2021/11/10 by Siru Wang, Wang, Siru, Guoqi Qian +1
Biochemistry, Genetics and Molecular Biology · #Applications (stat.AP) #FOS: Computer and information sciences #Gene expression and cancer classification #Genetic Associations and Epidemiology #Genetic and phenotypic traits in livestock #Methodology (stat.ME)
paper · pdf · doi:10.48550/arxiv.2111.05714
openalex publication_date 2021/11/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.