Power Analysis for Genetic Association Test (PAGEANT) provides insights to challenges for rare variant association studies

Bioinformatics. 2018 May 1;34(9):1506-1513. doi: 10.1093/bioinformatics/btx770.


Motivation: Genome-wide association studies are now shifting focus from analysis of common to rare variants. As power for association testing for individual rare variants may often be low, various aggregate level association tests have been proposed to detect genetic loci. Typically, power calculations for such tests require specification of large number of parameters, including effect sizes and allele frequencies of individual variants, making them difficult to use in practice. We propose to approximate power to a varying degree of accuracy using a smaller number of key parameters, including the total genetic variance explained by multiple variants within a locus.

Results: We perform extensive simulation studies to assess the accuracy of the proposed approximations in realistic settings. Using these simplified power calculations, we develop an analytic framework to obtain bounds on genetic architecture of an underlying trait given results from genome-wide association studies with rare variants. Finally, we provide insights into the required quality of annotation/functional information for identification of likely causal variants to make meaningful improvement in power.

Availability and implementation: A shiny application that allows a variety of Power Analysis of GEnetic AssociatioN Tests (PAGEANT), in R is made publicly available at https://andrewhaoyu.shinyapps.io/PAGEANT/.

Contact: nilanjan@jhu.edu.

Supplementary information: Supplementary data are available at Bioinformatics online.

Publication types

  • Research Support, N.I.H., Intramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Gene Frequency
  • Genetic Loci*
  • Genetic Variation*
  • Genetics, Population / methods
  • Genome-Wide Association Study / methods*
  • Humans
  • Models, Genetic*
  • Software*