title: A comparison of strategies for analyzing dichotomous outcomes in genome-wide association studies with general pedigrees.
abstract: Genome-wide association studies (GWAS) have been frequently conducted on general or isolated populations with related individuals. However, there is a lack of consensus on which strategy is most appropriate for analyzing dichotomous phenotypes in general pedigrees. Using simulation studies, we compared several strategies including generalized estimating equations (GEE) strategies with various working correlation structures, generalized linear mixed model (GLMM), and a variance component strategy (denoted LMEBIN) that treats dichotomous outcomes as continuous with special attentions to their performance with rare variants, rare diseases, and small sample sizes. In our simulations, when the sample size is not small, for type I error, only GEE and LMEBIN maintain nominal type I error in most cases with exceptions for GEE with very rare disease and genetic variants. GEE and LMEBIN have similar statistical power and slightly outperform GLMM when the prevalence is low. In terms of computational efficiency, GEE with sandwich variance estimator outperforms GLMM and LMEBIN. We apply the strategies to GWAS of gout in the Framingham Heart Study. Based on our results, we would recommend using GEE ind-san in the GWAS for common variants and GEE ind-fij or LMEBIN for rare variants for GWAS of dichotomous outcomes with general pedigrees.
authors.affiliation: Department of Neurology, Boston University School of Medicine, Boston, Massachusetts 02118, USA.
authors.forename: Ming-Huei
authors.initials: MH
authors.lastname: Chen
authors.forename: Xuan
authors.initials: X
authors.lastname: Liu
authors.forename: Fengrong
authors.initials: F
authors.lastname: Wei
authors.forename: Martin G
authors.initials: MG
authors.lastname: Larson
authors.forename: Caroline S
authors.initials: CS
authors.lastname: Fox
authors.forename: Ramachandran S
authors.initials: RS
authors.lastname: Vasan
authors.forename: Qiong
authors.initials: Q
authors.lastname: Yang
mesh_terms: D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D020022:Genetic Predisposition to Disease; D014644:Genetic Variation; D055106:Genome-Wide Association Study / Q000706:statistics & numerical data; D006073:Gout / Q000235:genetics; D006801:Humans; D016014:Linear Models; D008957:Models, Genetic; D010375:Pedigree; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D018401:Sample Size