Diabetes risk loci-associated pathways are shared across metabolic tissues.

AIMS/HYPOTHESIS: Numerous genome-wide association studies have been performed to understand the influence of genetic variation on type 2 diabetes etiology. Many identified risk variants are located in non-coding and intergenic regions, which complicates understanding of how genes and their downstream pathways are influenced. An integrative data approach will help to understand the mechanism and consequences of identified risk variants. METHODS: In the current study we use our previously developed method CONQUER to overlap 403 type 2 diabetes risk variants with regulatory, expression and protein data to identify tissue-shared disease-relevant mechanisms. RESULTS: One SNP rs474513 was found to be an expression-, protein- and metabolite QTL. Rs474513 influenced LPA mRNA and protein levels in the pancreas and plasma, respectively. On the pathway level, in investigated tissues most SNPs linked to metabolism. However, in eleven of the twelve tissues investigated nine SNPs were linked to differential expression of the ribosome pathway. Furthermore, seven SNPs were linked to altered expression of genes linked to the immune system. Among them, rs601945 was found to influence multiple HLA genes, including HLA-DQA2, in all twelve tissues investigated. CONCLUSION: Our results show that in addition to the classical metabolism pathways, other pathways may be important to type 2 diabetes that show a potential overlap with type 1 diabetes.

Multi-faceted analysis provides little evidence for recurrent whole-genome duplications during hexapod evolution.

BACKGROUND: Gene duplication events play an important role in the evolution and adaptation of organisms. Duplicated genes can arise through different mechanisms, including whole-genome duplications (WGDs). Recently, WGD was suggested to be an important driver of evolution, also in hexapod animals. RESULTS: Here, we analyzed 20 high-quality hexapod genomes using whole-paranome distributions of estimated synonymous distances (KS), patterns of within-genome co-linearity, and phylogenomic gene tree-species tree reconciliation methods. We observe an abundance of gene duplicates in the majority of these hexapod genomes, yet we find little evidence for WGD. The majority of gene duplicates seem to have originated through small-scale gene duplication processes. We did detect segmental duplications in six genomes, but these lacked the within-genome co-linearity signature typically associated with WGD, and the age of these duplications did not coincide with particular peaks in KS distributions. Furthermore, statistical gene tree-species tree reconciliation failed to support all but one of the previously hypothesized WGDs. CONCLUSIONS: Our analyses therefore provide very limited evidence for WGD having played a significant role in the evolution of hexapods and suggest that alternative mechanisms drive gene duplication events in this group of animals. For instance, we propose that, along with small-scale gene duplication events, episodes of increased transposable element activity could have been an important source for gene duplicates in hexapods.

CONQUER: an interactive toolbox to understand functional consequences of GWAS hits.

Numerous large genome-wide association studies have been performed to understand the influence of genetics on traits. Many identified risk loci are in non-coding and intergenic regions, which complicates understanding how genes and their downstream pathways are influenced. An integrative data approach is required to understand the mechanism and consequences of identified risk loci. Here, we developed the R-package CONQUER. Data for SNPs of interest are acquired from static- and dynamic repositories (build GRCh38/hg38), including GTExPortal, Epigenomics Project, 4D genome database and genome browsers. All visualizations are fully interactive so that the user can immediately access the underlying data. CONQUER is a user-friendly tool to perform an integrative approach on multiple SNPs where risk loci are not seen as individual risk factors but rather as a network of risk factors.