Ignite Creation Date:
2024-05-06 @ 12:06 PM
Last Modification Date:
2024-10-26 @ 12:54 PM
Study NCT ID:
NCT03676673
Status:
COMPLETED
Last Update Posted:
2022-09-26
First Post:
2018-09-17
Brief Title:
Deciphering the Autism Spectrum Disorder Beyond Genomics
Sponsor:
National Taiwan University Hospital
Organization:
National Taiwan University Hospital
Study Overview
Official Title:
Deciphering the Autism Spectrum Disorder Beyond Genomics AI Learning for Whole Exome Sequencing Metabolomics and Phenotype
Status:
COMPLETED
Status Verified Date:
2022-09
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
The investigators propose to study the molecular etiology of autism spectrum disorderASD 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 chromatographytime-of-flight mass spectrometry LC-MS and gas chromatographyquadrupole 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
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
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None