Every individual has millions of genomic variants compared to a reference genome. Only a small fraction of these variants can have significant impacts on human diseases. The challenge in human genomics research lies in identifying these large-effect variants and understanding how subtle changes in the DNA translate into disease phenotypes. This involves unraveling complex intermediate layers of how these genetic alterations exert their functional effects across multiple scales. In this dissertation, I present my research on bridging the gap between genetic variation and disease phenotypes, which is fundamental to advancing personalized medicine and targeted therapies. In Chapter 2, I present several computational approaches to identify functional disease-associated variants, leveraging genomic background mutability models, 3D protein structural information, transcription-based enhancer identification strategies, and enhancer-gene linkage mapping approaches. In Chapter 3, I developed a unified, end-to-end 3D structurally-informed protein interaction network propagation framework, NetFlow3D, that systematically maps the multiscale mechanistic effects of somatic mutations in cancer. NetFlow3D anisotropically propagates the impacts of spatial clusters of mutations on 3D protein structures across the protein interaction network, with propagation guided by the specific 3D structural interfaces involved, to identify significantly interconnected network “modules”, thereby uncovering key biological processes driving cancer. In Chapter 4, I established an integrative framework to delve into the etiology underlying autism spectrum disorder (ASD), which combines: (i) a gene-centric statistical model integrating coding and noncoding evidence of rare variant association, (ii) likely altered PPIs–as revealed by the presence of damaging de novo missense variants on their 3D structural interfaces, and (iii) the topology of the PPI network. The integration of noncoding data has nearly doubled the analytical power of gene discovery, and has uncovered an emerging class of potential ASD pathways. In summary, the theme of my thesis is identifying disease-associated variants by leveraging various biological data, and combining their complementary insights to decipher the complex mechanisms underlying in human diseases. The core principle of my approach is to strategically integrate these separate insights into unified framework architectures that closely aligns with the underlying biological nature, thereby effectively converging relevant signals while filtering out noise, and at the same time, systematically unraveling the complex intermediate layers that illustrate how subtle genetic changes translate into observable disease phenotypes.