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SNPxGE(2): a database for human SNP-coexpression associations.

Yupeng Wang¹, Sandeep J Joseph, Xinyu Liu

¹Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA. wyp1125@uga.edu

Bioinformatics (Oxford, England)

|December 3, 2011

Summary

The SNPxGE(2) database provides computationally predicted human SNP-coexpression associations. This resource aids in understanding how single nucleotide polymorphisms (SNPs) influence gene expression dynamics in populations.

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Area of Science:

Genomics
Bioinformatics
Population Genetics

Background:

Gene coexpression relationships are dynamic and conditional.
Single nucleotide polymorphisms (SNPs) are known to impact gene expression variations.

Purpose of the Study:

To computationally predict and database human SNP-coexpression associations.
To provide a resource for exploring the link between genetic variation and gene expression.

Main Methods:

Utilized a large-scale association study with HapMap phase I data (269 individuals, 4 populations).
Assessed SNP-coexpression associations using gap/substitution models for computational efficiency.
Analyzed 556,873 SNPs and 15,000 gene expression profiles.

Main Results:

Identified 44,769 SNP-coexpression associations in single populations and 50,792 in pooled populations at a 0.1 false discovery rate (FDR).
The SNPxGE(2) database stores these associations, linking SNP genotypes to differential gene coexpression.
Detailed information pages are available for each reported association.

Conclusions:

The SNPxGE(2) database offers a valuable resource for researchers studying genotype-phenotype relationships.
Facilitates the investigation of how genetic variations influence gene expression networks.
Enables querying associations by gene symbol or reference SNP ID.