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, 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. We discuss the problem of high dimensional statistical inference, especially capturing the dependence among multiple hypotheses. Chapter 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 elegant properties, including controlling false discovery rate at a specified level and significantly improving the power of the tests. In order to relax the parametric assumption within the model, Chapter 5 further proposes a semiparametric graphical model which estimates f1 adaptively. These statistical methods are based on graphical models, and their parameter learning is difficult due to the intractable normalization constant. Capturing the hidden patterns and heterogeneity within the parameters is even harder. Chapters 6 and 7 discuss the problem of learning large-scale graphical models, especially dealing with issues of heterogeneous parameters and latently-grouped parameters. Chapter 6 proposes a nonparametric approach which can adaptively integrate background knowledge about how the different parts of the graph can vary. For learning latently-grouped parameters in undirected graphical models, Chapter 7 imposes Dirichlet process priors over the parameters and estimates the parameters in a Bayesian framework. Chapter 8 explores the potential translation of GWAS discoveries to clinical breast cancer diagnosis. We discovered that, using SNPs known to be associated with breast cancer, we can better stratify patients and thereby significantly reduce false positives during breast cancer diagnosis, alleviating the risk of overdiagnosis. This result suggests that when radiologists are making medical decisions from mammograms (such as suggesting follow-up biopsies), they can consider these risky SNPs for more accurate decisions if the patients' genotype data are available.