Data for "Impact of regulatory variation across human iPSCs and differentiated cells"

This webpage provides data for our paper on understanding cell type-specific effects of DNA variation and on characterizing the suitability of induced pluripotent stem cells (iPSCs) as models for studies of complex disease:

Banovich NE*, Li YI*, Raj A*, Ward MC, Greenside P, Calderon D, Tung PY, Burnett JE, Myrthil M, Thomas SM, Burrows CL, Romero IG, Pavlovic BJ, Kundaje A, Pritchard JK, Gilad Y.
Impact of regulatory variation across human iPSCs and differentiated cells. bioRxiv, 2016.

Raw Data

The data generated by this study are available for download from GEO under accession GSE89895.

Processed Data

Expression QTL (LCL) from our LCL study

Expression QTL (iPSCs) top fastQTL, all fastQTL

Expression QTL (iPSC-CM) CHT all

caQTL (LCLs) CHT all

caQTL (iPSC) CHT significant, CHT all

caQTL (iPSC-CM) CHT all

meQTL (LCL) from our LCL study

meQTL (iPSC) top fastQTL, all fastQTL

YRI genotypes used: genotypes of 120 YRI individuals

Source Code

Source code for OrbWeaver, our Deep Learning method, is available here, and for RolyPoly, our polygenic method for identifying cell type most relevant to GWASs using gene expression specificity, is available here.

Contact

For questions please contact: Nicholas E Banovich (nick.banovich@gmail.com), Yang I Li (yangili1@uchicago.edu), Anil Raj (anil.calvin@gmail.com), Jonathan Pritchard (pritch@stanford.edu), or Yoav Gilad (gilad@uchicago.edu).


Last updated 11/15/2017.