In a recent study published in the journal Science Advances , researchers in the United States used 3D transport-based morphometry (TBM) to identify and visualize brain changes linked to 16p11.2 genetic copy number variation (CNV), enhancing prediction accuracy and advancing precision medicine in autism.
Study: Discovering the gene-brain-behavior link in autism via generative machine learning . Image Credit: jittawit21 / Shutterstock Background
Autism, characterized by social, communication, and behavioral impairments, is influenced by genetic and environmental factors, with heritability estimates up to 90%. Despite this, diagnosis is mainly behavioral, and genetic testing is infrequent. Over 200 autism-linked CNVs have been identified, notably the 16p11.2 region. Endophenotypes can bridge genetics and behavior. Emerging machine learning techniques, such as 3D TBM, have the potential to uncover gene-brain-behavior relationships, advancing precision medicine. Further research is essential to enhance understanding and develop better diagnostic and treatment approaches. About the study
In the present study, subjects were recruited from the Simons VIP project, reviewed by the Johns Hopkins Institutional Review Board, and acknowledged as exempt as subjects were deidentified from a preexisting database. Participants were referred by clinical genetic centers, testing laboratories, web-based networks, and self-referral. Screening and medical record reviews were conducted by Geisinger and Emory University, with 16p11.2 CNV tested via fluorescent in situ hybridization. Inclusion criteria included recurrent breakpoints of 16p11.2 without other pathogenic CNVs or unrelated syndromes. Exclusion criteria included environmental neurocognitive impacts, severe birth asphyxia, prematurity, and lack of fluency in English.
Behavioral testing involved the Autism Diagnostic Observation Schedule, Autism Diagnostic Interview, and Social Responsiveness Scale. Core phenotyping sites included the University of Washington Medical Center, Baylor University Medical Center, and Boston Children’s Hospital, using the Diagnostic and Statistical Manual of Mental Disorders, fourth edition, text revision (DSM-IV-TR) criteria. Cognitive measures assessed full-scale Intelligence Quotient (IQ) with standardized tests. High-resolution brain imaging was performed at the University of California and Children’s Hospital of Philadelphia.
Controls were recruited locally near imaging sites, matched for age, sex, handedness, and nonverbal IQ, excluding major DSM-IV diagnoses, Autism Spectrum Disorder (ASD) family history, other developmental disorders, dysmorphic features, or genetic abnormalities. The study […]
Researchers investigate the gene-brain-behavior link in autism using generative machine learning
