j5kim/datamed-admixture repository overview

⁠DataMed-Admixture

DataMed Admixture

⁠Installation and Preparation

⁠Install Docker

Download and install Docker community edition⁠

⁠Input Information

Input files are genotypes in plink text format (cohortname.ped and cohortname.map) and mapped to GRCh37 human reference genome (no "chr" in chromsome ID). Please refer to plink⁠ or plink 1.9⁠ documentation to obtain such files from other formats, including VCF formats

⁠Output Information

the analysis produces two file names after the input file

• output_cohortname.txt provides the admixture level of each subject for each reference population
• output_summary_cohortname.txt provides the cohort-wide cumulative admixture fraction as well as the diversity score.

⁠Reference populations

The admixture and diversity scores can be calculated from 2 reference cohorts: HapMap3 (8 population) and 1000G (5 continental super-population) and we provide 3 possible scripts to do so

• run_hapmap3.sh: using HapMap3 cohort.
• run_1000g_withLDpruning.sh: using pruned version of 1000G cohort: ideal for dense genotypes, such as whole genome, or genotyping arrays
• run_1000g_withoutLDpruning.sh: using unpruned version of 1000G cohort: ideal for small targeted genotypes (RNA-Seq, ChIP-Seq, Exomes)

⁠Testing

⁠Test data

In order to test the analysis we provide an accessory script 'prepare_example.sh', which downloads a public datasets and reformats it to plink ped and map format

Rankinen et al. PLoS One 2016

• No Evidence of a Common DNA Variant Profile Specific to World Class Endurance Athletes
• Article on PubMed⁠
• Data on Figshare⁠

⁠Test run

Commands to run an example run with a published data. Set the absolute path of your own local directory, where output will be created.

⁠Download and prepare test data

run the following set of commands to download the example data and convert them in your 'mydata' directory

export MY_LOCAL_DIR="/usr/john/mydata"
docker run -d -v ${MY_LOCAL_DIR}:/results j5kim/datamed-admixture:latest bash /opt/DataMed-Admixture/example/prepare_example.sh

⁠Run the test analysis

call hapmap3 based admixture on the test data by executing the following commands.

docker run -d -v ${MY_LOCAL_DIR}:/results j5kim/datamed-admixture:latest bash /opt/DataMed-Admixture/scripts/run_hapmap3.sh /results/rankinen

Above run is success if you see the 2 files output.rankinen.txt and output_summary_rankinen.txt

⁠Run

Provide your data in plink (cohortname.ped and cohortname.map) format and run the following command to call admixture and diversity score from the Hapmap3 reference.

export MY_LOCAL_DIR="/usr/john/mydata"
docker run -d -v ${MY_LOCAL_DIR}:/results j5kim/datamed-admixture:latest bash /opt/DataMed-Admixture/scripts/run_hapmap3.sh /results/cohortname

or run the following to call from the 1000G cohort (without pruning).

export MY_LOCAL_DIR="/usr/john/mydata"
docker run -d -v ${MY_LOCAL_DIR}:/results j5kim/datamed-admixture:latest bash /opt/DataMed-Admixture/scripts/run_1000g_withoutLDpruning.sh /results/cohortname

⁠DOI