Early Intervention Program for Children with Autism

Early Intervention Program for Children with Autism is a grant opportunity from the National Institutes of Health (NIH) that funds data science research to advance understanding of autism spectrum disorder (ASD) etiology, prevalence, and outcomes.

Through the NIH Autism Data Science Initiative (ADSI), awards under the OT2 mechanism support projects integrating genetic, environmental, and clinical datasets to identify causes of rising ASD prevalence, model gene-environment interactions, and improve clinical services for autistic individuals across the lifespan. Eligible applicants include universities and research institutions.

Award amounts vary by project; funded institutions include Boston Children's Hospital, UCLA, UNC Chapel Hill, Drexel University, and Emory University.

Autism Data Science Initiative Funded Research | DPCPSI Autism Data Science Initiative Funded Research Autism Data Science Initiative Funded Research Funded Projects #allabstracttable Click on the link for the project title below to see the full award abstract.

Contact PI/Project Leader Project Number Awardee Organization Title Areas of Research & ADSI Task Areas Chung, Wendy K 1OT2OD040407 Boston Children’s Hospital Integrating Genetic and Environmental Data to Predict Autism Susceptibility and Heterogeneity in the SPARK Cohort Etiology/Causation Cochran, Amy Louise 1OT2OD040368 University of Wisconsin-Madison Understanding causes of autism, its heterogeneity, and rising prevalence with emerging methods in causal inference Prevalence Task I: Dataset Aggregation Geschwind, Daniel H 1OT2OD040572 University of California Los Angeles Data science approaches to autism: environmental modulators of the transcriptome and gene-x-environment interactions Etiology/Causation Klinger, Laura G 1OT2OD040528 University of North Carolina Chapel Hill From Data to Action: Capturing Meaningful Outcomes in Autism Through Harmonized Data Clinical Services/Treatment Task I: Dataset Aggregation Liu, Zhandong 1OT2OD040565 Baylor College of Medicine Validate ASD: Independent Multimodal Replication and Validation of Autism Data-Science Models Task IV: Model Validation/Replication Lyall, Kristen 1OT2OD040445 Drexel University Diet and the Exposome in Autism: Discovering Complex Interactions with Diet in Autism Etiology And Variability Etiology/Causation Task I: Dataset Aggregation Miller, Judith Susanne 1OT2OD040373 Children's Hospital Of Philadelphia Genomic and Exposomic Factors in the Cause and Rise of Autism Prevalence Task I: Dataset Aggregation Sebat, Jonathan 1OT2OD040415 University of California, San Diego Elucidating the Interplay of Genes and Environment in Autism Using Genomic and Exposure Data from Large Populations Etiology/ Causation Task I: Dataset Aggregation Stein, Jason Louis 1OT2OD040436 University of North Carolina Chapel Hill Modeling autism-associated gene-by-environment interactions in human brain organoids Etiology/ Causation Task I: Dataset Aggregation Walker, Douglas Ian 1OT2OD040409 Emory University Mapping Internal Exposome-Metabolome Dynamics with Advanced Data Science to Identify Environmental Determinants of Autism.

Etiology/Causation Wang, Xiaobin 1OT2OD040418 Johns Hopkins University Boston Birth Cohort - Autism Data Science Initiative (BBC-ADSI) Etiology/Causation Zhong, Hua Judy 1OT2OD040511 Weill Medical College Of Cornell Univ AR² - Autism Replication, Validation, and Reproducibility Center Task IV: Model Zuckerman, Katharine Elizabeth 1OT2OD040512 Oregon Health & Science University Advancing Success & Developmental Outcomes in Autism Spectrum Disorder through Analysis of Secondary Data (ASD3 Outcomes Project) Clinical Services/Treatment Task I: Dataset Aggregation Contact PI/Project Leader Project Number Awardee Organization Areas of Research & ADSI Task Areas Chung, Wendy K 1OT2OD040407 Boston Children’s Hospital Etiology/Causation Integrating Genetic and Environmental Data to Predict Autism Susceptibility and Heterogeneity in the SPARK Cohort Although autism is known to be highly heritable, the lack of 100% concordance in monozygotic twins and stronger concordance between dizygotic twins over full siblings offers strong evidence for environmental susceptibility factors.

There is accumulating evidence for several non-genetic factors, including air pollution, pesticides, heavy metals and pregnancy-related complications.

However, studies of environmental susceptibility have been limited by modest sample sizes, indirect measurement and incomplete characterization of exposures, and lack of a multi-dimensional approach that accounts for the interplay between genetic and environmental factors that impact the probability of autism as well as its heterogenous trajectories.

The infrastructure already in place in SPARK, a large, US-based cohort of >150,000 recontactable people with autism, offers a unique opportunity to implement a large-scale integrative study design to identify prenatal and early life exposures that impact autism and how exposures interact with genetic risk.

Our long-term goal is to identify exposures that influence autism and understand the molecular mechanisms by which these exposures impact the genome, epigenome, and metabolome. The objective of this proposal is to characterize prenatal and early life exposures in >20,000 children with autism and identify exposures that impact probability of autism, developmental trajectories in autism and response to behavioral intervention.

We will also investigate the interaction between environmental exposures and genomic risk. In our first aim, we will use multivariate regression and machine learning methods to identify geospatial exposures associated with social communication abilities and response to educational/behavioral intervention in thousands of individuals with autism.

In the second aim, we will evaluate gene-environment interactions using the geospatial exposome and the entire distribution of autism genetic risk variants, including common and rare variants, in thousands of individuals with autism in SPARK.

In the third aim we will directly measure exogenous exposures and endogenous biological responses in the perinatal period by performing untargeted high-resolution exposomics on residual newborn blood spots from a subset of the SPARK cohort. We will also perform long-read DNA sequencing to assess DNA methylation epigenetic signatures associated with environmental exposures.

Ultimately, our goal is to build a model that can help clinicians and families assess genetic and nongenetic probability of autism and predict response to treatment to maximize the potential of individuals with autism.

The findings from this study will be significant because this will be the first large-scale investigation that comprehensively evaluates the interplay between exposomic and genomic risk factors in autism and will yield novel insights into the mechanisms that drive risk and resilience in this complex condition.

Contact PI/Project Leader Project Number Awardee Organization Areas of Research & ADSI Task Areas Cochran, Amy Louise 1OT2OD040368 University of Wisconsin-Madison Prevalence Task I: Dataset Aggregation Understanding causes of autism, its heterogeneity, and rising prevalence with emerging methods in causal inference Over the past five decades, autism prevalence in the United States has risen from fewer than 1 in 2,000 children to approximately 1 in 31.

Over the same period, early-life exposures—such as parental age, maternal diabetes, and prenatal complications—have changed dramatically. These parallel trends raise urgent questions: Are these early-life factors contributing to the increase in autism diagnoses? And how might these factors shape outcomes that are meaningful to autistic individuals and families?

This project addresses these questions by applying modern causal inference methods to large, population-based datasets. Unlike traditional regression models, which estimate associations, causal models allow researchers to simulate what would happen if a single exposure were changed while holding all else constant.

This enables valid estimation of both how much a risk factor contributes to autism diagnosis and related outcomes—such as communication or emotional regulation—and how much of the increase in autism prevalence can be attributed to historical shifts in these factors.

By combining these models with flexible, data-driven approaches that capture the heterogeneity in autism, the project may also identify distinct pathways by which early-life factors may lead to different autism phenotypes. The study has four specific aims.

First, researchers will prepare two complementary datasets for causal analysis: (1) the Study to Explore Early Development, a multisite case-control study led by the CDC that includes extensive behavioral, developmental, perinatal, and biological data; and (2) electronic health records from a large Midwestern health system linked to birth certificates.

Second, causal models will be used to estimate the effect of early-life exposures on autism occurrence. Third, exposures will be jointly modeled with autism phenotypes to uncover causal pathways that may be obscured by traditional analytic approaches. Fourth, these models will be applied to longitudinal data to estimate the portion of the increase in autism prevalence attributable to historical shifts in early-life exposures.

Findings will clarify which early-life factors have the strongest causal effects, which developmental outcomes they influence, and the extent to which they may have contributed to rising autism prevalence. Results will inform strategies for intervention and early identification and provide a foundation for prioritizing biological mechanisms and modifiable exposures.

Code and models will be publicly released in a modular, template-based format to enable replication across datasets such as CHARGE, SFARI, All of Us, and TriNetX.

The research team brings expertise in autism epidemiology, causal inference, clinical informatics, and participatory methods and includes individuals with lived experience of autism, ensuring that the work is rigorous, actionable, and grounded in the perspectives of the autism community.

Contact PI/Project Leader Project Number Awardee Organization Areas of Research & ADSI Task Areas Geschwind, Daniel H 1OT2OD040572 University of California Los Angeles Etiology/Causation Data science approaches to autism: environmental modulators of the transcriptome and gene-x-environment interactions The prevalence of autism spectrum disorder (ASD) has dramatically increased in recent years to a rate of 1 in 31 U. S children.

ASD risk is multifactorial, with genetic and environmental elements each playing a role. While major progress has been made in identifying causal genetic variants, environmental— particularly prenatal—exposures are increasingly recognized as critical contributors to neurodevelopmental risk. Over 200 high-production volume chemicals are routinely detected in American adults, including pregnant women.

The ways in which environmental factors contribute to ASD risk (and how they interact with genetic risk) remain unclear, however. Understanding how these environmental chemicals influence neurodevelopment at the cellular and molecular level is crucial for development of mitigation strategies.

Progress has been hampered by lack of large-scale data assessing the impact of chemicals at a cellular and molecular level in human neural cells that faithfully model early human brain development. A major barrier to progress has been the absence of scalable experimental platforms capable of evaluating compound exposures in ASD-relevant cell types and with comprehensive molecular readouts.

Most existing studies rely on immortalized cell lines, which do not faithfully model human brain development. Here, in this project: “Data science approaches to autism: environmental modulators of the transcriptome and gene-x-environment interactions,” we address this substantial gap in the field by capitalizing on recent advances in genomics, stem cell technology, and data science.

We will assess the activity of thousands of chemicals in the primary cell types implicated by ASD genetic risk, human neural progenitor cells and neurons. We leverage human stem cell-based model systems, which have been shown to accurately and reliably model key aspects of human brain development.

We couple this with high-throughput culturing and robotic systems, which provide a unique opportunity to efficiently screen the entire ToxCast II/III library of ~4,700 chemical compounds to which humans are exposed, in both human primary neural progenitors (phNPCs) and induced neurons (iNeurons).

We use an unbiased, genome-wide measurement at the cellular level, single-nucleus RNAseq, to comprehensively understand the effects that chemical exposures have on human neural development and to study the impacted gene regulatory networks and pathways. We will integrate this with public exposure data and molecular profiling in the human brain to find overlapping pathways.

We will then evaluate how genetic background modulates cellular responses by using cell villages to model gene–environment (GxE) interactions across a cohort of 150 donors, including 110 individuals with ASD and 40 neurotypical controls.

Using this framework, we will map exposure-responsive expression quantitative trait loci (eQTLs) for more than 40 chemicals with a novel method developed by one of the PIs, enabling the identification of GxE interactions at scale. We will integrate these data with publicly available data sources (e.g. PsychENCODE and ECHO).

We will model the biophysical interactions between relevant chemicals and proteins, to provide molecular insights into the underlying mechanisms of action – namely, the means by which compounds induce systemic gene expression via perturbations in gene regulatory networks. We will build a public resource that will enable further mechanistic investigation.

Our project will: 1) provide insight into mechanisms of action underlying known autism risk factors, 2) identify novel chemical risk factors that impact essential pathways in early brain development, and 3) query how genetic factors modulate susceptibility and resilience to chemical exposures, which will be comprehensively integrated with existing databases and resources.

Contact PI/Project Leader Project Number Awardee Organization Areas of Research & ADSI Task Areas Klinger, Laura G 1OT2OD040528 University of North Carolina Chapel Hill Clinical Services/Treatment Task I: Dataset Aggregation From Data to Action: Capturing Meaningful Outcomes in Autism Through Harmonized Data There are more autistic adults in the service system than ever before, and this number is estimated to increase by as much as 300% by 2030.

Outcomes for autistic adults are often characterized by low rates of employment and community participation, and high rates of anxiety, depression, and suicidality.

Our community partners, including autistic adults and caregivers, have identified the need for research across the adult lifespan that identifies intervention and service targets to promote a high quality of life characterized by good mental health and community participation.

We believe that generating a better understanding of autistic adults, including those in mid- and later life, is a pressing public health problem where rapid advancements are needed. To date, however, the field of adult autism research has primarily relied on small, non-representative samples limited to young adulthood.

This has hindered our ability to understand outcomes and identify service targets across the adult lifespan and the full autism spectrum (i.e., inclusive of those with and without intellectual disability and a variety of levels of support needs). In response to ROA-OTA-25-006, we leverage an unprecedented collaboration between senior researchers across 4 NIH Autism Center of Excellence (ACE) sites focused on autistic adults.

We employ a data driven approach to characterize service needs and targets in cross-sectional and longitudinal samples of autistic adults representative of the full adult lifespan and the full autism spectrum. We will deploy two complementary datasets that are optimally poised to make rapid advancements in service access, NIMH Data Archive (NDA) and Simons Powering Autism Research (SPARK).

The NDA data collected across ACE projects provides one of the largest samples of well-characterized autistic adults (n=1,400). The SPARK dataset represents one of the largest longitudinal samples (n=400) of autistic adults. This proposal has three tasks: (I) Dataset Aggregation from the NDA to harmonize the parallel assessment protocol developed by the study team and utilized across 4 ACE sites to identify modifiable adult outcomes.

(II) Data Generation to 1) generate geocodes to examine the influence of environmental and neighborhood factors (e.g., socioeconomic factors, provider density, environmental exposures) on service use and needs and trajectories of change in adult outcomes and 2) collect a third time point of data in a longitudinal cohort of 400 autistic adults from SPARK to examine trajectories of change in these modifiable outcomes across adulthood.

This additional data collection will complement our aggregated NDA data by advancing our ability to answer questions about drivers of service needs measured over time across the entirety of the dynamic adult years. (III) Data Analyses using both machine learning and (longitudinal) latent transition analyses.

We will use machine learning to identify subgroups in the NDA dataset based on their differing profiles of service use and needs and predict subgroup membership based on service targets and sociodemographic indicators.

This will allow us to examine specific hypotheses focused on identifying the greatest service needs in this population, the relation between service needs and service targets to improve adult outcomes (mental health, community participation, quality of life). We will use latent transition analysis in the SPARK dataset to identify drivers of service use and needs based on trajectories of change across time.

This will allow us to identify longitudinal trajectories of adult outcomes and their relation to service needs. Our work will be conducted in partnership with a 20-member Community Advisory Board (CAB) that includes autistic adults, family members/caregivers, researchers, clinicians and service providers for autistic adults, and state services representatives. The CAB will identify community-driven priorities for analysis.

It will also provide interpretation of findings that will inform innovative, implementation-ready approaches to enhance meaningful outcomes for autistic adults. The intensive exchange of knowledge between the CAB and the data science team will produce a team of autistic community research partners experienced in collaboration with potential for major contributions to public service well beyond this grant.

This OTA directly addresses the Autism Data Science Initiative goals by using multiple data sources to characterize service utilization patterns and pinpoint potential service targets that will lead to effective and scalable interventions across the lifespan.

This work will advance our understanding of adulthood and aging in autistic people and accelerate community-informed, efficient, and effective policy and program priorities that address service access to improve quality of life, increase community participation, and reduce co-occurring mental health challenges.

Contact PI/Project Leader Project Number Awardee Organization Areas of Research & ADSI Task Areas Liu, Zhandong 1OT2OD040565 Baylor College of Medicine Task IV: Model Validation/Replication Validate ASD: Independent Multimodal Replication and Validation of Autism Data-Science Models This project will develop a robust and transparent framework for the validation and replication of autism data science models through a multi-modal, cross-institutional approach.

The initiative will employ comprehensive datasets from various sources, including clinical, genomic, environmental, and physiological data from Texas Children’s Hospital (TCH) and other prominent research repositories. By leveraging advanced AI/ML methods, we will rigorously assess model performance across different pediatric populations, ensuring generalizability and fairness in clinical settings.

We will implement a two-pronged validation strategy, including intact model testing and code-blinded replication. This methodology will rigorously evaluate the accuracy, reproducibility, and population-specific calibration needs of predictive models.

Key to this effort will be a large-scale integration of structured clinical data from TCH's Research Data Warehouse, combined with genomic data from the SPARK (Simons Powering Autism Research for Knowledge) genetic cohort and metabolomics data from the BaBS (Bacteria and Birth Study). Additionally, environmental exposure models will be validated with placental biomarkers linked to neurodevelopmental outcomes.

The project will provide essential insights into the limitations and strengths of autism-related AI models in real-world applications, guiding their future clinical deployment.

Through a commitment to transparency, reproducibility, and stakeholder engagement, the project will deliver high-impact validation reports, including community-accessible tools that ensure models are utilized effectively across a broad range of patient populations. The outcomes of this work will enhance autism care and set a new standard for multimodal, multi-institutional model validation in pediatric health research.

Contact PI/Project Leader Project Number Awardee Organization Areas of Research & ADSI Task Areas Lyall, Kristen 1OT2OD040445 Drexel University Etiology/Causation Task I: Dataset Aggregation Diet and the Exposome in Autism: Discovering Complex Interactions with Diet in Autism Etiology And Variability Multiple factors contribute to the development of autism spectrum disorder (ASD), a developmental condition with early onset and heterogeneous phenotype.

Prenatal diet is one of the few factors that has been associated with reductions in risk of ASD, and emerging evidence also suggests certain nutrients may mitigate the effects of other environmental exposures. At the same time, packaged and highly processed foods represent a major source of exposure to classes of chemicals linked with adverse neurodevelopment.

The balance of risks and benefits in the diet, and effects on ASD, particularly within the context of other common risk factors, has not been well studied. The overarching goal of this project is to determine the role of diet and its complex interactions with other exposures in the development and presentation of autism spectrum disorder (ASD).

This project will use existing data from up to 10,000 mother-child participants from the Environmental influences on Child Health Outcomes (ECHO) consortium, a large US program initiated with over 60 cohorts following a common protocol to study child health from gestation to adulthood. We address key gaps in the field under 3 related aims. 1) Examine complex interactions of prenatal diet and exposure to common chemicals on ASD.

We focus on chemical exposures with diet as a major source and in common use, including pesticides, phthalates, and phenols, to better understand risks within the diet.

We will first examine independent effects of under-studied but highly consumed “high burden foods” on ASD and related traits, and next use advanced mixture models to capture interactions between nutrients and measured levels of chemicals, and address potential mitigation of chemical exposure effects across sources by healthy components of the diet.

We will utilize existing metabolomics data from prenatal measurements, which capture the biological effects of exposures, to determine pathways that may link these factors to ASD and bolster evidence for mechanisms that underlie associations. Next, we expand the consideration of the role of diet to: 2). Examine multi-exposure effects on ASD to determine key players in the prenatal exposome.

Building from our prior work in ECHO, we will examine the ability of prenatal dietary factors to modify the effects of another common exposure rising in prevalence in the US in parallel with ASD: maternal metabolic conditions during pregnancy (which include obesity, gestational diabetes, and gestational hypertension).

We will then use advanced data science approaches to determine how the wider set of prenatal risk factors across environmental, medical and lifestyle factors contributes to the gestational “exposome,” (the entirety of exposures), to influence ASD risk, and uncover key factors. As in aim 1, these analyses will be followed by mechanistic work using metabolomics data to determine key pathways underlying identified signals.

As secondary outcomes across Aims 1 and 2, we will examine risk of not just ASD itself, but also its highly co-occurring conditions and ASD-related traits, to advance understanding of contributors to variability in ASD. Finally, we will make use of the large numbers in ECHO with information on childhood exposures to: 3).

Determine dietary risks and deficiencies in children with ASD and examine how these contribute to phenotypic variability. These analyses will determine if autistic children experience worse nutrition than children without ASD, including higher intake of high burden foods, and assess whether these dietary differences contribute to symptom severity or risk of comorbidities, accounting for prenatal maternal exposures.

Completion of these aims will aid discovery of novel interactions and exposures whose effects may be lessened or made worse by different aspects of diet. Ultimately, findings from this project will advance understanding of the role of diet in ASD and the broader ASD exposome, and present opportunities for interventions with the potential to reduce risks and improve the lives of autistic individuals.

Contact PI/Project Leader Project Number Awardee Organization Areas of Research & ADSI Task Areas Miller, Judith Susanne 1OT2OD040373 Children's Hospital Of Philadelphia Prevalence Task I: Dataset Aggregation Genomic and Exposomic Factors in the Cause and Rise of Autism This collaboration between the Children’s Hospital of Philadelphia (CHOP) and the University of Pennsylvania (Penn) focuses exactly on the key priorities set out for Autism Data Science Initiative: 1) to identify genetic and environmental factors that lead to an autism diagnosis and 2) determine how these factors have either contributed to or are reflected in the rising prevalence.

Most of the existing research on environmental contributions to ASD focuses on isolated exposures, neglecting the systemic nature of real-world environmental interactions. This gap limits the development of actionable risk models for early diagnosis and personalized intervention.

By integrating multi-level exposome data (geocoded structural factors, individual social determinants, and perinatal exposures) with genomic and omic profiles, this study will advance understanding of how environmental contexts interact with biological susceptibility to shape ASD phenotypes, offering a foundation for tailored clinical management and novel preventive strategies.

While publicly available research datasets are critical to this effort, an integrated database on a well characterized, large, clinical population is essential. CHOP successfully implemented universal early autism screening in its primary care network in 2011, and has studied early screening, diagnostic outcomes, and prevalence through electronic health record (EHR) data since then.

This cohort now includes 104,405 children born between 2008-2017 who were screened for autism at an 18- or 24-month well-child visit and had at least one additional visit within the CHOP network at 4+ years of age. Thus, this is a well characterized and research-ready dataset of ~4000 children with autism and ~100,000 without.

Our proposed three-year project is to aggregate clinical and genomic data from CHOP’s EHR, clinical and research biorepositories; with Penn’s EHR data on pregnancy, maternal, and birth outcomes, and research databases (ROA Task 1). We will also generate geo-coded exposomic data including daily air and water components, greenspace, built environment, and the Childhood Opportunity Index during pregnancy, birth, and childhood (ROA Task 2).

We will then use advanced data science methods to generate hypotheses about relationships among and between variables to predict autism diagnosis and explain the increased prevalence over time (ROA Task 3).

With this resource and the predictive power of our machine learning analytic plan, this project will have the statistical power to identify potential causes of autism in specific populations (e.g., starting with genetic and phenotypic subgroups).

We will develop a prediction model that incorporates child-, family-, neighborhood- and community-level clinical, genomic, and exposomic data to predict autism in our cohort of 104,405 children who were all screened for autism as toddlers and who are now age 8-17 years.

We will also identify genomic, exposomic, clinical-practice factors, and their interactions, that contribute to the increase in autism prevalence and heterogeneity of the autism phenotype, including increases in medical and genetic risks; changes in environmental exposures; and critical changes in diagnostic practice, criteria, and service availability.

Our multi-disciplinary team represents the best clinical, informatics, genetics, data science, and community engagement expertise for developing machine learning methods and tools for large-scale, high-resolution, meaningful, and actionable autism research.

If successfully implemented, this study will create an unprecedented data resource for researchers to derive specific, testable causal hypotheses and definitively answer questions regarding causes of autism in a way that was not previously possible. Furthermore, the results will parse the variance across broad domains of genomic and exposomic factors that may cause autism, which is critical for setting future research priorities.

#Table8 Contact PI/Project Leader Project Number Awardee Organization Areas of Research & ADSI Task Areas Sebat, Jonathan 1OT2OD040415 University of California, San Diego Etiology/ Causation Task I: Dataset Aggregation Elucidating the Interplay of Genes and Environment in Autism Using Genomic and Exposure Data from Large Populations Autism spectrum disorder (ASD) is a multifactorial neurodevelopmental condition influenced by both genetic and environmental factors.

While rare and common genetic variants have identified over 100 ASD-associated genes and copy number variants (CNVs), the mechanisms through which environmental exposures modify genetic susceptibility remain poorly understood. A major barrier to progress has been insufficient sample size to rigorously investigate gene-environment interactions (G×E). To address this gap, we have assembled a combined dataset of 2.

7 million individuals with paired genetic, environmental, and clinical data across multiple cohorts. This study will apply innovative causal inference frameworks to disentangle direct environmental effects from gene-environment correlation, identify genetic modifiers of environmental exposures, and develop predictive models integrating genetic, environmental, and clinical data.

In Aim 1, we will investigate prenatal exposures and early-life environmental factors in two deeply phenotyped cohorts (ABCD, HBCD) with genome-wide genetic data, neurodevelopmental assessments, neuroimaging, and EEG. In Aim 2, we will examine G×E effects in neonatal intensive care using whole genome sequencing and perinatal electronic health record (EHR) data from the BeginNGS consortium.

In Aim 3, we will conduct large-scale meta-analyses of these studies with large biobank and health system cohorts and the SPARK ASD family cohort to identify G×E interactions, and to elucidate causal pathways through which environmental exposures contribute to ASD.

The proposed study will generate novel insights into modifiable environmental risk factors and their interaction with genetic susceptibility to ASD, providing a foundation for targeted prevention and personalized intervention strategies.

#Table9 Contact PI/Project Leader Project Number Awardee Organization Areas of Research & ADSI Task Areas Stein, Jason Louis 1OT2OD040436-01 University of North Carolina Chapel Hill Etiology/ Causation Task I: Dataset Aggregation Modeling autism-associated gene-by-environment interactions in human brain organoids Genetic studies have been highly successful in identifying causal factors associated with autism spectrum disorders (ASD).

However, current genetic associations do not completely explain liability for this complex disorder. Several environmental exposures, like air pollution, pesticide or heavy metal exposures, are associated with ASD through epidemiological studies.

However, unlike genetic studies, these environmental epidemiological associations have inherent problems including that environmental factors are difficult to quantify, and confounding variables are prevalent.

In addition, while epidemiological datasets can highlight risk factors for autism, developing new treatments requires an understanding of the cellular and molecular consequences of genetics and environmental exposures on brain cells.

While both genetic and environmental risk factors for ASD have been identified, how genetic background accentuates or blunts response to environmental risk factors, termed gene x environment interactions (GxE), has not been well studied.

Our group has pioneered the “GxE in a dish” approach to identify genetic variants modulating response to environmental toxicants using human neural cells including brain organoids where exposures can be tightly controlled.

To identify how the combination of genetic variation and environmental exposures lead to risk for ASD, to identify cellular and molecular mechanisms mediating these effects, and to suggest treatment targets to reverse these, we will conduct “GxE in a dish” studies using cortical organoids to understand the cellular and molecular consequences of autism-associated environmental toxicants modulated by genetic variation.

We will conduct acute and chronic exposures on cortical organoids derived from 115 unique participants to environmentally relevant concentrations of 6 toxicants and vehicle in cortical organoids, modeling exposures during pregnancy, and we will perform single cell RNA-seq (scRNA-seq) for each organoid.

We will identify common genetic variants associated with differences in gene regulation in response to environmental exposures within each identified cell type. Then, we will determine the impact of exposure-sensitive genetic variants on ASD diagnosis, intellectual ability, and brain development.

These analyses will highlight whether exposure-sensitive alleles accentuate ASD risk allele effects within specific cell types of the developing brain. Overall, our proposal will identify genetic variants exerting cell-type specific modulations on environmental exposures through a novel “GxE in a dish” design.

Successful completion of these aims may uncover unexplained risk for ASD that cannot be accounted for by genetics or exposures in isolation. #Table10 Contact PI/Project Leader Project Number Awardee Organization Areas of Research & ADSI Task Areas Walker, Douglas Ian 1OT2OD040409 Emory University Etiology/Causation Mapping Internal Exposome-Metabolome Dynamics with Advanced Data Science to Identify Environmental Determinants of Autism.

Neurodevelopmental disabilities among autistic people are an increasing public health concern in the U.S. Current prevalence estimates indicate that 1 in 31 school-aged children have autism, and the increase in recent decades strongly supports environmental factors as key contributors. However, there have been no systematic studies of complex environmental exposures contributing to the likelihood of developing autism.

Leveraging a powerful untargeted high-resolution mass spectrometry (HRMS) approach and two large, multi-site autism studies from the U.S. that already include extensive genetic, omics, questionnaire, targeted exposure, and phenotype data, we will create the largest exposome database for autism and an Autism Exposome Atlas well-powered for foundational discovery