Multi-Omics Integration for Early Autism Spectrum Disorder (ASD) Diagnosis in Children

Proposal Template

1. Overview

We propose to develop and validate an open-access multi-omics bioinformatics pipeline that integrates SNP-based genomic data and gut microbiome profiles to enable early risk prediction of Autism Spectrum Disorder (ASD) in children aged 12–24 months. ASD affects approximately 1 in 36 children (Maenner et al., 2023) and is typically diagnosed after age 3, missing the critical early intervention window (Zwaigenbaum et al., 2015; Pierce et al., 2019). While genome-wide association studies have identified recurrent variants in synaptic and neurodevelopmental genes such as SHANK3, CHD8, and NRXN1 (Satterstrom et al., 2020; Grove et al., 2019), pediatric gut microbiome studies consistently report alterations including reduced Bifidobacterium and shifts in short-chain fatty acid pathways (Coretti et al., 2018; Kang et al., 2013). However, no validated integrated biomarker panel currently exists for this age group.

We confirm access to computational resources (Munster Technological University bioinformatics lab and AWS/GCP cloud), open-access datasets (SFARI, MSSNG, PGC-ASD for genomics; PRJNA516054, PRJEB23835 for microbiome), and ethics exemption (secondary analysis of fully anonymized datasets; GDPR-compliant).

The project will run over 12 months: dataset acquisition & QC (2 months), genomic and microbiome analysis (4 months), multi-omics integration and model validation (4 months), and open-access pipeline release and preprint publication (2 months).

We commit to preregistration on ResearchHub, monthly progress updates, and sharing all processed data, biomarker lists, and analysis scripts under CC BY 4.0 via GitHub, and ResearchHub.

2. Introduction

Background and Rationale:
Autism Spectrum Disorder (ASD) affects ~1 in 36 children (Maenner et al., 2023) and is often diagnosed after age 3 (Zwaigenbaum et al., 2015), limiting early intervention. Behavioral tools (e.g., M-CHAT-R/F) have variable sensitivity (Wieckowski et al., 2023) and lower performance in girls (Lockwood Estrin et al., 2021). Genomic studies identify recurrent ASD-linked variants (e.g., SHANK3, CHD8, NRXN1) (Satterstrom et al., 2020; Grove et al., 2019), while pediatric microbiome research reports reproducible compositional and functional shifts (Coretti et al., 2018; Kang et al., 2013). No validated early-age multi-omics biomarker panel exists.

Research Questions:

Can integration of SNP-based genomic data and gut microbiome profiles improve early ASD risk prediction compared to single-modality approaches?
Which specific genetic variants and microbial taxa/functions show the strongest and most reproducible associations with ASD in children aged 12-24 months?
Do multi-omics models generalize across independent datasets?

Hypotheses:
H1: Multi-omics models integrating SNP and microbiome features will achieve higher predictive accuracy (AUC) than genomic-only or microbiome-only models.
H2: ASD-associated SNPs will correlate with distinct microbial taxa and functional pathways relevant to neurodevelopment.
H3: Identified biomarkers will replicate in independent pediatric datasets.

Impact Statement:
This study will produce an open-source multi-omics pipeline, a reproducible biomarker panel, and a framework for early molecular ASD screening.

3. Methods

Participants/Sample

We will use open-access, de-identified pediatric datasets:

Genomics: SFARI Base, MSSNG, PGC-ASD GWAS.
Microbiome: PRJNA516054, PRJEB23835.
Inclusion: Children aged 12–24 months, ASD diagnosis via ADOS/DSM-5 or neurotypical controls.
Exclusion: Preterm birth (<37 weeks), severe unrelated conditions, recent antibiotic use (<1 month).
Sample size: >400 subjects combined; power analysis indicates >80% power to detect moderate effect sizes (Cohen’s d = 0.5, α = 0.05).

Materials and Procedures

Genomic data will undergo quality control, imputation, and GWAS meta-analysis, followed by functional annotation using FUMA and Ensembl VEP.
Microbiome data (16S and shotgun) will be processed with QIIME2 for taxonomic profiles and HUMAnN3 for functional pathways.
Integration will be performed via Sparse Canonical Correlation Analysis (sCCA), network mapping in Cytoscape, and feature selection with Random Forest/XGBoost.
All analyses will be conducted on MTU HPC resources and AWS/GCP cloud instances.

Planned Analyses

Preprocessing:

Genomics — SNP QC (MAF >1%, HWE p>1e-6), imputation, population stratification adjustment.

Microbiome — read filtering, rarefaction, normalization.

Modeling: Compare genomic-only, microbiome-only, and integrated multi-omics models using Random Forest/XGBoost.

Metrics: AUC, precision, recall, F1-score with 95% CI.

Hypothesis tests:

H1: Multi-omics > single modality — paired t-test on cross-validation AUC.

H2: SNP–microbiome correlations — Spearman’s ρ, pathway enrichment via g:Profiler.

Contingent rules:

IF class imbalance >20% THEN apply SMOTE or class-weighting.

IF model AUC <0.7 THEN adjust feature selection and model parameters..

Ethics and Data Management

This study involves only secondary analysis of fully anonymized, publicly available datasets; therefore, formal IRB approval is not required. All work will comply with GDPR and FAIR data principles. No personal identifiers will be used or stored.
Processed datasets, biomarker lists, and all analysis scripts will be archived on GitHub, and ResearchHub under a CC BY 4.0 license, with full pipeline documentation to ensure reproducibility.

4. Pilot Data (Optional)

A preliminary analysis on a subset of the PRJNA516054 pediatric microbiome dataset achieved AUC = 0.76 for ASD vs. control classification using Random Forest on microbial taxa features alone.
When a polygenic risk score (PRS) from publicly available ASD GWAS data was added, AUC increased to 0.82 in 5-fold cross-validation.
These results demonstrate feasibility of multi-omics integration and support the potential for improved predictive accuracy over single-modality models.

5. Budget

Budget (USD 5,000)

Item	Cost	Item Cost Purpose
Cloud computing (AWS/GCP)	$1,500	Large-scale genomic & microbiome data processing
Dataset acquisition	$1,000	Access to paywalled genomic/microbiome datasets
Software/licenses	$500	Bioinformatics tools (FUMA premium, Cytoscape plugins)
Bioinformatics consulting	$1,000	Expert support for multi-omics integration
Publication fees	$1,000	Open-access preprint & journal submission

6. References

Maenner, M. J., Warren, Z., Williams, A. R., Amoakohene, E., Bakian, A. V., Bilder, D. A., Durkin, M. S., Esler, A., Furnier, S. M., Hughes, M. M., Patrick, M., Pierce, K., Salinas, A., Shenouda, J., White, T., Zahorodny, W., & Dietz, P. M. (2023). Prevalence and characteristics of autism spectrum disorder among children aged 8 years — Autism and Developmental Disabilities Monitoring Network, 11 sites, United States, 2020. MMWR Surveillance Summaries, 72(2), 1–14. https://doi.org/10.15585/mmwr.ss7202a1
Zwaigenbaum, L., Bauman, M. L., Choueiri, R., Kasari, C., Carter, A., Granpeesheh, D., Mailloux, Z., Roley, S. S., Wagner, S., Fein, D., Pierce, K., Buie, T., Davis, P. A., Newschaffer, C., Robins, D., Wetherby, A., Stone, W. L., Yirmiya, N., Estes, A., ... Natowicz, M. R. (2015). Early identification of autism spectrum disorder: Recommendations for practice and research. Pediatrics, 136(Supplement 1), S10–S40. https://doi.org/10.1542/peds.2014-3667C
Pierce, K., Gazestani, V. H., Bacon, E., Barnes, C. C., Cha, D., Nalabolu, S., Lopez, L., Moore, A., Pence-Stophaeros, S., Courchesne, E., & Members of the GET Network. (2019). Evaluation of the diagnostic stability of the early autism spectrum disorder phenotype in the general population starting at 12 months. JAMA Pediatrics, 173(6), 578–587. https://doi.org/10.1001/jamapediatrics.2019.0624
Satterstrom, F. K., Kosmicki, J. A., Wang, J., Breen, M. S., De Rubeis, S., An, J.-Y., Peng, M., Collins, R., Grove, J., Klei, L., Stevens, C., Reichert, J., Mulhern, M. S., Artomov, M., Gerges, S., Sheppard, B., Xu, X., Bhaduri, A., Norman, U., ... Buxbaum, J. D. (2020). Large-scale exome sequencing study implicates both developmental and functional changes in the neurobiology of autism. Cell, 180(3), 568–584.e23. https://doi.org/10.1016/j.cell.2019.12.036
Grove, J., Ripke, S., Als, T. D., Mattheisen, M., Walters, R. K., Won, H., Pallesen, J., Agerbo, E., Andreassen, O. A., Anney, R., Awashti, S., Belliveau, R., Bettella, F., Buxbaum, J. D., Bybjerg-Grauholm, J., Bækvad-Hansen, M., Cerrato, F., Chambert, K., Christensen, J. H., ... Børglum, A. D. (2019). Identification of common genetic risk variants for autism spectrum disorder. Nature Genetics, 51(3), 431–444. https://doi.org/10.1038/s41588-019-0344-8
Coretti, L., Paparo, L., Riccio, M. P., Amato, F., Cuomo, M., Natale, A., Borrelli, L., Corrado, G., Comegna, M., Buommino, E., Castaldo, G., Bravaccio, C., & Chiariotti, L. (2018). Gut microbiota features in young children with autism spectrum disorders. Frontiers in Microbiology, 9, 3146. https://doi.org/10.3389/fmicb.2018.03146
Kang, D.-W., Park, J. G., Ilhan, Z. E., Wallstrom, G., LaBaer, J., Adams, J. B., & Krajmalnik-Brown, R. (2013). Reduced incidence of Prevotella and other fermenters in intestinal microflora of autistic children. PLoS ONE, 8(7), e68322. https://doi.org/10.1371/journal.pone.0068322

Multi-Omics Integration for Early Autism Spectrum Disorder (ASD) Diagnosis in Children

Multi-Omics Integration for Early Autism Spectrum Disorder (ASD) Diagnosis in Children

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Request for Proposals - Neuroscience Research Project

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