Secondary Analysis of Publicly Available Data Reveals Superoxide and Oxygen Radical Pathways are Enriched for Associations Between Type 2 Diabetes and Low‐Frequency Variants

Abstract

Summary Genome-wide association studies explain at most 5%–10% of the heritable components of type 2 diabetes. Some of the “missing type 2 diabetes heritability” could be explained by low frequency variants. Pooling low frequency variants by genes into a composite test provides an alternative strategy to conventional analysis of individual SNPs for testing susceptibility genes. We examined the associations between low frequency variants and type 2 diabetes, using data from 2,538 diabetic and 2,977 non-diabetic subjects in the publicly available database of Genotypes and Phenotypes. We hypothesized that applying two approaches in combination would enable the identification of promising candidate genes. First, we combined information from all low frequency (1%–5%) variants at a locus in gene-centric analysis of associations with diabetes. Next, we searched for gene ontology (GO) biological processes that were enriched for gene-centric associations, after correcting for multiple testing to control the false discovery rate (FDR). We found three GO biological processes that were significantly enriched for associations to type 2 diabetes: ‘response to superoxide’ (FDR)-adjusted p-value =2.7×10−3), ‘response to oxygen radical’ (FDR-adjusted p-value =2.7×10−3), and ‘heart contraction’ (FDR-adjusted p-value =2.6×10−2). The ‘response to superoxide’ pathway was at the deepest level of the GO hierarchy. There were three genes which contributed to this pathway enrichment for association with type 2 diabetes, including SOD1 a gene which is involved in superoxide and oxygen radical pathways. Gene-centric tests of association with low-frequency variants, followed by analysis to evaluate which biological pathways are enriched for these associations has potential to recover, at least some proportion of, the “missing heritability” of type 2 diabetes.

From SNPs to pathways: Biological interpretation of type 2 diabetes (T2DM) genome wide association study (GWAS) results

“…The genes at the centers of these networks are: CDKAL1, ATP8A1, ARHGAP , and TCF7L2 , but considering LD only the gene CDKAL1 remains connected to at least 10 SNPs independent of each other. CDKAL1 variants have been reported to be associated with T2DM with highly significant p-values detected by different GWAS studies [ 5 ].…”

“…Nevertheless, it remains challenging to perform secondary analysis on GWAS results with the aim of obtaining a detailed biological understanding of the SNPs function and role in a disease [ 5 ]. Pathway analysis is an example of secondary analysis that has been applied to GWAS since 2007 [ 6 ], where the SNPs are contextualized in biological processes through the genes to which they are assigned [ 7 ].…”

“…In both cases the output is a list of pathways ranked by their significance based on the statistical test performed. Gene set enrichment was used to obtain interesting and germane pathway results linked to diabetes [ 5 , 12 ]. However, looking only at the highest ranked pathways does not assure an accounting of all genes detected by the significant association signals, interpretation of which could be relevant to understand the phenotype.…”

Big Data as a Driver for Clinical Decision Support Systems: A Learning Health Systems Perspective

“…This data model leverages standard terminologies and coding systems for healthcare (including ICD, SNOMED, CPT, HCPSC, and LOINC) to enable interoperability with and responsiveness to evolving data standards. Examples of applications to chronic diseases include the use of PCORNet (McGlynn et al, 2014) to create a common data model for patients affected by metabolic diseases, or of eMERGE to secondary data analysis for personalized medicine and phenotype definition in Type 2 Diabetes (Yazdanpanah et al, 2013;Hall et al, 2014).…”