Statistical screening method for genetic factors influencing susceptibility to common diseases in a two-stage genome-wide association study

Yasunori Sato, Nan Laird, Hideki Suganami, Chikuma Hamada, Naoto Niki, Isao Yoshimura, Teruhiko Yoshida

Research output: Contribution to journalArticle

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.

Original languageEnglish
Article number46
JournalStatistical Applications in Genetics and Molecular Biology
Volume8
Issue number1
DOIs
Publication statusPublished - 2009 Dec 1
Externally publishedYes

Keywords

  • False discovery rate (FDR)
  • Gastric cancer susceptibility genes
  • Single nucleotide polymorphisms (SNPs)
  • Statistical screening method

ASJC Scopus subject areas

  • Statistics and Probability
  • Molecular Biology
  • Genetics
  • Computational Mathematics

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