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research
Mapping the genetic architecture of gene expression in human liver
Authors
I Avila-Campillo
E Chudin
+29 more
J Derry
TA Drake
FP Guengerich
D GuhaThakurta
K Hao
JM Johnson
A Kasarskis
MJ Kruger
J Lamb
PY Lum
AJ Lusis
M Mehrabian
J Millstein
C Molony
CA Rohl
TH Rushmore
EE Schadt
E Schuetz
S Sieberts
RC Smith
JD Storey
SC Strom
C Suver
R Ulrich
A Van Nas
S Wang
X Yang
B Zhang
J Zhu
Publication date
1 May 2008
Publisher
'Public Library of Science (PLoS)'
Doi
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on
PubMed
Abstract
Genetic variants that are associated with common human diseases do not lead directly to disease, but instead act on intermediate, molecular phenotypes that in turn induce changes in higher-order disease traits. Therefore, identifying the molecular phenotypes that vary in response to changes in DNA and that also associate with changes in disease traits has the potential to provide the functional information required to not only identify and validate the susceptibility genes that are directly affected by changes in DNA, but also to understand the molecular networks in which such genes operate and how changes in these networks lead to changes in disease traits. Toward that end, we profiled more than 39,000 transcripts and we genotyped 782,476 unique single nucleotide polymorphisms (SNPs) in more than 400 human liver samples to characterize the genetic architecture of gene expression in the human liver, a metabolically active tissue that is important in a number of common human diseases, including obesity, diabetes, and atherosclerosis. This genome-wide association study of gene expression resulted in the detection of more than 6,000 associations between SNP genotypes and liver gene expression traits, where many of the corresponding genes identified have already been implicated in a number of human diseases. The utility of these data for elucidating the causes of common human diseases is demonstrated by integrating them with genotypic and expression data from other human and mouse populations. This provides much-needed functional support for the candidate susceptibility genes being identified at a growing number of genetic loci that have been identified as key drivers of disease from genome-wide association studies of disease. By using an integrative genomics approach, we highlight how the gene RPS26 and not ERBB3 is supported by our data as the most likely susceptibility gene for a novel type 1 diabetes locus recently identified in a large-scale, genome-wide association study. We also identify SORT1 and CELSR2 as candidate susceptibility genes for a locus recently associated with coronary artery disease and plasma low-density lipoprotein cholesterol levels in the process. © 2008 Schadt et al
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