Deciphering the Autism Spectrum Disorder Beyond Genomics
Condition: Autism Spectrum Disorder
MeSH terms: Autistic Disorder; Autism Spectrum Disorder; Child Development Disorders, Pervasive
Study type: Observational

Summary: 
      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.
    

Detailed description: 
      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.
    

Intervention name(s): Psychiatric diagnosis
Intervention type(s): Other
Intervention description(s): Kiddie Schedule for Affective Disorders & Schizophrenia (K-SADS) for DSM-5
Observational model: Case-Control
Time perspective: Cross-Sectional
Sampling method: Non-Probability Sample
Study population: 
        This study will recruit (1) 120 patients with clinical diagnosis of ASD according to the
        DSM-5 diagnostic criteria for the transcriptome NGS and the metabolites profiles from the
        Children's Mental Health Center at National Taiwan University Hospital (NTUH); (2) 40
        unaffected siblings (SIB) of ASD probands; and (3) 40 healthy age/gender-matched TD
        controls according to age and neighborhood distribution of the ASD group after interviewed
        by the Chinese K-SADS-E-DSM-5.
      
Biospecimen description: 
      20 mL of peripheral blood will be drawn for DNA and cell line preparation.