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Sex-dependent prediction of autism.

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  1. [1] § Materials and methods › Developmental enrichment analysis of SFARI genes ↔ ABA_enrichment_SFARI.Rmd, lines 16–24 · score 0.61 · Brain regions, SFARI gene, ABA, cutoff, adult, enrichment

Paper

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The authors' code

R Markdown · 50 lines · 2 KB · no license · 1 match

  1. ---
  2. title: "ABA_enrichment_SFARI"
  3. author: "Catriona Miller"
  4. date: "2024-09-24"
  5. output: html_document
  6. ---
  8. ```{r setup, include=FALSE}
  9. knitr::opts_chunk$set(echo = TRUE)
  10. ```
  12. ```{r}
  13. library(ABAEnrichment)
  14. ```
  16. ```{r}
  17. # find enriched brain regions with 1000 random sets to compare and controlling for gene length
  18. # female
  19. female_sig = read.csv('female_sig_SFARI_genes_incX.txt',header = FALSE)
  20. female_sig_in = data.frame(female_sig, is_candidate=1)
  21. res_female_devel = aba_enrich(female_sig_in, dataset='dev_effect', cutoff_quantiles=0.8, n_randsets=1000, gene_len = TRUE)
  22. res_female = aba_enrich(female_sig_in, dataset='adult', cutoff_quantiles=0.8, n_randsets=1000, gene_len = TRUE)
  23. res_female_5stage = aba_enrich(female_sig_in, dataset='5_stages', cutoff_quantiles=0.8, n_randsets=1000, gene_len = TRUE)
  24. ```
  26. ```{r}
  27. # same but male
  28. male_sig = read.csv('male_sig_SFARI_genes_incX.txt',header = FALSE)
  29. set.seed(10)
  31. male_vector <- male_sig[[1]]
  32. female_length <- length(female_sig[[1]])
  33. # Randomly sample male_vector to match the length of female_vector
  34. sampled_male_sig <- sample(male_vector, female_length, replace = FALSE)
  36. male_sig_in = data.frame(sampled_male_sig, is_candidate=1)
  37. res_male_devel = aba_enrich(male_sig_in, dataset='dev_effect', cutoff_quantiles=0.8, n_randsets=1000, gene_len = TRUE)
  38. res_male = aba_enrich(male_sig_in, dataset='adult', cutoff_quantiles=0.8, n_randsets=1000, gene_len = TRUE)
  39. res_male_5stages = aba_enrich(male_sig_in, dataset='5_stages', cutoff_quantiles=0.8, n_randsets=1000, gene_len = TRUE)
  40. ```
  43. ```{r}
  44. # both male and female
  45. all_sig = read.csv('all_sig_SFARI_genes.txt',header = FALSE)
  46. all_sig_in = data.frame(all_sig, is_candidate=1)
  47. res_all_devel = aba_enrich(all_sig_in, dataset='dev_effect', cutoff_quantiles=0.8, n_randsets=1000, gene_len = TRUE)
  48. res_all = aba_enrich(all_sig_in, dataset='adult', cutoff_quantiles=0.8, n_randsets=1000, gene_len = TRUE)
  49. res_all_5stages = aba_enrich(all_sig_in, dataset='5_stages', cutoff_quantiles=0.8, n_randsets=1000, gene_len = TRUE)
  50. ```

ABA_enrichment_SFARI.Rmd at commit c466614, no license · at the source

Overview

Authors: Catriona J. Miller1, Theo Portlock1, Denis M. Nyaga1, Justin M. O’Sullivan1,2,3,4
  1. The Liggins Institute, The University of Auckland, Auckland, New Zealand
  2. The Maurice Wilkins Centre, The University of Auckland, Auckland, New Zealand
  3. MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, United Kingdom
  4. Singapore Institute for Clinical Sciences, Agency for Science Technology and Research (A*STAR), Singapore, Singapore
Institutions: University of Auckland (New Zealand); Agency for Science, Technology and Research (Singapore); Singapore Institute for Clinical Sciences (Singapore); Maurice Wilkins Centre (New Zealand); MRC Lifecourse Epidemiology Unit (United Kingdom); University of Southampton (United Kingdom)
Journal: Frontiers in genetics, volume 17, article 1799530
Dates: received 29 January 2026; accepted 23 July 2026; published online 13 August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3389/fgene.2026.1799530 · PMID 42657396 · PMCID PMC13516016 · OpenAlex W7202263608
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), autism (population)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing
Keywords: autism, common variants, machine learning, sex-dependence, whole genome sequencing
Topic: Autism Spectrum Disorder Research (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Citations: not cited yet (Europe PMC); 60 references in the paper

Abstract

Introduction: Autism spectrum disorders (ASD) have a global prevalence of 1%, with a male-to- female diagnosis ratio of roughly 4:1. Several models have been developed to predict ASD using genetic information. However, the influence of biological sex on prediction outcomes remains underexplored.

Methods: We present an ensemble model to predict ASD, which integrates polygenic risk scores (PRSs), common genetic variants, and ASD risk genes with the MSSNG whole genome sequencing (WGS) dataset.

Results: Following training, our model achieved an accuracy of 0.68, an area under the receiver operating curve (AUROC) of 0.72, and a recall of 0.77 on the test dataset. Notably, common variants contributed more significantly to ASD prediction in males than females (p < 0.001), with accuracies of 0.69 and 0.66, respectively. The 16p11 locus emerged as particularly predictive for females (p < 0.001). Gene enrichment analysis using the Allen Brain Atlas revealed that expression of ASD risk genes that were significant in females were enriched (FWER < 0.05) in the primary somatosensory cortex, inferior parietal cortex, and parietal neocortex during fetal development. By contrast, male ASD risk gene expression was enriched (FWER < 0.05) in the dorsolateral prefrontal cortex and anterior cingulate cortex across developmental stages (fetal to adult).

Discussion: These findings underscore a sex-dependent role for common genetic variants in the risk of developing ASD. In doing so, they highlight the utility of ensemble models that incorporate common variation and biological sex for ASD prediction.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 1 match between paragraphs and lines of code.

Catriona-Miller/autism_ml

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: c4666144eceb2392a6e19859a2b822fb393ca1e5, 18 June 2026
Languages: R (2), Jupyter (2)
Size: 6 files, 4 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: 4 notebooks
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (2 files), NumPy (2 files), pandas (2 files), scikit-learn (2 files), SciPy (2 files), seaborn (2 files), SHAP (2 files), statsmodels (2 files), UMAP (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
4 files
At the source: github.com/Catriona-Miller/autism_ml

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

Tracing map

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  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 4 scripts, each with its path and the digest of its content;
  • 1 match 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

Supplementary Table S5 lists the datasets and software that were used in our analyses.

All scripts are available on GitHub (https://github.com/Catriona-Miller/autism_ml).

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, pages, dates, 4 authors, 5 keywords, 59 references.

Cite

This paper

Miller, C. J., Portlock, T., Nyaga, D. M., & O’Sullivan, J. M. (2026). Sex-dependent prediction of autism. Frontiers in genetics, 17, 1799530. https://doi.org/10.3389/fgene.2026.1799530

BibTeX

@article{miller2026sex,
author = {Miller, Catriona J. and Portlock, Theo and Nyaga, Denis M. and O’Sullivan, Justin M.},
title = {{Sex-dependent prediction of autism}},
journal = {Frontiers in genetics},
year = {2026},
month = aug,
volume = {17},
pages = {1799530},
publisher = {Frontiers Media SA},
issn = {1664-8021},
doi = {10.3389/fgene.2026.1799530},
url = {https://doi.org/10.3389/fgene.2026.1799530},
pmid = {42657396},
pmcid = {PMC13516016}
}

RIS

TY - JOUR
AU - Miller, Catriona J.
AU - Portlock, Theo
AU - Nyaga, Denis M.
AU - O’Sullivan, Justin M.
TI - Sex-dependent prediction of autism
T2 - Frontiers in genetics
J2 - Front Genet
PY - 2026
DA - 2026/08/13
VL - 17
SP - 1799530
SN - 1664-8021
PB - Frontiers Media SA
DO - 10.3389/fgene.2026.1799530
UR - https://doi.org/10.3389/fgene.2026.1799530
LA - en
ER -

CSL-JSON

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The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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