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].
| # | Section | Preview |
|---|---|---|
| 20 | Results β Integrating Genetic and Network Data across Species to Inform GWA Discoveries β Identifying candidate susceptibility genes for T1D. | The absence of an association between a T1D-associated SNP and the HLC expression valuesβ¦ |
| 21 | Results β Integrating Genetic and Network Data across Species to Inform GWA Discoveries β Identifying candidate susceptibility genes for T1D. | In cases where disease-associated traits and expression traits are scored in the same cohort, thereβ¦ |
| 22 | Results β Integrating Genetic and Network Data across Species to Inform GWA Discoveries β Identifying candidate susceptibility genes for T1D. | The association to RPS26 expression suggests that this gene warrants further study in the context ofβ¦ |
| 23 | Results β Integrating Genetic and Network Data across Species to Inform GWA Discoveries β Identifying candidate susceptibility genes for T1D. | What the genetic association and atlas data lack is a more refined context within which to assessβ¦ |
| 24 | Results β Integrating Genetic and Network Data across Species to Inform GWA Discoveries β Identifying candidate susceptibility genes for T1D. | Figure 1A highlights how RPS26 is directly connected to a number of known T1D genes. For example,β¦ |
| 25 | Results β Integrating Genetic and Network Data across Species to Inform GWA Discoveries β Identifying candidate susceptibility genes for coronary artery disease and LDL cholesterol levels. | Another GWAS involving the WTCCC resulted in the identification of seven loci associated withβ¦ |
| 26 | Results β Integrating Genetic and Network Data across Species to Inform GWA Discoveries β Identifying candidate susceptibility genes for coronary artery disease and LDL cholesterol levels. | To further characterize the association of these four expression traits with CAD-associated traits,β¦ |
| 27 | Results β Integrating Genetic and Network Data across Species to Inform GWA Discoveries β Identifying candidate susceptibility genes for coronary artery disease and LDL cholesterol levels. | To further elucidate the involvement of these genes in metabolic phenotypes associated with CAD, weβ¦ |
| 28 | Results β Integrating Genetic and Network Data across Species to Inform GWA Discoveries β Identifying candidate susceptibility genes for coronary artery disease and LDL cholesterol levels. | To establish whether PSRC1, CELSR2, and SORT1 are closely connected in human transcriptionalβ¦ |
| 29 | Results β Integrating Genetic and Network Data across Species to Inform GWA Discoveries β Identifying candidate susceptibility genes for coronary artery disease and LDL cholesterol levels. | Given the strong association between plasma LDL cholesterol levels and the expression of Psrc1,β¦ |
| 30 | Results β Integrating Genetic and Network Data across Species to Inform GWA Discoveries β Identifying candidate susceptibility genes for coronary artery disease and LDL cholesterol levels. | most logical candidate susceptibility gene in the 1p13.3 locus (Figure 2), although directβ¦ |
| 31 | Discussion | Previous studies on the genetics of gene expression in humans have focused primarily on⦠|
| 32 | Discussion | Whereas the eQTL data in the human liver cohort is valuable in its own right, when integrated with⦠|
| 33 | Discussion | In addition to the CAD locus, we highlighted RPS26 as a candidate susceptibility gene for T1D from a⦠|
| 34 | Discussion | unfolding in response to ER stress is hypothesized to disrupt processes associated with diabetes⦠|
| 35 | Discussion | It is important to note that a lack of association between expression traits in the HLC and⦠|
| 36 | Materials and Methods β HLC and tissue collection. | The HLC was assembled from a total of 780 liver samples (1β2 g) that were acquired from Caucasianβ¦ |
| 37 | Materials and Methods β HLC and tissue collection. | All samples were stored frozen at β80 Β°C from collection until processing for RNA and DNA; someβ¦ |
| 38 | Materials and Methods β Mouse crosses and tissue collection. | C57BL/6J (B6) mice were intercrossed with C3H/HeJ (C3H) mice to generate 321 F2 progeny (161β¦ |
| 39 | Materials and Methods β Mouse crosses and tissue collection. | The BXH cross on an ApoE null background (BXH/apoE) was previously described [41]. Briefly, C57BL/6Jβ¦ |
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