  Genomic data arising from a genome-wide association study (GWAS) are often
not only of large-scale, but also incomplete. A specific form of their
incompleteness is missing values with non-ignorable missingness mechanism. The
intrinsic complications of genomic data present significant challenges in
developing an unbiased and informative procedure of phenotype-genotype
association analysis by a statistical variable selection approach. In this
paper we develop a coherent procedure of categorical phenotype-genotype
association analysis, in the presence of missing values with non-ignorable
missingness mechanism in GWAS data, by integrating the state-of-the-art methods
of random forest for variable selection, weighted ridge regression with EM
algorithm for missing data imputation, and linear statistical hypothesis
testing for determining the missingness mechanism. Two simulated GWAS are used
to validate the performance of the proposed procedure. The procedure is then
applied to analyze a real data set from breast cancer GWAS.
