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...

Full description

Saved in:
Bibliographic Details
Published inAmerican journal of human genetics Vol. 82; no. 2; pp. 453 - 463
Main Authors Luca, Diana, Ringquist, Steven, Klei, Lambertus, Lee, Ann B., Gieger, Christian, Wichmann, H.-Erich, Schreiber, Stefan, Krawczak, Michael, Lu, Ying, Styche, Alexis, Devlin, Bernie, Roeder, Kathryn, Trucco, Massimo
Format Journal Article
LanguageEnglish
Published Chicago, IL Elsevier Inc 01.02.2008
University of Chicago Press
American Society of Human Genetics
Subjects
Biological and medical sciences
Case-Control Studies
Computer Simulation
Control Groups
Data Interpretation, Statistical
Databases, Genetic
Diabetes Mellitus, Type 1 - genetics
Fundamental and applied biological sciences. Psychology
General aspects. Genetic counseling
Genetics of eukaryotes. Biological and molecular evolution
Genetics, Population
Genomics - methods
Germany
Humans
Inheritance Patterns - genetics
Medical genetics
Medical sciences
Molecular and cellular biology
Polymorphism, Single Nucleotide - genetics
United States
United States
Germany
Human
Association
Sample
Use
Genetic variant
Genetics
Genome
Online AccessGet full text

Cover

More Information
Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
These authors contributed equally to this work.
ISSN:0002-9297
1537-6605
DOI:10.1016/j.ajhg.2007.11.003