Nature GeneticsGenomicsPeer-reviewed journal article
Systematic assessment of regulatory effects of human disease variants in pluripotent cells
Marc Jan BonderiD, Craig SmailiD, Michael J. GloudemansiD, Laure FrésardiD, David A. JakuboskyiD, Matteo D’AntonioiD, Xin LiiD, Nicole M. FerraroiD, Ivan Carcamo‐OriveiD, Bogdan Andrei MirautaiD, Daniel D. SeatoniD, Na CaiiD, Dara VakiliiD, Danilo HortaiD, Chunli ZhaoiD, Diane B. ZastrowiD, Devon E. BonneriD, Undiagnosed Diseases Network, Christopher D. BrowniD, YoSon ParkiD, Matthew Thomas WheeleriD, Helena KilpineniD, Joshua W. KnowlesiD, Erin N. SmithiD, Kelly A. FrazeriD, , Oliver StegleiD
Abstract
Identifying regulatory genetic effects in pluripotent cells provides important insights into disease variants with potentially transient or developmental origins. Combining existing and newly-generated data, we characterized 1,367 iPSC lines from 948 unique donors, collectively analyzed within the \"Integrated iPSC QTL\" (i2QTL) Consortium. The sample size of our study allowed us to derive the most comprehensive map of quantitative trait loci (QTL) in pluripotent human cells to date. We mapped the effects of nearby common genetic variants on five expression phenotypes, identifying cis-QTL at gene-, exon-level and transcript-, splicing-, alternative polyadenylation-ratio (APA) for a total of 18,556 genes. For gene-level, we further quantified the effects of rare and singleton variants, and the effect of distal variants that act in trans (trans-eQTL), which we replicated in independent samples. Our data are a valuable community resource, uncovering novel regulatory effects that have not previously been described in differentiated cells and tissues. Building on this regulatory map, we functionally explore GWAS signals for over 4,336 trait loci, finding evidence for colocalization with common and rare iPSC QTL for traits such as height and BMI, and diseases, such as cancer and coronary artery disease.
- organism: human
- assay: bulk RNA-seq
- research focus: gene regulatory network inference
- research focus: GWAS / association mapping
- approach: statistical model
- Citations
- 76
- FWCI
- 9.31
- Field percentile
- top 1%
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