deepnull 0.1.3

Models nonlinear interactions between covariates and phenotypes

DeepNull: Modeling non-linear covariate effects improves phenotype prediction and association power

This repository contains code implementing nonlinear covariate modeling to increase power in genome-wide association studies, as described in "DeepNull: Modeling non-linear covariate effects improves phenotype prediction and association power" (Hormozdiari et al 2021). The code is written using Python 3.7 and TensorFlow 2.4.

Installation

Installation is not required to run DeepNull end-to-end; you can just open DeepNull_e2e.ipynb in colab to try it out.

To install DeepNull locally, run

pip install --upgrade pip
pip install --upgrade deepnull

on a machine with Python 3.7+. This installs a CPU-only version, as there are typically few enough covariates that using accelerators does not provide meaningful speedups.

How to run DeepNull

To run locally, there is a single required input file. This file contains the phenotype of interest and covariates used to predict the phenotype, formatted as a tab-separated file suitable for GWAS analysis with PLINK or BOLT-LMM.

Briefly, the file must contain a single header line. The first two columns must be FID and IID, and all IID values must be unique.

An example command to train DeepNull to predict the phenotype pheno from covariates age, sex, and genotyping_array is the following:

python -m deepnull.main \
  --input_tsv=/input/YOUR_PHENOCOVAR_TSV \
  --output_tsv=/output/YOUR_OUTPUT_TSV \
  --target=pheno \
  --covariates="age,sex,genotyping_array"

To see all available flags, run

python -m deepnull.main --help 2> /dev/null

Data

Datasets used to reproduce the results from the above publication are available to researchers with approved access to the UK Biobank.

NOTE: the content of this research code repository (i) is not intended to be a medical device; and (ii) is not intended for clinical use of any kind, including but not limited to diagnosis or prognosis.

This is not an officially supported Google product.