Performing Genome-Wide Association Studies Using rMVP

Abstract

Genome wide association study (GWAS), which is a powerful tool to detect the relationship between the traits of interest and high-density markers, has provided unprecedented insights into the genetic basis of quantitative variation for complex traits. Along with the development of high-throughput sequencing technology, both sample sizes and marker sizes are increasing rapidly, which make computations more challenging than ever. Therefore, to efficiently process big data with limited computing resources in a reasonable time and to use state-of-the-art statistical models to reduce false positive and false negative rates have always been hot topics in the domain of GWAS. In this chapter, we describe how to perform GWAS using an R package, rMVP, which includes data preparation, evaluation of population structure, association tests by different models, and high-quality visualization of GWAS results.