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10.1093/bioinformatics/btw272 http://scihub22266oqcxt.onion/10.1093/bioinformatics/btw272 C4908356!4908356!27307612     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Bioinformatics 2016 ; 32 (12): i156-63 Nephropedia Template TP {{tp|p=27307612|t=2016. Using genomic annotations increases statistical power to detect eGenes |pdf=|usr=}}{{27307612}} gab.com Text Using genomic annotations increases statistical power to detect eGenes JO: Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=27307612 #FOAvip Motivation: Expression quantitative trait loci (eQTLs) are genetic variants that affect gene expression. In eQTL studies, one important task is to find eGenes or genes whose expressions are associated with at least one eQTL. The standard statistical method to determine whether a gene is an eGene requires association testing at all nearby variants and the permutation test to correct for multiple testing. The standard method however does not consider genomic annotation of the variants. In practice, variants near gene transcription start sites (TSSs) or certain histone modifications are likely to regulate gene expression. In this article, we introduce a novel eGene detection method that considers this empirical evidence and thereby increases the statistical power.Results: We applied our method to the liver Genotype-Tissue Expression (GTEx) data using distance from TSSs, DNase hypersensitivity sites, and six histone modifications as the genomic annotations for the variants. Each of these annotations helped us detected more candidate eGenes. Distance from TSS appears to be the most important annotation; specifically, using this annotation, our method discovered 50% more candidate eGenes than the standard permutation method.Contact:buhm.han@amc.seoul.kr or eeskin@cs.ucla.edu Twit Text FOAVip Using genomic annotations increases statistical power to detect eGenes ????Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=27307612 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27307612 #FOAvip English Wikipedia <ref>{{Cite pmid|27307612}}</ref> Using genomic annotations increases statistical power to detect eGenes #MMPMID27307612 Duong D; Zou J; Hormozdiari F; Sul JH; Ernst J; Han B; Eskin E Bioinformatics 2016[Jun]; 32 (12): i156-63 PMID27307612show ga Motivation: Expression quantitative trait loci (eQTLs) are genetic variants that affect gene expression. In eQTL studies, one important task is to find eGenes or genes whose expressions are associated with at least one eQTL. The standard statistical method to determine whether a gene is an eGene requires association testing at all nearby variants and the permutation test to correct for multiple testing. The standard method however does not consider genomic annotation of the variants. In practice, variants near gene transcription start sites (TSSs) or certain histone modifications are likely to regulate gene expression. In this article, we introduce a novel eGene detection method that considers this empirical evidence and thereby increases the statistical power.Results: We applied our method to the liver Genotype-Tissue Expression (GTEx) data using distance from TSSs, DNase hypersensitivity sites, and six histone modifications as the genomic annotations for the variants. Each of these annotations helped us detected more candidate eGenes. Distance from TSS appears to be the most important annotation; specifically, using this annotation, our method discovered 50% more candidate eGenes than the standard permutation method.Contact:buhm.han@amc.seoul.kr or eeskin@cs.ucla.edu äUsing genomic annotations increases statistical power to detect eGenes(epmc).pdf DeepDyve Pubget Overpricing