Our Research
The goal of our research group is to better understand the developmental mechanisms and trajectories of childhood neuropsychiatric disorders, from the prenatal period through adolescence. We are keen to leverage large-scale data and statistical and computational methods to facilitate precision psychiatric approaches.
Mental disorders are multifactorial in origin and arise from complex interactions of components across biological, psychological, and social factors. To understand mental disorders, we must understand the individual components of specific disorders in addition to the complex interactions between individuals, their parents, and their environment. With the availability of large longitudinal epidemiological and biological data, and the advancement of statistical and computational methods, we are rapidly advancing our understanding of mental disorders, progressing from a monocausal exposure-disease framework to a multi-causal paradigm.
At our lab, we foster a cross-disciplinary, collaborative research approach, enabling the exchange of each individual’s knowledge and experiences. We provide a high-level of supervision and didactic training for our staff, giving everyone a unique opportunity to pursue ground-breaking research and shed light on the complex etiology of childhood neuropsychiatric disorders
We have three working groups:
Biostatistics Working Group: The goal of this group is to develop and apply state-of-the-art statistical methods for the analysis of phenotypic data (medical records, clinical studies, etc.). Within this group we:
- Develop and apply statistical methods for the analysis of ‘big’ register data, and electronic health record data.
- Adapt machine learning methods to complex time-to-event outcomes for register data.
- Develop methods for causal inference from register data in the presence of unmeasured confounding.
Genomic Data Analysis Working Group: The goal this group is to develop and apply statistical methods and bioinformatics tools for the analysis of genomic data. The researchers in this working group explore all types of genetic variation for disease association. Within this group we:
- Employ novel statistical methods and adopt rigorous significance standards for rare-variant association studies.
- Develop machine learning algorithms for structural and functional genomics.
- Employ appropriate statistical methods to investigate common genetic variation (e.g., polygenic risk score, genetic risk score using Genomic-Best Linear Unbiased Prediction).
- Exert appropriate statistical methods for the analysis of structural variation.
Artificial Intelligence for Mental Health Working Group: The goal of this group is to utilize a combination of genetic, transcriptomic, and epidemiological data to develop predictive models for psychiatric and medical comorbidities in severe childhood neuropsychiatric disorders.
The Mahjani Lab is part of the Seaver Autism Center for Research and Treatment at the Icahn School of Medicine at Mount Sinai. The Seaver Autism Center is a fully integrated, translational research center that leads progressive studies and provides personalized care to individuals with autism and related rare disorders. As a team, we are dedicated to discovering the biological causes of autism and developing breakthrough treatments.
Learn more about the Seaver Autism Center’s translational research.
Recent Updates
April 14, 2024: Check out our new new preprint: Identification of moderate effect size genes in autism spectrum disorder through a novel gene pairing approach [Link].
September 12, 2023: The annual Seaver Celebration will celebrate the Center’s 30th anniversary on November 16 [Link].
August 10, 2023: Check out our new preprint on familial risk of postpartum psychosis [Link].
May 14, 2023: Excited that our newest publication in BMJ is out! “Direct additive genetics and maternal effect contribute to the risk of Tourette disorder”
January 23, 2023: We are extremely grateful to have recently received a grant from the National Institute of Mental Health (NIMH), R21, to study the risk arhcitecture of postpartum psychosis.
December 5, 2022: Happy to share our recent publication. A brilliant work by Benjamin Christoffersen.
October 5, 2022: Happy to share our recent publication, where we evaluated the distribution of potentially damaging copy number variation in obsessive-compulsive disorder and chronic tic disorders.
Research Projects – Women’s & Children’s Mental Health
Women’s Mental Health: Risk architecture of postpartum psychosis
Postpartum psychosis is a severe mood disorder, one of the most severe psychiatric conditions, with high risks of suicide and infanticide if untreated, and should be treated as a medical emergency. It is generally considered a bipolar spectrum disorder, yet, this disorder has not been classified in current disease classification systems because the underlying neurobiology and risk architecture is unclear. In particular, it is unknown how postpartum psychosis fits within the bipolar spectrum, and thus prevention and treatment guidelines are lacking. The goal of this project is to identify the distinct risk architecture of postpartum psychosis.
This project is a collaboration with Dr. Veerle Bergink.
Women’s & Children's Mental Health: Perinatal mental health and risk of childhood neuropsychiatric disorders
Mental disorders around pregnancy may pose significant concerns for both mothers and children due to the considerable morbidity they entail. In this project, we study women’s mental health throughout the reproductive cycle and its impact on the neurodevelopment of their children.
Maternal Genetic Nurture: The association between maternal factors around pregnancy and the risk of neuropsychiatric disorders in children involves two main pathways: direct transmission of genes and maternal effect, where the mother’s phenotype impacts the offspring’s phenotype through environmental pathways. Our analysis reveals that maternal effect contributes 5-15% of the risk for obsessive-compulsive disorder (OCD), Tourette syndrome, and attention-deficit/hyperactivity disorder (ADHD). We now study specific maternal factors that increase the risk of neuropsychiatric disorders in children through maternal effect mechanism.
Prenatal Factors and Child Development: One in five women suffer from mood and anxiety disorders during pregnancy, which are associated with the risk of neuropsychiatric disorders in children. The objective of this project is to differentiate between genetic and environmental pathways by which prenatal maternal mood and anxiety disorders (PMADs) during pregnancy increase the risk of autism spectrum disorder (ASD) in offspring. Our preliminary analysis revealed an odds ratio of 1.43 (95% CI: 1.28, 1.59) for the association between maternal PMADs and ASD in offspring.
This project is a collaboration with Dr. Veerle Bergink.
Children's Mental Health: Medical comorbidities in children with psychiatric disorders
This international project involves expertise from several fields and research centers with the common goal of improving the quality of life in children with childhood neuropsychiatric disorders and lower urinary tract symptoms. Lower urinary tract symptoms (LUTS) are symptoms from the urinary bladder and urethra which are related to problems with urinary storage, voiding, post-micturition, and pain. LUTS have significant adverse effects on the overall quality of life. LUTS can affect children’s social and emotional development and increase risk for social isolation, and being teased or bullied. LUTS frequently co-occur with psychiatric disorders such as anxiety disorders, attention-deficit/hyperactivity disorder (ADHD), and post-traumatic stress disorder (PTSD), particularly in youth.
This project is a collaboration with Dr. Dorothy Grice, Dr. Joseph Buxbaum, Dr. Olof Akre, and Dr. Keith Humphreys.
Children's Mental Health: Temporal and causal relationships between early-onset and late onset psychiatric disorders
Studies show that early-onset psychiatric disorders (e.g., OCD) are associated with a higher risk of late onset psychiatric disorders (e.g., bipolar disorder). Therefore, understanding the causes behind co-occurring psychiatric disorders are necessary. These causes may be shared genetic and/or environmental risk factors that increase the chance of both conditions occurring, and/or causal relationships where one disorder increases the likelihood of the other. Using a large national demographic and clinical dataset and large-scale genetic studies, this study aims to explore shared genetic and environmental risk between early-onset and late-onset psychiatric disorders. With these findings, the study will develop models to predict future late-onset psychiatric disorders in people with early-onset psychiatric disorders.
This project is a collaboration with Dr. Dorothy Grice, Dr. Joseph Buxbaum, and Dr. Niamh Mullins.
Research Projects – Computational Genetics
Genetic Architecture of Autism Spectrum Disorder
Our efforts encompass a range of projects aimed at unraveling the genetic risk architecture of autism spectrum disorder, encompassing both common and rare variation:
- Investigating genetic variants with a moderate effect size in ASD.
- Exploring tandem repeat expansions in the context of ASD.
- Analyzing differential gene mutations associated with ASD.
- Examining the interplay between gene-environment interactions and the heterogeneity of ASD.
- Assessing how ancestral differences contribute to the heterogeneity of ASD.
- Uncovering the impact of common genetic variations on ASD risk, particularly through the autoimmunity pathway.
Model Development: Variant effect prediction using deep learning
We are in the process of developing an innovative model utilizing deep learning methods to effectively differentiate between deleterious and non-deleterious mutations, including non-coding variants.
Model Development: Distributed analysis of sensitive data
The overarching aim of the proposed research environment is to develop a flexible and extendable informatics environment and new statistical methods for privacy-preserving co-analysis of distributed sensitive biomedical data.
This project is a collaboration with Dr. Salman Toor.
Funding and Awards
Funding:
- 2022, National Institute of Mental Health, PI
- 2021, Swedish Research Council, Site PI
- 2021, Seaver Autism Center for Research and Treatment Fellowship, PI
- 2020, Brain & Behavior Research Foundation Young Investigator Grants, PI
- 2018, Nordic strategic workshop grant, PI
Awards:
- 2021, The 2020 Eric Ziegel book review award from Technometrics
- 2020, Brain & Behavior Research Foundation Young Investigator Grants, PI
- 2020, Seaver Foundation Fellowship
- 2018, Seaver Foundation Fellowship
- 2015, Travel grant from Wallenberg Foundation
- 2015, Nordic travel scholarship
- 2015, Centre for Interdisciplinary Mathematics, funding for developing graduate courses
- 2014, Travel grant from the Anna Maria Lundin Foundation
- 2013, Travel grant from the Anna Maria Lundin Foundation
Computer-Intensive Methods in Statistics
Zwanzig, S., Mahjani, B. (2019). Computer-Intensive Methods in Statistics, Chapman and Hall/CRC, ISBN: 978-0-367-19423-9.
This textbook gives an overview of statistical methods that have been developed during recent years due to increased computer use, including random number generators, Monte Carlo methods, Markov Chain Monte Carlo (MCMC) methods, Bootstrap, EM algorithms, SIMEX, variable selection, density estimators, kernel estimators, orthogonal and local polynomial estimators, wavelet estimators, splines, and model assessment.
Team
Behrang Mahjani, PhD, Principal Investigator
Email: behrang.mahjani@mssm.com
Behrang is an Assistant Professor at the Department of Psychiatry at ISMMS, the Department of Genetics and Genomic Sciences, and the Department of Artificial Intelligence and Human Health. Dr. Mahjani is also a Research Specialist for clinical and epidemiological research at the Department of Molecular Medicine and Surgery at Karolinska Institutet (KI) and is affiliated with the Department of Medical Epidemiology and Biostatistics at the same place.
Alongside his research, Behrang has dedicated significant effort to teaching and mentoring students, obtaining certificates in academic teaching and in supervising degree projects. Since 2003, he has taught a broad spectrum of subjects, from advanced programming to big data and machine learning. Behrang was also an invited lecturer for a national course on Statistical Decision Theory. He has guided students in learning statistical and computational methods and co-authored a textbook on Computer-Intensive Methods in Statistics, showcasing his commitment to education and mentorship.
Teaching:
- Big Data and Machine Learning (Postgraduate Course)
- Statistical Decision Theory and Bayesian Methods (Postgraduate Course)
- Advanced Statistical Computing (Postgraduate Course)
- Advanced R Programming (Graduate Course)
- Computer-Intensive Statistics and Data Mining (Graduate Course)
- Scientific Computing II (Undergraduate Course)
- Design and Analysis of Algorithms (Undergraduate Course)
- Data Structures and Algorithms (Undergraduate Course)
- Advanced Object-Oriented Programming with C++ (Undergraduate Course)
- Foundations of Programming (Undergraduate Course)
Certificates:
- Professional Certificate in Biostatistics, 2021
- Certificate in Academic Teacher Training, 2010
- Certificate in Supervising Students For Degree Projects, 2010
Education:
- PhD, Statistical and Scientific Computing, 2016, Uppsala University, Sweden. Thesis: Methods from statistical computing for genetic analysis of complex traits.
- MSc, Engineering Mathematics and Computational Science (specialization in Mathematical Statistics), 2011, Chalmers University of Technology, Sweden. Thesis: Exploring connectivity of random subgraphs of a graph.
- MSc, Complex Adaptive Systems (specialization in Population Genetics), 2009, Chalmers University of Technology, Sweden. Thesis: Sequential Markov Coalescent algorithm for populations with demographic structure.
- BSc, Applied Mathematics, 2004, K.N.T. University of Technology.
Seulgi Jung, PhD, Postdoctoral Fellow
Email: seulgi.jung@mssm.edu
Seulgi is a postdoctoral fellow at Dr. Mahjani lab. He received his PhD in Biomedical Science from the University of Ulsan College of Medicine, Seoul, Korea. Currently, Dr. Jung seeks to understand how rare genetic variation influences the risk of developing neuropsychiatric disorders using whole exome sequencing (WES) data.
Adrianna Kępińska, PhD, Postdoctoral Fellow
Email: adrianna.kepinska@mssm.edu
Adrianna is a postdoctoral fellow at Dr. Mahjani and Dr. Laura Huckins labs. They received their PhD in Genetic Epidemiology from the Institute of Psychiatry, Psychology & Neuroscience, King’s College London, with a focus on genetics of psychosis symptoms across the lifespan. Currently, Dr. Kępińska is working with data from the Swedish national registers to shed light on genetic architecture of postpartum psychosis, with Dr. Veerle Bergik as an advisor. Dr. Kępińska also receives additional training in survival analysis and causal inference with the help of Dr. Keith Humphrey as an advisor.
Madison Caballero, PhD, Bioinformatician III
Email: madison.caballero@mssm.edu
Madison is a bioinformatician at Dr. Mahjani lab. She received her PhD in Genetics from the Department of Molecular Biology and Genetics, Cornell University. Her dissertation title was: “The Interplay Between DNA Replication Timing and Mutations”. Currently, Dr. Caballero seeks to understand how ultra-rare mutations influence the risk of autism.
Marina Natividad Avila, MSc, Bioinformatician, Researcher
Email: marina.natividadavila@mssm.edu
Marina is a bioinformatician at Dr. Joseph Buxbaum lab and a researcher at Dr. Mahjani lab. She received her master’s degree in Medical Genetics from the University of Glasgow, a UK leader in genetic diagnoses, and has therefore a strong understanding of genetic disease and diagnosis. She then moved to the US to study bioinformatics at Boston University. She is personally interested in the genetic architecture of admixed populations and subpopulations.
Christina Gustavsson Mahjani, MSc, RN, Research Nurse (flex time)
Email: christina.mahjani@mssm.edu
Christina is a research nurse at Dr. Mahjani lab. She received a Degree of Postgraduate Diploma in specialist nursing – mental health care and a Master of Medical Science in mental health care from Uppsala, Sweden. Christina has also done courses in the Master Programme in Public Health Science at Mid Sweden University. She has extensive experience in clinical epidemiology and register-based research in psychiatry.
Audrey is a Seaver Undergraduate Research Scholar at Dr. Mahjani lab. She is an undergraduate student pursuing her studies in Psychology at New York University. Her true passion lies in unraveling the etiology of autism and advocating for diversity and inclusion in the scientific community. At Dr. Mahjani’s lab, she investigates the varying prevalence and patterns of psychiatric comorbidities among individuals with autism from different ancestral populations. In addition to her studies, Audrey is receiving training in scientific research conduct and statistical analysis under the guidance of Dr. Mahjani, while also delving into the genetics of diverse populations through mentorship from Ms. Natividad Avila.
Shelby Smout, PhD, Postdoctoral Fellow
Email: shelby.smout@mssm.edu
Shelby Smout is a postdoctoral fellow at Dr. Mahjani and Dr. Bergink labs. They received their PhD in Health Psychology from Virginia Commonwealth University in 2022 where they conducted research assessing the role of discrimination on depression, anxiety, and healthcare avoidance among gender diverse populations. In March 2023, they received a grant from the American Society of Transplantation to conduct a qualitative study on barriers and contributors to transplantation among transgender patients with end-stage kidney or liver disease. In the Mahjani and Bergink labs, Dr. Smout studies women’s mental health throughout the reproductive cycle and its impact on the neurodevelopment of their children.
Lily Cohen, BSc, Research Associate
Lily was a research associate at Dr. Mahjani and Dr. Dorothy Grice lab. She assisted with data cleaning and statistical analyses of the lab’s data, in addition to helping Dr. Mahjani to handle the coordination, development, planning, and implementation of the lab’s projects.
Open Positions
We are actively seeking ambitious and highly motivated researchers with a strong background in computational science and statistics to join our team. Our work spans across statistical genetics, genomics, biostatistics, and bioinformatics, fostering a highly multidisciplinary and innovative research environment.
For PhD students interested in lab rotations, we offer not just a position but a collaborative learning journey. Together, we will develop a personalized rotation plan that aligns with your academic goals, research interests, and skill development needs. This plan ensures that your time in our lab is both productive and enriching, offering hands-on experience with cutting-edge research while contributing to your professional growth.
If our lab’s research interests you, please contact Behrang Mahjani (behrang.mahjani@mssm.edu) to inquire about our available positions.