ISLET eQTL EXPLORER

Explorer ID	Variant	Gene	Gene name	RefSeq ID	Position	Biotype	Chromatin state	Footprint	-log10(p-value)	Allele 1 (effect allele) / Allele 2	Effect size	Actions
Click on a variant in the plot to show its details here.

Name	LD threshold	Gene query	Biotypes	Chromatin states	Footprint	iESI	Variants	Actions
Click the Save button at the top to save your explorer state here.

The data from our 2017 paper Genetic regulatory signatures underlying islet gene expression and type 2 diabetes includes over 3000 variants in islet cis-eQTLs. This tool lets you quickly find those relevant to your research.

You can find processed data from the paper here, including:

1. Chromatin state annotations for 31 cell types (see Methods, Chromatin state analyses)
2. ATAC-seq peak calls for two islet samples, skeletal muscle, adipose and GM12878 (see Methods, Open chromatin profiling (ATAC-seq))
3. Transcription factor footprint calls for two islet samples (see Methods, ATAC-seq footprints)

If it's not working for you, please let us know: parkerlab-software at umich.edu.

General notes

Most recent browsers should work fine. We've noticed that Firefox, much as we love it, can be a little sluggish relative to the others, so if it's too slow for you, please try another browser. Chrome, Safari and Edge seem to be faster, at least for this.

How to use it

Filters

At any time, you can reset the plot to its initial state with the Reset plot button.

If you're interested in a particular gene, type part of its name or Ensembl ID in the text input labeled Gene.

You can filter the variants by chromatin state, by biotype, by iESI (islet expression specificity index), or whether an ATAC-seq footprint was observed at the variant location. Simply click any filter in the list. Hovering over a filter will preview the variants it matches. Clicking a filter again will remove it from the active filters. You can select multiple filters to combine sets of variants. Selecting all filters in a filter category is equivalent to selecting none.

The plot itself

Hovering over any shape in the plot will display a tooltip containing the variant's details.

Clicking any shape in the plot will add its details to the table below the plot labeled Variant Details. The Gene column in that table contains links to the Ensembl page for the variant's gene. The Position column contains links to 100kb windows centered on the variants in this UCSC Genome Browser session. If you click a variant by mistake, you can remove it from the table by clicking the Remove button in the last column.

You can zoom the plot with your mouse wheel or trackpad. Zooming in separates the variants, making them easier to click. You can also pan the plot by dragging with the mouse. At any time, you can get back to the starting zoom and position by clicking the Reset plot button.

The data

Islet expression specificity index

The islet expression specificity index (iESI) is a measure of islet-specific gene expression, with a range from 0 to 1. Here we partition the scores into deciles. A gene with an iESI score in decile 10 is expressed highly and specifically in islets compared to other tissue types.

Printing

You should be able to print the page at any time using your browser's default print dialog. If you notice problems with the output, please let us know.

Sessions

You can save the state of the explorer at any time with the Save button at the top right. In the resulting dialog, enter a name to save the session under, and it will be added to the Sessions table. In that table, you can press Load to load a saved session, or Remove to delete them.

At any time you can press the Share button at top right to get a URL to share your current session. It has to be copied to the clipboard manually, for browser security reasons; just press the Copy to clipboard button or use the keyboard: Command-C on Macs, Control-C elsewhere.

24 May 2017

• Added RefSeq IDs
• Added effect size
• Made allele ordering consistent in labels; allele 1 is now always the effect allele.

17 February 2017

We discovered some duplicate records in the data. This should have had no effect beyond overstated variant counts (3360 total, compared to 3314 after these duplicates were removed, and if you searched for a record with duplicates, e.g. SIX3, you'd see one spot in the plot but the tool would claim it was showing more).

We've kept the old data available, labeled 10 Feb 2017, so you can review any sessions you might have created.

This was of course found because someone with an interest in a particular variant spotted the discrepancy. We appreciate the help; if anything in the data doesn't make sense to you, please let us know at parkerlab-software at umich.edu.

10 February 2017

Published with session saving and sharing.

Name	Affiliation
Arushi Varshney1	Department of Human Genetics, University of Michigan, Ann Arbor, Michigan 48105, USA.
Laura J. Scott1	Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan 48109, USA.
Ryan P. Welch1	Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan 48109, USA.
Michael R. Erdos1	National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA.
Peter S. Chines	National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA.
Narisu Narisu	National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA.
Richard D'Oliveira Albanus	Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109, USA.
Peter Orchard	Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109, USA.
Brooke N. Wolford	Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109, USA.
Romy Kursawe	The Jackson Laboratory for Genomic Medicine, Farmington, CT 06030, USA.
Swarooparani Vadlamudi	Department of Genetics, University of North Carolina, Chapel Hill, North Carolina 27599, USA.
Maren Cannon	Department of Genetics, University of North Carolina, Chapel Hill, North Carolina 27599, USA.
John P. Didion	National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA.
John Hensley	Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109, USA.
Anthony Kirilusha	National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA.
Lori L. Bonnycastle	National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA.
D. Leland Taylor	National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA. European Bioinformatics Institute, European Molecular Biology Laboratory, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK.
Richard M. Watanabe	Department of Preventive Medicine, University of Southern California Keck School of Medicine, Los Angeles, California 90089, USA. Department of Physiology and Biophysics, University of Southern California Keck School of Medicine, Los Angeles, California 90089, USA.
Karen L. Mohlke	Department of Genetics, University of North Carolina, Chapel Hill, North Carolina 27599, USA.
Michael Boehnke1	Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan 48109, USA.
Francis S. Collins1	National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA.
Stephen C.J. Parker1, 2	Department of Human Genetics, University of Michigan, Ann Arbor, Michigan 48105, USA. Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109, USA.
Michael L. Stitzel1	The Jackson Laboratory for Genomic Medicine, Farmington, CT 06030, USA.