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Brain patterns linked to neuropsychiatric genetic risk mirror those seen in disease.

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Paper

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The authors' code

Python · 120 lines · 4.9 KB · MIT

  1. #!/usr/bin/env python
  3. """
  4. PRS-CS: a polygenic prediction method that infers posterior SNP effect sizes under continuous shrinkage (CS) priors
  5. using GWAS summary statistics and an external LD reference panel.
  7. Reference: T Ge, CY Chen, Y Ni, YCA Feng, JW Smoller. Polygenic Prediction via Bayesian Regression and Continuous Shrinkage Priors.
  8. Nature Communications, 10:1776, 2019.
  11. Usage:
  12. python PRScs.py --ref_dir=PATH_TO_REFERENCE --bim_prefix=VALIDATION_BIM_PREFIX --sst_file=SUM_STATS_FILE --n_gwas=GWAS_SAMPLE_SIZE --out_dir=OUTPUT_DIR
  13. [--a=PARAM_A --b=PARAM_B --phi=PARAM_PHI --n_iter=MCMC_ITERATIONS --n_burnin=MCMC_BURNIN --thin=MCMC_THINNING_FACTOR
  14. --chrom=CHROM --write_psi=WRITE_PSI --write_pst=WRITE_POSTERIOR_SAMPLES --seed=SEED]
  16. """
  19. import os
  20. import sys
  21. import getopt
  23. import parse_genet
  24. import mcmc_gtb
  25. import gigrnd
  28. def parse_param():
  29. long_opts_list = ['ref_dir=', 'bim_prefix=', 'sst_file=', 'a=', 'b=', 'phi=', 'n_gwas=',
  30. 'n_iter=', 'n_burnin=', 'thin=', 'out_dir=', 'chrom=', 'beta_std=', 'write_psi=', 'write_pst=', 'seed=', 'help']
  32. param_dict = {'ref_dir': None, 'bim_prefix': None, 'sst_file': None, 'a': 1, 'b': 0.5, 'phi': None, 'n_gwas': None,
  33. 'n_iter': 1000, 'n_burnin': 500, 'thin': 5, 'out_dir': None, 'chrom': range(1,23),
  34. 'beta_std': 'FALSE', 'write_psi': 'FALSE', 'write_pst': 'FALSE', 'seed': None}
  36. print('\n')
  38. if len(sys.argv) > 1:
  39. try:
  40. opts, args = getopt.getopt(sys.argv[1:], "h", long_opts_list)
  41. except:
  42. print('Option not recognized.')
  43. print('Use --help for usage information.\n')
  44. sys.exit(2)
  46. for opt, arg in opts:
  47. if opt == "-h" or opt == "--help":
  48. print(__doc__)
  49. sys.exit(0)
  50. elif opt == "--ref_dir": param_dict['ref_dir'] = arg
  51. elif opt == "--bim_prefix": param_dict['bim_prefix'] = arg
  52. elif opt == "--sst_file": param_dict['sst_file'] = arg
  53. elif opt == "--a": param_dict['a'] = float(arg)
  54. elif opt == "--b": param_dict['b'] = float(arg)
  55. elif opt == "--phi": param_dict['phi'] = float(arg)
  56. elif opt == "--n_gwas": param_dict['n_gwas'] = int(arg)
  57. elif opt == "--n_iter": param_dict['n_iter'] = int(arg)
  58. elif opt == "--n_burnin": param_dict['n_burnin'] = int(arg)
  59. elif opt == "--thin": param_dict['thin'] = int(arg)
  60. elif opt == "--out_dir": param_dict['out_dir'] = arg
  61. elif opt == "--chrom": param_dict['chrom'] = arg.split(',')
  62. elif opt == "--beta_std": param_dict['beta_std'] = arg.upper()
  63. elif opt == "--write_psi": param_dict['write_psi'] = arg.upper()
  64. elif opt == "--write_pst": param_dict['write_pst'] = arg.upper()
  65. elif opt == "--seed": param_dict['seed'] = int(arg)
  66. else:
  67. print(__doc__)
  68. sys.exit(0)
  70. if param_dict['ref_dir'] == None:
  71. print('* Please specify the directory to the reference panel using --ref_dir\n')
  72. sys.exit(2)
  73. elif param_dict['bim_prefix'] == None:
  74. print('* Please specify the directory and prefix of the bim file for the target dataset using --bim_prefix\n')
  75. sys.exit(2)
  76. elif param_dict['sst_file'] == None:
  77. print('* Please specify the summary statistics file using --sst_file\n')
  78. sys.exit(2)
  79. elif param_dict['n_gwas'] == None:
  80. print('* Please specify the sample size of the GWAS using --n_gwas\n')
  81. sys.exit(2)
  82. elif param_dict['out_dir'] == None:
  83. print('* Please specify the output directory using --out_dir\n')
  84. sys.exit(2)
  86. for key in param_dict:
  87. print('--%s=%s' % (key, param_dict[key]))
  89. print('\n')
  90. return param_dict
  93. def main():
  94. param_dict = parse_param()
  96. for chrom in param_dict['chrom']:
  97. print('##### process chromosome %d #####' % int(chrom))
  99. if '1kg' in os.path.basename(param_dict['ref_dir']):
  100. ref_dict = parse_genet.parse_ref(param_dict['ref_dir'] + '/snpinfo_1kg_hm3', int(chrom))
  101. elif 'ukbb' in os.path.basename(param_dict['ref_dir']):
  102. ref_dict = parse_genet.parse_ref(param_dict['ref_dir'] + '/snpinfo_ukbb_hm3', int(chrom))
  104. vld_dict = parse_genet.parse_bim(param_dict['bim_prefix'], int(chrom))
  106. sst_dict = parse_genet.parse_sumstats(ref_dict, vld_dict, param_dict['sst_file'], param_dict['n_gwas'])
  108. ld_blk, blk_size = parse_genet.parse_ldblk(param_dict['ref_dir'], sst_dict, int(chrom))
  110. mcmc_gtb.mcmc(param_dict['a'], param_dict['b'], param_dict['phi'], sst_dict, param_dict['n_gwas'], ld_blk, blk_size,
  111. param_dict['n_iter'], param_dict['n_burnin'], param_dict['thin'], int(chrom), param_dict['out_dir'], param_dict['beta_std'],
  112. param_dict['write_psi'], param_dict['write_pst'], param_dict['seed'])
  114. print('\n')
  117. if __name__ == '__main__':
  118. main()

PRScs.py at commit 5330390, under MIT · at the source

Overview

Authors: Chun-Ju Chou1,2, Elisabetta C. del Re3, Hao Wang1, Kareem Hamada3, Xiaoguang Tian1, Olena Iakunchykova1,4, Yunpeng Wang4, Mark Fiecas5, Chi-Hua Chen1
ORCID iDs: Chun-Ju Chou, Elisabetta C. del Re, Hao Wang, Kareem Hamada, Olena Iakunchykova, Mark Fiecas, Chi-Hua Chen
  1. Department of Radiology, University of California, San Diego, CA, United States
  2. Department of Bioengineering, University of California, San Diego, CA, United States
  3. Department of Psychiatry, Harvard Medical School, Boston, MA, United States
  4. Department of Psychology, University of Oslo, Oslo, Norway
  5. Division of Biostatistics, University of Minnesota School of Public Health, Minneapolis, MN, United States
Institutions: University of California San Diego (United States); Harvard University (United States); University of Oslo (Norway); University of Minnesota (United States)
Journal: Imaging neuroscience (Cambridge, Mass.), volume 4, article IMAG.a.1152
Dates: received 22 April 2025; accepted 24 January 2026; published online 10 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/imag.a.1152 · PMID 41821829 · PMCID PMC12977089 · OpenAlex W7128482915
Open access: diamond, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), structural MRI / diffusion (modality), human (organism), schizophrenia / psychosis (population), ADHD (population), clinical / translational (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Preprocessing
Keywords: imaging genetics, brain morphology, polygenic risk score, MRI, neuropsychiatric disorders, Mendelian randomization
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Institute of Mental Health (R01MH118281, R01MH132783)
Citations: not cited yet (Europe PMC); 96 references in the paper

Abstract

Analyzing brain morphological changes across individuals with varying genetic risk scores may reveal patterns of brain alterations from health to disease. This study investigates gray matter structural alterations in individuals with clinical diagnoses compared with those with genetic risk alone. UK Biobank MRI and genotypes (N = 34,720) were used to derive brain measures and polygenic risk scores, creating genetic risk brain maps for 14 neuropsychiatric disorders. Eight disorders from ENIGMA were used to construct disease brain maps. Brain maps of genetic risk and clinical diagnosis show overall alignment for ADHD, schizophrenia, bipolar disorder, and autism. Other conditions, including Alzheimer’s disease, show specific brain regions linked to genetic risk aligning with established patient patterns. Incomplete data for some conditions limit analyses. ADHD and PTSD polygenic burden was associated with smaller global brain sizes, while Parkinson’s disease was linked to larger brain volume. Mendelian randomization analyses revealed unidirectional relationships where the brain influences ADHD and Parkinson’s disease, while a bidirectional causal association was observed for schizophrenia. Focusing on schizophrenia and bipolar disorder, we found that individuals with high genetic risk combined with smaller brain structures were more likely to have these diagnoses. Overall, the study demonstrates marked similarities in brain changes between clinical diagnoses and genetic risk for several disorders, albeit with mild effect sizes in the latter. These findings underscore the importance of genetic risk in influencing brain anatomy and the progression of neuropsychiatric disorders.

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

Repositories

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

getian107/PRScs

License: MIT
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 53303906784c3360b9a1a7fa000b32c73eda9d5a, 21 November 2024
Languages: Python (4)
Size: 11 files, 4 scripts
Software Heritage: not archived
Found in: “Data and Code Availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (3 files), SciPy (2 files), h5py (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
6 files
At the source: github.com/getian107/PRScs

jianyang-lab/gsmr

License: none: the authors keep all their rights
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: 53f5034f4ebde2d149930a924449b57f6a36cb85, 13 September 2023
Languages: R (5)
Size: 18 files, 5 scripts
Software Heritage: not archived
Found in: “Data and Code Availability”
Holds: README, environment (DESCRIPTION), documentation, 2 notebooks
Not found: license file, CITATION.cff, tests, continuous integration
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
6 files
At the source: github.com/JianYang-Lab/gsmr/releases

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

Tracing map

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

What the map holds:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 9 scripts, 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 and Code Availability

The individual-level genetic and neuroimaging data used in this study were obtained from the UK Biobank (https://www.ukbiobank.ac.uk/). UK Biobank data are available to approved researchers. Summary-level neuroimaging results for neuropsychiatric disorders were obtained from published studies conducted by the ENIGMA consortium and related ENIGMA working groups, which are publicly available through the ENIGMA consortium and associated publications. Genome-wide association study (GWAS) summary statistics used for polygenic risk score construction were obtained from publicly available sources, as detailed in Supplementary Table S1.

All analyses were conducted using publicly available software, including FreeSurfer for neuroimaging processing, PRS-CS for polygenic risk score estimation (https://github.com/getian107/PRScs), GCTA for genetic analyses (https://yanglab.westlake.edu.cn/software/gcta/#Overview), and GSMR for Mendelian randomization analyses (https://github.com/JianYang-Lab/gsmr/releases). Custom scripts used for data processing and statistical analyses are available upon request.

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

Versions

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

Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 9 authors, 6 keywords, 1 funder, 96 references.

Cite

This paper

Chou, C.-J., del Re, E. C., Wang, H., Hamada, K., Tian, X., Iakunchykova, O., Wang, Y., Fiecas, M., & Chen, C.-H. (2026). Brain patterns linked to neuropsychiatric genetic risk mirror those seen in disease. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1152. https://doi.org/10.1162/imag.a.1152

BibTeX

@article{chou2026brain,
author = {Chou, Chun-Ju and del Re, Elisabetta C. and Wang, Hao and Hamada, Kareem and Tian, Xiaoguang and Iakunchykova, Olena and Wang, Yunpeng and Fiecas, Mark and Chen, Chi-Hua},
title = {{Brain patterns linked to neuropsychiatric genetic risk mirror those seen in disease}},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = mar,
volume = {4},
pages = {IMAG.a.1152},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/imag.a.1152},
url = {https://doi.org/10.1162/imag.a.1152},
pmid = {41821829},
pmcid = {PMC12977089}
}

RIS

TY - JOUR
AU - Chou, Chun-Ju
AU - del Re, Elisabetta C.
AU - Wang, Hao
AU - Hamada, Kareem
AU - Tian, Xiaoguang
AU - Iakunchykova, Olena
AU - Wang, Yunpeng
AU - Fiecas, Mark
AU - Chen, Chi-Hua
TI - Brain patterns linked to neuropsychiatric genetic risk mirror those seen in disease
T2 - Imaging neuroscience (Cambridge, Mass.)
J2 - Imaging Neurosci (Camb)
PY - 2026
DA - 2026/03/10
VL - 4
SP - IMAG.a.1152
SN - 2837-6056
PB - MIT Press
DO - 10.1162/imag.a.1152
UR - https://doi.org/10.1162/imag.a.1152
LA - en
ER -

CSL-JSON

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"author": [
{
"family": "Chou",
"given": "Chun-Ju"
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"given": "Elisabetta C."
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