Background: Autism spectrum disorder (ASD) is a complex neurodevelopmental condition
that poses several challenges in terms of clinical diagnosis and investigation of molecular etiology.
The lack of knowledge on the pathogenic mechanisms underlying ASD has hampered the clinical
trials that so far have tried to target ASD behavioral symptoms. In order to improve our understanding
of the molecular abnormalities associated with ASD, a deeper and more extensive genetic
profiling of targeted individuals with ASD was needed.
Methods: The recent availability of new and more powerful sequencing technologies (third-generation
sequencing) has allowed to develop novel strategies for the characterization of comprehensive
genetic profiles of individuals with ASD. In particular, this review will describe integrated approaches
based on the combination of various omics technologies that will lead to a better stratification
of targeted cohorts for the design of clinical trials in ASD.
Results: In order to analyze the big data collected by assays such as the whole genome, epigenome,
transcriptome, and proteome, it is critical to develop an efficient computational infrastructure. Machine
learning models are instrumental to identify non-linear relationships between the omics technologies
and, therefore, establish a functional informative network among the different data
Conclusion: The potential advantage provided by these new integrated omics-based strategies is
better characterization of the genetic background of ASD cohorts, to identify novel molecular targets
for drug development, and ultimately offer a more personalized approach in the design of clinical
trials for ASD.