Mapping the genetic architecture of gene expression in human liver.
paper
Cited
Public
- Authors
- Schadt, Eric E; Molony, Cliona; Chudin, Eugene; Hao, Ke; Yang, Xia; Lum, Pek Y; Kasarskis, Andrew; Zhang, Bin; Wang, Susanna; Suver, Christine; Zhu, Jun; Millstein, Joshua; Sieberts, Solveig; Lamb, John; GuhaThakurta, Debraj; Derry, Jonathan; Storey, John D; Avila-Campillo, Iliana; Kruger, Mark J; Johnson, Jason M; Rohl, Carol A; van Nas, Atila; Mehrabian, Margarete; Drake, Thomas A; Lusis, Aldons J; Smith, Ryan C; Guengerich, F Peter; Strom, Stephen C; Schuetz, Erin; Rushmore, Thomas H; Ulrich, Roger
- Year
- 2008
- Journal
- PLoS biology
- PMID
- 18462017
- DOI
- 10.1371/journal.pbio.0060107
- PMCID
- PMC2365981
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.
Figure 1
Local Networks for Rps26 and Erbb3 Derived from Causal, Probabilistic Whole-Gene Networks Constructed from the Liver, Adipose, Muscle, and Brain Gene Expression Data Generated from the BXH/wt and BXC Mouse Crosses(A) The Rps26 subnetwork includes a number of known T1D associated genes (green nodes), and RPS26 in this subnetwork is directly linked to H2-Eb1, a mouse ortholog of HLA-DRB1, a previously identified T1D susceptibility gene that is also strongly associated with a cis eSNP in the HLC (Table 2). The known T1D genes annotated by the Gene Ontology are significantly enriched in this subnetwork (Table 3).(B) The Erbb3 subnetwork is not associated with any pathways known or predicted to be involved in T1D.
Figure 2
PSRC1, CELSR2, and SORT1 Liver Expression Is Associated with a CAD Risk Allele and Plasma LDL Cholesterol LevelsThe CAD risk allele for SNP rs599839 was established in a previous WTCCC study [16] (lilac panel). In the HLC, this same SNP is strongly associated with PSRC1, CELSR2, and SORT1 expression, with the CAD risk allele associated with lower relative expression (pink panel). In the BXH/wt cross designed to study metabolic traits that increase cardiovascular risk (green panel), all three of these expression traits were strongly correlated with plasma LDL cholesterol levels, a major CAD risk factor (scatter plots associated with the green panel). Given the association of these genes to plasma LDL-cholesterol levels, we examined whether rs599839 was associated with LDL cholesterol in a previously published GWAS [35] and found this SNP was significantly associated with LDL cholesterol levels, where the CAD risk allele was associated with higher LDL cholesterol levels in this cohort. Lower levels of CELSR2 and SORT1 expression were associated with the risk allele in humans, and with higher LDL cholesterol levels in mouse, making them ideal candidate susceptibility genes for the CAD and LDL cholesterol associations to this locus. On the other hand, lower levels of PSRC1 expression were associated with the risk allele in humans, but with lower LDL cholesterol levels in mouse, suggesting that PSRC1 is not the gene increasing CAD risk, but instead may be acting to protect against it.
Figure 3
Local Networks for PSRC1, CELSR2, and SORT1 Derived from Causal, Probabilistic Whole-Gene Networks in Mouse and Human(A) Mouse network for Psrc1, Celsr2, and Sort1 derived from the liver, adipose, muscle, and brain gene expression data generated from the BXH/wt and BXC mouse crosses.(B) Human network for PSRC1, CELSR2, and SORT1 derived from the HLC and from a previously published adipose and blood tissue cohort [21].
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