Statistical Screening Method for Genetic Factors Influencing Susceptibility to Common Diseases in a Two-Stage Genome-Wide Association Study

Sato Yasunori, Laird Nan, Suganami Hideki, Hamada Chikuma, Niki Naoto, Yoshimura Isao and Yoshida Teruhiko
Additional contact information
Sato Yasunori: Harvard School of Public Health
Laird Nan: Harvard School of Public Health
Suganami Hideki: Tokyo University of Science
Hamada Chikuma: Tokyo University of Science
Niki Naoto: Tokyo University of Science
Yoshimura Isao: Tokyo University of Science
Yoshida Teruhiko: National Cancer Center Research Institute

Statistical Applications in Genetics and Molecular Biology, 2009, vol. 8, issue 1, 23

Abstract: A genome-wide association study (GWAS) is a standard strategy for detecting disease susceptibility genes, despite unsettled controversies on many aspects, including optimal study design and statistical analysis. As for study design, a two-stage design has been applied to maximize cost-effectiveness. However, there has been little consensus on appropriate statistical analysis for two-stage design. Thereby perplexing the researchers as to which statistical measures should be applied at the first stage, and how to determine the significance level of the differences at the second stage. Here, using simulation studies, we compared statistical operating characteristics of the screening in a two-stage GWAS by taking into consideration the proper balance of false-positive and false-negative error. As a result, the lower bound of confidence interval for odds ratios is recommended as the first stage measure, and then the second stage criteria should primarily depend on the purpose of the genome screen or its role in the overall gene-hunting scheme. Based on the simulation study, we suggest rules of thumb about which statistics to use in a given situation. An application of all operating characteristics of the screening method to an actual GWAS for gastric cancer illustrates the practical relevance of our discussion.

Keywords: single nucleotide polymorphisms (SNPs); gastric cancer susceptibility genes; false discovery rate (FDR); statistical screening method (search for similar items in EconPapers)
Date: 2009
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.2202/1544-6115.1490 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:sagmbi:v:8:y:2009:i:1:n:46

Ordering information: This journal article can be ordered from
https://www.degruyte ... urnal/key/sagmb/html

DOI: 10.2202/1544-6115.1490

Access Statistics for this article

Statistical Applications in Genetics and Molecular Biology is currently edited by Michael P. H. Stumpf

More articles in Statistical Applications in Genetics and Molecular Biology from De Gruyter
Bibliographic data for series maintained by Peter Golla ().