BACKGROUND & AIMS: Anti-tumor necrosis factor (anti-TNF) therapies are the most widely used biologic drugs for treating immune-mediated diseases, but repeated administration can induce the formation of anti-drug antibodies. The ability to identify patients at increased risk for development of anti-drug antibodies would facilitate selection of therapy and use of preventative strategies. METHODS: We performed a genomewide association study to identify variants associated with time to development of anti-drug antibodies in a discovery cohort of 1240 biologic-naïve patients with Crohn's disease starting infliximab or adalimumab therapy. Immunogenicity was defined as an anti-drug antibody titer !10 AU/mL using a drug-tolerant enzyme-linked immunosorbent assay. Significant association signals were confirmed in a replication cohort of 178 patients with inflammatory bowel disease. RESULTS: The HLA-DQA1*05 allele, carried by approximately 40% of Europeans, significantly increased the rate of immunogenicity (hazard ratio [HR], 1.90; 95% confidence interval [CI], 1.60-2.25; P ¼ 5.88 Â 10-13). The highest rates of immunogenicity, 92% at 1 year, were observed in patients treated with infliximab monotherapy who carried HLA-DQA1*05; conversely the lowest rates of immunogenicity, 10% at 1 year, were observed in patients treated with adalimumab combination therapy who did not carry HLA-DQA1*05. We confirmed this finding in the replication cohort (HR, 2.00; 95% CI, 1.35-2.98; P ¼ 6.60 Â 10-4). This association was consistent for patients treated with adalimumab (HR, 1.89; 95% CI, 1.32-2.70) or infliximab (HR, 1.92; 95% CI, 1.57-2.33), and for patients treated with anti-TNF therapy alone

We introduce NetworKit, an open-source software package for analyzing the structure of large complex networks. Appropriate algorithmic solutions are required to handle increasingly common large graph data sets containing up to billions of connections. We describe the methodology applied to develop scalable solutions to network analysis problems, including techniques like parallelization, heuristics for computationally expensive problems, efficient data structures, and modular software architecture. Our goal for the software is to package results of our algorithm engineering efforts and put them into the hands of domain experts. NetworKit is implemented as a hybrid combining the kernels written in C++ with a Python front end, enabling integration into the Python ecosystem of tested tools for data analysis and scientific computing. The package provides a wide range of functionality (including common and novel analytics algorithms and graph generators) and does so via a convenient interface. In an experimental comparison with related software, NetworKit shows the best performance on a range of typical analysis tasks.

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This article has an accompanying continuing medical education activity, also eligible for MOC credit, on page e21. Learning Objective: Upon completion of this CME activity, successful learners will be able to interpret the clinical impact of rare genetic variants in diverse populations on the prediction of inflammatory bowel disease risk and monogenic contribution to pathogenesis. BACKGROUND AND AIMS: Polygenic risk scores (PRS) may soon be used to predict inflammatory bowel disease (IBD) risk in prevention efforts. We leveraged exome-sequence and single nucleotide polymorphism (SNP) array data from 29,358 individuals in the multiethnic, randomly ascertained health system-based BioMe biobank to define effects of common and rare IBD variants on disease prediction and pathophysiology. METHODS: PRS were calculated from European, African American, and Ashkenazi Jewish (AJ) reference case-control studies, and a meta-GWAS run using all three association datasets. PRS were then combined using regression to assess which combination of scores best predicted IBD status in European, AJ, Hispanic, and African American cohorts in BioMe. Additionally, rare variants were assessed in genes associated with very early-onset IBD (VEO-IBD), by estimating genetic penetrance in each BioMe population. RESULTS: Combining risk scores based on association data from distinct ancestral populations improved IBD prediction for every population in BioMe and significantly improved prediction among European ancestry UK Biobank individuals. Lower predictive power for non-Europeans was observed, reflecting in part substantially lower African IBD case-control reference sizes. We replicated associations for two VEO-IBD genes, ADAM17 and LRBA, with high dominant model penetrance in BioMe. Autosomal recessive LRBA risk alleles are associated with severe, early-onset autoimmunity; we show that heterozygous carriage of an African-predominant LRBA protein-altering allele is associated with significantly decreased LRBA and CTLA-4 expression with T-cell activation. CONCLUSIONS: Greater genetic diversity in African populations improves prediction across populations, and generalizes some VEO-IBD genes. Increasing African American IBD case-collections should be prioritized to reduce health disparities and enhance pathophysiological insight.