Investigation of genetically regulated gene expression and response to treatment in rheumatoid arthritis highlights an association between IL18RAP expression and treatment response.

This article has been accepted for publication in Annals of the Rheumatic Diseases, 2020 following peer review, and the Version of Record can be accessed online at http://dx.doi.org/10.1136/annrheumdis-2020-217204OBJECTIVES: In this study, we sought to investigate whether there was any association between genetically regulated gene expression (as predicted using various reference panels) and anti-tumour necrosis factor (anti-TNF) treatment response (change in erythrocyte sedimentation rate (ESR)) using 3158 European ancestry patients with rheumatoid arthritis. METHODS: The genetically regulated portion of gene expression was estimated in the full cohort of 3158 subjects (as well as within a subcohort consisting of 1575 UK patients) using the PrediXcan software package with three different reference panels. Estimated expression was tested for association with anti-TNF treatment response. As a replication/validation experiment, we also investigated the correlation between change in ESR with measured gene expression at the Interleukin 18 Receptor Accessory Protein (IL18RAP) gene in whole blood and synovial tissue, using an independent replication data set of patients receiving conventional synthetic disease modifying anti-rheumatic drugs, with directly measured (via RNA sequencing) gene expression. RESULTS: We found that predicted expression of IL18RAP showed a consistent signal of association with treatment response across the reference panels. In our independent replication data set, IL18RAP expression in whole blood showed correlation with the change in ESR between baseline and follow-up (r=-0.35, p=0.0091). Change in ESR was also correlated with the expression of IL18RAP in synovial tissue (r=-0.28, p=0.02). CONCLUSION: Our results suggest that IL18RAP expression is worthy of further investigation as a potential predictor of treatment response in rheumatoid arthritis that is not specific to a particular drug type

Tools for efficient epistasis detection in genome-wide association study

<p>Abstract</p> <p>Background</p> <p>Genome-wide association study (GWAS) aims to find genetic factors underlying complex phenotypic traits, for which epistasis or gene-gene interaction detection is often preferred over single-locus approach. However, the computational burden has been a major hurdle to apply epistasis test in the genome-wide scale due to a large number of single nucleotide polymorphism (SNP) pairs to be tested.</p> <p>Results</p> <p>We have developed a set of three efficient programs, FastANOVA, COE and TEAM, that support epistasis test in a variety of problem settings in GWAS. These programs utilize permutation test to properly control error rate such as family-wise error rate (FWER) and false discovery rate (FDR). They guarantee to find the optimal solutions, and significantly speed up the process of epistasis detection in GWAS.</p> <p>Conclusions</p> <p>A web server with user interface and source codes are available at the website <url>http://www.csbio.unc.edu/epistasis/</url>. The source codes are also available at SourceForge <url>http://sourceforge.net/projects/epistasis/</url>.</p

AntEpiSeeker: detecting epistatic interactions for case-control studies using a two-stage ant colony optimization algorithm

<p>Abstract</p> <p>Background</p> <p>Epistatic interactions of multiple single nucleotide polymorphisms (SNPs) are now believed to affect individual susceptibility to common diseases. The detection of such interactions, however, is a challenging task in large scale association studies. Ant colony optimization (ACO) algorithms have been shown to be useful in detecting epistatic interactions.</p> <p>Findings</p> <p>AntEpiSeeker, a new two-stage ant colony optimization algorithm, has been developed for detecting epistasis in a case-control design. Based on some practical epistatic models, AntEpiSeeker has performed very well.</p> <p>Conclusions</p> <p>AntEpiSeeker is a powerful and efficient tool for large-scale association studies and can be downloaded from <url>http://nce.ads.uga.edu/~romdhane/AntEpiSeeker/index.html</url>.</p

Apparent correlation of palaeomagnetic intensity and climatic records in deep-sea sediments

Most reports of a correlation between Pleistocene climate and geomagnetic field intensity rely strongly on the assumption that sediment natural remanent magnetic (NRM) intensity provides a record of geomagnetic field strength and is not sensitive to local changes in properties of the sediment. Critical assessment of relevant data presented here and elsewhere from deep-sea sediment cores shows that a pronounced dependence of NRM intensity on sediment composition can occur which implies that this assumption is unlikely to be generally valid. As sediment composition often reflects varying depositional conditions induced by climatic change, the significance of correlations proposed between Pleistocene palaeomagnetism and climatic indicators in deep-sea sediments may be less dramatic than sometimes supposed

HLA-A Confers an HLA-DRB1 Independent Influence on the Risk of Multiple Sclerosis

A recent high-density linkage screen confirmed that the HLA complex contains the strongest genetic factor for the risk of multiple sclerosis (MS). In parallel, a linkage disequilibrium analysis using 650 single nucleotide polymorphisms (SNP) markers of the HLA complex mapped the entire genetic effect to the HLA-DR-DQ subregion, reflected by the well-established risk haplotype HLA-DRB1*15,DQB1*06. Contrary to this, in a cohort of 1,084 MS patients and 1,347 controls, we show that the HLA-A gene confers an HLA-DRB1 independent influence on the risk of MS (P = 8.4×10−10). This supports the opposing view, that genes in the HLA class I region indeed exert an additional influence on the risk of MS, and confirms that the class I allele HLA-A*02 is negatively associated with the risk of MS (OR = 0.63, P = 7×10−12) not explained by linkage disequilibrium with class II. The combination of HLA-A and HLA-DRB1 alleles, as represented by HLA-A*02 and HLA-DRB1*15, was found to influence the risk of MS 23-fold. These findings imply complex autoimmune mechanisms involving both the regulatory and the effector arms of the immune system in the triggering of MS

On the Use of Variance per Genotype as a Tool to Identify Quantitative Trait Interaction Effects: A Report from the Women's Genome Health Study

Testing for genetic effects on mean values of a quantitative trait has been a very successful strategy. However, most studies to date have not explored genetic effects on the variance of quantitative traits as a relevant consequence of genetic variation. In this report, we demonstrate that, under plausible scenarios of genetic interaction, the variance of a quantitative trait is expected to differ among the three possible genotypes of a biallelic SNP. Leveraging this observation with Levene's test of equality of variance, we propose a novel method to prioritize SNPs for subsequent gene–gene and gene–environment testing. This method has the advantageous characteristic that the interacting covariate need not be known or measured for a SNP to be prioritized. Using simulations, we show that this method has increased power over exhaustive search under certain conditions. We further investigate the utility of variance per genotype by examining data from the Women's Genome Health Study. Using this dataset, we identify new interactions between the LEPR SNP rs12753193 and body mass index in the prediction of C-reactive protein levels, between the ICAM1 SNP rs1799969 and smoking in the prediction of soluble ICAM-1 levels, and between the PNPLA3 SNP rs738409 and body mass index in the prediction of soluble ICAM-1 levels. These results demonstrate the utility of our approach and provide novel genetic insight into the relationship among obesity, smoking, and inflammation

A genetic ensemble approach for gene-gene interaction identification

<p>Abstract</p> <p>Background</p> <p>It has now become clear that gene-gene interactions and gene-environment interactions are ubiquitous and fundamental mechanisms for the development of complex diseases. Though a considerable effort has been put into developing statistical models and algorithmic strategies for identifying such interactions, the accurate identification of those genetic interactions has been proven to be very challenging.</p> <p>Methods</p> <p>In this paper, we propose a new approach for identifying such gene-gene and gene-environment interactions underlying complex diseases. This is a hybrid algorithm and it combines genetic algorithm (GA) and an ensemble of classifiers (called genetic ensemble). Using this approach, the original problem of SNP interaction identification is converted into a data mining problem of combinatorial feature selection. By collecting various single nucleotide polymorphisms (SNP) subsets as well as environmental factors generated in multiple GA runs, patterns of gene-gene and gene-environment interactions can be extracted using a simple combinatorial ranking method. Also considered in this study is the idea of combining identification results obtained from multiple algorithms. A novel formula based on pairwise <it>double fault </it>is designed to quantify the degree of complementarity.</p> <p>Conclusions</p> <p>Our simulation study demonstrates that the proposed genetic ensemble algorithm has comparable identification power to Multifactor Dimensionality Reduction (MDR) and is slightly better than Polymorphism Interaction Analysis (PIA), which are the two most popular methods for gene-gene interaction identification. More importantly, the identification results generated by using our genetic ensemble algorithm are highly complementary to those obtained by PIA and MDR. Experimental results from our simulation studies and real world data application also confirm the effectiveness of the proposed genetic ensemble algorithm, as well as the potential benefits of combining identification results from different algorithms.</p

Epistatic Module Detection for Case-Control Studies: A Bayesian Model with a Gibbs Sampling Strategy

The detection of epistatic interactive effects of multiple genetic variants on the susceptibility of human complex diseases is a great challenge in genome-wide association studies (GWAS). Although methods have been proposed to identify such interactions, the lack of an explicit definition of epistatic effects, together with computational difficulties, makes the development of new methods indispensable. In this paper, we introduce epistatic modules to describe epistatic interactive effects of multiple loci on diseases. On the basis of this notion, we put forward a Bayesian marker partition model to explain observed case-control data, and we develop a Gibbs sampling strategy to facilitate the detection of epistatic modules. Comparisons of the proposed approach with three existing methods on seven simulated disease models demonstrate the superior performance of our approach. When applied to a genome-wide case-control data set for Age-related Macular Degeneration (AMD), the proposed approach successfully identifies two known susceptible loci and suggests that a combination of two other loci—one in the gene SGCD and the other in SCAPER—is associated with the disease. Further functional analysis supports the speculation that the interaction of these two genetic variants may be responsible for the susceptibility of AMD. When applied to a genome-wide case-control data set for Parkinson's disease, the proposed method identifies seven suspicious loci that may contribute independently to the disease

All-cause and liver-related mortality risk factors in excessive drinkers: Analysis of data from the UK biobank

Background: High alcohol intake is associated with increased mortality. We aimed to identify factors affecting mortality in people drinking extreme amounts of alcohol. Methods: We obtained information from the UK Biobank on approximately 500,000 participants aged 40–70 years at baseline assessment in 2006–2010. Habitual alcohol intake, lifestyle and physiological data, laboratory test results, and hospital diagnoses and death certificate data (to June 2020) for 5136 men (2.20% of male participants) and 1504 women (0.60%) who reported consuming ≥80 or ≥50 g/day, respectively, were used in survival analysis. Results: Mortality hazard ratios for these excessive drinkers, compared to all other participants, were 2.02 (95% CI 1.89–2.17) for all causes, 1.89 (1.69–2.12) for any cancer, 1.87 (1.61–2.17) for any circulatory disease, and 9.40 (7.00–12.64) for any liver disease. Liver disease diagnosis or abnormal liver function tests predicted not only deaths attributed to liver disease but also those from cancers or circulatory diseases. Mortality among excessive drinkers was also associated with quantitative alcohol intake; diagnosed alcohol dependence, harmful use, or withdrawal syndrome; and current smoking at assessment. Conclusions: People with chronic excessive alcohol intake experience decreased average survival, but there is substantial variation in their mortality, with liver abnormality and alcohol dependence or other alcohol use disorders associated with a worse prognosis. Clinically, patients with these risk factors and high alcohol intake should be considered for early or intensive management. Research can usefully focus on the factors predisposing to dependence or liver abnormality

British Society of Gastroenterology (BSG) and British Society of Paediatric Gastroenterology, Hepatology and Nutrition (BSPGHAN) joint consensus guidelines on the diagnosis and management of eosinophilic oesophagitis in children and adults

Background: Eosinophilic oesophagitis (EoE) is an increasingly common cause of dysphagia in both children and adults, as well as one of the most prevalent oesophageal diseases with a significant impact on physical health and quality of life. We have provided a single comprehensive guideline for both paediatric and adult gastroenterologists on current best practice for the evaluation and management of EoE. Methods: The Oesophageal Section of the British Society of Gastroenterology was commissioned by the Clinical Standards Service Committee to develop these guidelines. The Guideline Development Group included adult and paediatric gastroenterologists, surgeons, dietitians, allergists, pathologists and patient representatives. The Population, Intervention, Comparator and Outcomes process was used to generate questions for a systematic review of the evidence. Published evidence was reviewed and updated to June 2021. The Grading of Recommendations, Assessment, Development and Evaluation (GRADE) system was used to assess the evidence and make recommendations. Two rounds of voting were held to assess the level of agreement and the strength of recommendations, with 80% consensus required for acceptance. Results: Fifty-seven statements on EoE presentation, diagnosis, investigation, management and complications were produced with further statements created on areas for future research. Conclusions: These comprehensive adult and paediatric guidelines of the British Society of Gastroenterology and British Society of Paediatric Gastroenterology, Hepatology and Nutrition are based on evidence and expert consensus from a multidisciplinary group of healthcare professionals, including patient advocates and patient support groups, to help clinicians with the management patients with EoE and its complications