DeNovoWEST

DeNovoWEST is a simulation-based method to test for a statistically significant enrichment of damaging de novo mutations (DNMs) in individual genes. This method scores all classes of variants (e.g. nonsense, missense, splice site) on a unified severity scale based on the empirically-estimated positive predictive value of being pathogenic, and incorporates a gene-based weighting derived from the deficit of protein truncating variants in the general population.

More information on the method can be found in Kaplanis, Samocha, Wiel, Zhang et al Nature 2020.

There are three main components of this repository:

1. DeNovoWEST code
2. Input files needed to recreate the figures in the manuscript
3. Code to recreate figures in the manuscript
4. A directory to recreate the weights used in DeNovoWEST

DeNovoWEST

To run DeNovoWEST, you will need Python 3.x and R. More details are provided in the README in the DeNovoWEST directory.

Input files

This directory contains the files needed to recreate some of the main text figures from the Kaplanis, Samocha, Wiel, Zhang et al manuscript. Specifically, the following files are provided:

1. De novo mutations from 31,058 individuals with a developmental disorder
2. Extended DeNovoWEST results file with information about per gene mutation rates
3. Sex information for probands in the study
4. Positive predictive value weight files that were generated as part of DeNovoWEST
5. Results of downsampling analysis
6. Results from simulations of modelling the number of remaining HI DD-associated genes
7. Classifications for likelihood of structural malformation of ultrasound
8. Gene features used to generate Figure 2(b) in manuscript
9. Results file from DeNovoNear
10. Results file from all variant enrichment part of DeNovoWEST
11. Results file from missense variant enrichment part of DeNovoWEST
12. List of diagnostic developmental disorders according to DDG2P

Paper figure code

For each of the main text figures, there is a separate Rmarkdown file or directory with the code to generate the figures from the provided input files.

Weight creation

We have provided an Rmarkdown that gives an overview of how to create the positive predictive value (PPV) weights that are used as part of DeNovoWEST. We have included some warnings about applying this aspect of the pipeline to other datasets in that directory's README.