SPECTRA Multi-Factor Analysis SETA; Genomics, 'Omics and Computational Biology

ARPA-H's SPECTRA program (Systems for Phenotypic Evaluation, Clinical Trajectories, Response, and Agency) aims to change how autism is understood and supported. It grounds research in objective, multi-factor measurement and computation instead of behavioral observation alone. The program's central idea is that genetic, environmental, family, metabolic, immune, microbiome, and developmental factors interact in nonlinear ways. Those interactions are thought to produce the very different paths autistic people follow in development, health, communication, and daily function (ARPA-H, n.d.

-b).

• TA1 - Analysis: tools that link genetic, environmental, family, metabolic, immune, and developmental factors to these different paths.
• TA2 - Modeling/Integration: AI platforms that map those paths across many sites while keeping data private, and identify factors that can be changed to improve outcomes.
• TA3 - Precision Intervention: choosing and rigorously testing interventions matched to each person.
• TA4 - Enabling Technologies: accessible tools for communication, quality of life, and embedded assessment that measure real-world function through ordinary use.

This SETA position supports TA1, the foundation of the program's science. TA1 projects will measure and analyze the human biological and environmental data that explain why outcomes differ from person to person. TA1 will also draw on real-world measurement from TA4 (ARPA-H, 2026). Its data and findings feed TA2's models and help TA3 identify targets that interventions can act on.

• Provide internal subject matter expertise on how to characterize the factors behind different autism trajectories. These include genetics and epigenetics, metabolomics, immune profiling, the microbiome, environmental exposures, family factors, and developmental history. The program's starting idea is that nonlinear interactions among these factors shape different developmental, health, communication, and functional outcomes (ARPA-H).
• Give insight on whether proposed approaches are conceptually and technically sound:
  • Study design: cohort design, sample size, and statistical power
  • Measurement: assay and measurement choices, and batch effects
  • Data handling: combining data from different sources, and quality control
  • Analysis: reproducibility, and statistical methods for gene‑environment and other multi‑factor interactions
  • Scope: any justifiable way of measuring a person's biological state is in scope, including neuroimaging and electrophysiology. Characterization is focused on humans, not model systems such as organoids (ARPA-H, n.d.
    
    -b).
• Help set goals, expectations, and budgets for TA1 projects, including milestones for generating, harmonizing, and sharing data.
• Help review and oversee funded TA1 projects, including:
  • coordinating with TA2 so TA1 data and features can feed the federated, privacy‑preserving modeling platforms (ARPA-H)
  • coordinating with TA3 so TA1 findings point to targets that interventions can act on

• Experience in human genetics/genomics or other 'omics research (e.g., sequencing, epigenomics, transcriptomics, proteomics, metabolomics, microbiome), covering both how the data are generated and how they're analyzed
• Experience with computational methods for high‑dimensional biological bioinformatics, statistical genetics, data science, computational modeling, and/or machine learning
• Experience working with large human…