UC Riverside

Genome-wide association studies (GWAS) are statistical tools widely used to identify the associations between genetic variants and a quantitative trait. Through GWAS, the genetic architectures of many complex traits in plants, animals and human have been revealed. A commonly used method in GWAS is the linear mixed model (LMM). This model is called the fixed model (FM) approach when the marker effect is treated as a fixed effect. In contrast to the FM approach, the scanned marker can also be treated as a random effect and such a method is called the random model (RM) approach. The RM approach allows the use of the effective number of tests to perform Bonferroni correction and thus significantly increases the statistical power. However, the RM approach requires estimation of two genetic variance components (the variance of the scanned marker and the polygenic variance) and thus involves high computational cost. The main focus of this dissertation is the development of a new method named randomized fixed model (RFM) methodology. By this method, we can perform the RM GWAS using results of the FM analysis without involving additional computation.

There are three chapters in this dissertation. The first chapter introduces the main concepts in GWAS, LMM and corrections for multiple hypotheses testing. The second chapter describes the RFM methodology, and demonstrates in both simulated data and real human data that the RFM is as powerful as the RM, with reduced computational complexity. In the third chapter, an outlier detection approach using a mixture model for significance test is described. Compared to Bonferroni correction method, this approach boosts the statistical power with the genome-wide type I error rate still controlled below 0.05. Thus, the outlier detection approach can be an alternative method for Bonferroni correction.