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Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration

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A data-driven framework linking the connectome to spatial gene expression gradients inspired by chemoaffinity theory.

Jigen Koike^1,2, Ken Nakae^3,4, Riichiro Hira⁵

¹Laboratory of Data-driven Biology, Graduate School of Integrated Sciences for Life, Hiroshima University, Higashihiroshima 739-8526, Japan.

Proceedings of the National Academy of Sciences of the United States of America

|March 3, 2026

Summary

SPERRFY operationalizes Sperry

Keywords:

canonical correlation analysis (CCA)chemoaffinity theory connectome neural wiring transcriptome

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Area of Science:

Neuroscience
Genetics
Computational Biology

Background:

Understanding genetic mechanisms of brain-wide neural circuit wiring is crucial.
Sperry's chemoaffinity theory explains axonal projections via molecular gradients, but is limited to sensory systems.

Purpose of the Study:

To develop a data-driven framework, SPERRFY, for applying Sperry's theory to whole-brain connectivity.
To infer latent positional gradients guiding axonal wiring across the entire brain.

Main Methods:

Integrated connectomic data with spatial transcriptomic profiles from the Allen Mouse Brain Atlas.
Employed canonical correlation analysis (CCA) to identify key positional gradients.
Developed connectivity reconstruction models based on inferred gradients.

Main Results:

SPERRFY successfully inferred latent positional gradients underlying axonal wiring.
Connectivity reconstruction demonstrated strong predictive performance.
Identified candidate genes potentially involved in positional wiring.

Conclusions:

SPERRFY extends Sperry's chemoaffinity theory to the whole-brain scale.
Provides a unified framework for understanding genetically encoded brain-wide circuitry.
Offers molecular insights into neural development and connectivity.