Search

Coalescent simulations are a widely used approach for simulating sample genealogies, but can become computationally burdensome in large samples. Methods exist to analytically calculate a sample's expected frequency spectrum without simulating full genealogies. However, statistics that rely on the distribution of the length of internal coalescent branches, such as the probability that two mutations of equal size arose on the same genealogical branch, have previously required full coalescent simulations to estimate. Here, we present a sampling method capable of efficiently generating limited portions of sample genealogies using a series of analytic equations that give probabilities for the number, start, and end of internal branches conditional on the number of final samples they subtend. These equations are independent of the coalescent waiting times and need only be calculated a single time, lending themselves to efficient computation. We compare our method with full coalescent simulations to show the resulting distribution of branch lengths and summary statistics are equivalent, but that for many conditions our method is at least 10 times faster.

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 genome-wide 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 = 5.88 × 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 = 6.60 × 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 (HR, 1.75; 95% CI, 1.37-2.22) or in combination with an immunomodulator (HR, 2.01; 95% CI, 1.57-2.58).CONCLUSIONS: In an observational study, we found a genome-wide significant association between HLA-DQA1*05 and the development of antibodies against anti-TNF agents. A randomized controlled biomarker trial is required to determine whether pretreatment testing for HLA-DQA1*05 improves patient outcomes by helping physicians select anti-TNF and combination therapies. ClinicalTrials.gov ID: NCT03088449.

Background Proteomic biomarkers have been linked to anti-TNF treatment failure, but previous studies have been insufficiently powered to stratify associations by drug level. The Personalised Anti-TNF Therapy in Crohn’s disease (PANTS) is a prospective UK-wide study investigating treatment failure in 1610 anti-TNF naïve patients. We aimed to identify proteomic markers of treatment failure. Methods We sampled patients with primary non-response (PNR) (n = 223) and remission (n = 219) who had a baseline CRP ≥4 mg/l and/or calprotectin >100 µg/g. PNR was defined at week 14 as ongoing steroids, or both of HBI failed to fall by ≥3 points or to ≤4 and CRP failed to fall by ≥50% or to ≤3 mg/l. Non-remission at week 54 was defined as HBI >4 and CRP >3 mg/l and no steroids. Targeted serum proteomic analysis of 180 proteins using Olink Inflammation and Immune Response panels were performed. Mann–Whitney U tests were used to identify baseline proteins that predicted PNR and non-remission. Sub-group analyses stratified by drug level were undertaken. Pharmacokinetic (PK) failure was defined as PNR with low drug level (infliximab level Results Elevated fibroblast growth factor 21 (FGF21) (OR 1.3, CI 1.1–1.4, p = 3.4 × 10–5) and interleukin-10 receptor subunit α (IL10RA) (OR 1.6, CI 1.2–2.1, p = 6.3 × 10–4) predicted PNR (Figure 1). At week 14, FGF21 (OR 1.5, CI 1.3–1.9, p = 1.8 × 10–6) and IL10RA (OR 1.8, CI 1.3–2.3, p = 5.8 × 10–5) levels were also associated with PNR. Sub-group analyses showed baseline FGF21 (OR 1.4, CI 1.2–1.7, p = 2.0 × 10–4) predicted PK failure and that IL10-RA (OR 1.6, CI 1.1–2.2, p = 6.7 × 10–3) predicted PD failure (Figure 2). In separate models, non-remission at week 54 was predicted by baseline (FGF21; OR 1.3, CI 1.1–1.4, p = 1.4 × 10–4, IL10-RA; OR 1.5, CI 1.1–2.0, p = 3.6 × 10–3) and week 14 (FGF21; OR 1.4, CI 1.2–1.7, p = 3.6 × 10–4, IL10-RA; OR 1.7, CI 1.3–2.4, p = 1.7 × 10–4) FGF21 and IL10-RA levels. Model validation of baseline FGF21 and IL10-RA showed an area under the curve of 0.61 (CI 0.57–0.64) for PNR and 0.60 (CI 0.56–0.64) for non-remission at week 54. Conclusion Our study identified FGF-21 and IL10-RA as proteins of interest associated with PK and PD treatment failure, respectively. Functional studies to determine the molecular mechanism driving dysregulation of these proteins are required.

SummaryBackgroundAnti-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.MethodsThe PANTS cohort consists of Crohn’s 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.FindingsThe 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=5.88×10-13). 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).InterpretationHLA-DQA1*05 is significantly associated with an increased rate of anti-drug antibody formation in patients with Crohn’s 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.

Current human sequencing projects observe an abundance of extremely rare genetic variation, suggesting recent acceleration of population growth. To better understand the impact of such accelerating growth on the quantity and nature of genetic variation, we present a new class of models capable of incorporating faster than exponential growth in a coalescent framework. Our work shows that such accelerated growth affects only the population size in the recent past and thus large samples are required to detect the models’ effects on patterns of variation. When we compare models with fixed initial growth rate, models with accelerating growth achieve very large current population sizes and large samples from these populations contain more variation than samples from populations with constant growth. This increase is driven almost entirely by an increase in singleton variation. Moreover, linkage disequilibrium decays faster in populations with accelerating growth. When we instead condition on current population size, models with accelerating growth result in less overall variation and slower linkage disequilibrium decay compared to models with exponential growth. We also find that pairwise linkage disequilibrium of very rare variants contains information about growth rates in the recent past. Finally, we demonstrate that models of accelerating growth may substantially change estimates of present-day effective population sizes and growth times.

A report on the 64th annual American Society of Human Genetics meeting held in San Diego, USA, 18-22 October, 2014.

There is substantial interest in the role of rare genetic variants in the etiology of complex human diseases. Several gene-based tests have been developed to simultaneously analyze multiple rare variants for association with phenotypic traits. The tests can largely be partitioned into two classes – ‘burden' tests and ‘joint' tests – based on how they accumulate evidence of association across sites. We used the empirical joint site frequency spectra of rare, nonsynonymous variation from a large multi-population sequencing study to explore the effect of realistic rare variant population structure on gene-based tests. We observed an important difference between the two test classes: their susceptibility to population stratification. Focusing on European samples, we found that joint tests, which allow variants to have opposite directions of effect, consistently showed higher levels of P-value inflation than burden tests. We determined that the differential stratification was caused by two specific patterns in the interpopulation distribution of rare variants, each correlating with inflation in one of the test classes. The pattern that inflates joint tests is more prevalent in real data, explaining the higher levels of inflation in these tests. Furthermore, we show that the different sources of inflation between tests lead to heterogeneous responses to genomic control correction and the number of variants analyzed. Our results indicate that care must be taken when interpreting joint and burden analyses of the same set of rare variants, in particular, to avoid mistaking inflated P-values in joint tests for stronger signals of true associations.

Summary: Recent genetic studies as well as recorded history point to massive growth in human population sizes during the recent past. To model and understand this growth accurately we introduce FTEC, an easy-to-use coalescent simulation program capable of simulating haplotype samples drawn from a population that has undergone faster than exponential growth. Samples drawn from a population that has undergone faster than exponential growth show an excess of very rare variation and more rapid LD decay when compared with samples drawn from a population that has maintained a constant size over time. Availability: Source code for FTEC is freely available for download from the University of Michigan Center for Statistical Genetics Wiki at http://genome.sph.umich.edu/wiki/FTEC Contact: mreppell@umich.edu; szoellne@umich.edu Supplementary information: Supplementary data are available at Bioinformatics online