BACKGROUND: Recent technologies enable genetic association studies of common clinical analytes on a genomewide basis in populations numbering thousands of individuals. The first publications using these technologies are already revealing novel biological functions for both genic and nongenic loci, and are promising to transform knowledge about the biological networks underlying disease pathophysiology. These early studies have also led to development of a set of principles for conducting a successful genomewide association study (GWAS). CONTENT: This review focuses on these principles with emphasis on the use of GWAS for plasma-based analytes to better understand human disease, with examples from cardiovascular biology. CONCLUSIONS: The correlation of common genetic variation on a genomewide basis with clinical analytes, or any other outcome of interest, promises to reveal how parts of the genome work together in human physiology. Nonetheless, performing a genomewide association study demands an awareness of very specific epidemiologic and analytic principles.
BACKGROUND: Recent technologies enable genetic association studies of common clinical analytes on a genomewide basis in populations numbering thousands of individuals. The first publications using these technologies are already revealing novel biological functions for both genic and nongenic loci, and are promising to transform knowledge about the biological networks underlying disease pathophysiology. These early studies have also led to development of a set of principles for conducting a successful genomewide association study (GWAS). CONTENT: This review focuses on these principles with emphasis on the use of GWAS for plasma-based analytes to better understand human disease, with examples from cardiovascular biology. CONCLUSIONS: The correlation of common genetic variation on a genomewide basis with clinical analytes, or any other outcome of interest, promises to reveal how parts of the genome work together in human physiology. Nonetheless, performing a genomewide association study demands an awareness of very specific epidemiologic and analytic principles.
Authors: Santhi K Ganesh; Daniel I Chasman; Martin G Larson; Xiuqing Guo; Germain Verwoert; Joshua C Bis; Xiangjun Gu; Albert V Smith; Min-Lee Yang; Yan Zhang; Georg Ehret; Lynda M Rose; Shih-Jen Hwang; George J Papanicolau; Eric J Sijbrands; Kenneth Rice; Gudny Eiriksdottir; Vasyl Pihur; Paul M Ridker; Ramachandran S Vasan; Christopher Newton-Cheh; Leslie J Raffel; Najaf Amin; Jerome I Rotter; Kiang Liu; Lenore J Launer; Ming Xu; Mark Caulfield; Alanna C Morrison; Andrew D Johnson; Dhananjay Vaidya; Abbas Dehghan; Guo Li; Claude Bouchard; Tamara B Harris; He Zhang; Eric Boerwinkle; David S Siscovick; Wei Gao; Andre G Uitterlinden; Fernando Rivadeneira; Albert Hofman; Cristen J Willer; Oscar H Franco; Yong Huo; Jacqueline C M Witteman; Patricia B Munroe; Vilmundur Gudnason; Walter Palmas; Cornelia van Duijn; Myriam Fornage; Daniel Levy; Bruce M Psaty; Aravinda Chakravarti Journal: Am J Hum Genet Date: 2014-06-26 Impact factor: 11.025