title: Imputation methods to improve inference in SNP association studies.
abstract: Missing single nucleotide polymorphisms (SNPs) are quite common in genetic association studies. Subjects with missing SNPs are often discarded in analyses, which may seriously undermine the inference of SNP-disease association. In this article, we develop two haplotype-based imputation approaches and one tree-based imputation approach for association studies. The emphasis is to evaluate the impact of imputation on parameter estimation, compared to the standard practice of ignoring missing data. Haplotype-based approaches build on haplotype reconstruction by the expectation-maximization (EM) algorithm or a weighted EM (WEM) algorithm, depending on whether case-control status is taken into account. The tree-based approach uses a Gibbs sampler to iteratively sample from a full conditional distribution, which is obtained from the classification and regression tree (CART) algorithm. We employ a standard multiple imputation procedure to account for the uncertainty of imputation. We apply the methods to simulated data as well as a case-control study on developmental dyslexia. Our results suggest that imputation generally improves efficiency over the standard practice of ignoring missing data. The tree-based approach performs comparably well as haplotype-based approaches, but the former has a computational advantage. The WEM approach yields the smallest bias at a price of increased variance.
authors.affiliation: Department of Biostatistics, University of Washington, Seattle, Washington, USA. yud@u.washington.edu
authors.forename: James Y
authors.initials: JY
authors.lastname: Dai
authors.forename: Ingo
authors.initials: I
authors.lastname: Ruczinski
authors.forename: Michael
authors.initials: M
authors.lastname: LeBlanc
authors.forename: Charles
authors.initials: C
authors.lastname: Kooperberg
mesh_terms: D000465:Algorithms; D016022:Case-Control Studies; D002874:Chromosome Mapping; D003198:Computer Simulation; D004410:Dyslexia / Q000235:genetics; D006239:Haplotypes; D006801:Humans; D016013:Likelihood Functions; D008957:Models, Genetic; D015233:Models, Statistical; D020641:Polymorphism, Single Nucleotide; D011336:Probability; D015203:Reproducibility of Results; D012107:Research Design