Reppell, M. | bioRxiv Science

bioRxiv2018

HLA-DQA1*05 is associated with the development of antibodies to anti-TNF therapy

BackgroundAnti-tumour necrosis factor (anti-TNF) therapies are the most widely used biologic therapies for treating immune-mediated diseases. Their efficacy is significantly reduced by the development of anti-drug antibodies which can lead to treatment failure and adverse reactions. The biological mechanisms underlying antibody development are unknown but the ability to identify subjects at higher risk would have significant clinical benefits.\n\nMethodsThe PANTS cohort consists of Crohns disease patients recruited prior to first administration of anti-TNF, with serial measurements of anti-drug antibody titres. We performed a genome-wide association study across 1240 individuals from this cohort to identify genetic variants associated with anti-drug antibody development.\n\nFindingsThe Human Leukocyte Antigen allele, HLA-DQA1*05, carried by approximately 40% of Europeans, significantly increased the rate of anti-drug antibody development (hazard ratio [HR], 1.90; 95% confidence interval [CI], 1.60 to 2.25; P=5.88x10-13). This association was consistent for patients treated with adalimumab (HR, 1.89; 95% CI, 1.32 to 2.70) and infliximab (HR, 1.92; 95% CI, 1.57 to 2.33), and for patients treated with mono-(HR, 1.75; 95% CI, 1.37 to 2.22) or combination therapy with immunomodulators (HR, 2.0; 95% CI, 1.57 to 2.58).\n\nInterpretationHLA-DQA1*05 is significantly associated with an increased rate of anti-drug antibody formation in patients with Crohns disease treated with infliximab and adalimumab. Pre-treatment HLA-DQA1*05 genetic testing may help personalise the choice of anti-TNF therapy and allow the targeted use of immunomodulator therapy to minimise risk and maximise response.

genomics↗

bioRxiv2017

Karp: Accurate and fast taxonomic classification using pseudoalignment

Pooled DNA from multiple unknown organisms arises in a variety of contexts, for example microbial samples from ecological or human health research. Determining the composition of pooled samples can be difficult, especially at the scale of modern sequencing data and reference databases. Here we propose the novel pooled DNA classification method Karp. Karp combines the speed and low-memory requirements of k-mer based pseudoalignment with a likelihood framework that uses base quality information to better resolve multiply mapped reads. In this text we apply Karp to the problem of classifying 16S rRNA reads, commonly used in microbiome research. Using simulations, we show Karp is accurate across a variety of read lengths and when samples contain reads originating from organisms absent from the reference. We also assess performance in real 16S data, and show that relative to other widely used classification methods Karp can reveal stronger statistical association signals and should empower future discoveries.

bioinformatics↗