Baseline Expression of Immune Gene Modules in Blood is Associated With Primary Response to Anti-TNF Therapy in Crohn's Disease Patients.

BACKGROUND AND AIMS: Anti-tumour necrosis factor [anti-TNF] therapy is widely used for the treatment of inflammatory bowel disease, yet many patients are primary non-responders, failing to respond to induction therapy. We aimed to identify blood gene expression differences between primary responders and primary non-responders to anti-TNF monoclonal antibodies [infliximab and adalimumab], and to predict response status from blood gene expression and clinical data. METHODS: The Personalised Anti-TNF Therapy in Crohn's Disease [PANTS] study is a UK-wide prospective observational cohort study of anti-TNF therapy outcome in anti-TNF-naive Crohn's disease patients [ClinicalTrials.gov identifier: NCT03088449]. Blood gene expression in 324 unique patients was measured by RNA-sequencing at baseline [week 0], and at weeks 14, 30, and 54 after treatment initiation [total sample size = 814]. RESULTS: After adjusting for clinical covariates and estimated blood cell composition, baseline expression of major histocompatibility complex, antigen presentation, myeloid cell enriched receptor, and other innate immune gene modules was significantly higher in anti-TNF responders vs non-responders. Expression changes from baseline to week 14 were generally of consistent direction but greater magnitude [i.e. amplified] in responders, but interferon-related genes were upregulated uniquely in non-responders. Expression differences between responders and non-responders observed at week 14 were maintained at weeks 30 and 54. Prediction of response status from baseline clinical data, cell composition, and module expression was poor. CONCLUSIONS: Baseline gene module expression was associated with primary response to anti-TNF therapy in PANTS patients. However, these baseline expression differences did not predict response with sufficient sensitivity for clinical use.

Genotyping, sequencing and analysis of 140,000 adults from Mexico City.

The Mexico City Prospective Study is a prospective cohort of more than 150,000 adults recruited two decades ago from the urban districts of Coyoacán and Iztapalapa in Mexico City1. Here we generated genotype and exome-sequencing data for all individuals and whole-genome sequencing data for 9,950 selected individuals. We describe high levels of relatedness and substantial heterogeneity in ancestry composition across individuals. Most sequenced individuals had admixed Indigenous American, European and African ancestry, with extensive admixture from Indigenous populations in central, southern and southeastern Mexico. Indigenous Mexican segments of the genome had lower levels of coding variation but an excess of homozygous loss-of-function variants compared with segments of African and European origin. We estimated ancestry-specific allele frequencies at 142 million genomic variants, with an effective sample size of 91,856 for Indigenous Mexican ancestry at exome variants, all available through a public browser. Using whole-genome sequencing, we developed an imputation reference panel that outperforms existing panels at common variants in individuals with high proportions of central, southern and southeastern Indigenous Mexican ancestry. Our work illustrates the value of genetic studies in diverse populations and provides foundational imputation and allele frequency resources for future genetic studies in Mexico and in the United States, where the Hispanic/Latino population is predominantly of Mexican descent.

Whole blood DNA methylation changes are associated with anti-TNF drug concentration in patients with Crohn's disease.

BACKGROUND AND AIMS: Anti-TNF treatment failure in patients with inflammatory bowel disease (IBD) is common and frequently related to low drug concentrations. In order to identify patients who may benefit from dose optimisation at the outset of anti-TNF therapy, we sought to define epigenetic biomarkers in whole blood at baseline associated with anti-TNF drug concentrations at week 14. METHODS: DNA methylation from 1,104 whole blood samples from 385 patients in the Personalised Anti-TNF Therapy in Crohn's disease (PANTS) study were assessed using the Illumina EPIC Beadchip (v1.0) at baseline, weeks 14, 30 and 54. We compared DNA methylation profiles in anti-TNF-treated patients who experienced primary non-response at week 14 and if they were assessed at subsequent time points, were not in remission at week 30 or 54 (infliximab n = 99, adalimumab n = 94), with patients who responded at week 14 and when assessed at subsequent time points, were in remission at week 30 or 54 (infliximab n = 99, adalimumab n = 93). RESULTS: Overall, between baseline and week 14, we observed 4,999 differentially methylated probes (DMPs) annotated to 2376 genes following anti-TNF treatment. Pathway analysis identified 108 significant gene ontology terms enriched in biological processes related to immune system processes and responses.Epigenome-wide association (EWAS) analysis identified 323 DMPs annotated to 210 genes at baseline associated with higher anti-TNF drug concentrations at week 14. Of these, 125 DMPs demonstrated shared associations with other common traits (proportion of shared CpGs compared to DMPs) including body mass index (23.2%), followed by CRP (11.5%), smoking (7.4%), alcohol consumption per day (7.1%) and IBD type (6.8%). EWAS of primary non-response to anti-TNF identified 20 DMPs that were associated with both anti-TNF drug concentration and primary non-response to anti-TNF with a strong correlation of the coefficients (Spearman's rho = -0.94, p < 0.001). CONCLUSION: Baseline DNA methylation profiles may be used as a predictor for anti-TNF drug concentration at week 14 to identify patients who may benefit from dose optimisation at the outset of anti-TNF therapy.

Systematic single-variant and gene-based association testing of thousands of phenotypes in 394,841 UK Biobank exomes.

Genome-wide association studies have successfully discovered thousands of common variants associated with human diseases and traits, but the landscape of rare variations in human disease has not been explored at scale. Exome-sequencing studies of population biobanks provide an opportunity to systematically evaluate the impact of rare coding variations across a wide range of phenotypes to discover genes and allelic series relevant to human health and disease. Here, we present results from systematic association analyses of 4,529 phenotypes using single-variant and gene tests of 394,841 individuals in the UK Biobank with exome-sequence data. We find that the discovery of genetic associations is tightly linked to frequency and is correlated with metrics of deleteriousness and natural selection. We highlight biological findings elucidated by these data and release the dataset as a public resource alongside the Genebass browser for rapidly exploring rare-variant association results.

HLA-DQA1*05 Carriage Associated With Development of Anti-Drug Antibodies to Infliximab and Adalimumab in Patients With Crohn's Disease.

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.

Using pseudoalignment and base quality to accurately quantify microbial community composition.

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 a novel method for taxonomic profiling in pooled DNA that 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. We apply the method to the problem of classifying 16S rRNA reads using a reference database of known organisms, a common challenge in microbiome research. Using simulations, we show the method is accurate across a variety of read lengths, with different length reference sequences, at different sample depths, and when samples contain reads originating from organisms absent from the reference. We also assess performance in real 16S data, where we reanalyze previous genetic association data to show our method discovers a larger number of quantitative trait associations than other widely used methods. We implement our method in the software Karp, for k-mer based analysis of read pools, to provide a novel combination of speed and accuracy that is uniquely suited for enhancing discoveries in microbial studies.

The impact of accelerating faster than exponential population growth on genetic variation.

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.

Analysis of rare variant population structure in Europeans explains differential stratification of gene-based tests.

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.

Surfing waves of data in San Diego: sophisticated analyses provide a broad view of human genetic diversity.

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

FTEC: a coalescent simulator for modeling faster than exponential growth.

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