Vahid H. Gazestani
University of California, Berkeley
GeneFunctional specializationWnt signaling pathwayCell fate determinationChromatin remodelingNeuroscienceCell typeProgenitor cellAutismVocal learningContext (language use)Intervention (counseling)Cerebral cortexEarly detectionYoung ageEarly languageGenetic modelCortical surfaceTreatment engagementTreatment outcomePre treatmentGeneticsGene regulatory networkClinical psychologyMedicineCohortBiology
Publications 3
#1Austin W. T. Chiang (UCSD: University of California, San Diego)H-Index: 8
#2Vahid H. Gazestani (UCSD: University of California, San Diego)H-Index: 10
Last. Nathan E. Lewis (UCSD: University of California, San Diego)H-Index: 44
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Omics technologies are ubiquitous in biomedical research. However, improper sample selection is an often-overlooked complication with large omics studies, resulting in confounding effects that can disrupt the internal validity of a study and lead to false conclusions. Here, we present a method called BalanceIT, which uses a genetic algorithm to identify an optimal set of samples with balanced clinical factors for large-scale omics experiments. We apply our approach to two large RNA-Seq studies i...
#1Michael V. Lombardo (IIT: Istituto Italiano di Tecnologia)H-Index: 56
#2Elena Maria Busuoli (IIT: Istituto Italiano di Tecnologia)
Last. Karen Pierce (UCSD: University of California, San Diego)H-Index: 51
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Early detection and intervention are believed to be key to facilitating better outcomes in children with autism, yet the impact of age at treatment start on outcome is poorly understood. While clinical traits such as language ability have been shown to predict treatment outcome, whether or not and how information at the genomic level can predict treatment outcome is unknown. Leveraging a cohort of toddlers with autism who all received the same standardized intervention at a very young age and pr...
#1Vahid H. Gazestani (University of California, Berkeley)H-Index: 1
#2Austin W. T. Chiang (University of California, Berkeley)H-Index: 8
Last. Nathan E. Lewis (University of California, Berkeley)H-Index: 44
view all 4 authors...
Numerous genes are associated with autism spectrum disorder (ASD); however, it remains unclear how most ASD risk genes influence neurodevelopment and result in similar traits. Recent genetic models of complex traits suggest non-tissue-specific genes converge on core disease genes; so we analyzed ASD genetics in this context. We found ASD risk genes partition cleanly into broadly-expressed and brain-specific genes. The two groups show sequential roles during neurodevelopment with broadly-expresse...
2 CitationsSource