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Edwards JS, Atlas SR, Wilson SM, et al. Integrated statistical and pathway approach to next-generation sequencing analysis: a family-based study of hypertension. BMC Proc. 2014;8:S104doi: 10.1186/1753-6561-8-S1-S104.
Edwards, J. S., Atlas, S. R., Wilson, S. M., Cooper, C. F., Luo, L., & Stidley, C. A. (2014). Integrated statistical and pathway approach to next-generation sequencing analysis: a family-based study of hypertension. BMC proceedings, 8S104. https://doi.org/10.1186/1753-6561-8-S1-S104
Edwards, Jeremy S, et al. "Integrated statistical and pathway approach to next-generation sequencing analysis: a family-based study of hypertension." BMC proceedings vol. 8 (2014): S104. doi: https://doi.org/10.1186/1753-6561-8-S1-S104
Edwards JS, Atlas SR, Wilson SM, Cooper CF, Luo L, Stidley CA. Integrated statistical and pathway approach to next-generation sequencing analysis: a family-based study of hypertension. BMC Proc. 2014 Jun 17;8:S104. doi: 10.1186/1753-6561-8-S1-S104. eCollection 2014. PMID: 25519358; PMCID: PMC4143684.
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BMC Proc. 2014 Jun 17;8:S104. doi: 10.1186/1753-6561-8-S1-S104. eCollection 2014.

Integrated statistical and pathway approach to next-generation sequencing analysis: a family-based study of hypertension.

BMC proceedings

Jeremy S Edwards, Susan R Atlas, Susan M Wilson, Candice F Cooper, Li Luo, Christine A Stidley

Affiliations

  1. Molecular Genetics and Microbiology, and Chemical and Nuclear Engineering, 1 University of New Mexico, University of New Mexico Cancer Center, Albuquerque, NM 87131, USA.
  2. Physics and Astronomy, Center for Advanced Research Computing, 1 University of New Mexico, University of New Mexico Cancer Center, Albuquerque, NM 87131, USA.
  3. Center for Advanced Research Computing, University of New Mexico Cancer Center, 1 University of New Mexico, Albuquerque, NM 87131, USA.
  4. University of New Mexico Cancer Center, Internal Medicine, 1 University of New Mexico, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA.

PMID: 25519358 PMCID: PMC4143684 DOI: 10.1186/1753-6561-8-S1-S104

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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