Statistical Methods for Genome-wide Association Studies and Personalized Medicine

iAbstract In genome-wide association studies (GWAS), researchers analyze the genetic variation across the entire human genome, searching for variations that are associated with observable traits or certain diseases. There are several inference challenges in GWAS, including the huge number of genetic markers to test, the weak association between truly associated markers and the traits, and the correlation structure between the genetic markers. This thesis mainly develops statistical methods that are suitable for genome-wide association studies and their clinical translation for personalized medicine. After we introduce more background and related work in Chapters 1 and 2, we further discuss the problem of high dimensional statistical inference, especially capturing the dependence among multiple hypotheses, which has been under-utilized in classical multiple testing procedures. Chap-ter 3 proposes a feature selection approach based on a unique graphical model which can leverage correlation structure among the markers. This graphical model-based feature selection approach significantly outperforms the conventional feature selection methods used in GWAS. Chapter 4 reformulates this feature selection approach as a multiple testing procedure that has many elegan