title: Bias due to two-stage residual-outcome regression analysis in genetic association studies.
abstract: Association studies of risk factors and complex diseases require careful assessment of potential confounding factors. Two-stage regression analysis, sometimes referred to as residual- or adjusted-outcome analysis, has been increasingly used in association studies of single nucleotide polymorphisms (SNPs) and quantitative traits. In this analysis, first, a residual-outcome is calculated from a regression of the outcome variable on covariates and then the relationship between the adjusted-outcome and the SNP is evaluated by a simple linear regression of the adjusted-outcome on the SNP. In this article, we examine the performance of this two-stage analysis as compared with multiple linear regression (MLR) analysis. Our findings show that when a SNP and a covariate are correlated, the two-stage approach results in biased genotypic effect and loss of power. Bias is always toward the null and increases with the squared-correlation between the SNP and the covariate (). For example, for , 0.1, and 0.5, two-stage analysis results in, respectively, 0, 10, and 50% attenuation in the SNP effect. As expected, MLR was always unbiased. Since individual SNPs often show little or no correlation with covariates, a two-stage analysis is expected to perform as well as MLR in many genetic studies; however, it produces considerably different results from MLR and may lead to incorrect conclusions when independent variables are highly correlated. While a useful alternative to MLR under , the two -stage approach has serious limitations. Its use as a simple substitute for MLR should be avoided.
authors.affiliation: Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts 02118, USA. demissie@bu.edu
authors.forename: Serkalem
authors.initials: S
authors.lastname: Demissie
authors.forename: L Adrienne
authors.initials: LA
authors.lastname: Cupples
mesh_terms: D015982:Bias; D005260:Female; D005269:Femur / Q000033:anatomy & histology; D056726:Genetic Association Studies / Q000706:statistics & numerical data; D055106:Genome-Wide Association Study; D005838:Genotype; D006801:Humans; D016014:Linear Models; D008297:Male; D020641:Polymorphism, Single Nucleotide; D019655:Quantitative Trait, Heritable; D012044:Regression Analysis