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

Evaluation of association tests for rare variants using simulated data sets in the Genetic Analysis Workshop 17 data

We evaluate four association tests for rare variants—the combined multivariate and collapsing (CMC) method, two weighted-sum methods, and a variable threshold method—by applying them to the simulated data sets of unrelated individuals in the Genetic Analysis Workshop 17 (GAW17) data. The family-wise error rate (FWER) and average power are used as criteria for evaluation. Our results show that when all nonsynonymous SNPs (rare variants and common variants) in a gene are jointly analyzed, the CMC method fails to control the FWER; when only rare variants (single-nucleotide polymorphisms with minor allele frequency less than 0.05) are analyzed, all four methods can control FWER well. All four methods have comparable power, which is low for the analysis of the GAW17 data sets. Three of the methods (not including the CMC method) involve estimation of p-values using permutation procedures that either can be computationally intensive or generate inflated FWERs. We adapt a fast permutation procedure into these three methods. The results show that using the fast permutation procedure can produce FWERs and average powers close to the values obtained from the standard permutation procedure on the GAW17 data sets. The standard permutation procedure is computationally intensive

MegaSNPHunter: a learning approach to detect disease predisposition SNPs and high level interactions in genome wide association study

<p>Abstract</p> <p>Background</p> <p>The interactions of multiple single nucleotide polymorphisms (SNPs) are highly hypothesized to affect an individual's susceptibility to complex diseases. Although many works have been done to identify and quantify the importance of multi-SNP interactions, few of them could handle the genome wide data due to the combinatorial explosive search space and the difficulty to statistically evaluate the high-order interactions given limited samples.</p> <p>Results</p> <p>Three comparative experiments are designed to evaluate the performance of MegaSNPHunter. The first experiment uses synthetic data generated on the basis of epistasis models. The second one uses a genome wide study on Parkinson disease (data acquired by using Illumina HumanHap300 SNP chips). The third one chooses the rheumatoid arthritis study from Wellcome Trust Case Control Consortium (WTCCC) using Affymetrix GeneChip 500K Mapping Array Set. MegaSNPHunter outperforms the best solution in this area and reports many potential interactions for the two real studies.</p> <p>Conclusion</p> <p>The experimental results on both synthetic data and two real data sets demonstrate that our proposed approach outperforms the best solution that is currently available in handling large-scale SNP data both in terms of speed and in terms of detection of potential interactions that were not identified before. To our knowledge, MegaSNPHunter is the first approach that is capable of identifying the disease-associated SNP interactions from WTCCC studies and is promising for practical disease prognosis.</p

Genome-wide association and HLA fine-mapping studies identify risk loci and genetic pathways underlying allergic rhinitis

Allergic rhinitis is the most common clinical presentation of allergy, affecting 400 million people worldwide, with increasing incidence in westernized countries1,2. To elucidate the genetic architecture and understand the underlying disease mechanisms, we carried out a meta-analysis of allergic rhinitis in 59,762 cases and 152,358 controls of European ancestry and identified a total of 41 risk loci for allergic rhinitis, including 20 loci not previously associated with allergic rhinitis, which were confirmed in a replication phase of 60,720 cases and 618,527 controls. Functional annotation implicated genes involved in various immune pathways, and fine mapping of the HLA region suggested amino acid variants important for antigen binding. We further performed genome-wide association study (GWAS) analyses of allergic sensitization against inhalant allergens and nonallergic rhinitis, which suggested shared genetic mechanisms across rhinitis-related traits. Future studies of the identified loci and genes might identify novel targets for treatment and prevention of allergic rhinitis

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

Replication of functional serotonin receptor type 3A and B variants in bipolar affective disorder: a European multicenter study

Serotonin type 3 receptors (5-HT3) are involved in learning, cognition and emotion, and have been implicated in various psychiatric phenotypes. However, their contribution to the pathomechanism of these disorders remains elusive. Three single nucleotide polymorphisms (SNPs) in the HTR3A and HTR3B genes (rs1062613, rs1176744 and rs3831455) have been associated with bipolar affective disorder (BPAD) in pilot studies, and all of them are of functional relevance. We performed a European multicenter study to confirm previous results and provide further evidence for the relevance of these SNPs to the etiology of neuropsychiatric disorders. This involved analysis of the distribution of the three SNPs among 1804 BPAD cases and 2407 healthy controls. A meta-analysis revealed a pooled odds ratio of 0.881 (P=0.009, 95% confidence intervals=0.802–0.968) for the non-synonymous functional SNP HTR3B p.Y129S (rs1176744), thereby confirming previous findings. In line with this, the three genome-wide association study samples BOMA (Bonn-Mannheim)-BPAD, WTCCC (Wellcome Trust Case Control Consortium)-BPAD and GAIN (Genetic Association Information Network)-BPAD, including >3500 patients and 5200 controls in total, showed an overrepresentation of the p.Y129 in patients. Remarkably, the meta-analysis revealed a P-value of 0.048 (OR=0.934, fixed effect model). We also performed expression analyses to gain further insights into the distribution of HTR3A and HTR3B mRNA in the human brain. HTR3A and HTR3B were detected in all investigated brain tissues with the exception of the cerebellum, and large differences in the A:B subunit ratio were observed. Interestingly, expression of the B subunit was most prominent in the brain stem, amygdalae and frontal cortex, regions of relevance to psychiatric disorders. In conclusion, the present study provides further evidence for the presence of impaired 5-HT3 receptor function in BPAD

Traits associated with innate and adaptive immunity in pigs: heritability and associations with performance under different health status conditions

There is a need for genetic markers or biomarkers that can predict resistance towards a wide range of infectious diseases, especially within a health environment typical of commercial farms. Such markers also need to be heritable under these conditions and ideally correlate with commercial performance traits. In this study, we estimated the heritabilities of a wide range of immune traits, as potential biomarkers, and measured their relationship with performance within both specific pathogen-free (SPF) and non-SPF environments. Immune traits were measured in 674 SPF pigs and 606 non-SPF pigs, which were subsets of the populations for which we had performance measurements (average daily gain), viz. 1549 SPF pigs and 1093 non-SPF pigs. Immune traits measured included total and differential white blood cell counts, peripheral blood mononuclear leucocyte (PBML) subsets (CD4+ cells, total CD8α+ cells, classical CD8αβ+ cells, CD11R1+ cells (CD8α+ and CD8α-), B cells, monocytes and CD16+ cells) and acute phase proteins (alpha-1 acid glycoprotein (AGP), haptoglobin, C-reactive protein (CRP) and transthyretin). Nearly all traits tested were heritable regardless of health status, although the heritability estimate for average daily gain was lower under non-SPF conditions. There were also negative genetic correlations between performance and the following immune traits: CD11R1+ cells, monocytes and the acute phase protein AGP. The strength of the association between performance and AGP was not affected by health status. However, negative genetic correlations were only apparent between performance and monocytes under SPF conditions and between performance and CD11R1+ cells under non-SPF conditions. Although we cannot infer causality in these relationships, these results suggest a role for using some immune traits, particularly CD11R1+ cells or AGP concentrations, as predictors of pig performance under the lower health status conditions associated with commercial farms

The Role of Host Genetics in Susceptibility to Influenza: A Systematic Review

Background: The World Health Organization has identified studies of the role of host genetics on susceptibility to severe influenza as a priority. A systematic review was conducted to summarize the current state of evidence on the role of host genetics in susceptibility to influenza (PROSPERO registration number: CRD42011001380). Methods and Findings: PubMed, Web of Science, the Cochrane Library, and OpenSIGLE were searched using a pre-defined strategy for all entries up to the date of the search. Two reviewers independently screened the title and abstract of 1,371 unique articles, and 72 full text publications were selected for inclusion. Mouse models clearly demonstrate that host genetics plays a critical role in susceptibility to a range of human and avian influenza viruses. The Mx genes encoding interferon inducible proteins are the best studied but their relevance to susceptibility in humans is unknown. Although the MxA gene should be considered a candidate gene for further study in humans, over 100 other candidate genes have been proposed. There are however no data associating any of these candidate genes to susceptibility in humans, with the only published study in humans being under-powered. One genealogy study presents moderate evidence of a heritable component to the risk of influenza-associated death, and while the marked familial aggregation of H5N1 cases is suggestive of host genetic factors, this remains unproven. Conclusion: The fundamental question ‘‘Is susceptibility to severe influenza in humans heritable?’ ’ remains unanswered. No

Metformin strongly affects transcriptome of peripheral blood cells in healthy individuals

Funding Information: The study was supported by the European Regional Development Fund under the project ?Investigation of interplay between multiple determinants influencing response to metformin: search for reliable predictors for efficacy of type 2 diabetes therapy? (Project No.: 1.1.1.1/16/A/091, https://ec.europa.eu/regional_policy/en/funding/ erdf/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors would like to thank all the volunteers for their participation and acknowledge the Genome Database of the Latvian Population for providing biological material and data. Publisher Copyright: © 2019 Ustinova et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Metformin is a commonly used antihyperglycaemic agent for the treatment of type 2 diabetes mellitus. Nevertheless, the exact mechanisms of action, underlying the various therapeutic effects of metformin, remain elusive. The goal of this study was to evaluate the alterations in longitudinal whole-blood transcriptome profiles of healthy individuals after a one-week metformin intervention in order to identify the novel molecular targets and further prompt the discovery of predictive biomarkers of metformin response. Next generation sequencing-based transcriptome analysis revealed metformin-induced differential expression of genes involved in intestinal immune network for IgA production and cytokine-cytokine receptor interaction pathways. Significantly elevated faecal sIgA levels during administration of metformin, and its correlation with the expression of genes associated with immune response (CXCR4, HLA-DQA1, MAP3K14, TNFRSF21, CCL4, ACVR1B, PF4, EPOR, CXCL8) supports a novel hypothesis of strong association between metformin and intestinal immune system, and for the first time provide evidence for altered RNA expression as a contributing mechanism of metformin’s action. In addition to universal effects, 4 clusters of functionally related genes with a subject-specific differential expression were distinguished, including genes relevant to insulin production (HNF1B, HNF1A, HNF4A, GCK, INS, NEUROD1, PAX4, PDX1, ABCC8, KCNJ11) and cholesterol homeostasis (APOB, LDLR, PCSK9). This inter-individual variation of the metformin effect on the transcriptional regulation goes in line with well-known variability of the therapeutic response to the drug.publishersversionPeer reviewe