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Liver and adipose expression associated SNPs are enriched for association to type 2 diabetes.

Zhong H, Beaulaurier J, Lum PY, Molony C, Yang X, Macneil DJ, Weingarth DT, Zhang B, Greenawalt D, Dobrin R, Hao K, Woo S, Fabre-Suver C, Qian S, Tota MR, Keller MP, Kendziorski CM, Yandell BS, Castro V, Attie AD, Kaplan LM, Schadt EE - PLoS Genet. (2010)

Bottom Line: This enrichment for T2D association increases as we restrict to eSNPs that correspond to genes comprising gene networks constructed from adipose gene expression data isolated from a mouse population segregating a T2D phenotype.We identified and validated malic enzyme 1 (Me1) as a key regulator of this T2D subnetwork in mouse and provided support for the association of this gene to T2D in humans.This integration of eSNPs and networks provides a novel approach to identify disease susceptibility networks rather than the single SNPs or genes traditionally identified through GWAS, thereby extracting additional value from the wealth of data currently being generated by GWAS.

View Article: PubMed Central - PubMed

Affiliation: Department of Genetics, Rosetta Inpharmatics, Seattle, Washington, United States of America.

ABSTRACT
Genome-wide association studies (GWAS) have demonstrated the ability to identify the strongest causal common variants in complex human diseases. However, to date, the massive data generated from GWAS have not been maximally explored to identify true associations that fail to meet the stringent level of association required to achieve genome-wide significance. Genetics of gene expression (GGE) studies have shown promise towards identifying DNA variations associated with disease and providing a path to functionally characterize findings from GWAS. Here, we present the first empiric study to systematically characterize the set of single nucleotide polymorphisms associated with expression (eSNPs) in liver, subcutaneous fat, and omental fat tissues, demonstrating these eSNPs are significantly more enriched for SNPs that associate with type 2 diabetes (T2D) in three large-scale GWAS than a matched set of randomly selected SNPs. This enrichment for T2D association increases as we restrict to eSNPs that correspond to genes comprising gene networks constructed from adipose gene expression data isolated from a mouse population segregating a T2D phenotype. Finally, by restricting to eSNPs corresponding to genes comprising an adipose subnetwork strongly predicted as causal for T2D, we dramatically increased the enrichment for SNPs associated with T2D and were able to identify a functionally related set of diabetes susceptibility genes. We identified and validated malic enzyme 1 (Me1) as a key regulator of this T2D subnetwork in mouse and provided support for the association of this gene to T2D in humans. This integration of eSNPs and networks provides a novel approach to identify disease susceptibility networks rather than the single SNPs or genes traditionally identified through GWAS, thereby extracting additional value from the wealth of data currently being generated by GWAS.

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Diagram depicting the process of filtering SNPs using eSNPs and disease associated networks.
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pgen-1000932-g001: Diagram depicting the process of filtering SNPs using eSNPs and disease associated networks.

Mentions: One way GGE studies can impact interpretation of GWAS is by providing a way to reduce the dimensionality of the DNA variation space, limiting focus to those DNA variants that have been associated with expression traits and testing whether such SNPs are associated with disease [12]. The set of SNPs associated with expression (eSNPs) in disease-relevant tissues can be considered a functionally relevant subset of all SNPs across the human genome, given they associate with a biologically relevant event (gene expression). However, the extent to which eSNPs inform on disease biology has not been comprehensively characterized for any disease. In this paper, we systematically examined whether eSNPs are more likely to associate with T2D compared to SNPs that a priori have no association to biologically relevant events. We assembled a comprehensive set of eSNPs identified in two GGE study cohorts representing three tissues [12]: liver, subcutaneous fat and omental fat tissues. Given the metabolic relevance of these tissues and the large-scale GWAS undertaken for T2D [26], we tested whether this set of eSNPs was more likely to associate with T2D than randomly selected SNPs. We further constructed a co-expression network from subcutaneous adipose tissue isolated from a mouse population segregating T2D traits and asked whether eSNPs associated with genes comprising these networks and sub-networks were enriched for association with T2D (Figure 1). By comparing the relative enrichments for association to T2D at these increasing levels of granularity, we sought to identify disease-associated subnetworks whose member genes might play important roles in T2D pathogenesis.


Liver and adipose expression associated SNPs are enriched for association to type 2 diabetes.

Zhong H, Beaulaurier J, Lum PY, Molony C, Yang X, Macneil DJ, Weingarth DT, Zhang B, Greenawalt D, Dobrin R, Hao K, Woo S, Fabre-Suver C, Qian S, Tota MR, Keller MP, Kendziorski CM, Yandell BS, Castro V, Attie AD, Kaplan LM, Schadt EE - PLoS Genet. (2010)

Diagram depicting the process of filtering SNPs using eSNPs and disease associated networks.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2865508&req=5

pgen-1000932-g001: Diagram depicting the process of filtering SNPs using eSNPs and disease associated networks.
Mentions: One way GGE studies can impact interpretation of GWAS is by providing a way to reduce the dimensionality of the DNA variation space, limiting focus to those DNA variants that have been associated with expression traits and testing whether such SNPs are associated with disease [12]. The set of SNPs associated with expression (eSNPs) in disease-relevant tissues can be considered a functionally relevant subset of all SNPs across the human genome, given they associate with a biologically relevant event (gene expression). However, the extent to which eSNPs inform on disease biology has not been comprehensively characterized for any disease. In this paper, we systematically examined whether eSNPs are more likely to associate with T2D compared to SNPs that a priori have no association to biologically relevant events. We assembled a comprehensive set of eSNPs identified in two GGE study cohorts representing three tissues [12]: liver, subcutaneous fat and omental fat tissues. Given the metabolic relevance of these tissues and the large-scale GWAS undertaken for T2D [26], we tested whether this set of eSNPs was more likely to associate with T2D than randomly selected SNPs. We further constructed a co-expression network from subcutaneous adipose tissue isolated from a mouse population segregating T2D traits and asked whether eSNPs associated with genes comprising these networks and sub-networks were enriched for association with T2D (Figure 1). By comparing the relative enrichments for association to T2D at these increasing levels of granularity, we sought to identify disease-associated subnetworks whose member genes might play important roles in T2D pathogenesis.

Bottom Line: This enrichment for T2D association increases as we restrict to eSNPs that correspond to genes comprising gene networks constructed from adipose gene expression data isolated from a mouse population segregating a T2D phenotype.We identified and validated malic enzyme 1 (Me1) as a key regulator of this T2D subnetwork in mouse and provided support for the association of this gene to T2D in humans.This integration of eSNPs and networks provides a novel approach to identify disease susceptibility networks rather than the single SNPs or genes traditionally identified through GWAS, thereby extracting additional value from the wealth of data currently being generated by GWAS.

View Article: PubMed Central - PubMed

Affiliation: Department of Genetics, Rosetta Inpharmatics, Seattle, Washington, United States of America.

ABSTRACT
Genome-wide association studies (GWAS) have demonstrated the ability to identify the strongest causal common variants in complex human diseases. However, to date, the massive data generated from GWAS have not been maximally explored to identify true associations that fail to meet the stringent level of association required to achieve genome-wide significance. Genetics of gene expression (GGE) studies have shown promise towards identifying DNA variations associated with disease and providing a path to functionally characterize findings from GWAS. Here, we present the first empiric study to systematically characterize the set of single nucleotide polymorphisms associated with expression (eSNPs) in liver, subcutaneous fat, and omental fat tissues, demonstrating these eSNPs are significantly more enriched for SNPs that associate with type 2 diabetes (T2D) in three large-scale GWAS than a matched set of randomly selected SNPs. This enrichment for T2D association increases as we restrict to eSNPs that correspond to genes comprising gene networks constructed from adipose gene expression data isolated from a mouse population segregating a T2D phenotype. Finally, by restricting to eSNPs corresponding to genes comprising an adipose subnetwork strongly predicted as causal for T2D, we dramatically increased the enrichment for SNPs associated with T2D and were able to identify a functionally related set of diabetes susceptibility genes. We identified and validated malic enzyme 1 (Me1) as a key regulator of this T2D subnetwork in mouse and provided support for the association of this gene to T2D in humans. This integration of eSNPs and networks provides a novel approach to identify disease susceptibility networks rather than the single SNPs or genes traditionally identified through GWAS, thereby extracting additional value from the wealth of data currently being generated by GWAS.

Show MeSH
Related in: MedlinePlus