Our research group investigates the developmental mechanisms and trajectories of neuropsychiatric disorders from the prenatal period through adolescence. We examine childhood conditions as well as the impact of severe perinatal psychiatric disorders on both maternal and child outcomes.
Mental disorders are multifactorial, arising from complex interactions between biological and environmental factors. To fully understand these conditions, it is essential to study both the specific components of each disorder and how individuals interact with their parents, caregivers, and the broader environmental contexts in which they live. Advances in statistical and computational methods, coupled with the availability of large-scale longitudinal data, are providing the tools necessary to investigate these intricate interactions. These developments are driving a shift from a monocausal exposure-disease framework, which attributes a disorder to a single cause, to a multi-causal paradigm that recognizes the combined influence of genetic, biological, psychological, social, and environmental factors in their development.
Our lab directly contributes to this evolving understanding by investigating the genetic and developmental mechanisms that shape neuropsychiatric disorders across the lifespan. We focus on severe psychiatric conditions occurring during pregnancy and the postpartum period, exploring how physiological, hormonal, and genetic factors impact maternal mental health and the developmental trajectories of their children. Additionally, we study the genetic contributions to childhood conditions such as attention-deficit hyperactivity disorder and autism spectrum disorder, examining both rare and common genetic variations.
At the core of our work lies an unwavering commitment to excellence, innovation, and collaboration. These core values shape every aspect of our research, from design to execution. For us, excellence means adhering to the highest standards of scientific rigor, ensuring that our findings are robust, reliable, and contribute meaningfully to the understanding and treatment of mental health disorders. Innovation drives our approach, inspiring us to adopt cutting-edge methodologies, challenge established paradigms, and explore complex questions with fresh perspectives. Collaboration is equally essential, fostering an inclusive and interdisciplinary culture where diverse expertise and viewpoints come together to drive transformative discoveries.
We provide comprehensive supervision and didactic training for our team, offering unique opportunities to engage in groundbreaking research and contribute to understanding the complex origins of neuropsychiatric disorders. Aligned with our commitment to excellence, we actively recognize and cultivate the strengths of each team member, fostering a supportive environment where everyone feels empowered to make meaningful contributions.
Our research is structured into three specialized 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.
Dr. Mahjani is also affiliated with the Women’s Mental Health Center and the Mindich Child Health and Development Institute at the Icahn School of Medicine at Mount Sinai.
Recent Updates
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 architecture of postpartum psychosis.
December 5, 2022: Happy to share our recent publication. A brilliant work by Benjamin Christoffersen.
Prenatal Risk Factors for Neuropsychiatric Disorders in Children: The Role of Indirect Genetic Effects
Some of our active projects are:
- Understanding the association between prenatal mood and anxiety disorders and the risk of neuropsychiatric disorders through analyzing placental DNA methylation patterns.
- Exploring the impact of prenatal autoimmune disorders on the risk of ASD and ADHD in children.
- Investigating the effects of exposure to maternal psychiatric medication during pregnancy on the risk of ASD and ADHD in children.
Postpartum Psychosis
Genetic Architecture of Autism Spectrum Disorder
Some of our active projects are:
- 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.
Genetic Architecture of Attention-deficit/hyperactivity disorder
Some of our active projects are:
- Investigating the role of ultra rare genetic variants in risk of ADHD.
- Exploring tandem repeat expansions in the context of ADHD.
- Examining the interplay between gene-environment interactions and the heterogeneity of ADHD.
Model Development: Variant effect prediction using deep learning
Temporal and causal relationships between early-onset and late onset psychiatric disorders
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.
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.
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.
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.
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.
Alumni
Marina Natividad Avila, MSc, Bioinformatician, Researcher
Email: marina.natividadavila@mssm.edu
Marina is a bioinformatician at Dr. Joseph Buxbaum 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.
Alumni
Sahar Jaffer, Summer Volunteer
Sahar is a senior high school student volunteering in our research group this summer. At our lab, she is learning about statistics, practicing statistical tests in Excel, and understanding how scientific studies are conducted. She is also developing skills in time management, ensuring reproducibility in research, writing reports, and science presentation. Additionally, she is learning about the autism phenotype, broadening her knowledge of behavioral science.
Audrey Paulin, Seavers Undergraduate Research Scholar (SURS)
Audrey was a Seaver Undergraduate Research Scholar in Dr. Mahjani’s lab, where she investigated the varying prevalence and patterns of psychiatric comorbidities among individuals with autism from different ancestral backgrounds. In addition to her studies, she received training in scientific research conduct and statistical analysis, contributing significantly to the understanding of her research topic.
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.
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.
- 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
- 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
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.