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10.1534/g3.116.038877 http://scihub22266oqcxt.onion/10.1534/g3.116.038877 C5345724!5345724 !28122950     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=28122950 &cmd=llinks): Failed to open stream: HTTP request failed! 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Reproduction and In-Depth Evaluation of Genome-Wide Association Studies and Genome-Wide Meta-analyses Using Summary Statistics |pdf=|usr=}}{{28122950 }} gab.com Text Reproduction and In-Depth Evaluation of Genome-Wide Association Studies and Genome-Wide Meta-analyses Using Summary Statistics JO: G3 (Bethesda) http://www.kidney.de/pget.php?&tw=1&pmid=28122950 #FOAvip In line with open-source genetics, we report a novel linear regression technique for genome-wide association studies (GWAS), called Open GWAS algoriTHm (OATH). When individual-level data are not available, OATH can not only completely reproduce reported results from an experimental model, but also recover underreported results from other alternative models with a different combination of nuisance parameters using naïve summary statistics (NSS). OATH can also reliably evaluate all reported results in-depth (e.g., p-value variance analysis), as demonstrated for 42 Arabidopsis phenotypes under three magnesium (Mg) conditions. In addition, OATH can be used for consortium-driven genome-wide association meta-analyses (GWAMA), and can greatly improve the flexibility of GWAMA. A prototype of OATH is available in the Genetic Analysis Repository (https://github.com/gc5k/GEAR). Twit Text FOAVip Reproduction and In-Depth Evaluation of Genome-Wide Association Studies and Genome-Wide Meta-analyses Using Summary Statistics ????G3 (Bethesda) http://www.kidney.de/pget.php?&tw=1&pmid=28122950 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28122950 #FOAvip English Wikipedia <ref>{{Cite pmid|28122950 }}</ref> Reproduction and In-Depth Evaluation of Genome-Wide Association Studies and Genome-Wide Meta-analyses Using Summary Statistics #MMPMID28122950 Niu YF ; Ye C ; He J ; Han F ; Guo LB ; Zheng HF ; Chen GB G3 (Bethesda) 2017[Mar]; 7 (3 ): 943-952 PMID28122950 show ga In line with open-source genetics, we report a novel linear regression technique for genome-wide association studies (GWAS), called Open GWAS algoriTHm (OATH). When individual-level data are not available, OATH can not only completely reproduce reported results from an experimental model, but also recover underreported results from other alternative models with a different combination of nuisance parameters using naïve summary statistics (NSS). OATH can also reliably evaluate all reported results in-depth (e.g., p-value variance analysis), as demonstrated for 42 Arabidopsis phenotypes under three magnesium (Mg) conditions. In addition, OATH can be used for consortium-driven genome-wide association meta-analyses (GWAMA), and can greatly improve the flexibility of GWAMA. A prototype of OATH is available in the Genetic Analysis Repository (https://github.com/gc5k/GEAR). |*Genome-Wide Association Study [MESH] |*Statistics as Topic [MESH] |Algorithms [MESH] |Arabidopsis/drug effects/genetics [MESH] |Asian People/genetics [MESH] |Cohort Studies [MESH] |Computer Simulation [MESH] |Genetic Loci [MESH] |Humans [MESH] |Magnesium/pharmacology [MESH] |Models, Genetic [MESH] |Polymorphism, Single Nucleotide/genetics [MESH] |Quantitative Trait, Heritable [MESH] |Reproducibility of Results [MESH]Reproduction and In-Depth Evaluation of Genome-Wide Association Studie(epmc).pdf DeepDyve Pubget Overpricing