Welcome to the public organisation to store software developed by members of the IMS-MRC Epidemiology Unit for the UK Biobank Research Access Platform (RAP). The information here describes individual software and workflows stored as repositories within this organisation.

This organisation and many of the repositories here-in are maintained by Eugene Gardner:

eugene.gardner at mrc-epid.cam.ac.uk

Other maintainers include:

T.B.D.

• Relevant DNA Nexus Projects
• Using Applets on DNANexus
  • Cloning an Applet
  • Docker Images
    • Building A Docker Image
• Workflows
  • VCF Filtering and Rare Variant Burden Testing
    • Command-Line Examples
    • Provided Resources
      • Filtered and Annotated BGEN Files
      • Sample Lists
      • Transcript Annotation
      • Collapsed Variants
• Tutorials
• Additional Applets

DNANexus stores data and resources within individual projects. Projects are whatever you choose for them to be, but in the case of this organisation, we have siloed individual "ideas" within a project to ensure that data and resources are kept separate. For example, we can have one project studying the effects of rare variation on testosterone levels and another on a GWAS of age at voice breaking. These two "ideas" will be stored in separate projects on the DNANexus platform and each will receive:

• A unique hash identifier like: project-1234567890ABCDEFGHIJ
• A separate set of linked UKBiobank resources (if requested) free of charge
• An independent billing profile (allowing for billing to different organisations/research groups)

This means we can essentially create as many projects as we would like to keep thoughts/ideas/people organised. The one limitation to this is sharing of software/tools created in one project versus another. Please see below what this means.

This organisation has three seperate projects:

When generating software/tools for the DNANexus platform, one can create either an Applet or an App. The differences are documented in more detail here, but in brief:

• Applets are specific to a project
• Apps are visible to all projects on the DNANexus platform (with some settable restrictions by the developer).

Here, we have provided the sourcecode for applets, and thus each repository within this organisation is the source code for one "applet". Please see the DNANexus documentation on applets to learn about how they are created, maintained, and run:

https://documentation.dnanexus.com/developer/apps.

Each applet in this organisation comes with three (among other) files:

1. Source code – Source code is located in src/ as a single script with a .py suffix. Source code should be heavily commented to enable use by others.
2. Applet metadata – A dxapp.json in the root directory. This file holds applet metadata to tell DNANexus how to build the app. How this file is constructed is documented here
3. Documentation – A github-format Markdown. This is where you will find the most relevant information on what the applet does and how to use it.

Brief instructions here on how to pull an applet from this organisation with github, and building it on the RAP within an individual project are below.

For a select few applets, we have also provided them in 'app' form. This means that you do not need to pull the applet source code from GitHub and build it within your own project. This will ensure you are always using the latest version of a given applet and keeps you from having to manually update your local version. To be able to access an app, you will need to be an authorised member of org-mrc_epid_group_1_2. Please contact Eugene Gardner if you would like to be added! For a list of applets available as apps, please refer to the VCF Filtering and Rare Variant Burden Testing section.

If you are using a project other than that used to generate a given applet, you will need to compile this applet for your respective DNANexus project (using the repostory for mrcepid-filterbcf as an example):

1. Clone this github repo to some directory:

git clone https://github.com/mrcepid-rap/mrcepid-filterbcf.git

This will create a folder named mrcepid-filterbcf, you can then:

2. Compile the source code:

dx build -f mrcepid-filterbcf

the -f flag just tells DNANexus to overwrite older versions of the applet within the same project if it is already there.

You can then run the following to run this applet:

dx run mrcepid-filterbcf <options>

To provide required software to applets, we use Docker images. All Docker images produced for this and other MRC projects are available as part of Eugene Gardner's Dockerhub profile: egardner413.

Note: If you are using an applet of this pipeline it is NOT necessary to explicitly download the relevant Docker images. This is handled by the applet itself.

See below for specifics regarding building Docker images for specific applets. Individual Dockerfiles used to build images are stored in the dockerimages repository which is part of this organisation. Individual Dockerfiles are:

Note: For most of the above I have chosen not to hardcode programme version numbers. This means that Docker will install the latest version of any software provided as part of the Dockerfile! The exception to this is plink2 as the developers do not provide static links to the latest version.

Docker images are built on an AWS Instance launched via DNANexus. I include here a brief example workflow using the "FilterBCF" Dockerfile to enable reproducibility. Please note that uploading to dockerhub using Eugene Gardner's dockerhub account will not work as described in step 5 below. If you want to store this docker image for yourself, you will need to change the commands accordingly. All commands are run either locally using the dx-toolkit or on a DNANexus AWS instance.

1. Launch a cloud-workstation:

dx run app-cloud_workstation --ssh --instance-type mem1_ssd1_v2_x4 -imax_session_length=2h

This command will generate a series of prompts and eventually lead to a query for your DNANexus ssh password. This will then log you into an AWS instance.

Note: One can adjust the amount of time the instance will exist for by changed the max_session_length parameter. 2 hours should be enough to build most Docker instances.

2. Set your permissions to root on the instance:

dx-su-contrib

3. Make and enter a fresh directory for building a Docker image and create a Dockerfile:

mkdir dockerbuild
cd dockerbuild

After entering this directory, you can either copy-and-paste the text from the provided Dockerfile (using something like vi), or upload the file using a combination of dx upload/dx download. Regardless of method, the following commands require that this file is named Dockerfile.

4. Build the Docker image:

docker build -t egardner413/mrcepid-filtering:latest .

5. Log in to dockerhub and upload the docker image:

docker login
# Enter prompted credentials
docker push egardner413/mrcepid-filtering:latest

After this last command, the Docker image will be available to pull with the Dockerhub address of egardner413/mrcepid-filtering:latest.

The purpose of this section is to provide information on our approach to processing, filtering, annotating, and burden testing UK Biobank Whole Exome Sequencing (WES) data at the IMS / MRC Epidemiology Unit using the UK Biobank (UKB) Research Access Platform (RAP). This workflow generates a single quality-controlled data set with annotations in order to perform rare variant burden testing analyses.

Graphical Outline of Workflow

This workflow is made up of six individual applets. Please see the individual READMEs within these repositories for more detailed information on what each step in the workflow does as well as accompanying source code:

Beyond descriptions included in the README's for each tool, Eugene Gardner has generated a R Markdown notebook that contains command-line examples for running individual tools described as part of this workflow. This notebook also contains examples for plotting of association tests and some quality control of QCd data. This notebook is included as a separate repository in this project:

QC_workflow

There are four primary outputs from this workflow:

1. Filtered and annotated bgen files – generated following running "mrcepid-makebgen"
   • Stored in the folder filtered_bgen/ in project-G6BJF50JJv8p4PjGB9yy7YQ2.
2. Filtered genetic data – generated by "mrcepid-buildgrms"
   • Stored in the folder project_resources/genetics/ in project-G6BJF50JJv8p4PjGB9yy7YQ2.
   • Includes plink files of QCd genetic (i.e. array-based) variants as well as sample inclusion/exclusion lists.
3. Collapsed Variant Masks for Burden Testing – generated following "mrcepid-collapsevariants"
   • Stored in the folder collapsed_variants_new/ in project-G6BJF50JJv8p4PjGB9yy7YQ2.
4. Association Statistics - generated by "mrcepid-runassociationtesting"
   • Stored in the folder results/ in project-G6BJF50JJv8p4PjGB9yy7YQ2

This workflow also generates several additional resources. These resources are contained in the folder project_resources/wes_resources/ in project-G6BJF50JJv8p4PjGB9yy7YQ2. Some of these resources are described below.

Filtered and annotated .bgen format files are provided per chromosome in the folder filtered_bgen/ in project-G6BJF50JJv8p4PjGB9yy7YQ2. There are 5 files per chromosome:

1. chr<CHR>.filtered.bgen – A standard .bgen file. This file was written by plink2 and has 8-bit encoding set with 'ref-last'.
2. chr<CHR>.filtered.sample – A corresponding .sample file. This is written in the sample v2 format, so it only has 1 ID column (instead of FID/IID like plink2 .bed).
3. chr<CHR>.filtered.bgen.bgi – A corresponding .bgi index of the bgen file. This was created by bgenix.
4. chr<CHR>.filtered.vep.tsv.gz – A tab-delimited file compressed with bgzip of annotations for all variants in the corresponding .bgen file. For more on the information stored in this file, please see the outputs documentation for mrcepid-annotatecadd.
5. chr<CHR>.filtered.vep.tsv.gz.tbi – A corresponding tabix .tbi index for the vep annotations.

VEP annotations for every variant in these files are also stored here with the file format like:

chr<CHR>.filtered

This file is bgzipped and tabix-indexed (file-G857Z5jJJv8vjVyzGQ5z1b0b) for easy querying with tabix. For the format of this file, please see the outputs section of mrcepid-annotatecadd.

We have provided sample lists of individuals specific to the 50k, 200k, and 450k releases of the UKBB WES data. These files have one individual ID per-line and are named like samples_<RELEASE>.txt.

Annotation information for transcripts expected to be included in the output of mrcepid-runassociationtesting is provided in the file file-G7xyzF8JJv8kyV7q5z8VV3Vb in project project-G6BJF50JJv8p4PjGB9yy7YQ2. A corresponding tabix index is provided as file file-G7xyzFjJJv8kyV7q5z8VV3Vj.

We have used mrcepid-collapsevariants to generate 16 variant masks. These masks are reasonable defaults to start performing association testing. They were generated by running collapsevariants on all combinations of Allele Frequency Cutoffs and consequence annotations. They are located in the folder collapsed_variants_new/ in project project-G6BJF50JJv8p4PjGB9yy7YQ2. Corresponding file names are included in parentheses. A list file to provide as the input via the input parameter association_tarballs to mrcepid-runassociationtesting is provided as file file-G7zPvZ0JJv8v06j8Gv2ppxpJ.

Allele Frequency Cutoffs:

1. Minor Allele Frequency < 0.1% (MAF_01)
2. Allele Count = 1 (AC_1)

Consequence Annotations:

1. All synonymous variants (SYN)
2. Missense CADD ≥ 25 (MISS_CADD25)
3. Missense REVEL ≥ 0.5 (REVEL0_5)
4. Missense REVEL ≥ 0.7 (REVEL0_7)
5. Missense CADD ≥ 25 & REVEL ≥0.7 (MISS_CADD25_REVEL0_7)
6. All PTVs (PTV)
7. High confidence PTVs (HC_PTV)
8. HC_PTV + MISS_CADD25 (DMG)

All of these files also have a corresponding .log file which provides information on the types of variants and counts per individual.

A tutorial on how to extract phenotype information has been provided by Alex Mörseburg. Please see the following repository for more information:

https://github.com/mrcepid-rap/mrcepid_raptutorial

The table below contains brief information on individual applets contained within this repository. Please see the individual READMEs within these repositories for more information on what applets are for: