Metadata-Version: 2.1
Name: deepnull
Version: 0.1.3
Summary: Models nonlinear interactions between covariates and phenotypes
Home-page: https://github.com/google-health/genomics-research/tree/main/nonlinear-covariate-gwas
Author: Google LLC
License: UNKNOWN
Description: # 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](https://colab.research.google.com/github/Google-Health/genomics-research/blob/main/nonlinear-covariate-gwas/DeepNull_e2e.ipynb)
        to try it out.
        
        To install DeepNull locally, run
        
        ```bash
        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](https://www.cog-genomics.org/plink/2.0/assoc) or
        [BOLT-LMM](https://alkesgroup.broadinstitute.org/BOLT-LMM/BOLT-LMM_manual.html).
        
        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:
        
        ```bash
        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
        
        ```bash
        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](https://www.ukbiobank.ac.uk/).
        
        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.
        
Keywords: GWAS
Platform: UNKNOWN
Requires-Python: >=3.7, <4
Description-Content-Type: text/markdown
