Urko M. Marigorta

Prediction of complicated disease course for children newly diagnosed with Crohn's disease: a multicentre inception cohort study

BACKGROUND Stricturing and penetrating complications account for substantial morbidity and health-care costs in paediatric and adult onset Crohns disease. Validated models to predict risk for complications are not available, and the effect of treatment on risk is unknown. METHODS We did a prospective inception cohort study of paediatric patients with newly diagnosed Crohns disease at 28 sites in the USA and Canada. Genotypes, antimicrobial serologies, ileal gene expression, and ileal, rectal, and faecal microbiota were assessed. A competing-risk model for disease complications was derived and validated in independent groups. Propensity-score matching tested the effect of anti-tumour necrosis factor α (TNFα) therapy exposure within 90 days of diagnosis on complication risk. FINDINGS Between Nov 1, 2008, and June 30, 2012, we enrolled 913 patients, 78 (9%) of whom experienced Crohns disease complications. The validated competing-risk model included age, race, disease location, and antimicrobial serologies and provided a sensitivity of 66% (95% CI 51-82) and specificity of 63% (55-71), with a negative predictive value of 95% (94-97). Patients who received early anti-TNFα therapy were less likely to have penetrating complications (hazard ratio [HR] 0·30, 95% CI 0·10-0·89; p=0·0296) but not stricturing complication (1·13, 0·51-2·51; 0·76) than were those who did not receive early anti-TNFα therapy. Ruminococcus was implicated in stricturing complications and Veillonella in penetrating complications. Ileal genes controlling extracellular matrix production were upregulated at diagnosis, and this gene signature was associated with stricturing in the risk model (HR 1·70, 95% CI 1·12-2·57; p=0·0120). When this gene signature was included, the models specificity improved to 71%. INTERPRETATION Our findings support the usefulness of risk stratification of paediatric patients with Crohns disease at diagnosis, and selection of anti-TNFα therapy. FUNDING Crohns and Colitis Foundation of America, Cincinnati Childrens Hospital Research Foundation Digestive Health Center.

A simulation study of gene-by-environment interactions in GWAS implies ample hidden effects

The switch to a modern lifestyle in recent decades has coincided with a rapid increase in prevalence of obesity and other diseases. These shifts in prevalence could be explained by the release of genetic susceptibility for disease in the form of gene-by-environment (GxE) interactions. Yet, the detection of interaction effects requires large sample sizes, little replication has been reported, and a few studies have demonstrated environmental effects only after summing the risk of GWAS alleles into genetic risk scores (GRSxE). We performed extensive simulations of a quantitative trait controlled by 2500 causal variants to inspect the feasibility to detect gene-by-environment interactions in the context of GWAS. The simulated individuals were assigned either to an ancestral or a modern setting that alters the phenotype by increasing the effect size by 1.05–2-fold at a varying fraction of perturbed SNPs (from 1 to 20%). We report two main results. First, for a wide range of realistic scenarios, highly significant GRSxE is detected despite the absence of individual genotype GxE evidence at the contributing loci. Second, an increase in phenotypic variance after environmental perturbation reduces the power to discover susceptibility variants by GWAS in mixed cohorts with individuals from both ancestral and modern environments. We conclude that a pervasive presence of gene-by-environment effects can remain hidden even though it contributes to the genetic architecture of complex traits.

Expression quantitative trait locus analysis for translational medicine

Expression quantitative trait locus analysis has emerged as an important component of efforts to understand how genetic polymorphisms influence disease risk and is poised to make contributions to translational medicine. Here we review how expression quantitative trait locus analysis is aiding the identification of which gene(s) within regions of association are causal for a disease or phenotypic trait; the narrowing down of the cell types or regulators involved in the etiology of disease; the characterization of drivers and modifiers of cancer; and our understanding of how different environments and cellular contexts can modify gene expression. We also introduce the concept of transcriptional risk scores as a means of refining estimates of individual liability to disease based on targeted profiling of the transcripts that are regulated by polymorphisms jointly associated with disease and gene expression.

Transcriptional risk scores link GWAS to eQTLs and predict complications in Crohn's disease

Gene expression profiling can be used to uncover the mechanisms by which loci identified through genome-wide association studies (GWAS) contribute to pathology. Given that most GWAS hits are in putative regulatory regions and transcript abundance is physiologically closer to the phenotype of interest, we hypothesized that summation of risk-allele-associated gene expression, namely a transcriptional risk score (TRS), should provide accurate estimates of disease risk. We integrate summary-level GWAS and expression quantitative trait locus (eQTL) data with RNA-seq data from the RISK study, an inception cohort of pediatric Crohns disease. We show that TRSs based on genes regulated by variants linked to inflammatory bowel disease (IBD) not only outperform genetic risk scores (GRSs) in distinguishing Crohns disease from healthy samples, but also serve to identify patients who in time will progress to complicated disease. Our dissection of eQTL effects may be used to distinguish genes whose association with disease is through promotion versus protection, thereby linking statistical association to biological mechanism. The TRS approach constitutes a potential strategy for personalized medicine that enhances inference from static genotypic risk assessment.

A Burden of Rare Variants Associated with Extremes of Gene Expression in Human Peripheral Blood

In order to evaluate whether rare regulatory variants in the vicinity of promoters are likely to impact gene expression, we conducted a novel burden test for enrichment of rare variants at the extremes of expression. After sequencing 2-kb promoter regions of 472 genes in 410 healthy adults, we performed a quadratic regression of rare variant count on bins of peripheral blood transcript abundance from microarrays, summing over ranks of all genes. After adjusting for common eQTLs and the major axes of gene expression covariance, a highly significant excess of variants with minor allele frequency less than 0.05 at both high and low extremes across individuals was observed. Further enrichment was seen in sites annotated as potentially regulatory by RegulomeDB, but a deficit of effects was associated with known metabolic disease genes. The main result replicates in an independent sample of 75 individuals with RNA-seq and whole-genome sequence information. Three of four predicted large-effect sites were validated by CRISPR/Cas9 knockdown in K562 cells, but simulations indicate that effect sizes need not be unusually large to produce the observed burden. Unusually divergent low-frequency promoter haplotypes were observed at 31 loci, at least 9 of which appear to be derived from Neandertal admixture, but these were not associated with divergent gene expression in blood. The overall burden test results are consistent with rare and private regulatory variants driving high or low transcription at specific loci, potentially contributing to disease.

Combining Multiple Hypothesis Testing with Machine Learning Increases the Statistical Power of Genome-wide Association Studies.

The standard approach to the analysis of genome-wide association studies (GWAS) is based on testing each position in the genome individually for statistical significance of its association with the phenotype under investigation. To improve the analysis of GWAS, we propose a combination of machine learning and statistical testing that takes correlation structures within the set of SNPs under investigation in a mathematically well-controlled manner into account. The novel two-step algorithm, COMBI, first trains a support vector machine to determine a subset of candidate SNPs and then performs hypothesis tests for these SNPs together with an adequate threshold correction. Applying COMBI to data from a WTCCC study (2007) and measuring performance as replication by independent GWAS published within the 2008–2015 period, we show that our method outperforms ordinary raw p-value thresholding as well as other state-of-the-art methods. COMBI presents higher power and precision than the examined alternatives while yielding fewer false (i.e. non-replicated) and more true (i.e. replicated) discoveries when its results are validated on later GWAS studies. More than 80% of the discoveries made by COMBI upon WTCCC data have been validated by independent studies. Implementations of the COMBI method are available as a part of the GWASpi toolbox 2.0.

Integrating genomics into evolutionary medicine

The application of the principles of evolutionary biology into medicine was suggested long ago and is already providing insight into the ultimate causes of disease. However, a full systematic integration of medical genomics and evolutionary medicine is still missing. Here, we briefly review some cases where the combination of the two fields has proven profitable and highlight two of the main issues hindering the development of evolutionary genomic medicine as a mature field, namely the dissociation between fitness and health and the still considerable difficulties in predicting phenotypes from genotypes. We use publicly available data to illustrate both problems and conclude that new approaches are needed for evolutionary genomic medicine to overcome these obstacles.

Replicability and Prediction: Lessons and Challenges from GWAS

Since the publication of the Wellcome Trust Case Control Consortium (WTCCC) landmark study a decade ago, genome-wide association studies (GWAS) have led to the discovery of thousands of risk variants involved in disease etiology. This success story has two angles that are often overlooked. First, GWAS findings are highly replicable. This is an unprecedented phenomenon in complex trait genetics, and indeed in many areas of science, which in past decades have been plagued by false positives. At a time of increasing concerns about the lack of reproducibility, we examine the biological and methodological reasons that account for the replicability of GWAS and identify the challenges ahead. In contrast to the exemplary success of disease gene discovery, at present GWAS findings are not useful for predicting phenotypes. We close with an overview of the prospects for individualized prediction of disease risk and its foreseeable impact in clinical practice.

Constraints on eQTL Fine Mapping in the Presence of Multisite Local Regulation of Gene Expression

Expression quantitative trait locus (eQTL) detection has emerged as an important tool for unraveling of the relationship between genetic risk factors and disease or clinical phenotypes. Most studies use single marker linear regression to discover primary signals, followed by sequential conditional modeling to detect secondary genetic variants affecting gene expression. However, this approach assumes that functional variants are sparsely distributed and that close linkage between them has little impact on estimation of their precise location and the magnitude of effects. We describe a series of simulation studies designed to evaluate the impact of linkage disequilibrium (LD) on the fine mapping of causal variants with typical eQTL effect sizes. In the presence of multisite regulation, even though between 80 and 90% of modeled eSNPs associate with normally distributed traits, up to 10% of all secondary signals could be statistical artifacts, and at least 5% but up to one-quarter of credible intervals of SNPs within r2 > 0.8 of the peak may not even include a causal site. The Bayesian methods eCAVIAR and DAP (Deterministic Approximation of Posteriors) provide only modest improvement in resolution. Given the strong empirical evidence that gene expression is commonly regulated by more than one variant, we conclude that the fine mapping of causal variants needs to be adjusted for multisite influences, as conditional estimates can be highly biased by interference among linked sites, but ultimately experimental verification of individual effects is needed. Presumably similar conclusions apply not just to eQTL mapping, but to multisite influences on fine mapping of most types of quantitative trait.

Distinct Effector B Cells Induced by Unregulated Toll-like Receptor 7 Contribute to Pathogenic Responses in Systemic Lupus Erythematosus

Graphical Abstract Figure. No caption available. SUMMARY Systemic Lupus Erythematosus (SLE) is characterized by B cells lacking IgD and CD27 (double negative; DN). We show that DN cell expansions reflected a subset of CXCR5‐ CD11c+ cells (DN2) representing pre‐plasma cells (PC). DN2 cells predominated in African‐American patients with active disease and nephritis, anti‐Smith and anti‐RNA autoantibodies. They expressed a T‐bet transcriptional network; increased Toll‐like receptor‐7 (TLR7); lacked the negative TLR regulator TRAF5; and were hyper‐responsive to TLR7. DN2 cells shared with activated naive cells (aNAV), phenotypic and functional features, and similar transcriptomes. Their PC differentiation and autoantibody production was driven by TLR7 in an interleukin‐21 (IL‐21)‐mediated fashion. An in vivo developmental link between aNAV, DN2 cells, and PC was demonstrated by clonal sharing. This study defines a distinct differentiation fate of autoreactive naive B cells into PC precursors with hyper‐responsiveness to innate stimuli, as well as establishes prominence of extra‐follicular B cell activation in SLE, and identifies therapeutic targets. HIGHLIGHTSAutoreactive CD27‐ IgD‐ CXCR5‐ CD11c+ (DN2) B cells expand in lupus patientsDN2 cells derive from naive cells and are poised to generate plasmablastsDN2 B cells are hyper‐responsive to Toll‐like receptor‐7 signalingThe properties of SLE DN2 B cells stem from distinct transcriptional networks &NA; The role of extrafollicular B cells in human systemic lupus is unknown. Jenks et al. define the main components of this pathway and its prominence in severe disease. Its activation is mediated by hyper‐responsiveness to Toll‐like receptor‐7 and leads to the generation of autoreactive antibody‐secreting plasmablasts.