Robust Score Tests with Missing Data in Genomics Studies

• Activity Number: 312 - Recent Methods Development for Sequence-Based Association Studies
• Type: Contributed
• Date/Time: Tuesday, July 31, 2018 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics in Genomics and Genetics
• Author(s): Kin Yau Wong* and Donglin Zeng and Danyu Lin
• Companies: Hong Kong Polytechnic University and UNC Chapel Hill and University of North Carolina
• Keywords: Association tests; Imputation; Integrative analysis; Multiple genomics platforms; semiparametric models; sieve estimation

Abstract

Analysis of genomic data is often complicated by the presence of missing values, which may arise due to cost or other reasons. The prevailing approach of single imputation is generally invalid if the imputation model is misspecified. We propose a robust score statistic based on imputed data for testing the association between a phenotype and a genomic variable with (partially) missing values. We fit a semiparametric regression model for the genomic variable against an arbitrary function of the linear predictor in the phenotype model and impute each missing value by its estimated posterior expectation. We show that the score statistic with such imputed values is asymptotically unbiased under general missing-data mechanisms. We develop a spline-based method to estimate the semiparametric imputation model and derive the asymptotic distribution of the corresponding score statistic. The proposed test is computationally feasible regardless of the number of independent variables in the imputation model. We demonstrate the advantages of the proposed method over existing methods through extensive simulation studies and provide an application to a major cancer genomics study.