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Find video protocols related to scientific articles indexed in Pubmed.
Chromatin profiling reveals regulatory network shifts and a protective role for hepatocyte nuclear factor 4? during colitis.
Mol. Cell. Biol.
PUBLISHED: 06-30-2014
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Transcriptional regulatory mechanisms likely contribute to the etiology of inflammatory bowel disease (IBD), as genetic variants associated with the disease are disproportionately found at regulatory elements. However, the transcription factors regulating colonic inflammation are unclear. To identify these transcription factors, we mapped epigenomic changes in the colonic epithelium upon inflammation. Epigenetic marks at transcriptional regulatory elements responded dynamically to inflammation and indicated a shift in epithelial transcriptional factor networks. Active enhancer chromatin structure at regulatory regions bound by the transcription factor hepatocyte nuclear factor 4? (HNF4A) was reduced during colitis. In agreement, upon an inflammatory stimulus, HNF4A was downregulated and showed a reduced ability to bind chromatin. Genetic variants that confer a predisposition to IBD map to HNF4A binding sites in the human colon cell line CaCo2, suggesting impaired HNF4A binding could underlie genetic susceptibility to IBD. Despite reduced HNF4A binding during inflammation, a temporal knockout model revealed HNF4A still actively protects against inflammatory phenotypes and promotes immune regulatory gene expression in the inflamed colonic epithelium. These findings highlight the potential for HNF4A agonists as IBD therapeutics.
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A novel method for analyzing genetic association with longitudinal phenotypes.
Stat Appl Genet Mol Biol
PUBLISHED: 03-19-2013
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Knowledge of genes influencing longitudinal patterns may offer information about predicting disease progression. We developed a systematic procedure for testing association between SNP genotypes and longitudinal phenotypes. We evaluated false positive rates and statistical power to localize genes for disease progression. We used genome-wide SNP data from the Framingham Heart Study. With longitudinal data from two real studies unrelated to Framingham, we estimated three trajectory curves from each study. We performed simulations by randomly selecting 500 individuals. In each simulation replicate, we assigned each individual to one of the three trajectory groups based on the underlying hypothesis (null or alternative), and generated corresponding longitudinal data. Individual Bayesian posterior probabilities (BPPs) for belonging to a specific trajectory curve were estimated. These BPPs were treated as a quantitative trait and tested (using the Wald test) for genome-wide association. Empirical false positive rates and power were calculated. Our method maintained the expected false positive rate for all simulation models. Also, our method achieved high empirical power for most simulations. Our work presents a method for disease progression gene mapping. This method is potentially clinically significant as it may allow doctors to predict disease progression based on genotype and determine treatment accordingly.
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Single-variant and multi-variant trend tests for genetic association with next-generation sequencing that are robust to sequencing error.
Hum. Hered.
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As with any new technology, next-generation sequencing (NGS) has potential advantages and potential challenges. One advantage is the identification of multiple causal variants for disease that might otherwise be missed by SNP-chip technology. One potential challenge is misclassification error (as with any emerging technology) and the issue of power loss due to multiple testing. Here, we develop an extension of the linear trend test for association that incorporates differential misclassification error and may be applied to any number of SNPs. We call the statistic the linear trend test allowing for error, applied to NGS, or LTTae,NGS. This statistic allows for differential misclassification. The observed data are phenotypes for unrelated cases and controls, coverage, and the number of putative causal variants for every individual at all SNPs. We simulate data considering multiple factors (disease mode of inheritance, genotype relative risk, causal variant frequency, sequence error rate in cases, sequence error rate in controls, number of loci, and others) and evaluate type I error rate and power for each vector of factor settings. We compare our results with two recently published NGS statistics. Also, we create a fictitious disease model based on downloaded 1000 Genomes data for 5 SNPs and 388 individuals, and apply our statistic to those data. We find that the LTTae,NGS maintains the correct type I error rate in all simulations (differential and non-differential error), while the other statistics show large inflation in type I error for lower coverage. Power for all three methods is approximately the same for all three statistics in the presence of non-differential error. Application of our statistic to the 1000 Genomes data suggests that, for the data downloaded, there is a 1.5% sequence misclassification rate over all SNPs. Finally, application of the multi-variant form of LTTae,NGS shows high power for a number of simulation settings, although it can have lower power than the corresponding single-variant simulation results, most probably due to our specification of multi-variant SNP correlation values. In conclusion, our LTTae,NGS addresses two key challenges with NGS disease studies; first, it allows for differential misclassification when computing the statistic; and second, it addresses the multiple-testing issue in that there is a multi-variant form of the statistic that has only one degree of freedom, and provides a single p value, no matter how many loci.
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What is Visualize?

JoVE Visualize is a tool created to match the last 5 years of PubMed publications to methods in JoVE's video library.

How does it work?

We use abstracts found on PubMed and match them to JoVE videos to create a list of 10 to 30 related methods videos.

Video X seems to be unrelated to Abstract Y...

In developing our video relationships, we compare around 5 million PubMed articles to our library of over 4,500 methods videos. In some cases the language used in the PubMed abstracts makes matching that content to a JoVE video difficult. In other cases, there happens not to be any content in our video library that is relevant to the topic of a given abstract. In these cases, our algorithms are trying their best to display videos with relevant content, which can sometimes result in matched videos with only a slight relation.