Computational Neurogenomics

The Computational Neurogenomics Lab at QIMR Berghofer combines genomics, neuroscience, sleep, mental health, data science, and machine learning to unravel the complexities of brain-related traits and diseases. Our aim is to identify the fundamental drivers of individual variation in cognition, behaviour, brain structure, and the risk of neuropsychiatric diseases by examining the natural variations in DNA sequence within populations.

To achieve this goal, we leverage advanced statistical and computational methods, collaborate with researchers from diverse fields, and analyse vast datasets from global scientific consortia and biobanks. Our overarching objective is to gain deeper insights into the underlying causes and mechanisms of human behaviour, neuroanatomy, and brain-related disorders.

Our research portfolio encompasses a wide range of complex health conditions, including but not limited to, Parkinson’s disease, Alzheimer’s disease and related dementias, chronic pain and migraines, self-harm behaviours, depression, sleep disorders, and other complex traits. We are constantly eager to learn and integrate new methodologies into our research, and our team is always willing to engage in multidisciplinary projects with researchers from other disciplines.

At the Computational Neurogenomics Lab, we are driven by the desire to advance scientific knowledge and improve human health through innovative research. Our diverse and multidisciplinary team is dedicated to pushing the boundaries of science and technology to unlock the secrets of the brain and unravel the mysteries of brain-related disorders.


Examples of our current research projects include:

  • Investigating the complex interplay of genetic and environmental factors that contribute to mental health disorders in different populations.
  • Examining the intricate relationships between genetics, brain structure, neurological and psychiatric diseases, cognition, and behaviour.
  • Characterising the genetic architecture of polygenic conditions, such as Parkinson’s disease, Alzheimer’s disease, vascular dementia, sleep apnoea, acne, self-harm behaviours, chronic pain, depression, and others.
  • Identifying the causal relationships between environmental stressors and health outcomes.


  • Acne
  • Ageing
  • Alzheimer’s disease
  • Chronic inflammation 
  • Chronic pain
  • Dementia
  • Major depressive disorder
  • Parkinson’s disease
  • Sleep disorders
  • Suicide prevention


  • Adrian Campos, Visiting Scientist
  • Freddy Chafota, Research Officer
  • Luis Garcia Marin, Research Assistant
  • Xochitl Diaz-Tellez, Visiting Student
  • Zuriel Ceja, PhD Student

Internal Collaborators

External Collaborators

  • Ignacio Mata, Cleveland Clinic
  • George Mellick, Griffith University
  • Kishore Kumar, Garvan Institute of Medical Research
  • Dale Nyholt, Queensland University of Technology
  • Jennifer Yokoyama, University of California San Francisco
  • Xianjun Dong, Brigham & Women’s Hospital, Boston, USA
  • Gunter Schumann, Charité – Universitätsmedizin Berlin
  • Claudia Durán-Aniotz, Latin American Brain Health Institute, Chile
  • Jill Rabinowitz, Johns Hopkins Bloomberg School of Public Health
  • Jiao Wang, Sun Yat-Sen University, Guangzhou, China
  • Sarael Alcauter, UNAM Mexico
  • Alejandra Medina-Rivera, UNAM Mexico
  • Alejandra Ruiz Contreras, UNAM Mexico
  • Carlos Cruz-Fuentes Instituto Nacional de Psiquiatría, Mexico
  • Gabriela Martínez-Levy Instituto Nacional de Psiquiatría, Mexico
  • National Health & Medical Research Council
  • Rebecca L Cooper Medical Research Foundation
  • National Institute of Mental Health, USA
  • Shake It Up Australia Foundation
  • American Parkinson’s Disease Association
  • The Michael J. Fox Foundation for Parkinson’s Research
  • Alzheimer’s Association
  • Medical Research Future Fund


Cracking the genetic code of familial Parkinson’s disease

This project is for PhD students only.  A background in (or a strong interest and passion to learn about) genetics, epidemiology, data science, statistics or bioinformatics is preferred. Previous research experience and coding and analysing data (genetic, clinical, or other kind) using R/Python is also desirable. BACKGROUND Parkinson’s disease (PD) affects ~150,000 Australians, and the […]

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