Modern genetics research constantly creates new types of high-dimensional genetic and genomic data and imposes new challenges in analyzing these data. This thesis deals with several important problems in analyzing high-dimensional genetic and genomic data, ranging from DNA methylation data to human microbiome data. First, we introduce a site selection and multiple imputation method to impute missing data in covariates in epigenome-wide analysis of DNA methylation data, which can help us adjust potential confounders, such as cell type composition. Second, to overcome low power issue of human microbiome association studies, we propose a powerful data-driven approach by weighting the variables (taxa) in a manner determined by the data itself. The increased power of the new test not only decreases the sample size required for a human microbiome association study but also allows for new discoveries with existing datasets. Third, we propose an adaptive test on a high-dimensional parameter of a generalized linear model (in the presence of a low-dimensional nuisance parameter). Benefiting from its adaptivity, the proposed test maintains high statistical power under various high-dimensional scenarios. We further establish its asymptotic null distribution. Finally, we propose a novel pathway-based association test by integrating gene expression, gene functional annotations, and a main genome-wide association study dataset. We applied it to a schizophrenia GWAS summary association dataset and identified 15 novel pathways associated with schizophrenia, such as GABA receptor complex (GO:1902710), which could not be uncovered by the standard single SNP-based analysis or gene-based TWAS.