On the use of general control samples for genome-wide association studies: genetic matching highlights causal variants.
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.
Luca
Diana
D
Department of Statistics, Carnegie Mellon University; Pittsburgh, PA 15213, USA.
Ringquist
Steven
S
Klei
Lambertus
L
Lee
Ann B
AB
Gieger
Christian
C
Wichmann
H-Erich
HE
Schreiber
Stefan
S
Krawczak
Michael
M
Lu
Ying
Y
Styche
Alexis
A
Devlin
Bernie
B
Roeder
Kathryn
K
Trucco
Massimo
M
D016022:Case-Control Studies; D003198:Computer Simulation; D035061:Control Groups; D003627:Data Interpretation, Statistical; D030541:Databases, Genetic; D003922:Diabetes Mellitus, Type 1 / Q000235:genetics; D005828:Genetics, Population; D023281:Genomics / Q000379:methods*; D005858:Germany; D006801:Humans; D040582:Inheritance Patterns / Q000235:genetics; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D014481:United States