each data point is a gene set. Y-axis: the percentage of significantly associated traits for a variant (functional pleiotropy); X-axis: the percentage of top decile LOEUF genes within the gene set. (B) Box plots show the distribution of constraint gene percentages for all Brain and Non-Brain gene sets (172). Each point represents a gene set, and asterisks (*) indicate statistically significant differences between groups. (C) Shows the same information on correlations in panel (A) for different constraint metrics. (D) Functional pleiotropy, normalized by the fraction of genetic constraint at the tissue level, is shown for 27 brain and 33 non-brain gene sets – deletions are presented on the left and duplications on the right. X-axis: represents the proportion of intolerant genes for the different gene sets. Y-axis: functional pleiotropy normalized by genetic constraint (centile). I.e., the 50th centile shows median functional pleiotropy computed across 100 randomly sampled gene sets. Circles and triangles represent brain and non-brain gene sets, respectively. Gray shaded ribbons indicating 25th–75th (ribbon 1), 10th–90th (ribbon 2), and 5th–95th (ribbon 3) centiles. This is followed by violin plots showing the distribution of normalized functional pleiotropy across brain and non-brain traits. Orange and green asterisks demonstrate significantly (FDR-corrected, q < 0.05) increased functional pleiotropy compared to what is expected for a gene set with a comparable fraction of genetic constraint. The grey star shows a significant difference between the brain and non-brain gene sets, normalized functional pleiotropy. (E) The same analyses at the whole-body cell type level, including 7 brain and 74 non-brain gene sets.
  12. #### Libraries ####
  13. ```{r setup, message=FALSE, warning=FALSE}
  14. library(ggplot2)
  15. library(ggprism)
  16. library(ggpubr)
  17. library(dplyr)
  18. library(tidyr)
  19. library(patchwork)
  20. library(knitr)
  21. library(openxlsx)
  22. library(svglite)
  24. # Define Core Colors and Sizes
  25. brain_colorcode <- "#d95f02"
  26. nonbrain_colorcode <- "#66a61e"
  27. in_base_size_ggprism <- 18
  28. ```
  30. #### Panel A: Correlation between constraint and Del/Dup/GWAS functional pleiotropy
  31. Description: Scatter plots evaluating the relationship between the fraction of constraint genes (LOEUF) and the percentage of significantly associated traits. ####
  33. ##### Panel A Data #####
  34. ```{r, fig.width=7, fig.height=3 ,dpi=100 }
  35. ##### Panel A Data #####
  36. # 1. Load the Pre-computed Correlation & P-Jaccard Statistics
  37. load("df_constraint_pleiotropy_corr.RData")
  39. stats_panel_a <- df_constraint_pleiotropy_corr %>%
  40. # Filter for the specific constraint measure used in Panel A
  41. filter(constraint == "measure_LEOUF_topdecile") %>%
  42. # Extract BOTH the correlation and the adjusted Jaccard p-value
  43. select(stat, cor, adj_pvalue_Jaccard)
  45. # 2. Load the Geneset-level plotting coordinates
  46. load("df_geneset_level_stats.RData")
  48. df_fp <- df_geneset_level_stats %>%
  49. mutate(
  50. # Convert constraint to percentage for the X-axis
  51. constraint_pct = constraint_measure_LEOUF_topdecile * 100
  52. ) %>%
  53. select(Geneset, Geneset_Type, Geneset_Cat, constraint_pct,
  54. functional_pleiotropy_deletion, functional_pleiotropy_duplication, functional_pleiotropy_GWASenrichment)
  56. kable(head(df_fp), format = "markdown")
  57. kable(stats_panel_a, align = "c", caption = "Exact Stats Extracted for Panel A")
  58. ```
  60. ##### Panel A Figure #####
  61. ```{r, fig.width=9, fig.height=3.5, dpi=300}
  62. # Function to plot scatter and map the exact 'cor' and 'p-jaccard' from the stats dataframe
  63. plot_pleio_scatter_jaccard <- function(data, y_var, color_hex, stat_label, stats_df) {
  65. # Map BOTH correlation and p-value from the stats dataframe
  66. row_match <- stats_df %>% filter(stat == stat_label)
  67. cor_val <- row_match$cor
  68. pval_adj <- row_match$adj_pvalue_Jaccard
  70. # Format the label (add asterisk if FDR < 0.05)
  71. cor_label <- paste0("r=", round(cor_val, 2), ifelse(pval_adj < 0.05, "*", ""))
  73. ggplot(data, aes(x = constraint_pct, y = .data[[y_var]])) +
  74. geom_point(color = color_hex, size = 2, alpha = 1) +
  75. # Add the extracted correlation label to the plot
  76. annotate("text", x = Inf, y = Inf, hjust = 1.1, vjust = 1.5, label = cor_label, size = 6, fontface="bold") +
  77. geom_smooth(method = "lm", formula=y ~ x, se = FALSE, linewidth = 1.5, fullrange = TRUE, color = "black") +
  78. scale_y_continuous(limits = c(0, 75)) +
  79. theme_prism(axis_text_angle = 90, base_size = 14) +
  80. labs(x = "% constraint genes", y = "% functional pleiotropy") +
  81. theme(plot.title = element_text(hjust = 0.5, face = "bold"))
  82. }
  84. # Generate the three plots using their exact stat labels
  85. p_scatter_del <- plot_pleio_scatter_jaccard(
  86. df_fp, "functional_pleiotropy_deletion", "red3",
  87. "functional_burden_pleiotropy_deletion", stats_panel_a
  88. )
  90. p_scatter_dup <- plot_pleio_scatter_jaccard(
  91. df_fp, "functional_pleiotropy_duplication", "royalblue",
  92. "functional_burden_pleiotropy_duplication", stats_panel_a
  93. ) + theme(axis.title.y = element_blank(), axis.text.y = element_blank(), axis.ticks.y = element_blank())
  95. p_scatter_gwas <- plot_pleio_scatter_jaccard(
  96. df_fp, "functional_pleiotropy_GWASenrichment", "darkgreen",
  97. "functional_pleiotropy_GWASenrichment", stats_panel_a
  98. ) + theme(axis.title.y = element_blank(), axis.text.y = element_blank(), axis.ticks.y = element_blank())
  100. # Layout the final figure
  101. p_scatter_Fig4A <- (p_scatter_del + p_scatter_dup + p_scatter_gwas) + plot_layout(widths = c(1, 1, 1))
  103. print(p_scatter_Fig4A)
  104. ```
  107. ##### Panel A Stats #####
  108. ```{r, fig.width=18, fig.height=7 ,dpi=800 }
  109. kable(head(stats_panel_a), format = "markdown")
  110. ```
  113. #### Panel B: Distribution of constraint across Brain and Non-Brain gene sets
  114. Description: Box plots comparing the percentage of constraint genes between functional categories.
  116. ##### Panel B Data #####
  117. ```{r, fig.width=18, fig.height=7 ,dpi=800 }
  118. df_box <- df_fp %>%
  119. select(Geneset, Geneset_Cat, constraint_pct) %>%
  120. mutate(Geneset_Cat = factor(Geneset_Cat, levels = c("Brain", "NonBrain")))
  122. kable(head(df_box), format = "markdown")
  123. ```
  126. ##### Panel B Figure #####
  127. ```{r, fig.width=7, fig.height=4, dpi=400}
  128. p_box_Fig4B <- ggplot(df_box, aes(x = Geneset_Cat, y = constraint_pct, fill = Geneset_Cat)) +
  129. # Boxplot with black outline
  130. geom_boxplot(outlier.shape = NA, width = 0.5, alpha = 0.5, linewidth = 0.8, color = "black") +
  131. # Jitter points: shape 21 enables 'fill' aesthetic
  132. geom_jitter(aes(color=Geneset_Cat,shape = Geneset_Cat), stroke = 0.4,
  133. width = 0.15, size = 2, alpha = 0.9) +
  134. scale_fill_manual(values = c("Brain" = brain_colorcode, "NonBrain" = nonbrain_colorcode)) +
  135. scale_color_manual(values = c("Brain" = brain_colorcode, "NonBrain" = nonbrain_colorcode)) +
  136. scale_shape_manual(values = c("Brain" = 16, "NonBrain" = 17)) +
  137. theme_prism(base_size = in_base_size_ggprism) +
  138. theme(
  139. legend.position = "none",
  140. axis.text.x = element_text(size = 14, face = "bold"),
  141. axis.text.y = element_text(size = 14, face = "bold"),
  142. axis.title.y = element_text(size = 16, face = "bold")
  143. ) +
  144. labs(x = NULL, y = "% constraint genes") +
  145. coord_flip()
  147. print(p_box_Fig4B)
  148. ```
  150. ##### Panel B Stats #####
  151. ```{r, fig.width=9, fig.height=5, dpi=400}
  152. res_wilcox_B <- wilcox.test(
  153. df_box %>% filter(Geneset_Cat == "Brain") %>% pull(constraint_pct),
  154. df_box %>% filter(Geneset_Cat == "NonBrain") %>% pull(constraint_pct)
  155. )
  157. stats_panel_b <- data.frame(
  158. Test = "Wilcoxon Rank Sum Test",
  159. Comparison = "Brain vs Non-Brain Constraint (%)",
  160. W_Statistic = res_wilcox_B$statistic,
  161. P_Value = format(res_wilcox_B$p.value, digits = 4)
  162. )
  164. kable(stats_panel_b, align = "c", caption = "Statistical Comparison of Constraint Percentages")
  165. ```
  168. #### Panel C: Constraint vs Functional pleiotropy across metrics
  169. Description: Evaluates functional pleiotropy correlations across seven different established metrics of genetic constraint.
  171. ##### Panel C Data & Stats #####
  172. ```{r, fig.width=5, fig.height=9, dpi=400}
  173. load("df_constraint_pleiotropy_corr.RData")
  175. # Map the pre-computed dataframe directly to plotting coordinates
  176. df_metrics_cor <- df_constraint_pleiotropy_corr %>%
  177. mutate(
  178. # Map the raw constraint strings to clean metric labels
  179. Metric = case_when(
  180. grepl("LEOUF", constraint) ~ "LOEUF",
  181. grepl("missense_Z", constraint) ~ "missense Z",
  182. grepl("s_het", constraint) ~ "s-het",
  183. grepl("CDS", constraint) ~ "constraint CDS",
  184. grepl("pHaplo", constraint) ~ "pHaplo",
  185. grepl("pTriplo", constraint) ~ "pTriplo",
  186. grepl("GeneLenBP", constraint) ~ "gene length",
  187. TRUE ~ constraint
  188. ),
  189. # Map the stat column to Del, Dup, GWAS
  190. Type = case_when(
  191. grepl("deletion", stat) ~ "Del",
  192. grepl("duplication", stat) ~ "Dup",
  193. grepl("GWASenrichment", stat) ~ "GWAS",
  194. TRUE ~ stat
  195. ),
  196. # Assign significance using the Jaccard adjusted p-value directly
  197. sig = ifelse(adj_pvalue_Jaccard < 0.05, "q<0.05", "n.s.")
  198. ) %>%
  199. # Set factors for proper plot ordering
  200. mutate(
  201. Type = factor(Type, levels = c("Del", "Dup", "GWAS")),
  202. sig = factor(sig, levels = c("q<0.05", "n.s."))
  203. ) %>%
  204. select(Metric, Type, cor, pvalue_Jaccard, adj_pvalue_Jaccard, sig)
  206. # Order Y-axis metrics based on Deletion correlation strength
  207. metric_order <- df_metrics_cor %>% filter(Type == "Del") %>% arrange(cor) %>% pull(Metric)
  208. df_metrics_cor$Metric <- factor(df_metrics_cor$Metric, levels = metric_order)
  210. kable(head(df_metrics_cor), format = "markdown")
  211. ```
  214. ##### Panel C Figure #####
  215. ```{r, fig.width=6, fig.height=7, dpi=90}
  216. p_Fig4C <- ggplot(df_metrics_cor, aes(y = Metric, x = cor, group = Type)) +
  217. geom_point(aes(color = Type, fill = Type, shape = sig), size = 5, position = position_dodge(width = 0.6)) +
  218. scale_fill_manual(values = c("Del" = "red3", "Dup" = "royalblue", "GWAS" = "darkgreen")) +
  219. scale_color_manual(values = c("Del" = "red3", "Dup" = "royalblue", "GWAS" = "darkgreen")) +
  220. scale_shape_manual(values = c("q<0.05" = 21, "n.s." = 4)) +
  221. geom_vline(xintercept = 0, linetype = "longdash", color = "black") +
  222. geom_vline(xintercept = 0.4, linetype = "dotted", color = "black") +
  223. theme_prism(base_size = in_base_size_ggprism) +
  224. theme(legend.position = c(0.85, 0.2)) +
  225. labs(x = "Correlation", y = NULL) +
  226. coord_cartesian(xlim = c(0, 0.7))
  228. print(p_Fig4C)
  229. ```
  231. #### Panel D & E: Normative Null Models (Tissue & Cell Type)
  232. Description: Functional pleiotropy normalized by the fraction of genetic constraint, plotted against null distributions for Tissue (Fantom60) and Cell Type (HPA81).
  235. ##### Panel D & E Data #####
  236. ```{r, fig.width=6, fig.height=7, dpi=90}
  237. # 1. Extract and format the exact plotting coordinates from geneset level stats
  238. # No external null distribution data is required.
  239. df_normative <- df_geneset_level_stats %>%
  240. mutate(
  241. # Convert constraint fraction to percentage for the X-axis
  242. constraint_pct = constraint_measure_LEOUF_topdecile * 100
  243. ) %>%
  244. select(Geneset, Geneset_Type, Geneset_Cat, constraint_pct,
  245. normative_model_centile_del_pleiotropy, normative_model_centile_dup_pleiotropy)
  247. # Ensure Geneset_Cat is a factor for consistent color mapping
  248. df_normative$Geneset_Cat <- factor(df_normative$Geneset_Cat, levels = c("Brain", "NonBrain"))
  250. kable(head(df_normative), format = "markdown")
  251. ```
  253. ##### Panel D & E Figure #####
  254. ```{r, fig.width=18, fig.height=9, dpi=90}
  255. # --- 1. Compute Stats First (Needed for Dynamic Annotations) ---
  256. compute_normative_stats <- function(data, gset_type, metric_col, variant_label) {
  257. sub_df <- data %>% filter(Geneset_Type == gset_type)
  259. brain_vals <- sub_df %>% filter(Geneset_Cat == "Brain") %>% pull(.data[[metric_col]])
  260. nbrain_vals <- sub_df %>% filter(Geneset_Cat == "NonBrain") %>% pull(.data[[metric_col]])
  262. # Unified Wilcoxon Rank-sum Tests
  263. p_brain_50 <- wilcox.test(brain_vals, mu = 50,exact = FALSE)$p.value
  264. p_nbrain_50 <- wilcox.test(nbrain_vals, mu = 50,exact = FALSE)$p.value
  265. p_b_vs_nb <- wilcox.test(brain_vals, nbrain_vals,exact = FALSE)$p.value
  267. return(data.frame(Geneset_Type = gset_type, Variant = variant_label,
  268. P_Brain_vs_50 = p_brain_50, P_NonBrain_vs_50 = p_nbrain_50, P_Brain_vs_NonBrain = p_b_vs_nb))
  269. }
  271. # Run across all categories to get raw p-values
  272. df_stats_raw <- bind_rows(
  273. compute_normative_stats(df_normative, "Tissue_Fantom60", "normative_model_centile_del_pleiotropy", "Del"),
  274. compute_normative_stats(df_normative, "Tissue_Fantom60", "normative_model_centile_dup_pleiotropy", "Dup"),
  275. compute_normative_stats(df_normative, "SC_HPA81", "normative_model_centile_del_pleiotropy", "Del"),
  276. compute_normative_stats(df_normative, "SC_HPA81", "normative_model_centile_dup_pleiotropy", "Dup")
  277. )
  279. # 1. Pool all 8 tests for 'shift from 50%' and adjust FDR
  280. pooled_vs_50_pvals <- c(df_stats_raw$P_Brain_vs_50, df_stats_raw$P_NonBrain_vs_50)
  281. pooled_vs_50_fdr <- p.adjust(pooled_vs_50_pvals, method = "fdr")
  283. # 2. Pool all 4 tests for 'brain vs non-brain' and adjust FDR
  284. pooled_b_vs_nb_fdr <- p.adjust(df_stats_raw$P_Brain_vs_NonBrain, method = "fdr")
  286. # Map the correctly pooled FDR values back
  287. df_stats_DE_final <- df_stats_raw %>%
  288. mutate(
  289. # Indices 1:4 belong to Brain, indices 5:8 belong to NonBrain
  290. FDR_Brain_vs_50 = pooled_vs_50_fdr[1:4],
  291. FDR_NonBrain_vs_50 = pooled_vs_50_fdr[5:8],
  292. FDR_Brain_vs_NonBrain = pooled_b_vs_nb_fdr
  293. )
  295. # --- 2. Helper Function to Build Individual Components ---
  296. build_normative_plots <- function(data_obs, gset_type, var_y, variant_type, stats_df, y_label = NULL, hide_y = FALSE) {
  298. df_sub <- data_obs %>% filter(Geneset_Type == gset_type)
  299. plot_stats <- stats_df %>% filter(Geneset_Type == gset_type, Variant == variant_type)
  301. # Scatter Plot (Using manual annotations for the Centile bands instead of a null dataset)
  302. p_scatter <- ggplot(df_sub, aes(x = constraint_pct, y = .data[[var_y]])) +
  303. # Background Centile Ribbons
  304. annotate("rect", ymin = 5, ymax = 95, xmin = -Inf, xmax = Inf, fill = "grey80", alpha = 0.4) +
  305. annotate("rect", ymin = 10, ymax = 90, xmin = -Inf, xmax = Inf, fill = "grey70", alpha = 0.4) +
  306. annotate("rect", ymin = 25, ymax = 75, xmin = -Inf, xmax = Inf, fill = "grey60", alpha = 0.4) +
  307. geom_hline(yintercept = 50, color = "black", linewidth = 1) +
  309. # Scatter Points
  310. geom_point(aes(color = Geneset_Cat, shape = Geneset_Cat), size = 3) +
  311. scale_color_manual(values = c("Brain" = brain_colorcode, "NonBrain" = nonbrain_colorcode)) +
  312. scale_shape_manual(values = c("Brain" = 16, "NonBrain" = 17)) +
  313. scale_x_continuous(expand = c(0, 0), limits = c(0, 22), breaks = seq(0, 22, by = 5)) +
  314. scale_y_continuous(limits = c(0, 125), breaks = c(10, 25, 50, 75, 90)) +
  315. theme_prism(base_size = in_base_size_ggprism, base_line_size = in_base_size_ggprism/24) +
  316. theme(legend.position = "none") +
  317. labs(x = "% constraint genes", y = y_label)
  319. if (hide_y) {
  320. p_scatter <- p_scatter + theme(axis.title.y = element_blank(), axis.text.y = element_blank(), axis.ticks.y = element_blank())
  321. }
  323. # Violin Plot
  324. p_violin <- ggplot(df_sub, aes(x = Geneset_Cat, y = .data[[var_y]], color = Geneset_Cat,shape=Geneset_Cat)) +
  325. geom_hline(yintercept = 50, linetype = "dotted", color = "black", linewidth = 2) +
  326. geom_violin(fill = "white", alpha = 1, trim = TRUE, linewidth = 0.8) +
  327. geom_point(position = position_jitter(seed = 1, width = 0.2), size = 1.5, alpha = 0.8) +
  328. stat_summary(color = "black", fun.data = "mean_cl_boot", geom = "pointrange", size = 0.7) +
  329. scale_color_manual(values = c("Brain" = brain_colorcode, "NonBrain" = nonbrain_colorcode)) +
  330. scale_shape_manual(values = c("Brain" = 16, "NonBrain" = 17)) +
  331. scale_x_discrete(labels = c("Brain", "NonBrain")) +
  332. scale_y_continuous(limits = c(0, 125), breaks = c(10, 25, 50, 75, 90)) +
  333. theme_prism(base_size = in_base_size_ggprism, base_line_size = in_base_size_ggprism/24) +
  334. theme(legend.position = "none", axis.title.x = element_blank(), axis.title.y = element_blank(),
  335. axis.line.y = element_blank(), axis.text.y = element_blank(), axis.ticks.y = element_blank())
  337. # Add Significance Annotations dynamically based on the stats
  338. sig_size <- 10
  340. if (plot_stats$FDR_Brain_vs_50 < 0.05) {
  341. p_violin <- p_violin + annotate("text", x = 1, y = 105, label = "*", size = sig_size, color = brain_colorcode, fontface = "bold")
  342. }
  343. if (plot_stats$FDR_NonBrain_vs_50 < 0.05) {
  344. p_violin <- p_violin + annotate("text", x = 2, y = 105, label = "*", size = sig_size, color = nonbrain_colorcode, fontface = "bold")
  345. }
  346. if (plot_stats$FDR_Brain_vs_NonBrain < 0.05) {
  347. p_violin <- p_violin +
  348. annotate("segment", x = 1, xend = 2, y = 115, yend = 115, color = "grey50", linewidth = 1) +
  349. annotate("segment", x = 1, xend = 1, y = 110, yend = 115, color = "grey50", linewidth = 1) +
  350. annotate("segment", x = 2, xend = 2, y = 110, yend = 115, color = "grey50", linewidth = 1) +
  351. annotate("text", x = 1.5, y = 118, label = "*", size = sig_size, color = "grey50", fontface = "bold")
  352. }
  354. return(p_scatter + p_violin + plot_layout(widths = c(0.72, 0.28)))
  355. }
  357. # --- 3. Assemble Final Composite Panels ---
  358. y_label_text <- "functional pleiotropy\nnormalized by constraint (centiles)"
  360. pD_Del <- build_normative_plots(df_normative, "Tissue_Fantom60", "normative_model_centile_del_pleiotropy", "Del", df_stats_DE_final, y_label = y_label_text)
  361. pD_Dup <- build_normative_plots(df_normative, "Tissue_Fantom60", "normative_model_centile_dup_pleiotropy", "Dup", df_stats_DE_final, hide_y = TRUE)
  362. row_D <- pD_Del | pD_Dup
  364. pE_Del <- build_normative_plots(df_normative, "SC_HPA81", "normative_model_centile_del_pleiotropy", "Del", df_stats_DE_final, y_label = y_label_text)
  365. pE_Dup <- build_normative_plots(df_normative, "SC_HPA81", "normative_model_centile_dup_pleiotropy", "Dup", df_stats_DE_final, hide_y = TRUE)
  366. row_E <- pE_Del | pE_Dup
  368. p_single_stacked <- row_D / row_E
  369. print(p_single_stacked)
  370. ```
  372. ##### Panel D & E Stats #####
  373. ```{r, fig.width=6, fig.height=7, dpi=90}
  374. # --- Simplified and Robust Stats Calculation ---
  375. # Display the stats dataframe that was computed and utilized in the Figure chunk
  376. kable(df_stats_DE_final %>% select(Geneset_Type, Variant, starts_with("FDR"), starts_with("P")), align = "c", caption = "FDR-Adjusted Wilcoxon Tests for Normative Centiles (Panels D & E)")
  377. ```

Figure4.Rmd at commit 4415dfe, under MIT · at the source

Overview

Authors: Sayeh Kazem1, Kuldeep Kumar1, Jane Yang1, Florian Benitiere1, Guillaume Huguet1, Josephine Mollon2,3, Thomas Renne1, Laura M Schultz4,5,6, Emma E M Knowles2,3, Worrawat Engchuan7, Omar Shanta8, Bhooma Thiruvahindrapuram7, Jeffrey R MacDonald7, Celia M T Greenwood9,10, Stephen W Scherer7,11, Laura Almasy4,5,6, Jonathan Sebat8, David C Glahn2,3, Guillaume Dumas1,12, Sébastien Jacquemont1
ORCID iDs: Sayeh Kazem, Kuldeep Kumar, Florian Benitiere, Guillaume Huguet, Josephine Mollon, Thomas Renne, Laura M Schultz, Emma E M Knowles, Worrawat Engchuan, Bhooma Thiruvahindrapuram, Jeffrey R MacDonald, Celia M T Greenwood, Stephen W Scherer, David C Glahn, Guillaume Dumas, Sébastien Jacquemont
  1. Centre de recherche Azrieli, CHU Sainte-Justine and University of Montréal, Montréal, QC Canada
  2. Department of Psychiatr, Harvard Medical School, Boston, MA USA
  3. Tommy Fuss Center for Neuropsychiatric Disease Research, Boston Children’s Hospital, Boston, MA USA
  4. Department of Biomedical and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, PA USA
  5. Lifespan Brain Institute, Children’s Hospital of Philadelphia, and Penn Medicine, Philadelphia, PA USA
  6. Department of Genetics, University of Pennsylvania, Philadelphia, PA USA
  7. The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, ON Canada
  8. Department of Psychiatry, University of California San Diego, La Jolla, CA USA
  9. Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC Canada
  10. Gerald Bronfman Department of Oncology, Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC Canada
  11. Department of Molecular Genetics, University of Toronto, Toronto, ON Canada
  12. Mila – Quebec AI Institute, University of Montréal, Montreal, QC Canada
Institutions: Centre Hospitalier Universitaire Sainte-Justine (Canada); CHU Sainte-Justine Research Center (Canada); Université de Montréal (Canada); Boston Children's Hospital (United States); Harvard University (United States); Children's Hospital of Philadelphia (United States); University of Pennsylvania Health System (United States); University of Pennsylvania (United States); Hospital for Sick Children (Canada); Centre for Applied Genomics (Canada); University of California San Diego (United States); Jewish General Hospital (Canada); Lady Davis Institute for Medical Research (Canada); McGill University (Canada); University of Toronto (Canada); Mila - Quebec Artificial Intelligence Institute (Canada)
Journal: Nature communications, volume 17, issue 1, article 9798
Dates: received 6 November 2025; accepted 31 July 2026; published online 14 August 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-76676-0 · PMID 42736290 · PMCID PMC13575219 · OpenAlex W7203458115
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), cellular / molecular (subfield)
Methods: Statistics, Machine learning, Preprocessing, Connectivity
Keywords: Rare variants, Functional genomics, Structural variation
MeSH: Gene Dosage*, Genetic Pleiotropy*, DNA Copy Number Variations, Genome-Wide Association Study, Humans, Phenotype, Polymorphism, Single Nucleotide (* major topic)
Topic: Growth Hormone and Insulin-like Growth Factors (Endocrinology, Diabetes and Metabolism, Medicine), according to OpenAlex
Funding: Fondation Brain Canada; Compute Canada; Canada First Research Excellence Fund; Institut de Valorisation des Données; National Institutes of Health (1u01mh119690-01); Canadian Institutes of Health Research (CIHR_400528)
Citations: cited by 1 paper (Europe PMC); 91 references in the paper

Abstract

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martineaujeanlouis.github.io/mind-genesparallelcnv

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Found in: the text, “Genotyping and CNV calling”
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Availability: 1 check, the latest on 27 September 2026: the link is dead (HTTP 404)
  • 27 September 2026: the link is dead (HTTP 404)
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linnarsson-lab/adult-human-brain

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Size: 130 files, 113 scripts
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SayehKazem/FunBurd

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Languages: R (5), Jupyter (2), Python (1), Shell (1)
Size: 318 files, 9 scripts
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11 files
At the source: github.com/SayehKazem/FunBurd

Zenodo 17038354

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Tools: ggplot2 (4 files), ggpubr (4 files), tidyverse (4 files), reshape2 (2 files), circlize (1 file), ComplexHeatmap (1 file), data.table (1 file)
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6 files
At the source:

funburd.readthedocs.io

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At the source: funburd.readthedocs.io/en/latest/

Code availability statement

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Read it in the paper: doi.org/10.1038/s41467-026-76676-0.

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  • 5 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
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Data

Datasets cited

Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41467-026-76676-0.

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Version 2, 28 September 2026

  • Funding: added Fondation Brain Canada; Compute Canada; Canada First Research Excellence Fund; Institut de Valorisation des Données; National Institutes of Health: 1u01mh119690-01; Canadian Institutes of Health Research: CIHR_400528

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 20 authors, 3 keywords, 7 MeSH terms, 86 references.

Cite

This paper

Kazem, S., Kumar, K., Yang, J., Benitiere, F., Huguet, G., Mollon, J., Renne, T., Schultz, L. M., Knowles, E. E. M., Engchuan, W., Shanta, O., Thiruvahindrapuram, B., MacDonald, J. R., Greenwood, C. M. T., Scherer, S. W., Almasy, L., Sebat, J., Glahn, D. C., Dumas, G., & Jacquemont, S. (2026). Determinants of functional burden pleiotropy and gene dosage responses across human traits. Nature communications, 17(1), 9798. https://doi.org/10.1038/s41467-026-76676-0

BibTeX

@article{kazem2026determinants,
author = {Kazem, Sayeh and Kumar, Kuldeep and Yang, Jane and Benitiere, Florian and Huguet, Guillaume and Mollon, Josephine and Renne, Thomas and Schultz, Laura M and Knowles, Emma E M and Engchuan, Worrawat and Shanta, Omar and Thiruvahindrapuram, Bhooma and MacDonald, Jeffrey R and Greenwood, Celia M T and Scherer, Stephen W and Almasy, Laura and Sebat, Jonathan and Glahn, David C and Dumas, Guillaume and Jacquemont, Sébastien},
title = {{Determinants of functional burden pleiotropy and gene dosage responses across human traits}},
journal = {Nature communications},
year = {2026},
month = aug,
volume = {17},
number = {1},
pages = {9798},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-76676-0},
url = {https://doi.org/10.1038/s41467-026-76676-0},
pmid = {42736290},
pmcid = {PMC13575219}
}

RIS

TY - JOUR
AU - Kazem, Sayeh
AU - Kumar, Kuldeep
AU - Yang, Jane
AU - Benitiere, Florian
AU - Huguet, Guillaume
AU - Mollon, Josephine
AU - Renne, Thomas
AU - Schultz, Laura M
AU - Knowles, Emma E M
AU - Engchuan, Worrawat
AU - Shanta, Omar
AU - Thiruvahindrapuram, Bhooma
AU - MacDonald, Jeffrey R
AU - Greenwood, Celia M T
AU - Scherer, Stephen W
AU - Almasy, Laura
AU - Sebat, Jonathan
AU - Glahn, David C
AU - Dumas, Guillaume
AU - Jacquemont, Sébastien
TI - Determinants of functional burden pleiotropy and gene dosage responses across human traits
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/08/14
VL - 17
IS - 1
SP - 9798
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-76676-0
UR - https://doi.org/10.1038/s41467-026-76676-0
LA - en
ER -

CSL-JSON

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