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10.1002/gepi.22027 http://scihub22266oqcxt.onion/10.1002/gepi.22027 C5604789!5604789!28019059     free     free     free Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Genet+Epidemiol 2017 ; 41 (2): 152-62 Nephropedia Template TP {{tp|p=28019059|t=2017. Impact of genotyping errors on statistical power of association tests in genomic analyses: A case study |pdf=|usr=}}{{28019059}} gab.com Text Impact of genotyping errors on statistical power of association tests in genomic analyses: A case study JO: Genet Epidemiol http://www.kidney.de/pget.php?&tw=1&pmid=28019059 #FOAvip A key step in genomic studies is to assess high throughput measurements across millions of markers for each participant?s DNA, either using microarrays or sequencing techniques. Accurate genotype calling is essential for downstream statistical analysis of genotype-phenotype associations, and next generation sequencing (NGS) has recently become a more common approach in genomic studies. How the accuracy of variant calling in NGS-based studies affects downstream association analysis has not, however, been studied using empirical data in which both microarrays and NGS were available. In this article, we investigate the impact of variant calling errors on the statistical power to identify associations between single nucleotides and disease, and on associations between multiple rare variants and disease. Both differential and nondifferential genotyping errors are considered. Our results show that the power of burden tests for rare variants is strongly influenced by the specificity in variant calling, but is rather robust with regard to sensitivity. By using the variant calling accuracies estimated from a substudy of a Cooperative Studies Program project conducted by the Department of Veterans Affairs, we show that the power of association tests is mostly retained with commonly adopted variant calling pipelines. An R package, GWAS.PC, is provided to accommodate power analysis that takes account of genotyping errors (http://zhaocenter.org/software/). Twit Text FOAVip Impact of genotyping errors on statistical power of association tests in genomic analyses: A case study ????Genet Epidemiol http://www.kidney.de/pget.php?&tw=1&pmid=28019059 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28019059 #FOAvip English Wikipedia <ref>{{Cite pmid|28019059}}</ref> Impact of genotyping errors on statistical power of association tests in genomic analyses: A case study #MMPMID28019059 Hou L; Sun N; Mane S; Sayward F; Rajeevan N; Cheung KH; Cho K; Pyarajan S; Aslan M; Miller P; Harvey PD; Gaziano JM; Concato J; Zhao H Genet Epidemiol 2017[Feb]; 41 (2): 152-62 PMID28019059show ga A key step in genomic studies is to assess high throughput measurements across millions of markers for each participant?s DNA, either using microarrays or sequencing techniques. Accurate genotype calling is essential for downstream statistical analysis of genotype-phenotype associations, and next generation sequencing (NGS) has recently become a more common approach in genomic studies. How the accuracy of variant calling in NGS-based studies affects downstream association analysis has not, however, been studied using empirical data in which both microarrays and NGS were available. In this article, we investigate the impact of variant calling errors on the statistical power to identify associations between single nucleotides and disease, and on associations between multiple rare variants and disease. Both differential and nondifferential genotyping errors are considered. Our results show that the power of burden tests for rare variants is strongly influenced by the specificity in variant calling, but is rather robust with regard to sensitivity. By using the variant calling accuracies estimated from a substudy of a Cooperative Studies Program project conducted by the Department of Veterans Affairs, we show that the power of association tests is mostly retained with commonly adopted variant calling pipelines. An R package, GWAS.PC, is provided to accommodate power analysis that takes account of genotyping errors (http://zhaocenter.org/software/). äImpact of genotyping errors on statistical power of association tests (epmc).pdf DeepDyve Pubget Overpricing