Shamil Sunyaev's research focuses on rapidly advancing DNA sequencing technology to provide a complete picture of population genetic variation and sequence divergence among species. His work involves the systematic re-sequencing of datasets aimed at understanding the origin of human mutations and their effects on molecular function, fitness, and phenotype. He analyzes human genetic variation in light of classic population genetics models, estimating parameters of demographic history and the distribution of fitness effects of mutations in the human genome. Additionally, he develops computational methods to predict the effects of mutations and single nucleotide polymorphisms using comparative genomics and protein structure data. His research program aims to apply insights gained to medical genetics studies, collaborating with medical genetics groups on clinical genetic diagnostics and large-scale sequencing studies of well-phenotyped populations. Thus, his research intertwines evolutionary models, functional predictions, and statistical methods for analyzing complex phenotypes. Furthermore, he is interested in developing computational statistical approaches applicable to proteomics and developmental biology.