Estimating the number of true discoveries in genome-wide association studies

Stat Med. 2012 May 20;31(11-12):1177-89. doi: 10.1002/sim.4391. Epub 2011 Oct 11.

Abstract

Recent genome-wide association studies have reported the discoveries of genetic variants of small to moderate effects. However, most studies of complex diseases face a great challenge because the number of significant variants is less than what is required to explain the disease heritability. A new approach is needed to recognize all potential discoveries in the data. In this paper, we present a practical model-free procedure to estimate the number of true discoveries as a function of the number of top-ranking SNPs together with the confidence bounds. This approach allows a practical methodology of general utility and produces relevant statistical quantities with simple interpretation.

MeSH terms

  • Asian People / genetics
  • Asian People / statistics & numerical data
  • Cholesterol, HDL / blood
  • Cholesterol, HDL / genetics
  • Computer Simulation / statistics & numerical data
  • Confidence Intervals
  • Data Interpretation, Statistical*
  • False Positive Reactions
  • Genetic Predisposition to Disease
  • Genome-Wide Association Study / statistics & numerical data*
  • Humans
  • Macular Degeneration / epidemiology
  • Macular Degeneration / genetics
  • Mathematical Computing
  • Models, Genetic
  • Polymorphism, Single Nucleotide
  • Schizophrenia / epidemiology
  • Schizophrenia / genetics
  • Singapore / epidemiology

Substances

  • Cholesterol, HDL