Synthesis-View: visualization and interpretation of SNP association results for multi-cohort, multi-phenotype data and meta-analysis

<p>Abstract</p> <p>Background</p> <p>Initial genome-wide association study (GWAS) discoveries are being further explored through the use of large cohorts across multiple and diverse populations involving meta-analyses within large consortia and networks. Many of the additional studies characterize less than 100 single nucleotide polymorphisms (SNPs), often include multiple and correlated phenotypic measurements, and can include data from multiple-sites, multiple-studies, as well as multiple race/ethnicities. New approaches for visualizing resultant data are necessary in order to fully interpret results and obtain a broad view of the trends between DNA variation and phenotypes, as well as provide information on specific SNP and phenotype relationships.</p> <p>Results</p> <p>The Synthesis-View software tool was designed to visually synthesize the results of the aforementioned types of studies. Presented herein are multiple examples of the ways Synthesis-View can be used to report results from association studies of DNA variation and phenotypes, including the visual integration of p-values or other metrics of significance, allele frequencies, sample sizes, effect size, and direction of effect.</p> <p>Conclusions</p> <p>To truly allow a user to visually integrate multiple pieces of information typical of a genetic association study, innovative views are needed to integrate multiple pieces of information. As a result, we have created "Synthesis-View" software for the visualization of genotype-phenotype association data in multiple cohorts. Synthesis-View is freely available for non-commercial research institutions, for full details see <url>https://chgr.mc.vanderbilt.edu/synthesisview</url>.</p

A General Framework for Formal Tests of Interaction after Exhaustive Search Methods with Applications to MDR and MDR-PDT

The initial presentation of multifactor dimensionality reduction (MDR) featured cross-validation to mitigate over-fitting, computationally efficient searches of the epistatic model space, and variable construction with constructive induction to alleviate the curse of dimensionality. However, the method was unable to differentiate association signals arising from true interactions from those due to independent main effects at individual loci. This issue leads to problems in inference and interpretability for the results from MDR and the family-based compliment the MDR-pedigree disequilibrium test (PDT). A suggestion from previous work was to fit regression models post hoc to specifically evaluate the null hypothesis of no interaction for MDR or MDR-PDT models. We demonstrate with simulation that fitting a regression model on the same data as that analyzed by MDR or MDR-PDT is not a valid test of interaction. This is likely to be true for any other procedure that searches for models, and then performs an uncorrected test for interaction. We also show with simulation that when strong main effects are present and the null hypothesis of no interaction is true, that MDR and MDR-PDT reject at far greater than the nominal rate. We also provide a valid regression-based permutation test procedure that specifically tests the null hypothesis of no interaction, and does not reject the null when only main effects are present. The regression-based permutation test implemented here conducts a valid test of interaction after a search for multilocus models, and can be applied to any method that conducts a search to find a multilocus model representing an interaction

Estimating cumulative pathway effects on risk for age-related macular degeneration using mixed linear models

BACKGROUND: Age-related macular degeneration (AMD) is the leading cause of irreversible visual loss in the elderly in developed countries and typically affects more than 10 % of individuals over age 80. AMD has a large genetic component, with heritability estimated to be between 45 % and 70 %. Numerous variants have been identified and implicate various molecular mechanisms and pathways for AMD pathogenesis but those variants only explain a portion of AMD’s heritability. The goal of our study was to estimate the cumulative genetic contribution of common variants on AMD risk for multiple pathways related to the etiology of AMD, including angiogenesis, antioxidant activity, apoptotic signaling, complement activation, inflammatory response, response to nicotine, oxidative phosphorylation, and the tricarboxylic acid cycle. While these mechanisms have been associated with AMD in literature, the overall extent of the contribution to AMD risk for each is unknown. METHODS: In a case–control dataset with 1,813 individuals genotyped for over 600,000 SNPs we used Genome-wide Complex Trait Analysis (GCTA) to estimate the proportion of AMD risk explained by SNPs in genes associated with each pathway. SNPs within a 50 kb region flanking each gene were also assessed, as well as more distant, putatively regulatory SNPs, based on DNaseI hypersensitivity data from ocular tissue in the ENCODE project. RESULTS: We found that 19 previously associated AMD risk SNPs contributed to 13.3 % of the risk for AMD in our dataset, while the remaining genotyped SNPs contributed to 36.7 % of AMD risk. Adjusting for the 19 risk SNPs, the complement activation and inflammatory response pathways still explained a statistically significant proportion of additional risk for AMD (9.8 % and 17.9 %, respectively), with other pathways showing no significant effects (0.3 % – 4.4 %). DISCUSSION: Our results show that SNPs associated with complement activation and inflammation significantly contribute to AMD risk, separately from the risk explained by the 19 known risk SNPs. We found that SNPs within 50 kb regions flanking genes explained additional risk beyond genic SNPs, suggesting a potential regulatory role, but that more distant SNPs explained less than 0.5 % additional risk for each pathway. CONCLUSIONS: From these analyses we find that the impact of complement SNPs on risk for AMD extends beyond the established genome-wide significant SNPs. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-015-0760-4) contains supplementary material, which is available to authorized users

Th17-Related Genes and Celiac Disease Susceptibility

Th17 cells are known to be involved in several autoimmune or inflammatory diseases. In celiac disease (CD), recent studies suggest an implication of those cells in disease pathogenesis. We aimed at studying the role of genes relevant for the Th17 immune response in CD susceptibility. A total of 101 single nucleotide polymorphisms (SNPs), mainly selected to cover most of the variability present in 16 Th17-related genes (IL23R, RORC, IL6R, IL17A, IL17F, CCR6, IL6, JAK2, TNFSF15, IL23A, IL22, STAT3, TBX21, SOCS3, IL12RB1 and IL17RA), were genotyped in 735 CD patients and 549 ethnically matched healthy controls. Case-control comparisons for each SNP and for the haplotypes resulting from the SNPs studied in each gene were performed using chi-square tests. Gene-gene interactions were also evaluated following different methodological approaches. No significant results emerged after performing the appropriate statistical corrections. Our results seem to discard a relevant role of Th17 cells on CD risk

Comparison of Strategies to Detect Epistasis from eQTL Data

Genome-wide association studies have been instrumental in identifying genetic variants associated with complex traits such as human disease or gene expression phenotypes. It has been proposed that extending existing analysis methods by considering interactions between pairs of loci may uncover additional genetic effects. However, the large number of possible two-marker tests presents significant computational and statistical challenges. Although several strategies to detect epistasis effects have been proposed and tested for specific phenotypes, so far there has been no systematic attempt to compare their performance using real data. We made use of thousands of gene expression traits from linkage and eQTL studies, to compare the performance of different strategies. We found that using information from marginal associations between markers and phenotypes to detect epistatic effects yielded a lower false discovery rate (FDR) than a strategy solely using biological annotation in yeast, whereas results from human data were inconclusive. For future studies whose aim is to discover epistatic effects, we recommend incorporating information about marginal associations between SNPs and phenotypes instead of relying solely on biological annotation. Improved methods to discover epistatic effects will result in a more complete understanding of complex genetic effects

Mutual Information for Testing Gene-Environment Interaction

Despite current enthusiasm for investigation of gene-gene interactions and gene-environment interactions, the essential issue of how to define and detect gene-environment interactions remains unresolved. In this report, we define gene-environment interactions as a stochastic dependence in the context of the effects of the genetic and environmental risk factors on the cause of phenotypic variation among individuals. We use mutual information that is widely used in communication and complex system analysis to measure gene-environment interactions. We investigate how gene-environment interactions generate the large difference in the information measure of gene-environment interactions between the general population and a diseased population, which motives us to develop mutual information-based statistics for testing gene-environment interactions. We validated the null distribution and calculated the type 1 error rates for the mutual information-based statistics to test gene-environment interactions using extensive simulation studies. We found that the new test statistics were more powerful than the traditional logistic regression under several disease models. Finally, in order to further evaluate the performance of our new method, we applied the mutual information-based statistics to three real examples. Our results showed that P-values for the mutual information-based statistics were much smaller than that obtained by other approaches including logistic regression models

Impact of surface adhesion and sample heterogeneity on the multiscale mechanical characterisation of soft biomaterials

Funding from the Engineering and Physical Sciences Research Council (EP/L505602/1) is gratefully acknowledged

Calcium Sulfate and Platelet-Rich Plasma make a novel osteoinductive biomaterial for bone regeneration

BACKGROUND: With the present study we introduce a novel and simple biomaterial able to induce regeneration of bone. We theorized that nourishing a bone defect with calcium and with a large amount of activated platelets may initiate a series of biological processes that culminate in bone regeneration. Thus, we engineered CS-Platelet, a biomaterial based on the combination of Calcium Sulfate and Platelet-Rich Plasma in which Calcium Sulfate also acts as an activator of the platelets, therefore avoiding the need to activate the platelets with an agonist. METHODS: First, we tested CS-Platelet in heterotopic (muscle) and orthotopic (bone) bone regeneration bioassays. We then utilized CS-Platelet in a variety of dental and craniofacial clinical cases, where regeneration of bone was needed. RESULTS: The heterotopic bioassay showed formation of bone within the muscular tissue at the site of the implantation of CS-Platelet. Results of a quantitative orthotopic bioassay based on the rat calvaria critical size defect showed that only CS-Platelet and recombinant human BMP2 were able to induce a significant regeneration of bone. A non-human primate orthotopic bioassay also showed that CS-Platelet is completely resorbable. In all human clinical cases where CS-Platelet was used, a complete bone repair was achieved. CONCLUSION: This study showed that CS-Platelet is a novel biomaterial able to induce formation of bone in heterotopic and orthotopic sites, in orthotopic critical size bone defects, and in various clinical situations. The discovery of CS-Platelet may represent a cost-effective breakthrough in bone regenerative therapy and an alternative or an adjuvant to the current treatments