Using public control genotype data to increase power and decrease cost of case-control genetic association studies.
Genome-wide association (GWA) studies are a powerful approach for identifying novel genetic risk factors associated with human disease. A GWA study typically requires the inclusion of thousands of samples to have sufficient statistical power to detect single nucleotide polymorphisms that are associated with only modest increases in risk of disease given the heavy burden of a multiple test correction that is necessary to maintain valid statistical tests. Low statistical power and the high financial cost of performing a GWA study remains prohibitive for many scientific investigators anxious to perform such a study using their own samples. A number of remedies have been suggested to increase statistical power and decrease cost, including the utilization of free publicly available genotype data and multi-stage genotyping designs. Herein, we compare the statistical power and relative costs of alternative association study designs that use cases and screened controls to study designs that are based only on, or additionally include, free public control genotype data. We describe a novel replication-based two-stage study design, which uses free public control genotype data in the first stage and follow-up genotype data on case-matched controls in the second stage that preserves many of the advantages inherent when using only an epidemiologically matched set of controls. Specifically, we show that our proposed two-stage design can substantially increase statistical power and decrease cost of performing a GWA study while controlling the type-I error rate that can be inflated when using public controls due to differences in ancestry and batch genotype effects.
Ho
Lindsey A
LA
Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Lange
Ethan M
EM
D016022:Case-Control Studies; D035061:Control Groups; D003365:Costs and Cost Analysis; D055106:Genome-Wide Association Study / Q000191:economics* / Q000379:methods*; D005838:Genotype; D006801:Humans; D008432:Mathematical Computing; D020641:Polymorphism, Single Nucleotide
Hum Hered. 2006;61(1):55-64
16612103
Am J Med Genet B Neuropsychiatr Genet. 2008 Oct 5;147B(7):1288-94
18500721
Ann Hum Genet. 2005 Sep;69(Pt 5):566-76
16138915
Ann Hum Genet. 2007 Mar;71(Pt 2):249-61
17096677
Genet Epidemiol. 2006 May;30(4):356-68
16607626
Nat Genet. 2006 Feb;38(2):209-13
16415888
Genet Epidemiol. 2004 Dec;27(4):401-14
15543639
Nat Genet. 2009 Feb;41(2):216-20
19122664
Nature. 2007 Jun 7;447(7145):661-78
17554300
Nat Genet. 2006 Aug;38(8):904-9
16862161
Science. 2010 Jul 1;2010:
20595579
Genet Epidemiol. 2001 Jan;20(1):4-16
11119293
Nat Genet. 2009 Aug;41(8):905-8
19578366
Pac Symp Biocomput. 2006;:523-34
17094266
Genet Epidemiol. 2010 May;34(4):319-26
20088020
Nat Genet. 2007 May;39(5):638-44
17401364
Stat Med. 2005 Mar 30;24(6):869-82
15568195
Am J Hum Genet. 2008 Feb;82(2):453-63
18252225
BMC Genet. 2005 Apr 08;6:18
15819990
Genet Epidemiol. 2007 Nov;31(7):776-88
17549752
N Engl J Med. 2008 Feb 28;358(9):900-9
18204098
Biometrics. 1968 Jun;24(2):315-27
5683872
Genomics. 2009 Jan;93(1):1-4
18519160
Biometrics. 2004 Sep;60(3):589-97
15339280
Biometrics. 2002 Mar;58(1):163-70
11890312
PLoS One. 2008 Jul 02;3(7):e2551
18596976
Hum Hered. 2001;52(3):149-53
11588398