PT  - JOURNAL ARTICLE
AU  - HEPING ZHANG
AU  - LEI LIU
AU  - XUEQIN WANG
AU  - JEFFREY R. GRUEN
TI  - Guideline for Data Analysis of Genomewide Association Studies
DP  - 2007 Jan 01
TA  - Cancer Genomics - Proteomics
PG  - 27--34
VI  - 4
IP  - 1
4099  - http://cgp.iiarjournals.org/content/4/1/27.short
4100  - http://cgp.iiarjournals.org/content/4/1/27.full
SO  - Cancer Genomics Proteomics2007 Jan 01; 4
AB  - Intensive efforts have been underway to identify common genetic factors that influence health and disease including cancer using genomewide association studies (GWAS). Our experiences have shown that while it is more advantageous to have large detailed data sets, the amount of information generated by GWAS also present major challenges for statistical analyses. While prospects for the oncoming flood of GWAS is exciting, the tools for conducting and evaluating these studies are still in early developmental stages creating some investigator uncertainty and prompting conferences and workshops specifically devoted to these topics. In this review, we summarize important steps for planning the statistical analysis involving genome-wide data from single nucleotide polymorphisms (SNPs). This review is purposely meant to be relatively short and of practical use for the space constraints of typical federal grant proposals.