Improving the signal-to-noise ratio in genome-wide association studies

Lisa J. Martin, Guimin Gao, Guolian Kang, Yixin Fang, Jessica G. Woo

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Genome-wide association studies employ hundreds of thousands of statistical tests to determine which regions of the genome may likely harbor disease-causing alleles. Such large-scale testing simultaneously requires stringent control over type I error and maintenance of sufficient power to detect true associations. These contradictory goals have led some researchers beyond Bonferroni correction of P-values to an exploration of methods to improve the detection of a few true effects in the presence of many unassociated loci. This article reviews how Genetic Analysis Workshop 16 Group 5 investigators proposed to adjust for multiple tests while simultaneously using information about the structure of the genome to improve the detection of true positives.

Original languageEnglish (US)
Pages (from-to)S29-S32
JournalGenetic Epidemiology
Volume33
Issue numberSUPPL. 1
DOIs
StatePublished - 2009

All Science Journal Classification (ASJC) codes

  • Epidemiology
  • Genetics(clinical)

Keywords

  • Genetics
  • Multiple testing
  • Statistics

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