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Genetic architectures of brain-related traits are shaped by strong selective constraints.

Code ↔ Paper

10 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 10 matches · 3 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Materials and Methods › Thresholding and Downsampling Quantitative Traits. ↔ scripts/gwas_deflation/05_thresholding_downsampling.R, lines 1–12 · score 0.89 · diastolic blood pressure, systolic blood pressure, HbA1c, heel bone mineral, BMI, LDL
  2. [2] § Materials and Methods › Thresholding and Downsampling Quantitative Traits. ↔ scripts/gwas_deflation/01_field_names.ipynb, lines 37–78 · score 0.85 · heel bone mineral, diastolic blood pressure, systolic blood pressure, HbA1c, medically relevant, fields
  3. [3] § Results › The Distinct Genetic Architectures of Brain-Related Traits. ↔ scripts/plotting/sldsc_plots.R, lines 1–48 · score 0.70 · Bonferroni correction, selected traits, DFP, FIS, IMR, AFS
  4. [4] § Materials and Methods › Distinct Subsets of (Approximately Uncorrelated) Brain-Related and Non-Brain-Related Traits. ↔ scripts/gwas_analyses/get_independent_traits.R, lines 1–69 · score 0.68 · Neale Lab, Genetic correlation, CNS traits, meta
  5. [5] § Results › Brain-Related Traits Retain Distinct Architectures After Accounting for Differences in Study Design. ↔ scripts/gwas_deflation/05_thresholding_downsampling.R, lines 22–67 · score 0.67 · reach equivalent power, original phenotypic, normal transformation, Deflation, downsampling, inverse
  6. [6] § Materials and Methods › Using Stratified LD Score Regression (S-LDSC) to Define Brain-Related Traits. ↔ scripts/gwas_analyses/07_sldsc_metaanalysis_ACAT.R, the whole file · a weak match · score 0.67 · Cauchy variables, Cauchy distribution, ACAT, meta, transformed, cell
  7. [7] § Materials and Methods › Thresholding and Downsampling Quantitative Traits. ↔ scripts/gwas_deflation/06_gwas_deflated.sh, the whole file · a weak match · score 0.64 · binary traits, fallback, firth, covar, glm, hide
  8. [8] § Materials and Methods › Identifying Approximately Independent Hits Using GCTA-COJO. ↔ scripts/gwas_analyses/02_cojo.sh, the whole file · a weak match · score 0.62 · cojo slct, GCTA COJO, GWAS
  9. [9] § Materials and Methods › Using Stratified LD Score Regression (S-LDSC) to Define Brain-Related Traits. ↔ ldsc.py, lines 547–617 · score 0.59 · munge_sumstats.py, LD scores, regression coefficient
  10. [10] § Materials and Methods › Thresholding and Downsampling Quantitative Traits. ↔ scripts/gwas_deflation/05_thresholding_downsampling.R, lines 22–67 · score 0.56 · normal transformed, nest, downsampling, inverse, rank, binary

Paper

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

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The authors' code

R · 67 lines · 2.6 KB · no license · 3 matches

The registry keeps no copy of this file: its repository has no license, so its authors keep all their rights to it. Your browser shows it from its source, with JavaScript.

It can be read at the source: scripts/gwas_deflation/05_thresholding_downsampling.R.

Overview

Authors: Huisheng Zhu1, Yuval B Simons2,3,4, Jeffrey P Spence2,5,6, Guy Sella7,8, Jonathan K Pritchard1,2
ORCID iDs: Huisheng Zhu, Yuval B Simons
  1. Department of Biology, Stanford University, Stanford, CA 94305
  2. Department of Genetics, Stanford University, Stanford, CA 94305
  3. Section of Genetic Medicine, University of Chicago, Chicago, IL 60637
  4. Department of Human Genetics, University of Chicago, Chicago, IL 60637
  5. Institute for Human Genetics, University of California, San Francisco, CA 94158
  6. Department of Epidemiology and Biostatistics, University of California, San Francisco, CA 94158
  7. Department of Biological Sciences, Columbia University, New York City, NY 10027
  8. Program for Mathematical Genomics, Columbia University, New York City, NY 10032
Institutions: Stanford University (United States); University of Chicago (United States); Columbia University (United States)
Journal: Proceedings of the National Academy of Sciences of the United States of America, volume 123, issue 36, article e2609814123
Dates: received 26 March 2026; accepted 29 July 2026; published online 1 September 2026; in print 8 September 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1073/pnas.2609814123 · PMID 42679042 · PMCID PMC13552918 · OpenAlex W7140029235
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), cellular / molecular (subfield)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Preprocessing, fMRI & imaging
Keywords: selection, GWAS, genetic architecture, brain-related traits, psychiatric disorders
MeSH: Brain*, Mental Disorders*, Selection, Genetic*, Gene Frequency, Genome-Wide Association Study, Humans, Models, Genetic, Phenotype, Polymorphism, Single Nucleotide, Quantitative Trait Loci (* major topic)
Topic: Genetic Associations and Epidemiology (Genetics, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: HHS | National Institutes of Health (R01HL175076, R01GM115889, R01HG014005, R01HG008140); National Science Foundation (DMS-2235451); NHGRI NIH HHS (R01 HG014005, R01 HG008140); NHLBI NIH HHS (R01 HL175076); NIGMS NIH HHS (R01 GM115889); Simons Foundation (SF) (MPS-NITMB-00005320)
Citations: not cited yet (Europe PMC); 99 references in the paper

Abstract

Genome-wide association studies (GWAS) have identified hundreds of significant loci for psychiatric disorders, yet the strength of these associations remains modest compared to other human complex traits with similar numbers of hits. Whether this pattern reflects statistical artifacts or real biological differences—and, if the latter, what underlies it—remains unclear. In addition to psychiatric disorders, we find that other traits with functional enrichment in the central nervous system (CNS), whether binary or quantitative, also share similar genetic architectures, characterized by GWAS hits of limited statistical significance and generally higher allele frequencies. In comparing the architecture of binary and quantitative traits, we adjust for statistical power in their respective studies. After this adjustment, we fit an evolutionary model of architecture and show that CNS-enriched traits have large mutational target sizes, with contributing variants and genes experiencing stronger selection than those for other traits. Our findings reveal heterogeneity among complex traits and provide insights into traits that more effectively capture fitness-relevant processes. More broadly, our results suggest that the genetic architectures of complex traits are shaped by the tissues through which these traits are mediated.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 10 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: the text, “Using Stratified LD Score Regression (S-LDSC) to”
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

huishengz/cns-selection

License: none: the authors keep all their rights
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: cff83096f0ca242980b30769973e0b3b222bd7eb, 20 March 2026
Languages: R (17), Shell (13), MATLAB (11), Jupyter (3)
Size: 46 files, 44 scripts
Software Heritage: not archived
Found in: “Data, Materials, and Software Availability”
Holds: README, 3 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (15 files), data.table (13 files), ggplot2 (13 files), Statistics and Machine Learning Toolbox (5 files), patchwork (4 files), NumPy (3 files), pandas (3 files), cowplot (1 file), Matplotlib (1 file), SciPy (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
45 files, not copied: shown from their source

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At the source: github.com/huishengz/cns-selection

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

Tracing map

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

Datasets cited

Data, Materials, and Software Availability

Codes used for this article are available at https://github.com/huishengz/cns-selection. Data and results are available at https://doi.org/10.5281/zenodo.19154957. To access UK Biobank data, eligible researchers can apply at https://www.ukbiobank.ac.uk/use-our-data/apply-for-access/ (99).

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

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 5 keywords, 10 MeSH terms, 6 funders, 93 references.

Cite

This paper

Zhu, H., Simons, Y. B., Spence, J. P., Sella, G., & Pritchard, J. K. (2026). Genetic architectures of brain-related traits are shaped by strong selective constraints. Proceedings of the National Academy of Sciences of the United States of America, 123(36), e2609814123. https://doi.org/10.1073/pnas.2609814123

BibTeX

@article{zhu2026genetic,
author = {Zhu, Huisheng and Simons, Yuval B and Spence, Jeffrey P and Sella, Guy and Pritchard, Jonathan K},
title = {{Genetic architectures of brain-related traits are shaped by strong selective constraints}},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
year = {2026},
month = sep,
volume = {123},
number = {36},
pages = {e2609814123},
publisher = {National Academy of Sciences},
issn = {0027-8424},
doi = {10.1073/pnas.2609814123},
url = {https://doi.org/10.1073/pnas.2609814123},
pmid = {42679042},
pmcid = {PMC13552918}
}

RIS

TY - JOUR
AU - Zhu, Huisheng
AU - Simons, Yuval B
AU - Spence, Jeffrey P
AU - Sella, Guy
AU - Pritchard, Jonathan K
TI - Genetic architectures of brain-related traits are shaped by strong selective constraints
T2 - Proceedings of the National Academy of Sciences of the United States of America
J2 - Proc Natl Acad Sci U S A
PY - 2026
DA - 2026/09/01
VL - 123
IS - 36
SP - e2609814123
SN - 0027-8424
PB - National Academy of Sciences
DO - 10.1073/pnas.2609814123
UR - https://doi.org/10.1073/pnas.2609814123
LA - en
ER -

CSL-JSON

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