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10.1186/1471-2164-14-S8-S8 http://scihub22266oqcxt.onion/10.1186/1471-2164-14-S8-S8 C4042241!4042241 !24564682     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=24564682 &cmd=llinks): Failed to open stream: HTTP request failed! 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Simultaneous inferences based on empirical Bayes methods and false discovery rates ineQTL data analysis |pdf=|usr=}}{{24564682 }} gab.com Text Simultaneous inferences based on empirical Bayes methods and false discovery rates ineQTL data analysis JO: BMC Genomics http://www.kidney.de/pget.php?&tw=1&pmid=24564682 #FOAvip BACKGROUND: Genome-wide association studies (GWAS) have identified hundreds of genetic variants associated with complex human diseases, clinical conditions and traits. Genetic mapping of expression quantitative trait loci (eQTLs) is providing us with novel functional effects of thousands of single nucleotide polymorphisms (SNPs). In a classical quantitative trail loci (QTL) mapping problem multiple tests are done to assess whether one trait is associated with a number of loci. In contrast to QTL studies, thousands of traits are measured alongwith thousands of gene expressions in an eQTL study. For such a study, a huge number of tests have to be performed (~10(6)). This extreme multiplicity gives rise to many computational and statistical problems. In this paper we have tried to address these issues using two closely related inferential approaches: an empirical Bayes method that bears the Bayesian flavor without having much a priori knowledge and the frequentist method of false discovery rates. A three-component t-mixture model has been used for the parametric empirical Bayes (PEB) method. Inferences have been obtained using Expectation/Conditional Maximization Either (ECME) algorithm. A simulation study has also been performed and has been compared with a nonparametric empirical Bayes (NPEB) alternative. RESULTS: The results show that PEB has an edge over NPEB. The proposed methodology has been applied to human liver cohort (LHC) data. Our method enables to discover more significant SNPs with FDR<10% compared to the previous study done by Yang et al. (Genome Research, 2010). CONCLUSIONS: In contrast to previously available methods based on p-values, the empirical Bayes method uses local false discovery rate (lfdr) as the threshold. This method controls false positive rate. Twit Text FOAVip Simultaneous inferences based on empirical Bayes methods and false discovery rates ineQTL data analysis ????BMC Genomics http://www.kidney.de/pget.php?&tw=1&pmid=24564682 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=24564682 #FOAvip English Wikipedia <ref>{{Cite pmid|24564682 }}</ref> Simultaneous inferences based on empirical Bayes methods and false discovery rates ineQTL data analysis #MMPMID24564682 Chakraborty A ; Jiang G ; Boustani M ; Liu Y ; Skaar T ; Li L BMC Genomics 2013[]; 14 Suppl 8 (Suppl 8 ): S8 PMID24564682 show ga BACKGROUND: Genome-wide association studies (GWAS) have identified hundreds of genetic variants associated with complex human diseases, clinical conditions and traits. Genetic mapping of expression quantitative trait loci (eQTLs) is providing us with novel functional effects of thousands of single nucleotide polymorphisms (SNPs). In a classical quantitative trail loci (QTL) mapping problem multiple tests are done to assess whether one trait is associated with a number of loci. In contrast to QTL studies, thousands of traits are measured alongwith thousands of gene expressions in an eQTL study. For such a study, a huge number of tests have to be performed (~10(6)). This extreme multiplicity gives rise to many computational and statistical problems. In this paper we have tried to address these issues using two closely related inferential approaches: an empirical Bayes method that bears the Bayesian flavor without having much a priori knowledge and the frequentist method of false discovery rates. A three-component t-mixture model has been used for the parametric empirical Bayes (PEB) method. Inferences have been obtained using Expectation/Conditional Maximization Either (ECME) algorithm. A simulation study has also been performed and has been compared with a nonparametric empirical Bayes (NPEB) alternative. RESULTS: The results show that PEB has an edge over NPEB. The proposed methodology has been applied to human liver cohort (LHC) data. Our method enables to discover more significant SNPs with FDR<10% compared to the previous study done by Yang et al. (Genome Research, 2010). CONCLUSIONS: In contrast to previously available methods based on p-values, the empirical Bayes method uses local false discovery rate (lfdr) as the threshold. This method controls false positive rate. |*Bayes Theorem [MESH] |*Quantitative Trait Loci [MESH] |Computational Biology/*methods [MESH] |Gene Expression [MESH] |Genetic Variation [MESH] |Genome-Wide Association Study [MESH] |Humans [MESH] |Liver/*metabolism [MESH] |Polymorphism, Single Nucleotide [MESH]Simultaneous inferences based on empirical Bayes methods and false dis(epmc).pdf DeepDyve Pubget Overpricing