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10.7555/JBR.29.20140007 http://scihub22266oqcxt.onion/10.7555/JBR.29.20140007 C4547377!4547377!26243515     free     free     free       J+Biomed+Res 2015 ; 29 (4): 285-97 Nephropedia Template TP {{tp|p=26243515|t=2015. Statistical analysis for genome-wide association study |pdf=|usr=}}{{26243515}} gab.com Text Statistical analysis for genome-wide association study JO: J Biomed Res http://www.kidney.de/pget.php?&tw=1&pmid=26243515 #FOAvip In the past few years, genome-wide association study (GWAS) has made great successes in identifying genetic susceptibility loci underlying many complex diseases and traits. The findings provide important genetic insights into understanding pathogenesis of diseases. In this paper, we present an overview of widely used approaches and strategies for analysis of GWAS, offered a general consideration to deal with GWAS data. The issues regarding data quality control, population structure, association analysis, multiple comparison and visual presentation of GWAS results are discussed; other advanced topics including the issue of missing heritability, meta-analysis, set-based association analysis, copy number variation analysis and GWAS cohort analysis are also briefly introduced. Twit Text FOAVip Statistical analysis for genome-wide association study ????J Biomed Res http://www.kidney.de/pget.php?&tw=1&pmid=26243515 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26243515 #FOAvip English Wikipedia <ref>{{Cite pmid|26243515}}</ref> Statistical analysis for genome-wide association study #MMPMID26243515 Zeng P; Zhao Y; Qian C; Zhang L; Zhang R; Gou J; Liu J; Liu L; Chen F J Biomed Res 2015[Jul]; 29 (4): 285-97 PMID26243515show ga In the past few years, genome-wide association study (GWAS) has made great successes in identifying genetic susceptibility loci underlying many complex diseases and traits. The findings provide important genetic insights into understanding pathogenesis of diseases. In this paper, we present an overview of widely used approaches and strategies for analysis of GWAS, offered a general consideration to deal with GWAS data. The issues regarding data quality control, population structure, association analysis, multiple comparison and visual presentation of GWAS results are discussed; other advanced topics including the issue of missing heritability, meta-analysis, set-based association analysis, copy number variation analysis and GWAS cohort analysis are also briefly introduced. äStatistical analysis for genome-wide association study (epmc).pdf DeepDyve Pubget Overpricing