Clinical trial NCT03676673: Deciphering the Autism Spectrum Disorder Beyond Genomics
Official title: Deciphering the Autism Spectrum Disorder Beyond Genomics: AI Learning for Whole Exome Sequencing, Metabolomics and Phenotype
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Autism Spectrum Disorder
Lead sponsor: National Taiwan University Hospital
Sponsor class: OTHER
First posted: 2018-09-19
Enrollment: 200
Sex: ALL
Minimum age (years): 3
Maximum age (years): 20
Standard ages: CHILD
ADULT
Accepts healthy volunteers: no
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.

Brief 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.

Eligibility criteria: Inclusion Criteria:

* a clinical diagnosis of ASD defined by the DSM-5 made by board-certificated child psychiatrists at the first visit and following visits
* ages range from 3 to 20
* at least one biological parent
* parents that are both Taiwanese
* subjects and their biological parents consent to participate in this study for complete phenotype assessments and blood withdraw for this study.

Exclusion Criteria:

* schizophrenia
* schizoaffective disorder
* organic psychosis.
* Probands with fragile X, intellectual disability, epilepsy, ADHD, and autoimmune diseases will be noted.

Primary outcomes:
measure: ASD-associated transcriptome profiles
timeFrame: Baseline
Secondary outcomes:
Other outcomes:
Locations:
city: Taipei
country: Taiwan
facility: National Taiwan Univeristy Hospital
Study officials:
References: