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Background Genome-wide association studies (GWAS) have revealed many susceptibility loci for complex genetic diseases. For most loci, the causal genes have not been identified. Currently, the identification of candidate genes is predominantly based on genes that localize close to or within identified loci. We have recently shown that 92 of the 163 inflammatory bowel disease (IBD)-loci co-localize with non-coding DNA regulatory elements (DREs). Mutations in DREs can contribute to IBD pathogenesis through dysregulation of gene expression. Consequently, genes that are regulated by these 92 DREs are to be considered as candidate genes. This study uses circular chromosome conformation capture-sequencing (4C-seq) to systematically analyze chromatin-interactions at IBD susceptibility loci that localize to regulatory DNA. Results Using 4C-seq, we identify genomic regions that physically interact with the 92 DRE that were found at IBD susceptibility loci. Since the activity of regulatory elements is cell-type specific, 4C-seq was performed in monocytes, lymphocytes, and intestinal epithelial cells. Altogether, we identified 902 novel IBD candidate genes. These include genes specific for IBD-subtypes and many noteworthy genes including ATG9A and IL10RA. We show that expression of many novel candidate genes is genotype-dependent and that these genes are upregulated during intestinal inflammation in IBD. Furthermore, we identify HNF4α as a potential key upstream regulator of IBD candidate genes. Conclusions We reveal many novel and relevant IBD candidate genes, pathways, and regulators. Our approach complements classical candidate gene identification, links novel genes to IBD and can be applied to any existing GWAS data. Electronic supplementary material The online version of this article (doi:10.1186/s13059-016-1100-3) contains supplementary material, which is available to authorized users.

The Kruskal-Wallis test is a popular non-parametric statistical test for identifying expression quantitative trait loci (eQTLs) from genome-wide data due to its robustness against variations in the underlying genetic model and expression trait distribution, but testing billions of marker-trait combinations one-by-one can become computationally prohibitive. We developed kruX, an algorithm implemented in Matlab, Python and R that uses matrix multiplications to simultaneously calculate the Kruskal-Wallis test statistic for several millions of marker-trait combinations at once. KruX is more than ten thousand times faster than computing associations one-by-one on a typical human dataset. We used kruX and a dataset of more than 500k SNPs and 20k expression traits measured in 102 human blood samples to compare eQTLs detected by the Kruskal-Wallis test to eQTLs detected by the parametric ANOVA and linear model methods. We found that the Kruskal-Wallis test is more robust against data outliers and heterogeneous genotype group sizes and detects a higher proportion of non-linear associations, but is more conservative for calling additive linear associations. In summary, kruX enables the use of robust non-parametric methods for massive eQTL mapping without the need for a high-performance computing infrastructure., minor revision; 6 pages, 5 figures; software available at http://krux.googlecode.com