title: Strategy to control type I error increases power to identify genetic variation using the full biological trajectory.
abstract: Genome-wide association studies have been successful in identifying loci that underlie continuous traits measured at a single time point. To additionally consider continuous traits longitudinally, it is desirable to look at SNP effects at baseline and over time using linear-mixed effects models. Estimation and interpretation of two coefficients in the same model raises concern regarding the optimal control of type I error. To investigate this issue, we calculate type I error and power under an alternative for joint tests, including the two degree of freedom likelihood ratio test, and compare this to single degree of freedom tests for each effect separately at varying alpha levels. We show which joint tests are the optimal way to control the type I error and also illustrate that information can be gained by joint testing in situations where either or both SNP effects are underpowered. We also show that closed form power calculations can approximate simulated power for the case of balanced data, provide reasonable approximations for imbalanced data, but overestimate power for complicated residual error structures. We conclude that a two degree of freedom test is an attractive strategy in a hypothesis-free genome-wide setting and recommend its use for genome-wide studies employing linear-mixed effects models.
authors: Johns Hopkins Bloomberg School of Public Health, Mental Health Department, Baltimore, Maryland 21205, USA. kbenke@jhsph.edu
authors: K S
authors: KS
authors: Benke
authors: Y
authors: Y
authors: Wu
authors: D M
authors: DM
authors: Fallin
authors: B
authors: B
authors: Maher
authors: L J
authors: LJ
authors: Palmer
mesh_terms: D015331:Cohort Studies; D003198:Computer Simulation; D014644:Genetic Variation; D055106:Genome-Wide Association Study; D006801:Humans; D016014:Linear Models; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide
references: Genet Epidemiol. 2010 Nov;34(7):665-73
references: 20839289
references: Genet Epidemiol. 2008 Apr;32(3):227-34
references: 18300295
references: Exp Gerontol. 2011 Dec;46(12):1010-9
references: 21968104
references: Science. 1996 Sep 13;273(5281):1516-7
references: 8801636
references: OMICS. 2011 Jun;15(6):393-8
references: 21348634
references: Nature. 2010 Aug 5;466(7307):707-13
references: 20686565
references: J Hum Genet. 2010 Apr;55(4):195-206
references: 20300123
references: Curr Opin Lipidol. 2012 Apr;23(2):98-103
references: 22418572
references: Nature. 2009 Aug 6;460(7256):748-52
references: 19571811
references: Genet Epidemiol. 2011 Feb;35(2):111-8
references: 21254218
references: PLoS One. 2011 Apr 27;6(4):e14808
references: 21556132
references: Hum Hered. 2007;63(2):111-9
references: 17283440
references: J Clin Endocrinol Metab. 2011 May;96(5):E810-5
references: 21307140
references: Nature. 2010 Oct 28;467(7319):1061-73
references: 20981092
references: Ann Epidemiol. 1991 Feb;1(3):263-76
references: 1669507
references: BMJ. 1995 Jan 21;310(6973):170
references: 7833759
references: Diabetologia. 2009 Jan;52(1):106-14
references: 19018513
references: BMC Med Genet. 2007 Sep 19;8 Suppl 1:S17
references: 17903299
references: PLoS Genet. 2010 Sep 09;6(9):e1001094
references: 20838585
references: Genet Epidemiol. 2010 Dec;34(8):879-91
references: 21104890
references: Epidemiology. 2010 Sep;21(5):621-5
references: 20699681
references: Biometrics. 1982 Dec;38(4):963-74
references: 7168798
references: PLoS One. 2012;7(5):e34861
references: 22567092
references: Bioinformatics. 2007 May 15;23(10):1294-6
references: 17384015
references: Stat Med. 2013 Jan 15;32(1):165-80
references: 22911890
references: J Clin Psychiatry. 1987 Aug;48(8):314-8
references: 3611032