Postdoctoral Research Fellow
Department of Biomedical Informatics, Harvard Medical School
Department of Neurology, Brigham and Women’s Hospital
Dr. Sumaiya Nazeen is a postdoctoral research fellow in the laboratories of Professor Shamil Sunyaev in the Department of Biomedical Informatics at the Harvard Medical School, and Professor Vikram Khurana in the Department of Neurology at the Brigham and Women's Hospital. She is also a remote associate member of the Broad Institute of MIT and Harvard. Her research focuses on the development of computational and statistical models for the interpretation of genetic foundation of complex human diseases. Computational integration of large-scale functional and comparative genomics datasets can provide insight into the disease-associated sequence variants and their likely molecular roles. Sumaiya’s research aims at designing better tools for furthering such insight. Her current projects include network-based rare variant analysis, patient stratification, biomarker discovery, and therapeutic target identification for complex diseases.
Sumaiya was awarded International Fulbright
Science & Technology Fellowship in 2012, and Ludwig Center for Molecular
Oncology Graduate Fellowship in 2015 for her research. Sumaiya pursued her bachelor’s
degree from the Department of Computer Science and Engineering (CSE) at
Bangladesh University of Engineering and Technology (BUET) and graduated summa
cum laude receiving both the Chancellor’s award and the Prime Minister’s
gold medal. She completed her S.M. and Ph.D. in Computer Science under the
supervision of Prof. Bonnie Berger at MIT in 2014 and 2019, respectively. Prior
to coming to MIT, she served as a lecturer in the Department of CSE, BUET.
Postdoctoral Research Fellow
Department of Biomedical Informatics, Harvard Medical School
Department of Neurology, Brigham and Women’s Hospital
Dr. Sumaiya Nazeen is a postdoctoral research fellow in the laboratories of Professor Shamil Sunyaev in the Department of Biomedical Informatics at the Harvard Medical School, and Professor Vikram Khurana in the Department of Neurology at the Brigham and Women's Hospital. She is also a remote associate member of the Broad Institute of MIT and Harvard. Her research focuses on the development of computational and statistical models for the interpretation of genetic foundation of complex human diseases. Computational integration of large-scale functional and comparative genomics datasets can provide insight into the disease-associated sequence variants and their likely molecular roles. Sumaiya’s research aims at designing better tools for furthering such insight. Her current projects include network-based rare variant analysis, patient stratification, biomarker discovery, and therapeutic target identification for complex diseases.
Sumaiya was awarded International Fulbright
Science & Technology Fellowship in 2012, and Ludwig Center for Molecular
Oncology Graduate Fellowship in 2015 for her research. Sumaiya pursued her bachelor’s
degree from the Department of Computer Science and Engineering (CSE) at
Bangladesh University of Engineering and Technology (BUET) and graduated summa
cum laude receiving both the Chancellor’s award and the Prime Minister’s
gold medal. She completed her S.M. and Ph.D. in Computer Science under the
supervision of Prof. Bonnie Berger at MIT in 2014 and 2019, respectively. Prior
to coming to MIT, she served as a lecturer in the Department of CSE, BUET.
Journal article
CONCLUSIONS: These findings expand the genetic architecture of TAD by identifying eight novel candidate genes and demonstrate the utility of phenotype-stratified rare variant burden analyses across large biobanks for gene discovery.
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...