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Identification of moderate effect size genes in autism spectrum disorder through a novel gene pairing approach.

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 · 6 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Incorporation and assessment of non-neuro cohorts ↔ 2_MES_non_neuro/example_commands_to_annotate.sh, the whole file · a weak match · score 0.78 · SNPeff, dbNSFP, missense variants, SIFT score, Ensembl, neuro
  2. [2] § Methods › Simulation of discovery pipeline ↔ 2_MES_non_neuro/simulate_MES_genes.R, lines 66–143 · score 0.74 · variant assignments, twins, orphans, rbinom, MES gene, simulated
  3. [3] § Methods › Gene expression ↔ 4_Expression/query_gene_expression.R, the whole file · a weak match · score 0.72 · tissue consensus, nTPM, genes expressed, GTEx, command, MES
  4. [4] § Methods › Incorporation and assessment of non-neuro cohorts ↔ 3_MES_synonymous/non_neuro_example_commands_to_annotate.sh, the whole file · a weak match · score 0.71 · SNPeff, dbNSFP, SIFT score, Ensembl, deleterious variants, neuro
  5. [5] § Results › Enrichment of MES risk gene expression in the brain ↔ 4_Expression/query_gene_expression.R, the whole file · a weak match · score 0.58 · nTPM, adrenal, cerebral, consensus, MES gene, tissues
  6. [6] § Methods › Family-based search for MES risk genes in SPARK ↔ 2_MES_non_neuro/example_commands_to_annotate.sh, the whole file · a weak match · score 0.58 · missense variant, SIFT score, Ensembl, PTV, neuro, deleterious
  7. [7] § Results › Controlling false positives in candidate gene pair analysis ↔ 1_MES_screen/4_table.R, the whole file · a weak match · score 0.50 · SPARK parents, ultra rare deleterious, rank, sum, probability, inheritance

Paper

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

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The authors' code

Shell · 39 lines · 1.6 KB · GPL-3.0 · 2 matches

  1. # Moderate effect size genes project
  2. # Author: Madison Caballero
  3. # Desription: A set of steps to annotate and extract variants in the non-neuro cohorts.
  4. # These steps were used for 1kGP, BioMe, and All of Us
  6. # For speed, I just extract one sample. This keeps all loci even if 0/0
  7. bcftools view --threads 4 -s [random_sample_id] input.vcf.gz > singlesamp.vcf
  9. # SNPeff annotate for function
  10. java -Xmx8g -jar snpEff.jar -v GRCh38.86 -canon singlesamp.vcf > singlesamp.ann.vcf
  12. # SNPsift to get dbNSFP information
  13. java -jar SnpSift.jar dbnsfp -v -db dbNSFP4.1a.txt.gz \
  14. -f gnomAD_exomes_AF,SIFT_score,SIFT4G_score,Polyphen2_HDIV_score,Polyphen2_HVAR_score,genename,Ensembl_geneid \
  15. singlesamp.ann.vcf > singlesamp.ann.dbNSFP.vcf
  17. # Get gene IDs that are SNPeff compatible
  18. awk '{print "missense_variant|MODERATE|" $1}' gene_names_Snpeffcompat.txt > grab.txt
  19. awk '{print "HIGH|" $1}' gene_names_Snpeffcompat.txt >> grab.txt
  21. # Grab variants that are missense or highly damaging (PTVs) for those genes
  22. # There is a later filtering step in case grep grabs partial matches
  23. grep -f grab.txt singlesamp.ann.dbNSFP.vcf > singlesamp.ann.dbNSFP.functional.noheader.vcf
  24. perl filter.pl singlesamp.ann.dbNSFP.functional.noheader.vcf > singlesamp.ann.dbNSFP.deleterious.noheader.vcf
  26. # header check
  27. grep -P "^" singlesamp1.ann.dbNSFP.vcf > header.txt
  28. cat header.txt singlesamp.ann.dbNSFP.deleterious.noheader.vcf > singlesamp.ann.dbNSFP.deleterious.vcf
  30. # Grab regions
  31. bcftools view \
  32. -T singlesamp.ann.dbNSFP.deleterious.vcf \
  33. -O v --threads 8 \
  34. -o filtered.input.vcf \
  35. input.vcf.gz
  37. # Get counts per gene
  38. perl grab_counts.pl

example_commands_to_annotate.sh at commit 17c5b6a, under GPL-3.0 · at the source

Overview

Authors: Madison Caballero1, F Kyle Satterstrom2,3,4, Silvia de Rubeis1,5,6,7, Joseph D Buxbaum1,5,6,7,8,9, Behrang Mahjani1,5,6,8,10,11,12
ORCID iDs: Madison Caballero, F Kyle Satterstrom, Silvia de Rubeis, Joseph D Buxbaum, Behrang Mahjani
  1. Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, NY USA
  2. The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY USA
  3. Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY USA
  4. Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA USA
  5. Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
  6. Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA USA
  7. Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY USA
  8. Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA USA
  9. Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY USA
  10. Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY USA
  11. Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
  12. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
Institutions: Icahn School of Medicine at Mount Sinai (United States); Broad Institute (United States); Massachusetts General Hospital (United States); Stanley Center for Psychiatric Research; Karolinska Institutet (Sweden)
Journal: Communications biology, volume 9, issue 1, article 1146
Dates: received 11 May 2025; accepted 20 May 2026; published online 30 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s42003-026-10380-z · PMID 42218271 · PMCID PMC13518923 · OpenAlex W4393942106
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2024.04.03.24305278
Status: code verified
Categories: genetics / omics (modality), human (organism), autism (population), cellular / molecular (subfield)
Methods: Statistics, Machine learning, Connectivity
Keywords: Genetics, Behavioural genetics
MeSH: Autism Spectrum Disorder*, Genetic Predisposition to Disease*, Gene Expression Profiling, Humans (* major topic)
Topic: Autism Spectrum Disorder Research (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: NIH HHS (OT2 OD023205, OT2 OD025276, OT2 OD026548, OT2 OD026551, OT2 OD026555, OT2 OD025315, OT2 OD026554, OT2 OD026556, U24 OD023163, U2C OD023196, OT2 OD026557, U24 OD023176, OT2 OD023206, OT2 OD025277, OT2 OD026553, OT2 OD025337, U24 OD023121, OT2 OD026549, OT2 OD026550, OT2 OD026552); NIMH NIH HHS (R01 MH129724, R01 MH139952, R01 MH128813); U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (R01MH129724, R01MH139952, R01MH128813); Beatrice and Samuel A. Seaver Foundation; U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH) (R01MH128813, R01MH129724, R01MH139952)
Citations: cited by 1 paper (Europe PMC); 51 references in the paper

Abstract

Autism Spectrum Disorder (ASD) arises from complex genetic and environmental factors, with inherited genetic variation playing a substantial role. This study introduces a novel approach to uncover moderate effect size (MES) risk genes in ASD, which individually do not meet the ASD liability threshold but contribute to risk when paired with another MES risk gene. Analyzing 10,795 families from the SPARK dataset, we identified 97 MES risk genes forming 50 significant gene pairs, demonstrating a substantial association with ASD when considered jointly, but not individually. Our method leverages familial inheritance patterns and statistical analyses, refined by comparisons against control cohorts, to elucidate these gene pairs’ contribution to ASD liability. Furthermore, expression profile analyses of these genes in brain tissues underscore their relevance to ASD pathology. This study underscores the complexity of ASD’s genetic landscape, suggesting that gene combinations, beyond high impact single-gene mutations, significantly contribute to the disorder’s etiology and heterogeneity. Our findings pave the way for new avenues in understanding ASD’s genetic underpinnings and developing targeted therapeutic strategies.

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 7 matches between paragraphs and lines of code.

MahjaniLab/MES_Code

License: GPL-3.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 17c5b6ac0fbf57aac32245c7c7f8b6d3e04bbefa, 12 September 2024
Languages: R (13), Perl (10), Shell (2)
Size: 40 files, 25 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (8 files), BCFtools (2 files), data.table (1 file), ggplot2 (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
27 files
At the source: github.com/MahjaniLab/MES_Code

Zenodo 19859234

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: tidyverse (8 files), BCFtools (2 files), data.table (1 file), ggplot2 (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
27 files
At the source:

Code availability

Code and resources used in this study is available both on GitHub at (https://github.com/MahjaniLab/MES_Code) and Zenodo (10.5281/zenodo.19859234). For any further inquiries or requests for code not available in the repository, please contact the corresponding author.

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

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;
  • 50 scripts, each with its path and the digest of its content;
  • 7 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

Whole genome sequencing from 1kGP is freely available from The International Genome Sample Resource (https://www.internationalgenome.org/). Allother individual-level data used in this study are not publicly available with access permission subject to review. The genetic and phenotypic data for SPARK can be requested at SFARI Base (https://www.sfari.org/resource/spark/). For convenience, we have included all ultra-rare deleterious variants in the SPARK cohort (Data S7). Access to the Sinai BioMe data can be requested through The Charles Bronfman Institute for Personalized Medicine (https://icahn.mssm.edu/research/ipm/programs/biome-biobank) and is subject to compliance with the Mount Sinai Health System’s data use agreements. All of Us controlled tier data is available for registered institutions and cohort selection is detailed in the workbench titled “Detecting the prevalence of ultra-rare gene mutations.”

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, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 2 keywords, 4 MeSH terms, 5 funders, 50 references.

Cite

This paper

Caballero, M., Satterstrom, F. K., de Rubeis, S., Buxbaum, J. D., & Mahjani, B. (2026). Identification of moderate effect size genes in autism spectrum disorder through a novel gene pairing approach. Communications biology, 9(1), 1146. https://doi.org/10.1038/s42003-026-10380-z

BibTeX

@article{caballero2026identification,
author = {Caballero, Madison and Satterstrom, F Kyle and de Rubeis, Silvia and Buxbaum, Joseph D and Mahjani, Behrang},
title = {{Identification of moderate effect size genes in autism spectrum disorder through a novel gene pairing approach}},
journal = {Communications biology},
year = {2026},
month = may,
volume = {9},
number = {1},
pages = {1146},
publisher = {Nature Publishing Group},
issn = {2399-3642},
doi = {10.1038/s42003-026-10380-z},
url = {https://doi.org/10.1038/s42003-026-10380-z},
pmid = {42218271},
pmcid = {PMC13518923}
}

RIS

TY - JOUR
AU - Caballero, Madison
AU - Satterstrom, F Kyle
AU - de Rubeis, Silvia
AU - Buxbaum, Joseph D
AU - Mahjani, Behrang
TI - Identification of moderate effect size genes in autism spectrum disorder through a novel gene pairing approach
T2 - Communications biology
J2 - Commun Biol
PY - 2026
DA - 2026/05/30
VL - 9
IS - 1
SP - 1146
SN - 2399-3642
PB - Nature Publishing Group
DO - 10.1038/s42003-026-10380-z
UR - https://doi.org/10.1038/s42003-026-10380-z
LA - en
ER -

CSL-JSON

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