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).

Motivation: Co-localization of trait associated SNPs for specific transcription-factor binding sites or regulatory regions in the genome can yield profound insight into underlying causal mechanisms. Analysis is complicated because the truly causal SNPs are generally unknown and can be either SNPs reported in GWAS studies or other proxy SNPs in their linkage disequilibrium. Hence, a comprehensive pipeline for SNP co-localization analysis that utilizes all relevant information about both the genotyped SNPs and their proxies is needed. Results: We developed an R package snpEnrichR for SNP co-localization analysis. The software integrates different tools for random SNP generation and genome co-localization analysis to automatize and help users to create custom SNP co-localization analysis. We show via an example that including proxy SNPs in SNP co-localization analysis enhances the sensitivity of co-localization detection. Availability and implementation: The software is available at https://github.com/kartiek/snpEnrichR.

MOTIVATION: Sample mix-ups can arise during sample collection, handling, genotyping or data management. It is unclear how often sample mix-ups occur in genome-wide studies, as there currently are no post hoc methods that can identify these mix-ups in unrelated samples. We have therefore developed an algorithm (MixupMapper) that can both detect and correct sample mix-ups in genome-wide studies that study gene expression levels. RESULTS: We applied MixupMapper to five publicly available human genetical genomics datasets. On average, 3% of all analyzed samples had been assigned incorrect expression phenotypes: in one of the datasets 23% of the samples had incorrect expression phenotypes. The consequences of sample mix-ups are substantial: when we corrected these sample mix-ups, we identified on average 15% more significant cis-expression quantitative trait loci (cis-eQTLs). In one dataset, we identified three times as many significant cis-eQTLs after correction. Furthermore, we show through simulations that sample mix-ups can lead to an underestimation of the explained heritability of complex traits in genome-wide association datasets. AVAILABILITY AND IMPLEMENTATION: MixupMapper is freely available at http://www.genenetwork.nl/mixupmapper/

Abstract Motivation: Several genes involved in the cellular import of copper and its subsequent incorporation into the high-affinity iron transport complex in Saccharomyces cerevisiae are known to be conserved between eukaryotes and prokaryotes. However, the degree to which these genes share their functional context as members of the same pathway in the prokaryotic domain is less clear. Results: The co-occurrence of gene families involved in Atx1p-mediated copper transport in the genomes and operon structures of 80 non-redundant prokaryotes was investigated. For this purpose, we developed a Web tool (SHOPS) to display the operon context for a given set of proteins. In total, a set of 43 putative operons was identified. These were found to be involved in a variety of pathways and indicate a large diversity in the functional context of the individual gene family members. Availability: The SHOPS tool can be found at http://www.bioinformatics.med.uu.nl/shops. Supplemental data are available at http://humgen.med.uu.nl/publications/vanbakel/pathway/ Supplementary information: For additional information on our datasets and for the supplemental figures referred to in the text, please see http://humgen.med.uu.nl/publications/vanbakel/pathway/. The Web-based tool, SHOPS, can be found at http://www.bioinformatics.med.uu.nl/shops/