Use my Search Websuite to scan PubMed, PMCentral, Journal Hosts and Journal Archives, FullText. Kick-your-searchterm to multiple Engines kick-your-query now !>

A dictionary by aggregated review articles of nephrology, medicine and the life sciences Your one-stop-run pathway from word to the immediate pdf of peer-reviewed on-topic knowledge.

10.1093/bioinformatics/btw128 http://scihub22266oqcxt.onion/10.1093/bioinformatics/btw128 C4920127!4920127!27153677     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Bioinformatics 2016 ; 32 (13): 1990-2000 Nephropedia Template TP {{tp|p=27153677|t=2016. Assessing statistical significance in multivariable genome wide association analysis |pdf=|usr=}}{{27153677}} gab.com Text Assessing statistical significance in multivariable genome wide association analysis JO: Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=27153677 #FOAvip Motivation: Although Genome Wide Association Studies (GWAS) genotype a very large number of single nucleotide polymorphisms (SNPs), the data are often analyzed one SNP at a time. The low predictive power of single SNPs, coupled with the high significance threshold needed to correct for multiple testing, greatly decreases the power of GWAS.Results: We propose a procedure in which all the SNPs are analyzed in a multiple generalized linear model, and we show its use for extremely high-dimensional datasets. Our method yields P-values for assessing significance of single SNPs or groups of SNPs while controlling for all other SNPs and the family wise error rate (FWER). Thus, our method tests whether or not a SNP carries any additional information about the phenotype beyond that available by all the other SNPs. This rules out spurious correlations between phenotypes and SNPs that can arise from marginal methods because the ?spuriously correlated? SNP merely happens to be correlated with the ?truly causal? SNP. In addition, the method offers a data driven approach to identifying and refining groups of SNPs that jointly contain informative signals about the phenotype. We demonstrate the value of our method by applying it to the seven diseases analyzed by the Wellcome Trust Case Control Consortium (WTCCC). We show, in particular, that our method is also capable of finding significant SNPs that were not identified in the original WTCCC study, but were replicated in other independent studies.Availability and implementation: Reproducibility of our research is supported by the open-source Bioconductor package hierGWAS.Contact:peter.buehlmann@stat.math.ethz.chSupplementary information:Supplementary data are available at Bioinformatics online. Twit Text FOAVip Assessing statistical significance in multivariable genome wide association analysis ????Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=27153677 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27153677 #FOAvip English Wikipedia <ref>{{Cite pmid|27153677}}</ref> Assessing statistical significance in multivariable genome wide association analysis #MMPMID27153677 Buzdugan L; Kalisch M; Navarro A; Schunk D; Fehr E; Bühlmann P Bioinformatics 2016[Jul]; 32 (13): 1990-2000 PMID27153677show ga Motivation: Although Genome Wide Association Studies (GWAS) genotype a very large number of single nucleotide polymorphisms (SNPs), the data are often analyzed one SNP at a time. The low predictive power of single SNPs, coupled with the high significance threshold needed to correct for multiple testing, greatly decreases the power of GWAS.Results: We propose a procedure in which all the SNPs are analyzed in a multiple generalized linear model, and we show its use for extremely high-dimensional datasets. Our method yields P-values for assessing significance of single SNPs or groups of SNPs while controlling for all other SNPs and the family wise error rate (FWER). Thus, our method tests whether or not a SNP carries any additional information about the phenotype beyond that available by all the other SNPs. This rules out spurious correlations between phenotypes and SNPs that can arise from marginal methods because the ?spuriously correlated? SNP merely happens to be correlated with the ?truly causal? SNP. In addition, the method offers a data driven approach to identifying and refining groups of SNPs that jointly contain informative signals about the phenotype. We demonstrate the value of our method by applying it to the seven diseases analyzed by the Wellcome Trust Case Control Consortium (WTCCC). We show, in particular, that our method is also capable of finding significant SNPs that were not identified in the original WTCCC study, but were replicated in other independent studies.Availability and implementation: Reproducibility of our research is supported by the open-source Bioconductor package hierGWAS.Contact:peter.buehlmann@stat.math.ethz.chSupplementary information:Supplementary data are available at Bioinformatics online. äAssessing statistical significance in multivariable genome wide associ(epmc).pdf DeepDyve Pubget Overpricing