Professor of Biomedical Informatics, Harvard Medical School
Professor of Medicine, Brigham and Women’s Hospital
Dr. Sunyaev
is a computational genomicist and geneticist. Research in his lab encompasses
many aspects of population genetic variation including the origin of mutations,
the effect of allelic variants on molecular function, population and
evolutionary genetics, and genetics of human complex and Mendelian traits. He
developed several computational and statistical methods widely adopted by the
community.
Dr. Sunyaev
obtained a PhD in molecular biophysics from the Moscow Institute of Physics and
Technology and completed his postdoctoral training in bioinformatics at the
European Molecular Biology Laboratory (EMBL). He is an Associate Member at
Broad Institute of MIT and Harvard. He co-leads the NHGRI-funded Genome
Sequencing Program Analysis Center and is actively involved in the Undiagnosed
Diseases Network and in the Brigham Genomic Medicine program. He also
co-organizes the Boston Evolutionary Genomics Group.
Professor of Biomedical Informatics, Harvard Medical School
Professor of Medicine, Brigham and Women’s Hospital
Dr. Sunyaev
is a computational genomicist and geneticist. Research in his lab encompasses
many aspects of population genetic variation including the origin of mutations,
the effect of allelic variants on molecular function, population and
evolutionary genetics, and genetics of human complex and Mendelian traits. He
developed several computational and statistical methods widely adopted by the
community.
Dr. Sunyaev
obtained a PhD in molecular biophysics from the Moscow Institute of Physics and
Technology and completed his postdoctoral training in bioinformatics at the
European Molecular Biology Laboratory (EMBL). He is an Associate Member at
Broad Institute of MIT and Harvard. He co-leads the NHGRI-funded Genome
Sequencing Program Analysis Center and is actively involved in the Undiagnosed
Diseases Network and in the Brigham Genomic Medicine program. He also
co-organizes the Boston Evolutionary Genomics Group.
Journal article
Studying the genetic basis of human phenotypes involves two primary strategies. Model-system experiments generate interpretable gene networks but do not establish relevance to human disease. In contrast, statistical genetics identifies variant- and gene-level associations but cannot test mechanistic models. Here, we bridge these approaches by introducing NERINE, a hierarchical model-based rare variant association test that incorporates gene network topology while remaining robust to network...
Journal article
Genomic sequencing is now widely accessible for genetic diagnostics and is emerging as a component of newborn screening. This technological development generates the need to characterize incoming mutations, create comprehensive datasets of genes causing rare Mendelian disorders, and identify pathogenic variants. Large-scale exome sequencing datasets such as Genome Aggregation Database (gnomAD) have been assembled to help address these challenges. The recent release of gnomAD (v4; n = 730,947)...