It is of vital importance to find genetic variants that underlie human complex diseases and locate genes that are responsible for these diseases. Since proteins are typically composed of several structural domains, it is reasonable to assume that harmful genetic variants may alter structures of protein domains, affect functions of proteins and eventually cause disorders. With this understanding, the authors explore the possibility of recovering associations between protein domains and complex diseases. The authors define associations between protein domains and disease families on the basis of associations between non-synonymous single nucleotide polymorphisms (nsSNPs) and complex diseases, similarities between diseases, and relations between proteins and domains. Based on a domain-domain interaction network, the authors propose a 'guilt-by-proximity' principle to rank candidate domains according to their average distance to a set of seed domains in the domain-domain interaction network. The authors validate the method through large-scale cross-validation experiments on simulated linkage intervals, random controls and the whole genome. Results show that areas under receiver operating characteristic curves (AUC scores) can be as high as 77.90%, and the mean rank ratios can be as low as 21.82%. The authors further offer a freely accessible web interface for a genome-wide landscape of associations between domains and disease families.