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Autism Spectrum Disorder-Associated Genes Enrich in Discrete Cortical, Limbic and Cerebellar Brain Regions.

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Paper

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

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The authors' code

Python · 118 lines · 4.3 KB · no license

  1. import os
  2. import pandas as pd
  3. import numpy as np
  4. from tqdm import tqdm
  5. from numba import jit
  6. import warnings
  8. CONFIG = {
  9. 'working_directory': 'C:/Users/godierna/Datafiles',
  10. 'rna_brain_file': 'rna_brain_HPA.tsv',
  11. 'regional_expression_file': 'HPA_RegionalGeneExpression_2025.csv',
  12. 'target_genes_file': 'target_genes.csv',
  13. 'target_genes_column': 'GENES',
  14. 'percentile_threshold': 95,
  15. 'n_target_genes':, # input lenth of gene list here
  16. 'n_simulations': 1000000,
  17. 'chunk_size': 1000,
  18. 'confidence_multiplier': 3.471,
  19. 'output_filtered_genes': 'filtered_target_genes.csv',
  20. 'output_frequencies': 'observed_frequencies.csv',
  21. 'output_final_results': 'enrichment_analysis_results.csv',
  22. }
  24. os.chdir(CONFIG['working_directory'])
  26. Data_raw = pd.read_table(CONFIG['rna_brain_file'])
  27. nTPM = Data_raw.drop(['Gene', 'TPM', 'pTPM'], axis=1)
  28. BrainRegions_temp = nTPM['Subregion'].unique()
  29. all_genes = nTPM['Gene name'].unique()
  30. filtered_data = []
  31. for gene in tqdm(all_genes, desc="Filtering genes"):
  32. gene_data = nTPM[nTPM['Gene name'] == gene].copy()
  33. threshold = np.percentile(gene_data['nTPM'], CONFIG['percentile_threshold'])
  34. gene_filtered = gene_data[gene_data['nTPM'] >= threshold]
  35. filtered_data.append(gene_filtered)
  36. Result_preprocessed = pd.concat(filtered_data, ignore_index=True)
  37. Result_preprocessed.to_csv(CONFIG['regional_expression_file'], index=False)
  40. @jit(nopython=True)
  41. def count_frequencies_numba(subregion_indices, brain_region_indices, n_regions):
  42. counts = np.zeros(n_regions)
  43. for idx in range(len(subregion_indices)):
  44. region_idx = brain_region_indices[idx]
  45. if 0 <= region_idx < n_regions:
  46. counts[region_idx] += 1
  47. return counts
  50. Data = pd.read_table(CONFIG['rna_brain_file'])
  51. Result = pd.read_csv(CONFIG['regional_expression_file'])
  52. target_genes = pd.read_csv(CONFIG['target_genes_file'])
  54. BrainRegions = Result['Subregion'].unique()
  55. region_to_idx = {region: idx for idx, region in enumerate(BrainRegions)}
  57. Target_Genes = Result[Result['Gene name'].isin(target_genes[CONFIG['target_genes_column']])]
  58. Target_Genes.to_csv(CONFIG['output_filtered_genes'])
  60. n_genes = CONFIG['n_target_genes']
  62. target_subregion_indices = np.array([region_to_idx.get(region, -1) for region in Target_Genes['Subregion']])
  63. brain_region_indices = np.array([region_to_idx[region] for region in Target_Genes['Subregion']])
  64. numbers = count_frequencies_numba(target_subregion_indices, brain_region_indices, len(BrainRegions))
  66. freqdata = pd.DataFrame({
  67. 'Frequency': numbers,
  68. 'BrainRegion': BrainRegions
  69. })
  70. freqdata.to_csv(CONFIG['output_frequencies'])
  72. humangenome = Data['Gene name'].unique()
  73. result_genes = Result['Gene name'].unique()
  74. gene_to_idx = {gene: idx for idx, gene in enumerate(result_genes)}
  76. gene_subregions = {}
  77. for gene in tqdm(humangenome, desc="Building gene mappings"):
  78. gene_rows = Result[Result['Gene name'] == gene]
  79. if not gene_rows.empty:
  80. subregions = gene_rows['Subregion'].values
  81. gene_subregions[gene] = [region_to_idx.get(sr, -1) for sr in subregions]
  83. n_sets = CONFIG['n_simulations']
  84. chunk_size = CONFIG['chunk_size']
  85. results = np.zeros((len(BrainRegions), n_sets))
  87. for chunk_start in tqdm(range(0, n_sets, chunk_size), desc="Processing chunks"):
  88. chunk_end = min(chunk_start + chunk_size, n_sets)
  89. chunk_size_actual = chunk_end - chunk_start
  91. for i in range(chunk_size_actual):
  92. random_genes = np.random.choice(humangenome, size=n_genes, replace=False)
  93. chunk_counts = np.zeros(len(BrainRegions))
  95. for gene in random_genes:
  96. if gene in gene_subregions:
  97. for region_idx in gene_subregions[gene]:
  98. if region_idx >= 0:
  99. chunk_counts[region_idx] += 1
  101. results[:, chunk_start + i] = chunk_counts
  103. means = np.mean(results, axis=1)
  104. stdevs = np.std(results, axis=1)
  106. output = pd.DataFrame({
  107. 'BrainRegion': freqdata['BrainRegion'],
  108. 'Means': means,
  109. 'Stdevs': stdevs,
  110. 'MOE': stdevs * CONFIG['confidence_multiplier'],
  111. })
  113. output['Upper'] = dff['Means'] + dff['MOE']
  114. output['Lower'] = dff['Means'] - dff['MOE']
  115. output['Observed'] = freqdata['Frequency']
  116. output['SigHigh'] = dff['Observed'] > dff['Upper']
  118. dff.to_csv(CONFIG['output_final_results'])

ATLANTE.py at commit 17feb39, no license · at the source

Overview

Authors: G. Lorenzo Odierna1, Vicki Bitsika1, Christopher F. Sharpley1
ORCID iDs: G. Lorenzo Odierna, Vicki Bitsika, Christopher F. Sharpley
  1. Brain‐Behavior Research Group University of New England Armidale New South Wales Australia
Institutions: University of New England (Australia)
Journal: The European journal of neuroscience, volume 64, issue 2, article e70638
Dates: received 9 December 2025; accepted 2 July 2026; published online 24 July 2026; in print July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1111/ejn.70638 · PMID 42499009 · PMCID PMC13400753 · OpenAlex W7171186436
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), other condition (population), autism (population), cellular / molecular (subfield)
Methods: Statistics, Graphs
Keywords: autism spectrum disorder, biomarkers, brain regions, early diagnosis, gene expression, neurodevelopmental disorder, synaptic dysfunction
MeSH: Autism Spectrum Disorder*, Cerebellum*, Cerebral Cortex*, Limbic System*, Female, Humans, Male (* major topic)
Topic: Autism Spectrum Disorder Research (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Citations: cited by 1 paper (Europe PMC); 78 references in the paper

Abstract

Autism spectrum disorder (ASD) is a highly heritable neurodevelopmental condition with a well‐characterised genetic architecture, yet how ASD‐associated genes map onto discrete brain regions remains poorly understood. This study applied a recently validated analysis pipeline (ATLANTE) to discover regions of the human brain enriched for high expression of 234 high‐confidence ASD‐associated genes. Eleven discrete brain regions were identified, spanning cortical, limbic and cerebellar systems including the anterior orbitofrontal gyrus, cerebellar cortex, flocculonodular lobe, vermis, posterior cingulate cortex, temporo‐occipital transitional zone, hippocampal subfields CA1 and CA3, postcentral gyrus, occipital cortex and perirhinal gyrus. Notably, the anterior orbitofrontal gyrus exhibited significant enrichment for syndromic ASD genes, suggesting a core role in the neuropsychiatric features of syndromic presentations. Network and community clustering analyses revealed three major gene communities corresponding to distinct biological processes: synaptic dysfunction in limbic regions, histone modification in cerebellar regions and axonal ion channel regulation in cortical regions. Nodal analysis identified eight high‐priority genes with broad relevance across multiple brain regions, including NR3C2, GABRB2 and NBEA. These findings provide a refined neuroanatomical framework for ASD pathophysiology, identify understudied brain regional targets and imply that ASD‐associated genes exert primary effects in specific brain regions.

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

Repositories

Its files are read in the Code ↔ Paper reader above.

LorenzoOdierna/Analysis-Tool-for-Local-Association-of-Neuronal-Transcript-Expression-ATLANTE

License: none: the authors keep all their rights
State: the link is dead, verified on 27 September 2026
Evidence: found in the paper
Software Heritage: not archived
Found in: the text, “Analysis Using the ATLANTE Pipeline”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link is dead
  • 27 September 2026: the link is dead
At the source: github.com/LorenzoOdierna/Analysis-Tool-for-Local-Association-of-Neuronal-Transcript-Expression-ATLANTE

LorenzoOdierna/Analysis-Tool-for-Local-Association-of-Neuronal-Transcript-Expression-ATLANTE-

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 17feb3948a4905e6a7f4275c97f95bc80455c2e1, 10 December 2025
Languages: Python (1)
Size: 2 files, 1 script
Software Heritage: not archived
Found in: the text, “Analysis Using the ATLANTE Pipeline”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Numba (1 file), NumPy (1 file), pandas (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
2 files
At the source: github.com/LorenzoOdierna/Analysis-Tool-for-Local-Association-of-Neuronal-Transcript-Expression-ATLANTE-

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:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 1 script, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • 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

Data used in this study were derived from the following resources available in the public domain: Human Proteome Atlas, www.proteinatlas.org; SFARI GENE, https://gene.sfari.org/.

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

Versions

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Version 2, 28 September 2026

  • Publisher: n/a → Wiley

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 7 keywords, 7 MeSH terms, 73 references.

Cite

This paper

Odierna, G. L., Bitsika, V., & Sharpley, C. F. (2026). Autism Spectrum Disorder-Associated Genes Enrich in Discrete Cortical, Limbic and Cerebellar Brain Regions. The European journal of neuroscience, 64(2), e70638. https://doi.org/10.1111/ejn.70638

BibTeX

@article{odierna2026autism,
author = {Odierna, G. Lorenzo and Bitsika, Vicki and Sharpley, Christopher F.},
title = {{Autism Spectrum Disorder-Associated Genes Enrich in Discrete Cortical, Limbic and Cerebellar Brain Regions}},
journal = {The European journal of neuroscience},
year = {2026},
month = jul,
volume = {64},
number = {2},
pages = {e70638},
publisher = {Wiley},
issn = {0953-816X},
doi = {10.1111/ejn.70638},
url = {https://doi.org/10.1111/ejn.70638},
pmid = {42499009},
pmcid = {PMC13400753}
}

RIS

TY - JOUR
AU - Odierna, G. Lorenzo
AU - Bitsika, Vicki
AU - Sharpley, Christopher F.
TI - Autism Spectrum Disorder-Associated Genes Enrich in Discrete Cortical, Limbic and Cerebellar Brain Regions
T2 - The European journal of neuroscience
J2 - Eur J Neurosci
PY - 2026
DA - 2026/07/01
VL - 64
IS - 2
SP - e70638
SN - 0953-816X
PB - Wiley
DO - 10.1111/ejn.70638
UR - https://doi.org/10.1111/ejn.70638
LA - en
ER -

CSL-JSON

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"family": "Odierna",
"given": "G. Lorenzo"
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"given": "Christopher F."
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"container-title-short": "Eur J Neurosci",
"volume": "64",
"issue": "2",
"page": "e70638",
"DOI": "10.1111/ejn.70638",
"PMID": "42499009",
"PMCID": "PMC13400753",
"ISSN": "0953-816X",
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"date-parts": [
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1
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}

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