UC Riverside

The state of the art GWAS under the linear mixed model framework, although vastly improved, still suffers from high computational cost and type I error rate. Approaches like EMMA (Kanget al. 2008), GEMMA (Zhou and Stephens, 2012) and EMMAX (Kang et al. 2010) among others are better when compared to the traditional GWAS approach, but they are still computationally slow. The purpose of this dissertation is to illustrate our new approach called the RFM, which can be applied to the linear mixed model GWAS. We will show that the RFM approach is more efficient approach that saves tremendous computational time while also lowering the type I error rate.

Chapter one will introduce GWAS and briefly discuss the generalized linear mixed models theory that are typically used in GWAS. Chapter two will detail the linear mixed model theory and methodology and its application to GWAS. We will overview the different techniques that can be used to estimate the unknown parameters in the linear mixed models, as well as discuss in details the mathematics behind those techniques that are directly used in our research. Chapter 3 will illustrate the application of the RFM method to GWAS. We will apply the RFM method on the simulated datasets used in chapter two and compare the results. We will also use RFM methodology to conduct GWAS on two actual datasets, both containing whole genome sequences. We will compare and discuss our GWAS results obtained using the RFM method to the GWAS results obtained using SAS's proc mixed and R.