Through an ongoing collaboration with Prof. Manfred Kayser of Erasmus University in the Netherlands, HIrisPlex-S is an all-in-one system with the ability to predict groups of color using probabilistic prediction. This interactive webtool can be found here hirisplex.erasmusmc.nl
If you are interested in learning about this method and its applicability to case work, please contact the lab. We do currently perform casework for both single source and mixtures. This system will provide intelligence information on the physical appearance of the person from DNA. Currently this tool provides categorical eye, hair and skin color predictions with approx. 80% correct prediction output per trait.
For those with genotypes from sequence data, the following R script can be used to convert your genotype data into the correct allele orientation (0,1,2) for direct upload into the online HirisPlex-S model for prediction. In order to use the script, please enter your genotypes into this template file. Formats can be G/G or GG etc. but do require either format, if you have missing data, NA must be inserted. The script works for all platforms MAC/Linux/PC and will generate a output file which can be directly uploaded for prediction on the HIrisPlex-S website
For those with raw fasta files that would like to use the HPS-MPS pipeline, please download the following zip folder with all the necessary scripts and follow the guidelines from the HPS-MPS publication.
For those with Commercial array data and you would like to check what SNPs are available to make a file to predict eye, hair and skin color using HIrisPlex-S, download the following R scripts for 23 and me, Ancestry.com, Family Tree DNA. Please contact us if you have any issues running these files in R.
Published in BMC Bioinformatics, this easy to use tool combines multiple programs in an easy workflow to go from raw genotype data to Manhattan plot in several hours, by utilising a simple input file that covers phasing, imputation, population stratification, and GWAS, including QC metrics. Available on github: Odyssey