Abstract
Meta-analyses of genome-wide association studies (GWAS) have improved our understanding of the genetic foundations of a number of diseases, including diabetes. However, single nucleotide polymorphisms (SNPs) that are identified by GWAS, especially those that fall outside of gene regions, do not always clearly link to the underlying biology. Despite this, these SNPs have often been validated through re-sequencing efforts as not just tag SNPs, but as causative SNPs, and so must play a role in disease development or progression. In this study, we show how the 3D genome (spatial connections) and trans-expression Quantitative Trait Loci connect diabetes loci from different GWAS meta-analyses, informing the backbone of regulatory networks. Our findings include a three-way functional–spatial connection between the TM6SF2, CTRB1–BCAR1, and CELSR2–PSRC1 loci (rs201189528, rs7202844, and rs7202844, respectively) connected through the KCNIP3 and BCAR1/BCAR3 loci, respectively. These spatial hubs serve as an example of how loci in genes with little biological connection to disease come together to contribute to the diabetes phenotype.
Highlights
The genome-wide association study (GWAS) is a test for statistical associations between common gene variants and a phenotype
The large number of previous studies into the genetic contribution to type 2 diabetes (T2D) (2) make it a model phenotype for the application of GWAS meta-analyses to further identify the genetics that underlie the phenotype (2, 3, 5–7). This was recognized by the DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) (1) consortium, which performed a meta-analysis of genetic variants associated with T2D (1)
A GWAS meta-analysis can only analyze genotypes and phenotypes that are homogenous across cohorts, missing any findings that are lost by heterogeneity of methods of detection
Summary
The genome-wide association study (GWAS) is a test for statistical associations between common gene variants (single nucleotide polymorphisms, SNPs) and a phenotype. There is a strong trend for studies to combine data from multiple GWAS studies into a meta-analysis to (1) validate previous findings (2), (2) expand findings from single populations to universal effects (3), and (3) identify novel gene effects (1) These changes have increased the power of the GWAS studies, reduced the numbers of false positives, and enabled the detection of small genetic effects that are associated with a number of diseases, including diabetes (1–7). Single nucleotide polymorphisms identified by GWAS studies may provide critical clues toward unraveling the regulatory network that underlies phenotypic complexity This is true of SNPs that are highly associated with disease but do not occur in exons or promoters and, as such, have no obvious biological relevance to the disease state (9, 10). This analysis identifies distant elements that are significantly associated by gene expression to T2D loci
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