title: Integrated statistical and pathway approach to next-generation sequencing analysis: a family-based study of hypertension.
abstract: Genome wide association studies (GWAS) have been used to search for associations between genetic variants and a phenotypic trait of interest. New technologies, such as next-generation sequencing, hold the potential to revolutionize GWAS. However, millions of polymorphisms are identified with next-generation sequencing technology. Consequently, researchers must be careful when performing such a large number of statistical tests, and corrections are typically made to account for multiple testing. Additionally, for typical GWAS, the p value cutoff is set quite low (approximately <10(-8)). As a result of this p value stringency, it is likely that there are many true associations that do not meet this threshold. To account for this we have incorporated a priori biological knowledge to help identify true associations that may not have reached statistical significance. We propose the application of a pipelined series of statistical and bioinformatic methods, to enable the assessment of the association of genetic polymorphisms with a disease phenotype--here, hypertension--as well as the identification of statistically significant pathways of genes that may play a role in the disease process.
authors.affiliation: Molecular Genetics and Microbiology, and Chemical and Nuclear Engineering, 1 University of New Mexico, University of New Mexico Cancer Center, Albuquerque, NM 87131, USA.
authors.forename: Jeremy S
authors.initials: JS
authors.lastname: Edwards
authors.affiliation: Physics and Astronomy, Center for Advanced Research Computing, 1 University of New Mexico, University of New Mexico Cancer Center, Albuquerque, NM 87131, USA.
authors.forename: Susan R
authors.initials: SR
authors.lastname: Atlas
authors.affiliation: Center for Advanced Research Computing, University of New Mexico Cancer Center, 1 University of New Mexico, Albuquerque, NM 87131, USA.
authors.forename: Susan M
authors.initials: SM
authors.lastname: Wilson
authors.affiliation: Molecular Genetics and Microbiology, and Chemical and Nuclear Engineering, 1 University of New Mexico, University of New Mexico Cancer Center, Albuquerque, NM 87131, USA.
authors.forename: Candice F
authors.initials: CF
authors.lastname: Cooper
authors.affiliation: University of New Mexico Cancer Center, Internal Medicine, 1 University of New Mexico, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA.
authors.forename: Li
authors.initials: L
authors.lastname: Luo
authors.affiliation: University of New Mexico Cancer Center, Internal Medicine, 1 University of New Mexico, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA.
authors.forename: Christine A
authors.initials: CA
authors.lastname: Stidley