An efficient process for enhancing genotype imputation in Genome-wide association studies using high performance computing

Genotype imputation based analysis usually consumes computational and data intensive. This paper presents a practical and efficient process for enhancing the genotype imputation based analysis on Single Nucleotide Polymorphism (SNP) using High Performance Computing (HPC). Our process is split into data quality control, haplotype estimation, and imputation. We validate and measure the process on a standard workstation and a server for pilot dataset of chromosome 22 from Genetic Analysis Workshop 16 (GAW16) provided by the North American Rheumatoid Arthritis Consortium (NARAC). The NARAC dataset consists of 2,062 individuals and 545,080 SNP variants. We use 1000 Genomes database as reference panels. Our process correctly and rapidly produces results more than ordinary steps of the genotype imputation based analysis.