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10.1038/ejhg.2015.190 http://scihub22266oqcxt.onion/10.1038/ejhg.2015.190 C4930094!4930094!26306642     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Eur+J+Hum+Genet 2016 ; 24 (5): 761-6 Nephropedia Template TP {{tp|p=26306642|t=2016. FAPI: Fast and accurate P-value Imputation for genome-wide association study |pdf=|usr=}}{{26306642}} gab.com Text FAPI: Fast and accurate P-value Imputation for genome-wide association study JO: Eur J Hum Genet http://www.kidney.de/pget.php?&tw=1&pmid=26306642 #FOAvip Imputing individual-level genotypes (or genotype imputation) is now a standard procedure in genome-wide association studies (GWAS) to examine disease associations at untyped common genetic variants. Meta-analysis of publicly available GWAS summary statistics can allow more disease-associated loci to be discovered, but these data are usually provided for various variant sets. Thus imputing these summary statistics of different variant sets into a common reference panel for meta-analyses is impossible using traditional genotype imputation methods. Here we develop a fast and accurate P-value imputation (FAPI) method that utilizes summary statistics of common variants only. Its computational cost is linear with the number of untyped variants and has similar accuracy compared with IMPUTE2 with prephasing, one of the leading methods in genotype imputation. In addition, based on the FAPI idea, we develop a metric to detect abnormal association at a variant and showed that it had a significantly greater power compared with LD-PAC, a method that quantifies the evidence of spurious associations based on likelihood ratio. Our method is implemented in a user-friendly software tool, which is available at http://statgenpro.psychiatry.hku.hk/fapi. Twit Text FOAVip FAPI: Fast and accurate P-value Imputation for genome-wide association study ????Eur J Hum Genet http://www.kidney.de/pget.php?&tw=1&pmid=26306642 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26306642 #FOAvip English Wikipedia <ref>{{Cite pmid|26306642}}</ref> FAPI: Fast and accurate P-value Imputation for genome-wide association study #MMPMID26306642 Kwan JS; Li MX; Deng JE; Sham PC Eur J Hum Genet 2016[May]; 24 (5): 761-6 PMID26306642show ga Imputing individual-level genotypes (or genotype imputation) is now a standard procedure in genome-wide association studies (GWAS) to examine disease associations at untyped common genetic variants. Meta-analysis of publicly available GWAS summary statistics can allow more disease-associated loci to be discovered, but these data are usually provided for various variant sets. Thus imputing these summary statistics of different variant sets into a common reference panel for meta-analyses is impossible using traditional genotype imputation methods. Here we develop a fast and accurate P-value imputation (FAPI) method that utilizes summary statistics of common variants only. Its computational cost is linear with the number of untyped variants and has similar accuracy compared with IMPUTE2 with prephasing, one of the leading methods in genotype imputation. In addition, based on the FAPI idea, we develop a metric to detect abnormal association at a variant and showed that it had a significantly greater power compared with LD-PAC, a method that quantifies the evidence of spurious associations based on likelihood ratio. Our method is implemented in a user-friendly software tool, which is available at http://statgenpro.psychiatry.hku.hk/fapi. äFAPI: Fast and accurate P-value Imputation for genome-wide association(epmc).pdf DeepDyve Pubget Overpricing