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Mapping the genetic architecture of gene expression in human liver.

Schadt EE, Molony C, Chudin E, Hao K, Yang X, Lum PY, Kasarskis A, Zhang B, Wang S, Suver C, Zhu J, Millstein J, Sieberts S, Lamb J, GuhaThakurta D, Derry J, Storey JD, Avila-Campillo I, Kruger MJ, Johnson JM, Rohl CA, van Nas A, Mehrabian M, Drake TA, Lusis AJ, Smith RC, Guengerich FP, Strom SC, Schuetz E, Rushmore TH, Ulrich R - PLoS Biol. (2008)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Rosetta Inpharmatics, Seattle, Washington, United States of America. eric_schadt@merck.com

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.

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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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pbio-0060107-g003: 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].

Mentions: To further elucidate the involvement of these genes in metabolic phenotypes associated with CAD, we examined Psrc1, Celsr2, and Sort1 in the context of the probabilistic, causal network constructed as described above for the Erbb3/Rps26 example. All three genes not only fell in the same subnetwork, they were all directly connected to the same gene, 2010200O16Rik, demonstrating that these genes are tightly co-regulated, possibly driven by common regulatory factors (Figure 3A). This same subnetwork also included genes like Tgfbr2, Pparg, Lpl, Ppm1l, and Alox5ap, all of which have been previously identified and validated as being associated with traits related to obesity, diabetes, cholesterol levels, and cardiovascular disease [25,31–33]. More generally, Psrc1 and Sort1 participate in a previously defined macrophage-enriched metabolic (MEM) subnetwork validated as causal for obesity-, diabetes-, and atherosclerosis-related traits [34]. In fact, the subnetwork depicted in Figure 3A is composed of 1,346 genes, with 226 of these genes overlapping the set of 1,406 genes composing the MEM subnetwork (82 would have been expected by chance). This 2.76-fold enrichment in this case is highly significant, with a Fisher exact test p = 8.20 × 10−47.


Mapping the genetic architecture of gene expression in human liver.

Schadt EE, Molony C, Chudin E, Hao K, Yang X, Lum PY, Kasarskis A, Zhang B, Wang S, Suver C, Zhu J, Millstein J, Sieberts S, Lamb J, GuhaThakurta D, Derry J, Storey JD, Avila-Campillo I, Kruger MJ, Johnson JM, Rohl CA, van Nas A, Mehrabian M, Drake TA, Lusis AJ, Smith RC, Guengerich FP, Strom SC, Schuetz E, Rushmore TH, Ulrich R - PLoS Biol. (2008)

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].
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2365981&req=5

pbio-0060107-g003: 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].
Mentions: To further elucidate the involvement of these genes in metabolic phenotypes associated with CAD, we examined Psrc1, Celsr2, and Sort1 in the context of the probabilistic, causal network constructed as described above for the Erbb3/Rps26 example. All three genes not only fell in the same subnetwork, they were all directly connected to the same gene, 2010200O16Rik, demonstrating that these genes are tightly co-regulated, possibly driven by common regulatory factors (Figure 3A). This same subnetwork also included genes like Tgfbr2, Pparg, Lpl, Ppm1l, and Alox5ap, all of which have been previously identified and validated as being associated with traits related to obesity, diabetes, cholesterol levels, and cardiovascular disease [25,31–33]. More generally, Psrc1 and Sort1 participate in a previously defined macrophage-enriched metabolic (MEM) subnetwork validated as causal for obesity-, diabetes-, and atherosclerosis-related traits [34]. In fact, the subnetwork depicted in Figure 3A is composed of 1,346 genes, with 226 of these genes overlapping the set of 1,406 genes composing the MEM subnetwork (82 would have been expected by chance). This 2.76-fold enrichment in this case is highly significant, with a Fisher exact test p = 8.20 × 10−47.

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Rosetta Inpharmatics, Seattle, Washington, United States of America. eric_schadt@merck.com

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.

Show MeSH
Related in: MedlinePlus