Integrating genetic and gene expression evidence into genome-wide association analysis of gene sets

Abstract

Single variant or single gene analyses generally account for only a small proportion of the phenotypic variation in complex traits. Alternatively, gene set or pathway association analyses are playing an increasingly important role in uncovering genetic architectures of complex traits through the identification of systematic genetic interactions. Two dominant paradigms for gene set analyses are association analyses based on SNP genotypes and those based on gene expression profiles. However, gene–disease association can manifest in many ways, such as alterations of gene expression, genotype, and copy number; thus, an integrative approach combining multiple forms of evidence can more accurately and comprehensively capture pathway associations. We have developed a single statistical framework, Gene Set Association Analysis (GSAA), that simultaneously measures genome-wide patterns of genetic variation and gene expression variation to identify sets of genes enriched for differential expression and/or trait-associated genetic markers. Simulation studies illustrate that joint analyses of genomic data increase the power to detect real associations when compared with gene set methods that use only one genomic data type. The analysis of two human diseases, glioblastoma and Crohn's disease, detected abnormalities in previously identified disease-associated pathways, such as pathways related to PI3K signaling, DNA damage response, and the activation of NFKB. In addition, GSAA predicted novel pathway associations, for example, differential genetic and expression characteristics in genes from the ABC transporter family in glioblastoma and from the HLA system in Crohn's disease. These demonstrate that GSAA can help uncover biological pathways underlying human diseases and complex traits.

Genetic Association and Altered Gene Expression of Osteoprotegerin in Otosclerosis Patients

“…In parallel, genetic association studies were carried out and showed the association of OTSC with polymorphisms in the COL1A1, TGFB1, BMP2, BMP4, and RELN genes in multiple populations (McKenna et al, 1998;Thys et al, 2007a;Schrauwen et al, 2008;Schrauwen et al, 2009;Khalfallah et al, 2010;Khalfallah et al, 2011;Priyadarshi et al, 2013). Gene expression profiling of disease tissue compared to controls has been suggested as a method to investigate the genetic basis of complex disorders (Xiong et al, 2012). Some of the studies have reported reduced or missing OPG expression in the otosclerotic tissue (Karosi et al, 2011;Potocka-Baklazec et al, 2014) but the exact mechanism that controls the altered OPG expression in OTSC is unclear.…”

WITHDRAWN: Downregulation of AC061961.2, LINGO1-AS1, and RP11-13E1.5 is associated with dilated cardiomyopathy progression

“…Additionally, based on the expression values of module genes, we analyzed the correlation between module genes and the DCM disease state using a toolset of Gene Set Association Analysis for RNA-Seq with Sample Permutation (GSAASeqSP, http://gsaa.unc.edu) [38], and the module with a nominal p-value of <0.05 was considered to be significantly correlated with the DCM disease state. Furthermore, according to the core enrichment gene obtained from GSAASeqSP, we screened key lncRNAs and constructed the lncRNA regulatory network on the basis of the WGCNA module.…”

Applying Genomic and Genetic Tools to Understand and Mitigate Damage from Exposure to Toxins

“…The Fisher's exact test found that there was not a greater than expected number of SNPs within the differentially expressed genes within any of the strains. However, we anticipate that we will use the Gene Set Association Analysis-SNP (GSAA-SNP) software (30) to further investigate possible relationships between the SNPs identified within each strain and the differentially expressed gene data sets. This work could be translated into a greater ability to predict whether there are additional human populations that might be more susceptible to the effects of PB and whether the response to PB varies by genotypic differences at other loci beyond those for detoxification enzymes, such as butyrylcholinesterase and paraoxonase.…”