Leveraging gene co-expression patterns to infer trait-relevant tissues in genome-wide association studies

ABSTRACTGenome-wide association studies (GWAS) have proven to be a valuable approach for identifying genetic intervals associated with phenotypic variation in Medicago truncatula. These intervals can vary in size, depending on the historical local recombination near each significant interval. Typically, significant intervals span numerous gene models, limiting the ability to resolve high-confidence candidate genes underlying the trait of interest. Additional genomic data, including gene co-expression networks, can be combined with the genetic mapping information to successfully identify candidate genes. Co-expression network analysis provides information about the functional relationships of each gene through its similarity of expression patterns to other well-defined clusters of genes. In this study, we integrated data from GWAS and co-expression networks to pinpoint candidate genes that may be associated with nodule-related phenotypes in Medicago truncatula. We further investigated a subset of these genes and confirmed that several had existing evidence linking them nodulation, including MEDTR2G101090 (PEN3-like), a previously validated gene associated with nodule number.

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Abstract 1444: Weighted Gene Co-expression Network Analysis of Adipose and Liver Reveals Gene Modules Related to Plasma HDL Levels and Containing Candidate Genes at Loci Identified in Genome Wide Association Studies

Circulation ◽

10.1161/circ.118.suppl_18.s_327 ◽

2008 ◽

Vol 118 (suppl_18) ◽

Author(s):

Peter Langfelder ◽

Margarete Mehrabian ◽

Eric E Schadt ◽

Aldons J Lusis ◽

Steve Horvath

Keyword(s):

Network Analysis ◽

Association Studies ◽

Expression Patterns ◽

Screening Method ◽

Functional Enrichment ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Genome Wide ◽

Gene Modules ◽

Hdl Metabolism

The genetic and environmental factors contributing to HDL-cholesterol levels are highly complex. For example, a recent meta-analysis of three genome wide association studies (GWAS), consisting of over 9000 individuals, revealed several loci, but altogether these explained less than 10% of HDL variation. Since HDL has a heritability of about 50%, there clearly must be many as yet unidentified factors. To better address this complexity, we have utilized integrative genomic approaches to relate common DNA variation to gene networks and HDL metabolism. We report a Weighted Gene Co-expression Network Analysis (WGCNA) of genome-wide expression data from a CAST X C57BL6/J F2 intercross. WGCNA is a systems-based gene expression analysis and gene screening method. It utilizes co-expression patterns among genes to identify gene modules (groups of highly co-expressed genes) significantly associated with a clinical trait, in this case plasma HDL levels. Co-expression modules may represent cellular processes and interacting pathways that provide a bridge between individual genes and a systems-level view of the organism. A module-centric analysis effectively alleviates the multiple testing problems inherent in microarray data analysis and can be considered a biologically motivated data-reduction scheme. Using data from liver and adipose tissues, we have identified several modules strongly associated with plasma HDL levels (p-values ranging from below 1e-20 to 1e-5). Gene ontology and functional enrichment analysis indicate that these modules are indeed biologically meaningful. The modules contain variants of several genes under loci that were recently implicated by three GWA studies: liver modules include GCKR, ANGPTL4, ABCA3, APOA1, and APOA4, while the adipose modules include ABCA6, ANGPTL11 and 12, MMAB, MLXIPL, SORT1, PBX4, PLTP, and APOL6. Thus, our study also serves to help identify likely candidates from GWAS. In conclusion, applying WGCNA methods reveals modules that are biologically meaningful, statistically significant, and enriched for genes and pathways related to HDL metabolism and transport.

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