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Shared genetic architecture between the topology of brain white matter structural connectome and fluid intelligence.

Code ↔ Paper

6 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 6 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Genotyping and quality control ↔ codes/samples_qc.py, lines 32–66 · score 0.81 · genetic sex, White British, aneuploidy, heterozygosity, outliers, field
  2. [2] § Methods › Identification of shared genetic variants ↔ pleioFDR_amd.m, the whole file · a weak match · score 0.65 · linkage disequilibrium, discovery rate, conjunctional, conjFDR, pleiotropic, variants
  3. [3] § Methods › Genotyping and quality control ↔ munge_sumstats.py, lines 105–123 · score 0.52 · allele frequency, imputation quality, genotype, variants, score, SNP
  4. [4] § Methods › Phenotypic and genetic correlation estimation ↔ codes/genetic_correlation_munge.sh, the whole file · a weak match · score 0.51 · LD score regression, genetic correlations, GWAS
  5. [5] § Methods › Identification of shared genetic variants ↔ runme.m, lines 1–127 · score 0.51 · discovery rate, MHC, conjunctional, conjFDR, pleiotropic, SNPs
  6. [6] § Methods › Prediction analysis ↔ codes/predict.py, lines 22–50 · score 0.51 · CV, training, prediction, Pearson, fold, components

Paper

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The authors' code

Python · 87 lines · 2.9 KB · no license · 1 match

  1. import pandas as pd
  2. import numpy as np
  3. from pathlib import Path
  5. # ==========================================
  6. # 1. 路径配置
  7. # ==========================================
  8. PROJECT_ROOT = Path(__file__).resolve().parent
  9. DATA_DIR = PROJECT_ROOT / "data" / "ukb"
  10. RAW_DATA_PATH = Path("E:/sample_QC/ukb676304.csv") # 外部超大原始数据路径
  12. # 确保输出目录存在
  13. DATA_DIR.mkdir(parents=True, exist_ok=True)
  15. # ==========================================
  16. # 2. 核心功能函数
  17. # ==========================================
  19. def merge_imaging_genetics(imaging_id_file, genetic_id_file):
  20. """合并影像数据与基因数据共有的 Sample"""
  21. df_imaging = pd.read_csv(imaging_id_file, header=None, names=['eid'])
  23. # 读取基因 ID 文件
  24. df_genetic = pd.read_csv(genetic_id_file, sep=' ', header=None)
  25. df_genetic = df_genetic.iloc[:, :2]
  26. df_genetic.columns = ['eid', 'iid']
  28. # 取交集
  29. df_merged = pd.merge(df_imaging, df_genetic, on='eid')
  30. return df_merged
  32. def run_sample_qc(input_df, raw_pheno_path):
  33. # 定义需要提取的 UKB Field ID
  34. target_fields = {
  35. 'eid': 'eid',
  36. '22006-0.0': 'genetic_ethnic',
  37. '22020-0.0': 'used_in_pca',
  38. '31-0.0': 'self_reported_sex',
  39. '22001-0.0': 'genetic_sex',
  40. '22019-0.0': 'sex_aneuploidy',
  41. '22027-0.0': 'heterozygosity_outlier',
  42. '22021-0.0': 'genetic_kinship'
  43. }
  45. # 仅读取必要的列以节省内存
  46. df_raw = pd.read_csv(raw_pheno_path, usecols=list(target_fields.keys()))
  47. df_raw = df_raw.rename(columns=target_fields)
  49. # 与待处理 ID 合并
  50. df_qc = pd.merge(df_raw, input_df, on='eid')
  52. # 筛选条件:
  53. # 1. 自述性别与基因性别一致
  54. # 2. 排除性染色体非整倍体
  55. # 3. 排除杂合度或缺失率异常
  56. # 4. 遗传背景为 Unrelated White British
  57. # 5. 排除亲缘关系过近者 (Ten or more third-degree relatives)
  58. mask = (
  59. (df_qc['self_reported_sex'] == df_qc['genetic_sex']) &
  60. (df_qc['sex_aneuploidy'].isnull()) &
  61. (df_qc['heterozygosity_outlier'].isnull()) &
  62. (df_qc['genetic_ethnic'] == df_qc['used_in_pca']) &
  63. (df_qc['genetic_kinship'] != 10)
  64. )
  66. return df_qc[mask]
  68. # ==========================================
  69. # 3. 执行流程
  70. # ==========================================
  72. if __name__ == "__main__":
  73. # A. 合并初步 ID
  74. merged_ids = merge_imaging_genetics(
  75. imaging_id_file = DATA_DIR / "id_all.csv",
  76. genetic_id_file = DATA_DIR / "c1.txt"
  77. )
  79. # B. 执行 QC 筛选
  80. final_qc_df = run_sample_qc(merged_ids, RAW_DATA_PATH)
  82. # C. 保存结果
  83. output_path = DATA_DIR / "id_qc_final.csv"
  84. final_qc_df.to_csv(output_path, index=None)
  86. print(f"Done! Final sample size: {len(final_qc_df)}")
  87. print(f"Result saved to: {output_path}")

samples_qc.py at commit 89402d4, no license · at the source

Overview

Authors: Xinyi Dong1, Weijie Huang2, Hao-Jie Chen1, Yunhao Zhang3,4, Bing Liu1, Daoqiang Zhang2, Zhanjun Zhang1,5, Guolin Ma6, Ni Shu1,5,7
ORCID iDs: Xinyi Dong, Weijie Huang, Hao-Jie Chen, Bing Liu, Daoqiang Zhang, Ni Shu
  1. State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University,Beijing, China
  2. College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics, the Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education,Nanjing, China
  3. State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences,Beijing, China
  4. School of Artificial Intelligence, University of Chinese Academy of Sciences,Beijing, China
  5. BABRI Centre, Beijing Normal University,Beijing, China
  6. Department of Radiology, China-Japan Friendship Hospital,Beijing, China
  7. Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University,Beijing, China
Institutions: Beijing Normal University (China); Nanjing University of Aeronautics and Astronautics (China); Chinese Academy of Sciences (China); University of Chinese Academy of Sciences (China); China-Japan Friendship Hospital (China)
Journal: Communications biology, volume 9, issue 1, article 942
Dates: received 2 July 2025; accepted 15 April 2026; published online 6 May 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s42003-026-10131-0 · PMID 42092107 · PMCID PMC13358041 · OpenAlex W7160405124
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), structural MRI / diffusion (modality), human (organism)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Graphs, fMRI & imaging
Keywords: Intelligence, Genome-wide association studies
MeSH: Brain*, Connectome*, Intelligence*, White Matter*, Diffusion Magnetic Resonance Imaging, Female, Genome-Wide Association Study, Humans, Male, Middle Aged, Polymorphism, Single Nucleotide (* major topic)
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Natural Science Foundation of China (National Science Foundation of China) (82301608, 32271145, 81871425, 210510238); Beijing Natural Science Foundation (L252087); Brain Science and Brain-like Intelligence Technology - National Science and Technology Major Project (2022ZD0213300), Open Research Fund of the State Key Laboratory of Cognitive Neuroscience and Learning (CNLZD2101, CNLZD2303), Fundamental Research Funds for the Central Universities (2017XTCX04), the Key Research and Development Program of Hebei Province of China (223777112D), Science and Technology Research and Development Plan of Chengde City of China (202109A057), Government Funded Outstanding Talent Project of Hebei Province
Citations: not cited yet (Europe PMC); 96 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.

Repositories

Its files are read in the Code ↔ Paper reader above, with 6 matches between paragraphs and lines of code.

bulik/ldsc

License: GPL-3.0
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: 2fdeeb3b44379408794154993dbd6101b8946b7e, 16 January 2026
Languages: Python (19), MATLAB (4), Perl (1), R (1)
Size: 1,093 files, 25 scripts
Software Heritage: archived
Found in: “Code availability”
Holds: README, license file, environment (environment.yml, requirements.txt, setup.py), tests
Not found: CITATION.cff, continuous integration, documentation
Tools: NumPy (18 files), pandas (11 files), SciPy (5 files), BEDTools (2 files)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
27 files
At the source: github.com/bulik/ldsc/

precimed/pleiofdr

License: GPL-3.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 0da963cac22fe8de9030166d7aea974bcbb0a367, 3 June 2025
Languages: MATLAB (33), Python (9), R (3), Shell (2), Jupyter (1)
Size: 55 files, 48 scripts
Software Heritage: archived
Found in: “Code availability”
Holds: README, license file, 1 notebook
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (8 files), pandas (8 files), Statistics and Machine Learning Toolbox (6 files), data.table (3 files), SciPy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
50 files
At the source: github.com/precimed/pleiofdr/

Zenodo 19436734

License: CC-BY-4.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (3 files), pandas (3 files), Pingouin (1 file), scikit-learn (1 file), SciPy (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
7 files
At the source:

sheeya-dong/wm_intelligence

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 89402d47a9f6cc93fbae53040626df643dbcc84e, 6 April 2026
Languages: Shell (3), Python (3)
Size: 7 files, 6 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (3 files), pandas (3 files), Pingouin (1 file), scikit-learn (1 file), SciPy (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
7 files
At the source: github.com/Sheeya-Dong/WM_intelligence/tree/v1.0.0

Code availability statement

The paper has a code 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/s42003-026-10131-0.

Tracing map

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What the map holds:

  • 4 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 85 scripts, each with its path and the digest of its content;
  • 6 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • 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

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

  • no repository, dataset or request procedure was recognized in it

Read it in the paper: doi.org/10.1038/s42003-026-10131-0.

Versions

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 2 keywords, 11 MeSH terms, 3 funders, 93 references.

Cite

This paper

Dong, X., Huang, W., Chen, H.-J., Zhang, Y., Liu, B., Zhang, D., Zhang, Z., Ma, G., & Shu, N. (2026). Shared genetic architecture between the topology of brain white matter structural connectome and fluid intelligence. Communications biology, 9(1), 942. https://doi.org/10.1038/s42003-026-10131-0

BibTeX

@article{dong2026shared,
author = {Dong, Xinyi and Huang, Weijie and Chen, Hao-Jie and Zhang, Yunhao and Liu, Bing and Zhang, Daoqiang and Zhang, Zhanjun and Ma, Guolin and Shu, Ni},
title = {{Shared genetic architecture between the topology of brain white matter structural connectome and fluid intelligence}},
journal = {Communications biology},
year = {2026},
month = may,
volume = {9},
number = {1},
pages = {942},
publisher = {Nature Publishing Group},
issn = {2399-3642},
doi = {10.1038/s42003-026-10131-0},
url = {https://doi.org/10.1038/s42003-026-10131-0},
pmid = {42092107},
pmcid = {PMC13358041}
}

RIS

TY - JOUR
AU - Dong, Xinyi
AU - Huang, Weijie
AU - Chen, Hao-Jie
AU - Zhang, Yunhao
AU - Liu, Bing
AU - Zhang, Daoqiang
AU - Zhang, Zhanjun
AU - Ma, Guolin
AU - Shu, Ni
TI - Shared genetic architecture between the topology of brain white matter structural connectome and fluid intelligence
T2 - Communications biology
J2 - Commun Biol
PY - 2026
DA - 2026/05/06
VL - 9
IS - 1
SP - 942
SN - 2399-3642
PB - Nature Publishing Group
DO - 10.1038/s42003-026-10131-0
UR - https://doi.org/10.1038/s42003-026-10131-0
LA - en
ER -

CSL-JSON

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