Validity of using ad hoc methods to analyze secondary traits in case-control association studies.
Case-control association studies often collect from their subjects information on secondary phenotypes. Reusing the data and studying the association between genes and secondary phenotypes provide an attractive and cost-effective approach that can lead to discovery of new genetic associations. A number of approaches have been proposed, including simple and computationally efficient ad hoc methods that ignore ascertainment or stratify on case-control status. Justification for these approaches relies on the assumption of no covariates and the correct specification of the primary disease model as a logistic model. Both might not be true in practice, for example, in the presence of population stratification or the primary disease model following a probit model. In this paper, we investigate the validity of ad hoc methods in the presence of covariates and possible disease model misspecification. We show that in taking an ad hoc approach, it may be desirable to include covariates that affect the primary disease in the secondary phenotype model, even though these covariates are not necessarily associated with the secondary phenotype. We also show that when the disease is rare, ad hoc methods can lead to severely biased estimation and inference if the true disease model follows a probit model instead of a logistic model. Our results are justified theoretically and via simulations. Applied to real data analysis of genetic associations with cigarette smoking, ad hoc methods collectively identified as highly significant (P<10-5) single nucleotide polymorphisms from over 10 genes, genes that were identified in previous studies of smoking cessation.
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America.
Godwin
G
Yung
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America.
Xihong
X
Lin
D016022:Case-Control Studies; D005819:Genetic Markers / Q000235:genetics*; D020022:Genetic Predisposition to Disease; D055106:Genome-Wide Association Study / Q000379:methods; D006801:Humans; D016015:Logistic Models; D008175:Lung Neoplasms / Q000235:genetics*; D008957:Models, Genetic; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D019655:Quantitative Trait, Heritable; D012907:Smoking / Q000235:genetics*
D005819:Genetic Markers
SNPs; case-control sampling; genome-wide association studies; linear regression; logistic regression; secondary phenotypes
Genet Epidemiol. 2010 Jul;34(5):427-33
20583284
Nat Genet. 2013 Jun;45(6):690-6
23583978
Stat Med. 2006 Apr 30;25(8):1323-39
16220494
Nat Genet. 2006 Aug;38(8):904-9
16862161
Mol Med. 2010 Jul-Aug;16(7-8):247-53
20379614
Ann Hum Genet. 2012 Nov;76(6):484-99
22881407
Nat Genet. 2012 Feb 19;44(3):307-11
22344219
Mol Med. 2010 Nov-Dec;16(11-12):513-26
20811658
Science. 2002 Dec 20;298(5602):2381-5
12493913
PLoS Genet. 2010 Feb 26;6(2):e1000864
20195508
Biostatistics. 2014 Jan;15(1):117-28
24152770
Genet Epidemiol. 2011 Apr;35(3):190-200
21308766
Am J Hum Genet. 2011 Mar 11;88(3):294-305
21376301
Nat Genet. 2010 Nov;42(11):937-48
20935630
PLoS Genet. 2012;8(11):e1003032
23144628
Stat Med. 1995 Apr 30;14(8):769-75
7644857
Carcinogenesis. 2014 May;35(5):1039-45
24419231
Am J Hum Genet. 2013 May 2;92(5):744-59
23643383
Nat Genet. 2012 Jul 22;44(8):848-51
22820511
PLoS Genet. 2010 Dec 02;6(12):e1001230
21151957
Biostatistics. 2012 Jul;13(3):497-508
21933777
BMC Res Notes. 2010 Dec 19;3:339
21167073
Genet Epidemiol. 2009 Apr;33(3):256-65
19051285
Genet Epidemiol. 2009 Dec;33(8):717-28
19365863
PLoS Genet. 2012;8(3):e1002559
22412388