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
Large biobank-scale whole genome sequencing (WGS) studies are rapidly identifying a multitude of coding and non-coding variants. They provide an unprecedented resource for illuminating the genetic basis of human diseases. Variant functional annotations play a critical role in WGS analysis, result interpretation, and prioritization of disease- or trait-associated causal variants. Existing functional annotation databases have limited scope to perform online queries and functionally annotate the...
Journal article
Large-scale whole-genome sequencing studies have enabled analysis of noncoding rare-variant (RV) associations with complex human diseases and traits. Variant-set analysis is a powerful approach to study RV association. However, existing methods have limited ability in analyzing the noncoding genome. We propose a computationally efficient and robust noncoding RV association detection framework, STAARpipeline, to automatically annotate a whole-genome sequencing study and perform flexible noncoding...
