10.5061/DRYAD.PJ105
Keller, Mark P.
University of Wisconsin-Madison
Gatti, Daniel M.
Jackson Laboratory
Schueler, Kathryn L.
University of Wisconsin-Madison
Rabaglia, Mary E.
University of Wisconsin-Madison
Stapleton, Donnie S.
University of Wisconsin-Madison
Simecek, Peter
Vincent, Matthew
Jackson Laboratory
Allen, Sadie
Maine School of Science and Mathematics
Broman, Aimee Teo
University of Wisconsin-Madison
Bacher, Rhonda
University of Wisconsin-Madison
Kendziorski, Christina
University of Wisconsin-Madison
Broman, Karl W.
University of Wisconsin-Madison
Yandell, Brian S.
University of Wisconsin-Madison
Churchill, Gary A.
Jackson Laboratory
Attie, Alan D.
University of Wisconsin-Madison
Simecek, Petr
Jackson Laboratory
Data from: Genetic drivers of pancreatic islet function
Dryad
dataset
2018
mediation analysis
Mus musculus
T2D
module-QTL
genome-wide association
β-cell
2018-12-20T00:00:00Z
2018-12-20T00:00:00Z
en
https://doi.org/10.1534/genetics.118.300864
1832603668 bytes
1
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Nearly all gene loci that have been associated with type 2 diabetes play a
role in pancreatic islet function. To evaluate the role of islet gene
expression in the etiology of diabetes, we sensitized a genetically
diverse mouse population with a Western diet and carried out genome-wide
association mapping of diabetes-related phenotypes. We quantified mRNA
abundance in the islets, and identified 18,775 expression quantitative
trait loci. We applied mediation analysis to identify candidate causal
driver genes at loci where numerous transcripts co-map. These include two
genes previously associated with monogenic diabetes (PDX1 and HNF4A), as
well as three genes with nominal association with diabetes-related traits
in humans (FAM83E, IL6ST, and SAT2). We grouped transcripts into gene
modules and show that these modules enrich for physiological pathways that
also map to distinct loci. We identified and mapped regulatory loci for
modules enriched with transcripts specific for α-cells, and another
specific for δ-cells. However, no single module enriched for
β-cell-specific transcripts, reflecting heterogeneity within the β-cell
population. A module enriched in transcripts associated with branched
chain amino acid metabolism was the most strongly correlated with clinical
traits that reflect insulin resistance. Although the mice in this study
were not overtly diabetic, the analysis of pancreatic islet gene
expression under dietary-induced stress, enabled us to identify genes and
pathways linked to diabetes-associated clinical traits. Our analysis
reveals a high degree of concordance between diabetes-associated loci in
the mouse with those found in human populations, and demonstrates how the
mouse can provide evidence to support nominal associations found in human
genome-wide association mapping.
Attie Islet eQTL dataThis is an R compressed binary file. The objects are
described in the README.Attie_DO378_eQTL_viewer_v1.Rdata