Deciphering the Autism Spectrum Disorder Beyond Genomics

Autism Spectrum Disorder

The investigators propose to study the molecular etiology of autism spectrum disorder(ASD) from a genomic, metabolomics and network biology perspective by combining data of gene expression, sequence variations and metabolism conditions of patients with ASD. As the complexity of ASD, the investigators consider both science-based and clinic-based measurements to ensure no missing of any relevant domain of the complex relations. In addition to the collection of biological factors, the investigators will also collect the comprehensive clinical, environmental, neurocognitive, MRI images to integrate the multiple factors into the matrix features. Finally the investigators will apply the machine learning to provide us the aspects of the underline pathway back into the other sample distribution published as the open dataset to verify and adjust the features in order to achieve satisfactory level of the reliability and stability of the algorithms. With Next Generation Sequencing (NGS) technology, the investigators will sequence the whole exome sequencing (WES) (MiSeq System) of approximately 120 ASD probands, 40 unaffecting siblings and 40 healthy controls of Taiwanese Han population to identify ASD-associated transcriptome profiles. The results will be using real-time PCR (qPCR) or conventional Sanger sequencing to verified. The investigators will use both liquid chromatography/time-of-flight mass spectrometry (LC-MS) and gas chromatography/quadrupole mass spectrometry (GC-MS) for a full assessment of a wide range of metabolites with over 820 metabolites. Hence, this 3-year proposal consists two main parts - the ASD transcriptome sequence analysis by NGS technology and the metabolomics study of ASD via LC-MS and GC-MS technology.

Primary Aim: To establish a stable and reliable neurogenesis molecular level pathways and potential pathogenesis mechanisms for ASD by using the machine learning approach of the integrated data of biological variables (NGS data and metabolomics) and the comprehensive clinical, environmental, neurocognitive, and MRI images data. 1. To investigate the majority of candidate risk factors from the multiple domains collected in this project; 2. To apply network-based algorithms (including deep learning) to approach the underlining pathogenesis mechanism of ASD; 3. To further verify the machine learning algorithm based on the data collected in this project through other open access database for stability and reliability of our algorithm. Secondary Aims: Aim I: To identify the ASD biomarkers and disease mechanism using NGS technology. 1. To investigate the transcriptome profiles occurring in ASD patients; 2. To identify ASD-associated exome sequence variations from a network biology perspective; 3. To identify ASD-associated gene-gene interaction sub-networks; and 4. To explore how the sequencing outcomes, regulate and interact with brain structure and function even linking to neuropsychological functions and behavioral phenotypes. Aim II: To characterize ASD-affected metabolites. 1. By using LC-MS and GC-MS, we will perform metabolomics analysis, including targeted and untargeted analysis; 2. To identify the potential metabolomics profiles and pathways related to behavioral phenotypes, neuropsychological functions, neuroanatomy and brain functions in patients with ASD; and 3. To identify how the metabolites variance distributions are manipulated through the genetic expressions.

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