Ulcerative colitis and Crohn’s disease are the two main forms of inflammatory bowel disease (IBD). Here, we report the first trans-ethnic association study of IBD, with genome-wide or Immunochip genotype data from an extended cohort of 86,640 European individuals and Immunochip data from 9,846 individuals of East-Asian, Indian or Iranian descent. We implicate 38 loci in IBD risk for the first time. For the majority of IBD risk loci, the direction and magnitude of effect is consistent in European and non-European cohorts. Nevertheless, we observe genetic heterogeneity between divergent populations at several established risk loci driven by a combination of differences in allele frequencies (NOD2), effect sizes (TNFSF15, ATG16L1) or a combination of both (IL23R, IRGM). Our results provide biological insights into the pathogenesis of IBD, and demonstrate the utility of trans-ethnic association studies for mapping complex disease loci and understanding genetic architecture across diverse populations.

ObjectivePatients with IBD display substantial heterogeneity in clinical characteristics. We hypothesise that individual differences in the complex interaction of the host genome and the gut microbiota can explain the onset and the heterogeneous presentation of IBD. Therefore, we performed a case–control analysis of the gut microbiota, the host genome and the clinical phenotypes of IBD.DesignStool samples, peripheral blood and extensive phenotype data were collected from 313 patients with IBD and 582 truly healthy controls, selected from a population cohort. The gut microbiota composition was assessed by tag-sequencing the 16S rRNA gene. All participants were genotyped. We composed genetic risk scores from 11 functional genetic variants proven to be associated with IBD in genes that are directly involved in the bacterial handling in the gut: NOD2, CARD9, ATG16L1, IRGM and FUT2.ResultsStrikingly, we observed significant alterations of the gut microbiota of healthy individuals with a high genetic risk for IBD: the IBD genetic risk score was significantly associated with a decrease in the genus Roseburia in healthy controls (false discovery rate 0.017). Moreover, disease location was a major determinant of the gut microbiota: the gut microbiota of patients with colonic Crohn's disease (CD) is different from that of patients with ileal CD, with a decrease in alpha diversity associated to ileal disease (p=3.28×10−13).ConclusionsWe show for the first time that genetic risk variants associated with IBD influence the gut microbiota in healthy individuals. Roseburia spp are acetate-to-butyrate converters, and a decrease has already been observed in patients with IBD.

We combined large-scale mRNA expression analysis and gene mapping to identify genes and loci that control hematopoietic stem cell (HSC) function. We measured mRNA expression levels in purified HSCs isolated from a panel of densely genotyped recombinant inbred mouse strains. We mapped quantitative trait loci (QTLs) associated with variation in expression of thousands of transcripts. By comparing the physical transcript position with the location of the controlling QTL, we identified polymorphic cis-acting stem cell genes. We also identified multiple trans-acting control loci that modify expression of large numbers of genes. These groups of coregulated transcripts identify pathways that specify variation in stem cells. We illustrate this concept with the identification of candidate genes involved with HSC turnover. We compared expression QTLs in HSCs and brain from the same mice and identified both shared and tissue-specific QTLs. Our data are accessible through WebQTL, a web-based interface that allows custom genetic linkage analysis and identification of coregulated transcripts.

Changes in the gut microbiota have been associated with two of the most common gastrointestinal diseases, inflammatory bowel disease (IBD) and irritable bowel syndrome (IBS). Here, we performed a case-control analysis using shotgun metagenomic sequencing of stool samples from 1792 individuals with IBD and IBS compared with control individuals in the general population. Despite substantial overlap between the gut microbiome of patients with IBD and IBS compared with control individuals, we were able to use gut microbiota composition differences to distinguish patients with IBD from those with IBS. By combining species-level profiles and strain-level profiles with bacterial growth rates, metabolic functions, antibiotic resistance, and virulence factor analyses, we identified key bacterial species that may be involved in two common gastrointestinal diseases.

Many investigations have reported the successful mapping of quantitative trait loci (QTLs) for gene expression phenotypes (eQTLs). Local eQTLs, where expression phenotypes map to the genes themselves, are of especially great interest, because they are direct candidates for previously mapped physiological QTLs. Here we show that many mapped local eQTLs in genetical genomics experiments do not reflect actual expression differences caused by sequence polymorphisms in cis-acting factors changing mRNA levels. Instead they indicate hybridization differences caused by sequence polymorphisms in the mRNA region that is targeted by the microarray probes. Many such polymorphisms can be detected by a sensitive and novel statistical approach that takes the individual probe signals into account. Applying this approach to recent mouse and human eQTL data, we demonstrate that indeed many local eQTLs are falsely reported as “cis-acting” or “cis” and can be successfully detected and eliminated with this approach.

BackgroundThe analysis of expression quantitative trait loci (eQTL) is a potentially powerful way to detect transcriptional regulatory relationships at the genomic scale. However, eQTL data sets often go underexploited because legacy QTL methods are used to map the relationship between the expression trait and genotype. Often these methods are inappropriate for complex traits such as gene expression, particularly in the case of epistasis.ResultsHere we compare legacy QTL mapping methods with several modern multi-locus methods and evaluate their ability to produce eQTL that agree with independent external data in a systematic way. We found that the modern multi-locus methods (Random Forests, sparse partial least squares, lasso, and elastic net) clearly outperformed the legacy QTL methods (Haley-Knott regression and composite interval mapping) in terms of biological relevance of the mapped eQTL. In particular, we found that our new approach, based on Random Forests, showed superior performance among the multi-locus methods.ConclusionsBenchmarks based on the recapitulation of experimental findings provide valuable insight when selecting the appropriate eQTL mapping method. Our battery of tests suggests that Random Forests map eQTL that are more likely to be validated by independent data, when compared to competing multi-locus and legacy eQTL mapping methods.

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Inbred mouse strains exhibit differences in susceptibility to influenza A infections. However, the molecular mechanisms underlying these differences are unknown. Therefore, we infected a highly susceptible mouse strain (DBA/2J) and a resistant strain (C57BL/6J) with influenza A H1N1 (PR8) and performed genome-wide expression analysis. We found genes expressed in lung epithelium that were specifically downregulated in DBA/2J mice, whereas a cluster of genes on chromosome 3 was only down-regulated in C57BL/6J. In both mouse strains, chemokines, cytokines and interferon-response genes were up-regulated, indicating that the main innate immune defense pathways were activated. However, many immune response genes were up-regulated in DBA/2J much stronger than in C57BL/6J, and several immune response genes were exclusively regulated in DBA/2J. Thus, susceptible DBA/2J mice showed a hyper-inflammatory response. This response is similar to infections with highly pathogenic influenza virus and may serve as a paradigm for a hyper-inflammatory host response to influenza A virus.