Imaging Genetics Models Skill

Overview

imaging-genetics-models provides matrix-based association, polygenic scoring, and multivariate genotype-imaging analysis. It also builds explicit PLINK2 commands without embedding or redistributing the external executable.

Supported modes

Mode	Required arrays	Output
association	genotype, phenotype	covariate-adjusted variant tests
lmm	above plus kinship	kinship-aware variant tests
prs	genotype, weights	subject polygenic score
pls	X, Y	paired latent components
cca	X, Y	canonical variates

Population structure, ancestry, batch, age, sex, site, and relatedness must be handled before genetic effects are interpreted.

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Installation

pip install numpy pandas scipy scikit-learn statsmodels joblib

For genome-wide command-line analyses, install PLINK2 separately and execute the generated command through NeuroClaw's audited shell workflow.

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Workflows

1. Variant-imaging association

Create an NPZ bundle:

genotype:   [subjects, variants]
phenotype:  [subjects] or [subjects, phenotypes]
variant_id: [variants] (optional)
covariates: [subjects, covariates] (optional)
kinship:    [subjects, subjects] (required only for `lmm`)

python skills/imaging-genetics-models/scripts/train_reference.py \
  --input imaging_genetics.npz \
  --model association \
  --output-dir run_models_output/imaging_gwas

Use --model lmm when the bundle contains a kinship matrix.

2. Polygenic score

genotype:   [subjects, variants]
weights:    [variants]
subject_id: [subjects] (optional)

python skills/imaging-genetics-models/scripts/train_reference.py \
  --input prs_bundle.npz \
  --model prs \
  --output-dir run_models_output/prs

3. PLS or CCA

X: [subjects, genetic features]
Y: [subjects, imaging phenotypes]

python skills/imaging-genetics-models/scripts/train_reference.py \
  --input imaging_genetics.npz \
  --model cca \
  --components 3 \
  --output-dir run_models_output/cca

Fit dimensionality reduction and covariate residualization inside the training data when the analysis is evaluated predictively.

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Input / Output Summary

Mode	Main output
Association/LMM	association_results.csv
PRS	predictions.csv with polygenic_score
PLS/CCA	predictions.csv with paired component scores
PLS/CCA model	checkpoint.joblib
All modes	metrics.json, config.json, run_manifest.json

Never split related individuals across train and test folds. Record genome build, allele orientation, QC thresholds, ancestry definition, and phenotype construction with every analysis.

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Testing

pytest models/tests/test_extended_models.py -q
python skills/imaging-genetics-models/scripts/train_reference.py --help

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Directory Reference

models/imaging_genetics/
├── association.py      association, LMM, and polygenic score utilities
├── multivariate.py     PLS and CCA
├── plink.py            audited PLINK2 command builder
└── train.py            matrix-analysis CLI

skills/imaging-genetics-models/
├── SKILL.md
└── scripts/train_reference.py

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Reference

• PLINK2 remains an external executable with independent installation terms.
• Association outputs include effect estimates and multiplicity-ready P values.

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Created At: 2026-07-26 HKT Last Updated At: 2026-07-29 HKT Author: chengwang96