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Genomic data arising from a genome-wide association study (GWAS) are oftennot only of large-scale, but also incomplete. A specific form of theirincompleteness is missing values with non-ignorable missingness mechanism. Theintrinsic complications of genomic data present significant challenges indeveloping an unbiased and informative procedure of phenotype-genotypeassociation analysis by a statistical variable selection approach. In thispaper we develop a coherent procedure of categorical phenotype-genotypeassociation analysis, in the presence of missing values with non-ignorablemissingness mechanism in GWAS data, by integrating the state-of-the-art methodsof random forest for variable selection, weighted ridge regression with EMalgorithm for missing data imputation, and linear statistical hypothesistesting for determining the missingness mechanism. Two simulated GWAS are usedto validate the performance of the proposed procedure. The procedure is thenapplied to analyze a real data set from breast cancer GWAS.

variable selection and missing data imputation in categorical genomic data analysis by integrated ridge regression and random forest