On the use of general control samples for genome-wide association studies: genetic matching highlights causal variants.

Authors:
Diana Luca, Steven Ringquist, Lambertus Klei, Ann B Lee, Christian Gieger, H-Erich Wichmann, Stefan Schreiber, Michael Krawczak, Ying Lu, Alexis Styche, Bernie Devlin, Kathryn Roeder, Massimo Trucco
Year of publication:
2008
Volume:
82
Issue:
2
Issn:
0002-9297
Journal title abbreviated:
AM J HUM GENET
Journal title long:
American journal of human genetics
Impact factor:
10.794
Abstract: 
Resources being amassed for genome-wide association (GWA) studies include "control databases" genotyped with a large-scale SNP array. How to use these databases effectively is an open question. We develop a method to match, by genetic ancestry, controls to affected individuals (cases). The impact of this method, especially for heterogeneous human populations, is to reduce the false-positive rate, inflate other spuriously small p values, and have little impact on the p values associated with true positive loci. Thus, it highlights true positives by downplaying false positives. We perform a GWA by matching Americans with type 1 diabetes (T1D) to controls from Germany. Despite the complex study design, these analyses identify numerous loci known to confer risk for T1D.