Yuval Itan, PhD
img_Yuval Itan
ASSOCIATE PROFESSOR | Genetics and Genomic Sciences
Research Topics
Bioinformatics, Biomedical Informatics, Biomedical Sciences, Biostatistics, Cardiovascular, Clinical Genomics, Computational Biology, Computer Simulation, Coronavirus, Evolution, Gene Discovery, Genetics, Genomics, Immune Deficiency, Infectious Disease, Inflammatory Bowel Disease (IBD), Mathematical Modeling of Biomedical Systems, Mathematical and Computational Biology, Neural Networks, Obesity, Parkinson's Disease, Personalized Medicine, Proteomics, Systems Biology, Technology & Innovation, Theoretical Biology, Translational Research
Multi-Disciplinary Training Area
Artificial Intelligence and Emerging Technologies in Medicine [AIET], Genetics and Genomic Sciences [GGS]
Investigating population-specific disease-causing mutations, genes and pathways
Different human populations display varying genomic architectures, that are likely to result in population-specific disease-causing mutations, genes and pathways. We currently investigate this concept with Ashkenazi Jewish (AJ) inflammatory bowel disease (IBD) patients from the IBD genetics consortium (IBDGC) whole exome sequencing data, that we identify by admixture and principal component analyses (PCA). We perform a gene burden analysis of cases vs controls, focusing on high-impact rare genetic variants. We use PheWAS to further validate our results. We aim to then extend the analysis to other human populations (Hispanic, African American and European) for identifying population-specific IBD genomic signals.

Itan lab webpage
Deep neural network predictions of pathogenic mutations
While there has been an extensive effort in identifying pathogenic mutations in patients’ genomes, current methods still cannot efficiently prioritize the true pathogenic mutations in patients. We showed that by using extensive annotations it is possible to cluster mutations by disease groups. We aim to deep neural network (aka “deep learning”) classifier to efficiently and automatically prioritize pathogenic mutations in patients’ genomes, by considering the disease of the patient, train based on extensive annotations at the variant-, gene- and pathway-levels, and separate the training sets by disease groups and high-quality non-trivial neutral genetic variants.
Predicting the functional consequence of mutations
Gain-of-function (GOF) and loss-of-function (LOF) mutations in the same gene result in different diseases and require different treatment. We aim to develop the first computational method to efficiently predict if a mutation is GOF, LOF or neutral by: (1) creating the first extensive GOF and LOF database by extracting data with natural language processing (NLP) algorithm on abstracts of known pathogenic mutations; (2) applying statistical and feature selection approach to detect protein-level and gene-level features that best differentiate GOF from LOF and neutral mutations; and (3) developing a Random Forest classifier and a public server to predict the functional consequence of mutations. We use Phenome-Wide Associations (PheWAS) on Mount Sinai’s BioMe resource for validating our resource and detect novel GOF/LOF phenotypes.

BSc, Bar-Ilan University

PhD, University College London

Postdoc, The Rockefeller University


Selected Publications

Identifying high-impact variants and genes in exomes of Ashkenazi Jewish inflammatory bowel disease patients. Yiming Wu, Kyle Gettler, Meltem Ece Kars, Mamta Giri, Dalin Li, Cigdem Sevim Bayrak, Peng Zhang, Aayushee Jain, Patrick Maffucci, Ksenija Sabic, Tielman Van Vleck, Girish Nadkarni, Lee A. Denson, Harry Ostrer, Adam P. Levine, Elena R. Schiff, Anthony W. Segal, Subra Kugathasan, Peter D. Stenson, David N. Cooper, L. Philip Schumm, Scott Snapper, Mark J. Daly, Talin Haritunians, Richard H. Duerr, Mark S. Silverberg, John D. Rioux, Steven R. Brant, Dermot P.B. McGovern, Judy H. Cho, Yuval Itan. Nature Communications

Rare predicted loss-of-function variants of type I IFN immunity genes are associated with life-threatening COVID-19. Daniela Matuozzo, Estelle Talouarn, Astrid Marchal, Peng Zhang, Jeremy Manry, Yoann Seeleuthner, Yu Zhang, Alexandre Bolze, Matthieu Chaldebas, Baptiste Milisavljevic, Adrian Gervais, Paul Bastard, Takaki Asano, Lucy Bizien, Federica Barzaghi, Hassan Abolhassani, Ahmad Abou Tayoun, Alessandro Aiuti, Ilad Alavi Darazam, Luis M. Allende, Rebeca Alonso-Arias, Andrés Augusto Arias, Gokhan Aytekin, Peter Bergman, Simone Bondesan, Yenan T. Bryceson, Ingrid G. Bustos, Oscar Cabrera-Marante, Sheila Carcel, Paola Carrera, Giorgio Casari, Khalil Chaïbi, Roger Colobran, Antonio Condino-Neto, Laura E. Covill, Ottavia M. Delmonte, Loubna El Zein, Carlos Flores, Peter K. Gregersen, Marta Gut, Filomeen Haerynck, Rabih Halwani, Selda Hancerli, Lennart Hammarström, Nevin Hatipoğlu, Adem Karbuz, Sevgi Keles, Dusan Bogunovic, Yuval Itan, Nadjib Hammoudi. Genome Medicine

Genetic susceptibility to diabetic kidney disease is linked to promoter variants of XOR. Qin Wang, Haiying Qi, Yiming Wu, Liping Yu, Rihab Bouchareb, Shuyu Li, Emelie Lassén, Gabriella Casalena, Krisztian Stadler, Kerstin Ebefors, Zhengzi Yi, Shaolin Shi, Fadi Salem, Ronald Gordon, Lu Lu, Robert W. Williams, Jeremy Duffield, Weijia Zhang, Yuval Itan, Erwin Böttinger, Ilse Daehn. Nature Metabolism

View All Publications

Physicians and scientists on the faculty of the Icahn School of Medicine at Mount Sinai often interact with pharmaceutical, device and biotechnology companies to improve patient care, develop new therapies and achieve scientific breakthroughs. In order to promote an ethical and transparent environment for conducting research, providing clinical care and teaching, Mount Sinai requires that salaried faculty inform the School of their relationships with such companies.

Dr. Itan did not report having any of the following types of financial relationships with industry during 2022 and/or 2023: consulting, scientific advisory board, industry-sponsored lectures, service on Board of Directors, participation on industry-sponsored committees, equity ownership valued at greater than 5% of a publicly traded company or any value in a privately held company. Please note that this information may differ from information posted on corporate sites due to timing or classification differences.

Mount Sinai's faculty policies relating to faculty collaboration with industry are posted on our website. Patients may wish to ask their physician about the activities they perform for companies.