REMC SNP annotations for transcriptome prediction - epiXcan

Question

I have been trying to use a pipeline for predicting gene expression for a target gene (Although you can do it for many) - the name of this method is epiXcan. Here is a link to the paper:

https://www.nature.com/articles/s41467-019-11874-7#Sec22

One of the very first steps in the pipeline is obtaining SNP 'annotations' from an epigenome reference consortium known as the REMC. In the methods paper they say:

"We first prepare eQTL statistics (computed with MatrixEQTL4) and SNP annotations (extracted from REMC https://egg2.wustl.edu/roadmap/web_portal/). For each eQTL tissue, we use the matched REMC tissue to extract the corresponding annotations".

I have completed the first step of obtaining eQTL statistics. However obtaining SNP annotations is where I am lost.

In my case, I am only trying to predict the expression of one gene (DRD4) and so my region of interest is the pre-frontal cortex. If you visit the website they provide though, it doesn't seem as though annotating a large list of SNPs - at least in the way they are presenting - is possible.

Can anyone provide some insight or direction on how to proceed? How does one obtain SNP annotations from the REMC? I have tried emailing the authors of the paper and they have not been responsive.

Thank you,

Answer 1

After much research I found out how to obtain the annotations.

You must go to the REMC website: https://egg2.wustl.edu/roadmap/web_portal/

Go to the chromatin-state-learning tab, and choose the n-chromatin state model desired - the core has 15 states, then 18, then 25 (imputed states).

Following this, you must go to the download URL link and, you will find a lot of files, primarily with the following coding information: E0*.. This corresponds to a particular tissue/cell type and so you must choose the one relevant to your research question. The way they are correspond to each tissue/cell type can be found in the following paper:

https://www.nature.com/articles/s41467-019-11874-7#Sec22