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
In renewing tissues, mutations conferring selective advantage may result in clonal expansions^(1-4). In contrast to somatic tissues, mutations driving clonal expansions in spermatogonia (CES) are also transmitted to the next generation. This results in an effective increase of de novo mutation rate for CES drivers^(5-8). CES was originally discovered through extreme recurrence of de novo mutations causing Apert syndrome⁵. Here, we develop a systematic approach to discover CES drivers as hotspots...
Journal article
CONCLUSION: Importantly, our approach showcases the bioinformatic advancements in genome analysis for RED detection and highlights its practical applications.