OSCR

Offspring genetic diversity regulates rearing experiences that predict differential susceptibility to Chd8 haploinsufficiency.

Code ↔ Paper

7 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 7 matches
  1. [1] § Results › Offspring genetic background shapes individual differences in litter survival, B6-Chd8+/− maternal care, and nest quality ↔ data_visualization.ipynb, lines 270–282 · score 0.98 · B6 CC13, B6 CC16, B6 CC24, B6 CC28, B6 CC32, B6 CC44
  2. [2] § Results › Genetic background impacts the age to reach developmental milestones and pup activity levels during the third postnatal week ↔ data_visualization.ipynb, lines 270–282 · score 0.98 · B6 CC13, B6 CC16, B6 CC24, B6 CC28, B6 CC32, B6 CC44
  3. [3] § Results › Variation in the preweaning environment predicts postweaning traits and differential susceptibility to Chd8 haploinsufficiency ↔ data_visualization.ipynb, lines 25–38 · score 0.84 · adult body weights, fear extinction, pup crawl, DSI aggression, brain weight, fear expression
  4. [4] § Methods › Litter observations ↔ data_visualization.ipynb, lines 284–395 · score 0.83 · pup crawling, eye opening, Passive nursing, Active nursing, eating solid, dam resting
  5. [5] § Methods › Postweaning behavioral tests and weights ↔ data_visualization.ipynb, lines 199–225 · score 0.72 · Fear extinction, brain weights, body weights, Fear expression, Fear acquisition, room
  6. [6] § Methods › Statistics and reproducibility ↔ data_visualization.ipynb, lines 284–395 · score 0.69 · dam passive nursing, active nursing, Dam lick, Nest quality, nest build, outliers
  7. [7] § Results › Genetic background impacts the age to reach developmental milestones and pup activity levels during the third postnatal week ↔ data_visualization.ipynb, lines 172–196 · score 0.67 · pup eyes open, pup autogrooming, pup crawling, eating solid, R2, digging

Paper

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The authors' code

Jupyter notebook · 441 lines · 15 KB · no license · 7 matches

  1. # %% [markdown]
  2. # # SHAP Modeling
  3. # ## "Offspring genetic diversity regulates rearing experiences that predict differential susceptibility to Chd8 haploinsufficiency"
  5. # %%
  6. # Required Import Statements
  7. import pandas as pd
  8. import numpy as np
  9. import shap
  10. from sklearn.tree import DecisionTreeRegressor
  11. from sklearn.metrics import r2_score
  12. import matplotlib.pyplot as plt
  13. from matplotlib.ticker import FuncFormatter
  14. import matplotlib.image as mpimg
  15. import seaborn as sns
  16. from matplotlib.lines import Line2D
  18. # %% [markdown]
  19. # ## Data Cleaning
  21. # %%
  22. # Import Data
  23. chd8_het_effect = pd.read_excel('chd8_het_effect.xlsx')
  25. # %%
  26. # Rename Columns for chd8 het effects dataframe
  27. chd8_het_effect = chd8_het_effect.rename(columns={'Weaning body weight': 'Weaning Body Weight', 'Adolescent body weight':'Adolescent Body Weight',
  28. 'Adult body weight':'Adult Body Weight', 'Terminal body weight':'Terminal Body Weight', 'Brain weight':'Brain Weight',
  29. 'DOF distance':'DOF Distance', 'DSI aggression':'DSI Aggression', 'DSI sniffing':'DSI Sniffing',
  30. 'BOF % center distance': 'BOF % Center Distance', 'Fear acquisition':'Fear Acquisition', 'Fear expression':'Fear Expression',
  31. 'Fear extinction': 'Fear Extinction'})
  33. chd8_het_effect = chd8_het_effect.rename(columns={'Nest quality':'Nest Quality', 'dam_active_nurse': 'Dam Active Nurse', 'dam run': 'Dam Run',
  34. 'dam_eat':'Dam Eat', 'dam_lick': 'Dam Lick', 'dam_nest_build': 'Dam Nest Build', 'dam_outnest': 'Dam Out of Nest',
  35. 'dam_pass nurse': 'Dam Passive Nurse', 'dam_rest':'Dam Rest', 'total nurse': 'Total Nurse',
  36. 'Pup_climb': 'Pup Climb', 'Pup_crawl/run': 'Pup Crawl/Run', 'Pup dig': 'Pup Dig', 'Pup autogroom':'Pup Autogroom',
  37. 'pup_eat solid':'Pup Eat Solid', 'pup_eyes open':'Pup Eyes Open', 'pup jump': 'Pup Jump', 'pup L/G':'Pup L/G',
  38. 'pup nest build':'Pup Nest Build', 'Litter size':'Litter Size'})
  40. # %%
  41. # List of target variables
  42. target_variables = ['Weaning Body Weight', 'Adolescent Body Weight',
  43. 'Adult Body Weight', 'Terminal Body Weight', 'Brain Weight',
  44. 'DOF Distance', 'DSI Aggression', 'DSI Sniffing',
  45. 'BOF % Center Distance', 'Fear Acquisition', 'Fear Expression',
  46. 'Fear Extinction']
  48. # %% [markdown]
  49. # ## Compute SHAP Models
  51. # %%
  52. def get_shap_tree(x, y):
  53. # Step 1: Fit a decision tree
  54. tree = DecisionTreeRegressor(random_state=42, max_depth=5)
  55. tree.fit(x, y)
  57. # Step 2: Compute SHAP values
  58. explainer = shap.TreeExplainer(tree)
  59. shap_values = explainer.shap_values(x)
  61. # Step 3: Get mean absolute SHAP values for feature importance
  62. shap_importance = np.abs(shap_values).mean(axis=0)
  64. shap_df = pd.DataFrame({
  65. 'feature': x.columns,
  66. 'importance': shap_importance
  67. }).sort_values(by='importance', ascending=False)
  69. # Step 4: Filter features with SHAP < 0.01 and keep top 5 (plus 'Sex')
  70. filtered_features = shap_df[shap_df['importance'] > 0.01].head(6)['feature'].tolist()
  71. if 'Sex' not in filtered_features:
  72. filtered_features.append('Sex')
  73. x_filtered = x[filtered_features]
  75. # Step 5: Refit the decision tree with filtered features
  76. tree_filtered = DecisionTreeRegressor(random_state=42, max_depth=5)
  77. tree_filtered.fit(x_filtered, y)
  79. # Step 6: Compute SHAP values again for the filtered model
  80. explainer_filtered = shap.TreeExplainer(tree_filtered)
  81. shap_values_filtered = explainer_filtered.shap_values(x_filtered)
  83. return shap_values_filtered, x_filtered, tree_filtered
  85. # %% [markdown]
  86. # ## SHAP Plotting
  88. # %%
  89. def graph_with_r2_and_clustering_sex_shaded(shap_vals, x, y, title, grad_boost):
  90. """
  91. Generate SHAP bar plot with sex cohorts and display R^2 value.
  93. Parameters:
  94. shap_vals: SHAP values
  95. x: Features dataframe
  96. y: Target variable series
  97. title: Title for the plot
  98. grad_boost: Trained model
  99. """
  100. ############## SHAP CALCULATIONS ##############
  102. # Calculate R2
  103. r2 = r2_score(y, grad_boost.predict(x))
  105. # Define sex cohorts for shading
  106. sex = ['Male' if x.iloc[i]['Sex'] == 0 else 'Female' for i in range(x.shape[0])]
  108. # Exclude 'Sex' column from features
  109. x_no_sex = x.drop(columns=['Sex'])
  111. # Filter SHAP values to exclude 'Sex'
  112. feature_names = x.columns.tolist()
  113. valid_features = [name for name in feature_names if name != 'Sex']
  114. feature_indices = [x.columns.get_loc(name) for name in valid_features]
  115. shap_vals_no_sex = shap_vals[:, feature_indices]
  117. shap_values_explanation = shap.Explanation(values=shap_vals_no_sex,
  118. data=x_no_sex,
  119. feature_names=x_no_sex.columns)
  121. clustering = shap.utils.hclust(x_no_sex, y)
  123. ############## PLOTTING ##############
  124. fig = plt.figure(figsize=[12,8]) # Explicit figure size
  125. ax = fig.add_subplot(111)
  126. ax.xaxis.set_major_formatter(FuncFormatter(lambda x, pos: f"{x:.2f}"))
  128. # Create the SHAP bar plot with sex cohorts
  129. shap.plots.bar(
  130. shap_values_explanation.cohorts(sex).abs.mean(0),
  131. clustering=clustering,
  132. clustering_cutoff=1,
  133. max_display=10,
  134. show=False,
  135. ax=ax,
  137. )
  139. ############## MANAGE LABEL FORMATTING ##############
  141. for text in ax.get_xticklabels() + ax.get_yticklabels():
  142. text.set_fontsize(24)
  144. for text in ax.get_xticklabels():
  145. text.set_text
  147. xlabel = ax.get_xlabel()
  148. if xlabel:
  149. ax.set_xlabel("Mean |SHAP|", fontsize=24)
  151. for child in ax.get_children():
  152. if hasattr(child, 'set_fontsize'):
  153. child.set_fontsize(24)
  155. legend = ax.get_legend()
  156. if legend is not None:
  157. if legend.get_title() is not None:
  158. legend.get_title().set_fontsize(20)
  159. for text in legend.get_texts():
  160. text.set_fontsize(20)
  162. ax.set_title(f'{title}\nR² = {r2:,.2f}', y=1, fontsize=24)
  164. ############## FINAL PLOTTING ##############
  166. # Save the plot with lower dpi to reduce file size
  167. plt.savefig(f'Images/{title}.png', bbox_inches='tight')
  169. # Display the plot
  170. plt.show()
  172. # %% [markdown]
  173. # ## Display SHAP Plots
  175. # %%
  176. for target in target_variables:
  177. # Define target variable
  178. y = chd8_het_effect[target]
  179. x = chd8_het_effect[['Nest Quality', 'Dam Active Nurse', 'Dam Run',
  180. 'Dam Eat', 'Dam Lick', 'Dam Nest Build', 'Dam Out of Nest',
  181. 'Dam Passive Nurse', 'Dam Rest', 'Total Nurse', 'Dam_PC1', 'Dam_PC2',
  182. 'Pup Climb', 'Pup Crawl/Run', 'Pup Dig', 'Pup Autogroom',
  183. 'Pup Eat Solid', 'Pup Eyes Open', 'Pup Jump', 'Pup L/G',
  184. 'Pup Nest Build', 'Litter Size', '% Chd8 Hets', '% Males', 'Sex']]
  186. # Delete parts of SHAP tree with values of ~0
  187. x_filtered = x
  188. zeros = True
  189. while zeros:
  190. shap_values_filtered, x_filtered, tree_filtered = get_shap_tree(x_filtered, y)
  191. if np.all(np.abs(shap_values_filtered).mean(axis=0) >= 0.01):
  192. zeros = False
  194. # Call the graphing function
  195. graph_with_r2_and_clustering_sex_shaded(shap_values_filtered, x_filtered, y, title=target, grad_boost=tree_filtered)
  196. plt.show()
  199. # %%
  200. ################## COMBINE PLOTS ##################
  201. fig, axes = plt.subplots(4, 3, figsize=(16, 12))
  202. images = [['Weaning Body Weight', 'Adolescent Body Weight',
  203. 'Adult Body Weight'], ['Terminal Body Weight', 'Brain Weight',
  204. 'DOF Distance'], ['DSI Aggression', 'DSI Sniffing',
  205. 'BOF % Center Distance'], ['Fear Acquisition', 'Fear Expression',
  206. 'Fear Extinction']]
  208. # Loop over the rows and columns to display images
  209. for row in [0, 1, 2, 3]:
  211. for column in [0, 1, 2]:
  212. ax = axes[row, column]
  213. ax.axis('off')
  214. image_name = images[row][column]
  215. img = mpimg.imread(f'Images/{image_name}.png')
  216. ax.imshow(img)
  218. # Adjust layout to make room for the figure title
  219. plt.tight_layout()
  220. # Save the figure with higher resolution
  221. plt.savefig('Images/decision_tree_shap.png', dpi=300, bbox_inches='tight')
  222. plt.rc('font', size=14)
  224. # Display the plot
  225. plt.show()
  228. # %% [markdown]
  229. # ## Distribution Plots
  231. # %%
  232. def get_final_cohort_df(cohorts, valid_features):
  233. # Create initial lists/dicts
  234. data = {feat: [] for feat in valid_features}
  235. data['Sex'] = []
  236. sexes = [k for k in cohorts.cohorts.keys() if k in ("Male", "Female")]
  238. # Add features and values to lists/dicts
  239. for sex in sexes:
  240. rows = cohorts.cohorts[sex].values
  241. for pup in rows:
  242. length = len(pup)
  243. for idx, feat in enumerate(valid_features):
  244. if idx < length:
  245. data[feat].append(pup[idx])
  246. data['Sex'].append(sex)
  248. return pd.DataFrame(data)
  250. # %%
  251. def get_cohorts(x_filtered, shap_values_filtered):
  252. # Define sex cohorts for shading
  253. sex = ['Male' if x_filtered.iloc[i]['Sex'] == 0 else 'Female' for i in range(x_filtered.shape[0])]
  255. # Exclude 'Sex' column from features
  256. x_no_sex = x_filtered.drop(columns=['Sex'])
  258. # Filter SHAP values to exclude 'Sex'
  259. feature_names = x_filtered.columns.tolist()
  260. valid_features = [name for name in feature_names if name != 'Sex']
  261. feature_indices = [x_filtered.columns.get_loc(name) for name in valid_features]
  262. shap_vals_no_sex = shap_values_filtered[:, feature_indices]
  264. shap_values_explanation = shap.Explanation(values=shap_vals_no_sex,
  265. data=x_no_sex,
  266. feature_names=x_no_sex.columns)
  268. return shap_values_explanation.cohorts(sex).abs
  270. # %%
  271. all_strains = ['B6-B6', 'B6-CC1', 'B6-CC2', 'B6-CC7', 'B6-CC10', 'B6-CC12',
  272. 'B6-CC13', 'B6-CC16', 'B6-CC22', 'B6-CC24', 'B6-CC25',
  273. 'B6-CC28', 'B6-CC32', 'B6-CC44', 'B6-CC57', 'B6-CC75']
  274. color_palette = ['#8ebaee', '#b3efee', '#5879a3', '#bec0bf', '#ac2e32',
  275. '#cc615d', '#e98733', '#5a8d53', '#e49fb6', '#c5905e',
  276. '#f5db7a', '#0b195d', '#726967', '#955fea', '#d5cea5',
  277. '#7cb872']
  278. # Same palette used earlier
  279. palette = dict(zip(
  280. all_strains,
  281. sns.color_palette(color_palette)
  282. ))
  284. # %%
  285. for target in target_variables:
  286. y = chd8_het_effect[target]
  287. x = chd8_het_effect[['Nest Quality', 'Dam Active Nurse', 'Dam Run',
  288. 'Dam Eat', 'Dam Lick', 'Dam Nest Build', 'Dam Out of Nest',
  289. 'Dam Passive Nurse', 'Dam Rest', 'Total Nurse', 'Dam_PC1', 'Dam_PC2',
  290. 'Pup Climb', 'Pup Crawl/Run', 'Pup Dig', 'Pup Autogroom',
  291. 'Pup Eat Solid', 'Pup Eyes Open', 'Pup Jump', 'Pup L/G',
  292. 'Pup Nest Build', 'Litter Size', '% Chd8 Hets', '% Males', 'Sex']]
  293. # --- Ensure Strain is present (add it to cohort_df if it's in the original x) ---
  294. # If chd8_het_effect contains 'Strain', grab it before dropping/transforming
  295. # Delete parts of SHAP tree with values of ~0
  296. x_filtered = x
  297. zeros = True
  298. while zeros:
  299. shap_values_filtered, x_filtered, tree_filtered = get_shap_tree(x_filtered, y)
  300. if np.all(np.abs(shap_values_filtered).mean(axis=0) >= 0.01):
  301. zeros = False
  303. # Get cohorts
  304. cohorts = get_cohorts(x_filtered, shap_values_filtered)
  305. feature_names = x_filtered.columns.tolist()
  306. valid_features = [name for name in feature_names if name != 'Sex']
  307. cohort_df = get_final_cohort_df(cohorts, valid_features)
  308. if 'Strain' in chd8_het_effect.columns and 'Strain' not in cohort_df.columns:
  309. # assume cohort_df index aligns with chd8_het_effect index; adjust merge as needed
  310. cohort_df = cohort_df.merge(chd8_het_effect[['Strain']], left_index=True, right_index=True, how='left')
  312. # Make dataframe long-format and keep Strain as an id_var
  313. long_df = cohort_df.melt(id_vars=['Sex','Strain'],
  314. value_vars=valid_features,
  315. var_name='Feature',
  316. value_name='Value')
  318. # Helper to extract outliers by Feature using IQR rule
  319. def find_outliers(df):
  320. def _outliers(group):
  321. q1 = group['Value'].quantile(0.25)
  322. q3 = group['Value'].quantile(0.75)
  323. iqr = q3 - q1
  324. lower = q1 - 1.5 * iqr
  325. upper = q3 + 1.5 * iqr
  326. return group[(group['Value'] < lower) | (group['Value'] > upper)]
  327. return df.groupby('Feature', group_keys=False).apply(_outliers)
  329. # Plot: one column per Sex so box grouping remains by Feature but outliers are colored by Strain
  330. sexes = sorted(long_df['Sex'].unique())
  331. ncols = 2
  332. fig, axes = plt.subplots(1, ncols, figsize=(6 * ncols, 6), sharey=True)
  334. # Store legend handles once
  335. legend_handles = None
  337. for ax, sex in zip(axes, sexes):
  338. subset = long_df[long_df['Sex'] == sex]
  340. sns.boxplot(
  341. data=subset,
  342. x='Feature',
  343. y='Value',
  344. showfliers=False,
  345. ax=ax,
  346. color='gray'
  347. )
  349. outliers = find_outliers(subset)
  350. strain_order = all_strains
  352. sns.stripplot(
  353. data=outliers,
  354. x='Feature',
  355. y='Value',
  356. hue='Strain',
  357. hue_order=strain_order,
  358. dodge=False,
  359. jitter=0.25,
  360. ax=ax,
  361. size=6,
  362. marker='o',
  363. edgecolor='gray',
  364. linewidth=0.3
  365. )
  367. # Grab handles only once (from first axis)
  368. if legend_handles is None:
  369. legend_handles, legend_labels = ax.get_legend_handles_labels()
  371. # Remove axis-level legend
  372. if ax.legend_:
  373. ax.legend_.remove()
  375. ax.set_title(f'{target} — Sex: {sex}')
  376. ax.set_xlabel('')
  377. ax.tick_params(axis='x', rotation=45)
  379. plt.ylabel('|SHAP Value|')
  381. # ✅ Add ONE legend to the full figure
  382. if legend_handles:
  383. legend = fig.legend(
  384. legend_handles,
  385. legend_labels,
  386. title='Strain',
  387. loc='center right',
  388. bbox_to_anchor=(1.02, 0.5)
  389. )
  391. legend.remove()
  393. plt.tight_layout(rect=[0, 0, 0.88, 1]) # leave space for legend
  394. plt.savefig(f'Images/boxplot_{target}_outliers_by_strain.png', bbox_inches='tight')
  395. plt.show()
  397. # %%
  398. legend_elements = [
  399. Line2D([0], [0],
  400. marker='o',
  401. color='w',
  402. label=strain,
  403. markerfacecolor=palette[strain],
  404. markeredgecolor='gray',
  405. markersize=8)
  406. for strain in all_strains
  407. ]
  409. # %%
  410. ################## COMBINE PLOTS ##################
  411. fig, axes = plt.subplots(6, 2, figsize=(14, 24))
  412. images = [['Weaning Body Weight', 'Adolescent Body Weight'],
  413. ['Adult Body Weight', 'Terminal Body Weight'], ['Brain Weight',
  414. 'DOF Distance'], ['DSI Aggression', 'DSI Sniffing'],
  415. ['BOF % Center Distance', 'Fear Acquisition'], ['Fear Expression',
  416. 'Fear Extinction']]
  417. plt.subplots_adjust(wspace=0.01)
  418. # Loop over the rows and columns to display images
  419. for row in [0, 1, 2, 3, 4, 5]:
  420. for column in [0, 1]:
  421. ax = axes[row, column]
  422. ax.axis('off')
  423. image_name = images[row][column]
  424. img = mpimg.imread(f'Images/boxplot_{image_name}_outliers_by_strain.png')
  425. ax.imshow(img)
  427. plt.subplots_adjust(right=0.90)
  428. plt.rc('font', size=14)
  430. fig.legend(
  431. handles=legend_elements,
  432. title='Strain',
  433. loc='center right',
  434. bbox_to_anchor=(1.02, 0.5)
  435. )
  436. # Save the figure with higher resolution
  437. plt.savefig('Images/boxplot_shap.png', dpi=300, bbox_inches='tight')
  440. # Display the plot
  441. plt.show()

data_visualization.ipynb at commit 840aae5, no license · at the source

Overview

Authors: Manal Tabbaa1,2, Alexis Gamez1, A’di Dust3, Maja Matarić3, Pat Levitt1,2
ORCID iDs: Manal Tabbaa, A’di Dust
  1. Division of Neurology, Department of Pediatrics and Developmental Neuroscience and Neurogenetics Program, Children’s Hospital Los Angeles, The Saban Research Institute,Los Angeles, CA USA
  2. Keck School of Medicine of the University of Southern California,Los Angeles, CA USA
  3. Department of Computer Science, University of Southern California,Los Angeles, CA USA
Institutions: Saban Research Institute (United States); University of Southern California (United States)
Journal: Communications biology, volume 9, issue 1, article 1028
Dates: received 27 June 2025; accepted 10 March 2026; published online 11 May 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s42003-026-09908-0 · PMID 42115306 · PMCID PMC13424357 · OpenAlex W4408096176
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.21203/rs.3.rs-6058389/v1
Status: code verified
Categories: mouse (organism), other condition (population), autism (population), developmental (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Preprocessing, Connectivity, Physiology & signal measures
Keywords: Autism spectrum disorders, Behavioural genetics, Disease model
MeSH: DNA-Binding Proteins*, Genetic Predisposition to Disease*, Genetic Variation*, Haploinsufficiency*, Neurodevelopmental Disorders*, Animals, Behavior, Animal, Female, Male, Maternal Behavior, Mice, Phenotype (* major topic)
Topic: Autism Spectrum Disorder Research (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Institute of Mental Health (R21MH118685); NSF (DBI2011039); Saban Research Institute, Children's Hospital Los Angeles
Citations: cited by 1 paper (Europe PMC); 41 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

Its files are read in the Code ↔ Paper reader above, with 7 matches between paragraphs and lines of code.

adidust4/Offspring-genetic-diversity-Chd8-haploinsufficiency

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 840aae596ae66870d0703c15dc195c65305732ac, 21 February 2026
Languages: Jupyter (1)
Size: 43 files, 1 script
Software Heritage: not archived
Found in: “Code availability”
Holds: README, 1 notebook
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (1 file), NumPy (1 file), pandas (1 file), scikit-learn (1 file), seaborn (1 file), SHAP (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
2 files
At the source: github.com/adidust4/Offspring-genetic-diversity-Chd8-haploinsufficiency

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Read it in the paper: doi.org/10.1038/s42003-026-09908-0.

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Data

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  • it points to a dataset: figshare 31395966
  • it says that the data are available on request

Read it in the paper: doi.org/10.1038/s42003-026-09908-0.

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 3 keywords, 12 MeSH terms, 3 funders, 39 references.

Cite

This paper

Tabbaa, M., Gamez, A., Dust, A., Matarić, M., & Levitt, P. (2026). Offspring genetic diversity regulates rearing experiences that predict differential susceptibility to Chd8 haploinsufficiency. Communications biology, 9(1), 1028. https://doi.org/10.1038/s42003-026-09908-0

BibTeX

@article{tabbaa2026offspring,
author = {Tabbaa, Manal and Gamez, Alexis and Dust, A’di and Matarić, Maja and Levitt, Pat},
title = {{Offspring genetic diversity regulates rearing experiences that predict differential susceptibility to Chd8 haploinsufficiency}},
journal = {Communications biology},
year = {2026},
month = may,
volume = {9},
number = {1},
pages = {1028},
publisher = {Nature Publishing Group},
issn = {2399-3642},
doi = {10.1038/s42003-026-09908-0},
url = {https://doi.org/10.1038/s42003-026-09908-0},
pmid = {42115306},
pmcid = {PMC13424357}
}

RIS

TY - JOUR
AU - Tabbaa, Manal
AU - Gamez, Alexis
AU - Dust, A’di
AU - Matarić, Maja
AU - Levitt, Pat
TI - Offspring genetic diversity regulates rearing experiences that predict differential susceptibility to Chd8 haploinsufficiency
T2 - Communications biology
J2 - Commun Biol
PY - 2026
DA - 2026/05/11
VL - 9
IS - 1
SP - 1028
SN - 2399-3642
PB - Nature Publishing Group
DO - 10.1038/s42003-026-09908-0
UR - https://doi.org/10.1038/s42003-026-09908-0
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s42003-026-09908-0",
"type": "article-journal",
"title": "Offspring genetic diversity regulates rearing experiences that predict differential susceptibility to Chd8 haploinsufficiency",
"container-title": "Communications biology",
"author": [
{
"family": "Tabbaa",
"given": "Manal"
},
{
"family": "Gamez",
"given": "Alexis"
},
{
"family": "Dust",
"given": "A’di"
},
{
"family": "Matarić",
"given": "Maja"
},
{
"family": "Levitt",
"given": "Pat"
}
],
"container-title-short": "Commun Biol",
"volume": "9",
"issue": "1",
"page": "1028",
"DOI": "10.1038/s42003-026-09908-0",
"PMID": "42115306",
"PMCID": "PMC13424357",
"ISSN": "2399-3642",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s42003-026-09908-0",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
11
]
]
}
}

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