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Abstract We evaluate the shared genetic regulation of mRNA molecules, proteins and metabolites derived from whole blood from 3029 human donors. We find abundant allelic heterogeneity, where multiple variants regulate a particular molecular phenotype, and pleiotropy, where a single variant associates with multiple molecular phenotypes over multiple genomic regions. The highest proportion of share genetic regulation is detected between gene expression and proteins (66.6%), with a further median shared genetic associations across 49 different tissues of 78.3% and 62.4% between plasma proteins and gene expression. We represent the genetic and molecular associations in networks including 2828 known GWAS variants, showing that GWAS variants are more often connected to gene expression in trans than other molecular phenotypes in the network. Our work provides a roadmap to understanding molecular networks and deriving the underlying mechanism of action of GWAS variants using different molecular phenotypes in an accessible tissue.

Genome-wide association studies (GWAS) have identified over 40 type 1 diabetes risk loci. The clinical impact of these loci on β-cell function during disease progression is unknown. We aimed at testing whether a genetic risk score could predict glycemic control and residual β-cell function in type 1 diabetes (T1D). As gene expression may represent an intermediate phenotype between genetic variation and disease, we hypothesized that genes within T1D loci which are expressed in islets and transcriptionally regulated by proinflammatory cytokines would be the best predictors of disease progression. Two-thirds of 46 GWAS candidate genes examined were expressed in human islets, and 11 of these significantly changed expression levels following exposure to proinflammatory cytokines (IL-1β + IFNγ + TNFα) for 48 h. Using the GWAS single nucleotide polymorphisms (SNPs) from each locus, we constructed a genetic risk score based on the cumulative number of risk alleles carried in children with newly diagnosed T1D. With each additional risk allele carried, HbA1c levels increased significantly within first year after diagnosis. Network and gene ontology (GO) analyses revealed that several of the 11 candidate genes have overlapping biological functions and interact in a common network. Our results may help predict disease progression in newly diagnosed children with T1D which can be exploited for optimizing treatment.

Long non-coding RNAs are a new class of non-coding RNAs that are at the crosshairs in many human diseases such as cancers, cardiovascular disorders, inflammatory and autoimmune disease like Inflammatory Bowel Disease (IBD) and Type 1 Diabetes (T1D). Nearly 90% of the phenotype-associated single-nucleotide polymorphisms (SNPs) identified by genome-wide association studies (GWAS) lie outside of the protein coding regions, and map to the non-coding intervals. However, the relationship between phenotype-associated loci and the non-coding regions including the long non-coding RNAs (lncRNAs) is poorly understood. Here, we systemically identified all annotated IBD and T1D loci-associated lncRNAs, and mapped nominally significant GWAS/ImmunoChip SNPs for IBD and T1D within these lncRNAs. Additionally, we identified tissue-specific cis-eQTLs, and strong linkage disequilibrium (LD) signals associated with these SNPs. We explored sequence and structure based attributes of these lncRNAs, and also predicted the structural effects of mapped SNPs within them. We also identified lncRNAs in IBD and T1D that are under recent positive selection. Our analysis identified putative lncRNA secondary structure-disruptive SNPs within and in close proximity (+/-5 kb flanking regions) of IBD and T1D loci-associated candidate genes, suggesting that these RNA conformation-altering polymorphisms might be associated with diseased-phenotype. Disruption of lncRNA secondary structure due to presence of GWAS SNPs provides valuable information that could be potentially useful for future structure-function studies on lncRNAs.