This repository contains R code to perform TreeWAS analysis. For a full description of the method see preprint here or available within this repository.

• The code is available as an R package. To install the package clone the repository and install:

R CMD INSTALL TreeWAS

or alternatively, do this from within an R session

library(devtools)
install_github("mcveanlab/TreeWAS/TreeWAS")

Data for TreeWAS analysis is encoded in three files.

Assumes two columns with header. First column has samples IDs and second column contains either a genotype (0/1/2) or a continuous variable such as a genetic risk score (GRS). Sample ID must be the first column.

ID GRS
1 2.91109682117209
2 3.58725581286855
3 4.0187625426125
4 4.25960701964853
5 3.38657230426473
6 4.27097017945458
7 3.72455637560711

A file with multiple columns and a header. One row per individual. The column ID contains sample IDs and it doesn’t need to be the first column. All other columns are assumed to be phenotypes coded as 0 or 1, column names are phenotype codes which should be present in the tree file (matching column coding in the tree file).

ID R198 R104 M512 L720 M8414 L031 K802 S662 K267
1 1 1 1 0 0 0 0 0 0 
2 0 0 0 1 0 0 0 0 0 
3 0 0 0 0 1 0 0 0 0 
4 0 0 0 0 0 1 0 0 0 
5 0 0 0 0 0 0 1 1 0 
6 0 1 0 0 0 0 0 0 1 
7 0 0 0 0 0 0 0 0 0 
8 0 0 0 0 0 0 0 0 0 
9 0 0 0 0 0 0 0 0 0 

File defining the topology of the diagnosis tree. These files can be downloaded from the UK Biobank Showcase website. For example, the tree describing the encoding of Non-cancer Illnesses (data-field 20002) is encoded using Data-Coding 6 of the UK Biobank. This file can be downloaded here. File is assumed to be tab-delimited. A function provided in the package will parse and sort the tree for TreeWAS analysis. The first few lines of the tree for Data-Coding 19 (corresponding to the ICD-10 tree) are shown below.

coding	meaning	node_id	parent_id	selectable
A00	A00 Cholera	286	23	N
A000	A00.0 Cholera due to Vibrio cholerae 01, biovar cholerae	287	286	Y
A001	A00.1 Cholera due to Vibrio cholerae 01, biovar el tor	288	286	Y
A009	A00.9 Cholera, unspecified	289	286	Y
A01	A01 Typhoid and paratyphoid fevers	290	23	N
A010	A01.0 Typhoid fever	291	290	Y
A011	A01.1 Paratyphoid fever A	292	290	Y
A012	A01.2 Paratyphoid fever B	293	290	Y
A013	A01.3 Paratyphoid fever C	294	290	Y

A list of sample IDs to include in the analysis can be parsed to the script with the --keep argument. We assume one sample ID per line.

Three scripts are provided to run TreeWAS analysis with different type of genetic variation and/or genetic models.

The script grs_tree_analysis.R performs TreeWAS analysis on a GRS. The script takes the following arguments:

To do a GRS analysis on the test data, use the following command.

./scripts/grs_tree_analysis.R \
    --sample_file=example_data/sample_file_grs.txt \
    --tree_file=example_data/tree_example_ICD10_Chap_VI.txt \
    --pheno_file=example_data/phenotype_file.txt \
    --outprefix=test_grs.res \
    --num.cores=1

The scripts cc_snp_tree_analysis.R and cc_snp_tree_analysis_additive.R perform case-control association analysis. The scripts take the following arguments:

To Run the analysis with the test data fitting an additive model do:

./scripts/cc_snp_tree_analysis_additive.R \
    --sample_file='example_data/sample_file_gen.txt' \
    --tree_file='example_data/tree_example_ICD10_Chap_VI.txt' \
    --pheno_file='example_data/phenotype_file.txt' \
    --outprefix='test_gen.res' \
    --b1_max_mag=2 \
    --b1_spac=0.02 \
    --num.cores=1

or with a full genetic model:

./scripts/cc_snp_tree_analysis.R \
    --sample_file='example_data/sample_file_gen.txt' \
    --tree_file='example_data/tree_example_ICD10_Chap_VI.txt' \
    --pheno_file='example_data/phenotype_file.txt' \
    --outprefix='test_gen2.res' \
    --theta=0.33333 \
    --p1=0.001 \
    --b1_max_mag=3 \
    --b2_max_mag=3 \
    --b1_spac=0.02 \
    --b2_spac=0.02 \
    --num.cores=1

If you use TreeWAS in your work, please cite us:

Cortes A., et al. (2017) Bayesian analysis of genetic association across tree-structured routine healthcare data in the UK Biobank. bioRxiv 105122. doi: https://doi.org/10.1101/105122