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10.1002/gepi.21912 http://scihub22266oqcxt.onion/10.1002/gepi.21912 C4543537!4543537!26198599     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=26198599&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 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Genet+Epidemiol 2015 ; 39 (6): 439-45 Nephropedia Template TP {{tp|p=26198599|t=2015. Contemporary considerations for constructing a Genetic Risk Score: An Empirical Approach |pdf=|usr=}}{{26198599}} gab.com Text Contemporary considerations for constructing a Genetic Risk Score: An Empirical Approach JO: Genet Epidemiol http://www.kidney.de/pget.php?&tw=1&pmid=26198599 #FOAvip Genetic Risk Scores are an increasingly popular tool for summarizing the cumulative risk of a set of SNPs with disease. Typically only the set of the SNPs that have reached genome-wide significance compose these scores. However recent work suggests that including additional SNPs may aid risk assessment. In this paper, we used the Atherosclerosis Risk in Communities Study (ARIC) cohort to illustrate how one can choose the optimal set of SNPs for a GRS. In addition to p-value threshold, we also examined linkage disequilibrium, imputation quality and imputation type. We provide a variety of evaluation metrics. Results suggest that p-value threshold had the greatest impact on GRS quality for the outcome of coronary heart disease, with an optimal threshold around 0.001. However, GRSs are relatively robust to both linkage disequilibrium and imputation quality. We also show that the optimal GRS partially depends on the evaluation metric and consequently the way one intends to use the GRS. Overall the implications highlight both the robustness of GRS and a means to empirically choose the best set of GRSs. Twit Text FOAVip Contemporary considerations for constructing a Genetic Risk Score: An Empirical Approach ????Genet Epidemiol http://www.kidney.de/pget.php?&tw=1&pmid=26198599 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26198599 #FOAvip English Wikipedia <ref>{{Cite pmid|26198599}}</ref> Contemporary considerations for constructing a Genetic Risk Score: An Empirical Approach #MMPMID26198599 Goldstein BA; Yang L; Salfati E; Assimes TL Genet Epidemiol 2015[Sep]; 39 (6): 439-45 PMID26198599show ga Genetic Risk Scores are an increasingly popular tool for summarizing the cumulative risk of a set of SNPs with disease. Typically only the set of the SNPs that have reached genome-wide significance compose these scores. However recent work suggests that including additional SNPs may aid risk assessment. In this paper, we used the Atherosclerosis Risk in Communities Study (ARIC) cohort to illustrate how one can choose the optimal set of SNPs for a GRS. In addition to p-value threshold, we also examined linkage disequilibrium, imputation quality and imputation type. We provide a variety of evaluation metrics. Results suggest that p-value threshold had the greatest impact on GRS quality for the outcome of coronary heart disease, with an optimal threshold around 0.001. However, GRSs are relatively robust to both linkage disequilibrium and imputation quality. We also show that the optimal GRS partially depends on the evaluation metric and consequently the way one intends to use the GRS. Overall the implications highlight both the robustness of GRS and a means to empirically choose the best set of GRSs. äContemporary considerations for constructing a Genetic Risk Score: An (epmc).pdf DeepDyve Pubget Overpricing