Integrated Statistical and Pathway Approach to Next-generation Sequencing Analysis: a Family-based Study of Hypertension

Overview

Journal BMC Proc

Publisher Biomed Central

Specialty Biology

Date 2014 Dec 19

PMID 25519358

Citations 5

Authors

Jeremy S Edwards

Susan R Atlas

Susan M Wilson

Candice F Cooper

Li Luo

Christine A Stidley

Affiliations

Soon will be listed here.

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.

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