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Admixed GWAS for Related Individuals Conditioning On Local Ancestry

Reason this release was yanked:

Bug in score tests for quant traits

Project description

agricola

agricola is a Python package and command-line tool for conducting genome-wide association studies (GWAS) in admixed populations. Inspired by regenie and Tractor, agricola provides a scalable, local-ancestry–aware framework that handles relatedness, population structure, and ancestry effect heterogeneity.

Full documentation can be found here


Why agricola?

Admixed individuals have unique LD patterns that can improve signal localization and improve power for population-specific causal variants. However, standard GWAS tools fail to adjust for local ancestry or model effect heterogeneity in admixed individuals.

Tools like Tractor and SAIGE-Tractor address this gap. agricola follows the same conceptual approach-performing single-variant association tests with explicit local ancestry adjustment-but combines it with:

  • Accelerated linear algebra via JAX
  • CUDA GPU, TPU, or CPU support for flexible compute environments
  • Efficient local ancestry queries using lanctools
  • Multi-phenotype modeling
  • Adjustment for sample relatedness

Installation

Requirements: Python 3.10+

Install via pip:

pip install agricola

For GPU or TPU support:

pip install agricola[cuda]
pip install agricola[tpu]

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