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10.1080/01621459.2014.901223 http://scihub22266oqcxt.onion/10.1080/01621459.2014.901223 C4219655!4219655!25382885     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Am+Stat+Assoc 2014 ; 109 (507): 905-30 Nephropedia Template TP {{tp|p=25382885|t=2014. Identifying Genetic Variants for Addiction via Propensity Score Adjusted Generalized Kendall?s Tau |pdf=|usr=}}{{25382885}} gab.com Text Identifying Genetic Variants for Addiction via Propensity Score Adjusted Generalized Kendall s Tau JO: J Am Stat Assoc http://www.kidney.de/pget.php?&tw=1&pmid=25382885 #FOAvip Identifying replicable genetic variants for addiction has been extremely challenging. Besides the common difficulties with genome-wide association studies (GWAS), environmental factors are known to be critical to addiction, and comorbidity is widely observed. Despite the importance of environmental factors and comorbidity for addiction study, few GWAS analyses adequately considered them due to the limitations of the existing statistical methods. Although parametric methods have been developed to adjust for covariates in association analysis, difficulties arise when the traits are multivariate because there is no ready-to-use model for them. Recent nonparametric development includes U-statistics to measure the phenotype-genotype association weighted by a similarity score of covariates. However, it is not clear how to optimize the similarity score. Therefore, we propose a semiparametric method to measure the association adjusted by covariates. In our approach, the nonparametric U-statistic is adjusted by parametric estimates of propensity scores using the idea of inverse probability weighting. The new measurement is shown to be asymptotically unbiased under our null hypothesis while the previous non-weighted and weighted ones are not. Simulation results show that our test improves power as opposed to the non-weighted and two other weighted U-statistic methods, and it is particularly powerful for detecting gene-environment interactions. Finally, we apply our proposed test to the Study of Addiction: Genetics and Environment (SAGE) to identify genetic variants for addiction. Novel genetic variants are found from our analysis, which warrant further investigation in the future. Twit Text FOAVip Identifying Genetic Variants for Addiction via Propensity Score Adjusted Generalized Kendall s Tau ????J Am Stat Assoc http://www.kidney.de/pget.php?&tw=1&pmid=25382885 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=25382885 #FOAvip English Wikipedia <ref>{{Cite pmid|25382885}}</ref> Identifying Genetic Variants for Addiction via Propensity Score Adjusted Generalized Kendall?s Tau #MMPMID25382885 Jiang Y; Li N; Zhang H J Am Stat Assoc 2014[]; 109 (507): 905-30 PMID25382885show ga Identifying replicable genetic variants for addiction has been extremely challenging. Besides the common difficulties with genome-wide association studies (GWAS), environmental factors are known to be critical to addiction, and comorbidity is widely observed. Despite the importance of environmental factors and comorbidity for addiction study, few GWAS analyses adequately considered them due to the limitations of the existing statistical methods. Although parametric methods have been developed to adjust for covariates in association analysis, difficulties arise when the traits are multivariate because there is no ready-to-use model for them. Recent nonparametric development includes U-statistics to measure the phenotype-genotype association weighted by a similarity score of covariates. However, it is not clear how to optimize the similarity score. Therefore, we propose a semiparametric method to measure the association adjusted by covariates. In our approach, the nonparametric U-statistic is adjusted by parametric estimates of propensity scores using the idea of inverse probability weighting. The new measurement is shown to be asymptotically unbiased under our null hypothesis while the previous non-weighted and weighted ones are not. Simulation results show that our test improves power as opposed to the non-weighted and two other weighted U-statistic methods, and it is particularly powerful for detecting gene-environment interactions. Finally, we apply our proposed test to the Study of Addiction: Genetics and Environment (SAGE) to identify genetic variants for addiction. Novel genetic variants are found from our analysis, which warrant further investigation in the future. äIdentifying Genetic Variants for Addiction via Propensity Score Adjust(epmc).pdf DeepDyve Pubget Overpricing