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10.1016/j.humimm.2015.11.004 http://scihub22266oqcxt.onion/10.1016/j.humimm.2015.11.004 C5609807!5609807!26582005     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=26582005&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Hum+Immunol 2016 ; 77 (3): 307-12 Nephropedia Template TP {{tp|p=26582005|t=2016. HLA imputation in an admixed population: An assessment of the 1000 Genomes data as a training set |pdf=|usr=}}{{26582005}} gab.com Text HLA imputation in an admixed population: An assessment of the 1000 Genomes data as a training set JO: Hum Immunol http://www.kidney.de/pget.php?&tw=1&pmid=26582005 #FOAvip Methods to impute HLA alleles based on dense single nucleotide polymorphism (SNP) data provide a valuable resource to association studies and evolutionary investigation of the MHC region. The availability of appropriate training sets is critical to the accuracy of HLA imputation, and the inclusion of samples with various ancestries is an important pre-requisite in studies of admixed populations. We assess the accuracy of HLA imputation using 1000 Genomes Project data as a training set, applying it to a highly admixed Brazilian population, the Quilombos from the state of São Paulo. To assess accuracy, we compared imputed and experimentally determined genotypes for 146 samples at 4 HLA classical loci. We found imputation accuracies of 82.9%, 81.8%, 94.8% and 86.6% for HLA-A, -B, -C and -DRB1 respectively (two-field resolution). Accuracies were improved when we included a subset of Quilombo individuals in the training set. We conclude that the 1000 Genomes data is a valuable resource for construction of training sets due to the diversity of ancestries and the potential for a large overlap of SNPs with the target population. We also show that tailoring training sets to features of the target population substantially enhances imputation accuracy. Twit Text FOAVip HLA imputation in an admixed population: An assessment of the 1000 Genomes data as a training set ????Hum Immunol http://www.kidney.de/pget.php?&tw=1&pmid=26582005 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26582005 #FOAvip English Wikipedia <ref>{{Cite pmid|26582005}}</ref> HLA imputation in an admixed population: An assessment of the 1000 Genomes data as a training set #MMPMID26582005 Nunes K; Zheng X; Torres M; Moraes ME; Piovezan BZ; Pontes GN; Kimura L; Carnavalli JE; Mingroni Netto RC; Meyer D Hum Immunol 2016[Mar]; 77 (3): 307-12 PMID26582005show ga Methods to impute HLA alleles based on dense single nucleotide polymorphism (SNP) data provide a valuable resource to association studies and evolutionary investigation of the MHC region. The availability of appropriate training sets is critical to the accuracy of HLA imputation, and the inclusion of samples with various ancestries is an important pre-requisite in studies of admixed populations. We assess the accuracy of HLA imputation using 1000 Genomes Project data as a training set, applying it to a highly admixed Brazilian population, the Quilombos from the state of São Paulo. To assess accuracy, we compared imputed and experimentally determined genotypes for 146 samples at 4 HLA classical loci. We found imputation accuracies of 82.9%, 81.8%, 94.8% and 86.6% for HLA-A, -B, -C and -DRB1 respectively (two-field resolution). Accuracies were improved when we included a subset of Quilombo individuals in the training set. We conclude that the 1000 Genomes data is a valuable resource for construction of training sets due to the diversity of ancestries and the potential for a large overlap of SNPs with the target population. We also show that tailoring training sets to features of the target population substantially enhances imputation accuracy. äHLA imputation in an admixed population: An assessment of the 1000 Gen(epmc).pdf DeepDyve Pubget Overpricing