Genetic and phenotypic heterogeneity in autism and autism spectrum disorders (ASD) pose significant challenges for research focused on defining biomarkers, developmental trajectory and treatment and outcomes. This provides a key rationale for requiring Fragile X testing and high resolution karyotyping using SNP arrays on all subjects enrolled in ACE Centers or Networks, as we will continue to in this ACE. In our previous ACE Center Project II, we hypothesized that studying ASD endophenotypes, would aid in identification of more homogeneous patient subgroups and hasten identification of genetic loci. We showed how a common ASD susceptibility variant in CNTNAP2 modulates brain function, connecting gene to brain to endophenotype for the first time in ASD. We also identified several cases of rare, large copy number variation (CNV) and smaller variants of less certain pathogenecity. Here we propose to continue to genetically characterize all ACE probands, hypothesizing that identifying certain etiological subclasses may provide more homogeneous populations that will be more predictive of trajectory and outcome. We will integrate identification of CNV with gene expression data to identify dysregulated genes within and near CNVs, thus improving classification of pathogenecity, and identify those with mutations currently undetectable by structural variant analysis alone. We will take a systems approach to functionally group these genes into biological pathways, and thus to group patients by shared molecular defects. We will then relate shared molecular pathway defects in the patient subsets to the phenotypic biomarker measurements collected in projects l-IV. We will test the relationship between known and newly discovered genetic variants and measures of behavior, eye-tracking/pupillometry, EEG, and brain imaging at both single time points and examining longitudinal trajectories, as well as their influence on response to treatment. In this way we seek to connect genetic variation to measures of brain function as a means of unraveling the genetic and phenotypic heterogeneity observed in ASD, and to develop improved predictors of diagnosis and treatment response.
Autism Spectrum Disorder varies widely in both symptoms and causes and perhaps is best thought of as the autisms. ASD has a strong genetic component, but the mutations causing disease are largely unknown. We will use genetic measures to define more homogeneous sub-types of autism so as to increase power in clinical trajectory and treatment studies, identify biomarkers and integrate genetic data with measures of behavior and brain function to identify biological processes that are disrupted in ASD.
|Brenner, Laurie A; Shih, Vivian H; Colich, Natalie L et al. (2015) Time reproduction performance is associated with age and working memory in high-functioning youth with autism spectrum disorder. Autism Res 8:29-37|
|Jann, Kay; Gee, Dylan G; Kilroy, Emily et al. (2015) Functional connectivity in BOLD and CBF data: similarity and reliability of resting brain networks. Neuroimage 106:111-22|
|Del Rosario, Mithi; Gillespie-Lynch, Kristen; Johnson, Scott et al. (2014) Parent-reported temperament trajectories among infant siblings of children with autism. J Autism Dev Disord 44:381-93|
|Alaerts, Kaat; Woolley, Daniel G; Steyaert, Jean et al. (2014) Underconnectivity of the superior temporal sulcus predicts emotion recognition deficits in autism. Soc Cogn Affect Neurosci 9:1589-600|
|Gaugler, Trent; Klei, Lambertus; Sanders, Stephan J et al. (2014) Most genetic risk for autism resides with common variation. Nat Genet 46:881-5|
|Di Martino, A; Yan, C-G; Li, Q et al. (2014) The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Mol Psychiatry 19:659-67|
|Zwaigenbaum, Lonnie; Young, Gregory S; Stone, Wendy L et al. (2014) Early head growth in infants at risk of autism: a baby siblings research consortium study. J Am Acad Child Adolesc Psychiatry 53:1053-62|
|Schneider, Maude; Debbané, Martin; Bassett, Anne S et al. (2014) Psychiatric disorders from childhood to adulthood in 22q11.2 deletion syndrome: results from the International Consortium on Brain and Behavior in 22q11.2 Deletion Syndrome. Am J Psychiatry 171:627-39|
|Tak, Sungho; Wang, Danny J J; Polimeni, Jonathan R et al. (2014) Dynamic and static contributions of the cerebrovasculature to the resting-state BOLD signal. Neuroimage 84:672-80|
|Mullen, Brian R; Khialeeva, Elvira; Hoffman, Daniel B et al. (2013) Decreased reelin expression and organophosphate pesticide exposure alters mouse behaviour and brain morphology. ASN Neuro 5:e00106|
Showing the most recent 10 out of 41 publications