title: Guideline for data analysis of genomewide association studies.
abstract: 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.
authors.affiliation: Yale University School of Medicine, New Haven, CT 06520-8034, USA. Heping.Zhang@Yale.EDU
authors.forename: Heping
authors.initials: H
authors.lastname: Zhang
authors.forename: Lei
authors.initials: L
authors.lastname: Liu
authors.forename: Xueqin
authors.initials: X
authors.lastname: Wang
authors.forename: Jeffrey R
authors.initials: JR
authors.lastname: Gruen
mesh_terms: D020022:Genetic Predisposition to Disease / Q000235:genetics*; D015894:Genome, Human / Q000235:genetics*; D017408:Guidelines as Topic; D006801:Humans; D011157:Population Dynamics; D011786:Quality Control; D018401:Sample Size