A General Framework for Two-Stage Analysis of Genome-wide Association Studies and Its Application to Case-Control Studies

Two-stage analyses of genome-wide association studies have been proposed as a means to improving power for designs including family-based association and gene-environment interaction testing. In these analyses, all markers are first screened via a statistic that may not be robust to an underlying as...

Full description

Saved in:
Bibliographic Details
Published in:American journal of human genetics 2012-05, Vol.90 (5), p.760-773
Main Authors: Wason, James M.S., Dudbridge, Frank
Format: Article
Language:English
Subjects:
Biological and medical sciences
Biomarkers
Case-Control Studies
Computer Simulation
Data processing
Environment
Fundamental and applied biological sciences. Psychology
Gene Frequency
Gene-Environment Interaction
General aspects. Genetic counseling
Genetics of eukaryotes. Biological and molecular evolution
Genome, Human
Genome-Wide Association Study - methods
Genomics
Genotype
Humans
Linear Models
Medical genetics
Medical sciences
Molecular and cellular biology
Personality traits
Regression analysis
Reproducibility of Results
Values
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Two-stage analyses of genome-wide association studies have been proposed as a means to improving power for designs including family-based association and gene-environment interaction testing. In these analyses, all markers are first screened via a statistic that may not be robust to an underlying assumption, and the markers thus selected are then analyzed in a second stage with a test that is independent from the first stage and is robust to the assumption in question. We give a general formulation of two-stage designs and show how one can use this formulation both to derive existing methods and to improve upon them, opening up a range of possible further applications. We show how using simple regression models in conjunction with external data such as average trait values can improve the power of genome-wide association studies. We focus on case-control studies and show how it is possible to use allele frequencies derived from an external reference to derive a powerful two-stage analysis. An illustration involving the Wellcome Trust Case-Control Consortium data shows several genome-wide-significant associations, subsequently validated, that were not significant in the standard analysis. We give some analytic properties of the methods and discuss some underlying principles.
ISSN:0002-9297
1537-6605
DOI:10.1016/j.ajhg.2012.03.007