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Abstract Background The genetic background of cancer remains complex and challenging to integrate. Many somatic mutations within genes are known to cause and drive cancer, while genome-wide association studies (GWAS) of cancer have revealed many germline risk factors associated with cancer. However, the overlap between known somatic driver genes and positional candidate genes from GWAS loci is surprisingly small. We hypothesised that genes from multiple independent cancer GWAS loci should show tissue-specific co-regulation patterns that converge on cancer-specific driver genes. Results We studied recent well-powered GWAS of breast, prostate, colorectal and skin cancer by estimating co-expression between genes and subsequently prioritising genes that show significant co-expression with genes mapping within susceptibility loci from cancer GWAS. We observed that the prioritised genes were strongly enriched for cancer drivers defined by COSMIC, IntOGen and Dietlein et al. The enrichment of known cancer driver genes was most significant when using co-expression networks derived from non-cancer samples of the relevant tissue of origin. Conclusion We show how genes within risk loci identified by cancer GWAS can be linked to known cancer driver genes through tissue-specific co-expression networks. This provides an important explanation for why seemingly unrelated sets of genes that harbour either germline risk factors or somatic mutations can eventually cause the same type of disease.

Abstract Background Recent studies highlight the role of metabolites in immune diseases, but it remains unknown how much of this effect is driven by genetic and non-genetic host factors. Result We systematically investigate circulating metabolites in a cohort of 500 healthy subjects (500FG) in whom immune function and activity are deeply measured and whose genetics are profiled. Our data reveal that several major metabolic pathways, including the alanine/glutamate pathway and the arachidonic acid pathway, have a strong impact on cytokine production in response to ex vivo stimulation. We also examine the genetic regulation of metabolites associated with immune phenotypes through genome-wide association analysis and identify 29 significant loci, including eight novel independent loci. Of these, one locus (rs174584-FADS2) associated with arachidonic acid metabolism is causally associated with Crohn’s disease, suggesting it is a potential therapeutic target. Conclusion This study provides a comprehensive map of the integration between the blood metabolome and immune phenotypes, reveals novel genetic factors that regulate blood metabolite concentrations, and proposes an integrative approach for identifying new disease treatment targets.

Abstract Background Expression quantitative trait loci (eQTL) studies are used to interpret the function of disease-associated genetic risk factors. To date, most eQTL analyses have been conducted in bulk tissues, such as whole blood and tissue biopsies, which are likely to mask the cell type-context of the eQTL regulatory effects. Although this context can be investigated by generating transcriptional profiles from purified cell subpopulations, current methods to do this are labor-intensive and expensive. We introduce a new method, Decon2, as a framework for estimating cell proportions using expression profiles from bulk blood samples (Decon-cell) followed by deconvolution of cell type eQTLs (Decon-eQTL). Results The estimated cell proportions from Decon-cell agree with experimental measurements across cohorts (R ≥ 0.77). Using Decon-cell, we could predict the proportions of 34 circulating cell types for 3194 samples from a population-based cohort. Next, we identified 16,362 whole-blood eQTLs and deconvoluted cell type interaction (CTi) eQTLs using the predicted cell proportions from Decon-cell. CTi eQTLs show excellent allelic directional concordance with eQTL (≥ 96–100%) and chromatin mark QTL (≥87–92%) studies that used either purified cell subpopulations or single-cell RNA-seq, outperforming the conventional interaction effect. Conclusions Decon2 provides a method to detect cell type interaction effects from bulk blood eQTLs that is useful for pinpointing the most relevant cell type for a given complex disease. Decon2 is available as an R package and Java application ( https://github.com/molgenis/systemsgenetics/tree/master/Decon2 ) and as a web tool ( www.molgenis.org/deconvolution ).