Dataset Information

Diverse convergent evidence in the genetic analysis of complex disease: coordinating omic, informatic, and experimental evidence to better identify and validate risk factors.

ABSTRACT: In omic research, such as genome wide association studies, researchers seek to repeat their results in other datasets to reduce false positive findings and thus provide evidence for the existence of true associations. Unfortunately this standard validation approach cannot completely eliminate false positive conclusions, and it can also mask many true associations that might otherwise advance our understanding of pathology. These issues beg the question: How can we increase the amount of knowledge gained from high throughput genetic data? To address this challenge, we present an approach that complements standard statistical validation methods by drawing attention to both potential false negative and false positive conclusions, as well as providing broad information for directing future research. The Diverse Convergent Evidence approach (DiCE) we propose integrates information from multiple sources (omics, informatics, and laboratory experiments) to estimate the strength of the available corroborating evidence supporting a given association. This process is designed to yield an evidence metric that has utility when etiologic heterogeneity, variable risk factor frequencies, and a variety of observational data imperfections might lead to false conclusions. We provide proof of principle examples in which DiCE identified strong evidence for associations that have established biological importance, when standard validation methods alone did not provide support. If used as an adjunct to standard validation methods this approach can leverage multiple distinct data types to improve genetic risk factor discovery/validation, promote effective science communication, and guide future research directions.

SUBMITTER: Ciesielski TH

PROVIDER: S-EPMC4112852 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Diverse convergent evidence in the genetic analysis of complex disease: coordinating omic, informatic, and experimental evidence to better identify and validate risk factors.

Ciesielski Timothy H TH Pendergrass Sarah A SA White Marquitta J MJ Kodaman Nuri N Sobota Rafal S RS Huang Minjun M Bartlett Jacquelaine J Li Jing J Pan Qinxin Q Gui Jiang J Selleck Scott B SB Amos Christopher I CI Ritchie Marylyn D MD Moore Jason H JH Williams Scott M SM

BioData mining 20140630

In omic research, such as genome wide association studies, researchers seek to repeat their results in other datasets to reduce false positive findings and thus provide evidence for the existence of true associations. Unfortunately this standard validation approach cannot completely eliminate false positive conclusions, and it can also mask many true associations that might otherwise advance our understanding of pathology. These issues beg the question: How can we increase the amount of knowledg ...[more]

PMID: 25071867

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Project description:In an era of biodiversity crisis, arthropods have great potential to inform conservation assessment and test hypotheses about community assembly. This is because their relatively narrow geographic distributions and high diversity offer high-resolution data on landscape-scale patterns of biodiversity. However, a major impediment to the more widespread application of arthropod data to a range of scientific and policy questions is the poor state of modern arthropod taxonomy, especially in the tropics. Inventories of spiders and other megadiverse arthropods from tropical forests are dominated by undescribed species. Such studies typically organize their data using morphospecies codes, which make it difficult for data from independent inventories to be compared and combined. To combat this shortcoming, we offer cyberdiversity, an online community-based approach for reconciling results of independent inventory studies where current taxonomic knowledge is incomplete. Participating scientists can upload images and DNA barcode sequences to dedicated databases and submit occurrence data and links to a web site (www.digitalSpiders.org). Taxonomic determinations can be shared with a crowdsourcing comments feature, and researchers can discover specimens of interest available for loan and request aliquots of genomic DNA extract. To demonstrate the value of the cyberdiversity framework, we reconcile data from three rapid structured inventories of spiders conducted in Vietnam with an independent inventory (Doi Inthanon, Thailand) using online image libraries. Species richness and inventory completeness were assessed using non-parametric estimators. Community similarity was evaluated using a novel index based on the Jaccard replacing observed with estimated values to correct for unobserved species. We use a distance-decay framework to demonstrate a rudimentary model of landscape-scale changes in community composition that will become increasingly informative as additional inventories participate. With broader adoption of the cyberdiversity approach, networks of information-sharing taxonomists can more efficiently and effectively address taxonomic impediments while elucidating landscape scale patterns of biodiversity.

Project description:ObjectiveTo define inflammation-related host-microbe interactions in experimental spondyloarthritis (SpA) using novel inter-omic approaches.MethodsThe relative frequency of gut microbes was determined by 16S ribosomal RNA (rRNA) gene sequencing, and gene expression using RNA-Seq of host tissue. HLA-B27/human β2 -microglobulin-transgenic (HLA-B27-transgenic) and wild-type rats from dark agouti, Lewis, and Fischer backgrounds were used. Inter-omic analyses using Cytoscape were employed to identify relevant relationships. PICRUSt was used to predict microbial functions based on known metagenomic profiles.ResultsInter-omic analysis revealed several gut microbes that were strongly associated with dysregulated cytokines driving inflammatory response pathways, such as interleukin-17 (IL-17), IL-23, IL-17, IL-1, interferon-γ (IFNγ), and tumor necrosis factor (TNF). Many microbes were uniquely associated with inflammation in Lewis or Fischer rats, and one was relevant on both backgrounds. Several microbes that were strongly correlated with immune dysregulation were not differentially abundant in HLA-B27-transgenic compared to wild-type controls. A multi-omic network analysis revealed non-overlapping clusters of microbes in Lewis and Fischer rats that were strongly linked to overlapping dysregulated immune/inflammatory genes. Prevotella, Clostridiales, and Blautia were important in Lewis rats, while Akkermansia muciniphila and members of the Lachnospiraceae family dominated in Fischer rats. Inflammation-associated metabolic pathway perturbation (e.g., butanoate, propanoate, lipopolysaccharide, and steroid biosynthesis) was also predicted from both backgrounds.ConclusionInter-omic and network analysis of gut microbes and the host immune response in experimental SpA provides an unprecedented view of organisms strongly linked to dysregulated IL-23, IL-17, IL-1, IFNγ, and TNF. Functional similarities between these organisms may explain why animals of different genetic backgrounds exhibit common patterns of immune dysregulation, possibly through perturbation of similar metabolic pathways. These results highlight the power of linking analyses of gut microbiota with the host immune response to gain insights into the role of dysbiotic microbes in SpA beyond taxonomic profiling.

Dataset Information

Diverse convergent evidence in the genetic analysis of complex disease: coordinating omic, informatic, and experimental evidence to better identify and validate risk factors.

Publications

Diverse convergent evidence in the genetic analysis of complex disease: coordinating omic, informatic, and experimental evidence to better identify and validate risk factors.

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