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Non-Coding Variants in the Genomic Architecture of Autism and Intellectual Impairment /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Non-Coding Variants in the Genomic Architecture of Autism and Intellectual Impairment // Eric Alexander Sosa.
作者:	Sosa, Eric Alexander,
面頁冊數:	1 electronic resource (229 pages)
附註:	Source: Dissertations Abstracts International, Volume: 86-02, Section: A.
Contained By:	Dissertations Abstracts International86-02A.
標題:	Genetics. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31486140
ISBN:	9798383624296

Non-Coding Variants in the Genomic Architecture of Autism and Intellectual Impairment /
Sosa, Eric Alexander,

Non-Coding Variants in the Genomic Architecture of Autism and Intellectual Impairment /Eric Alexander Sosa. - 1 electronic resource (229 pages)

Source: Dissertations Abstracts International, Volume: 86-02, Section: A.

Non-coding variants (NCVs) represent a distinctive and understudied type of genetic variation beyond alterations in the coding regions of our DNA and an increasingly valuable area of research. The non-coding genome is significantly larger than its protein-coding counterpart and requires strategical analyses for variant prioritization. The large number of NCVs and limited knowledge of regulatory elements challenges our ability to prioritize and characterize pathogenic variants from background noise. This thesis explores the intricate interplay between de novo variants (DNVs) and chromatin through a computational genomics framework, enriching our understanding of NCVs in autism -a trait for which limited diagnostic success rates stem in part from our inability to interpret non-coding variation. This thesis reviews the parameters that best predict the contribution(s) of NCVs, introduces a toolkit for the analysis of variant enrichment across cell-specific chromatin architecture, and establishes a link between non-coding variants and intellectual impairment in autism. The timing of this thesis is critical given the evolving feasibility of whole genome sequencing (WGS) technologies for unresolved diseases in the clinical setting.Chapter I provides an overview to understand DNA sequence variation at non-coding regulatory elements and reviews the range of mechanisms by which they alter gene expression and contribute to rare disease. It highlights specific case studies that elucidate functionality in non-coding regions and discusses their interpretation for clinical application through computational methods. This chapter reviews the role of non-coding variants in autism development, the current challenges in predicting their functional consequences, and considerations for strategical prioritization.Chapter II discusses the need for computational toolkits that integrate context-specific chromatin data to identify candidate regulatory variants and introduces Regulatory Landscape Enrichment Analysis (RLEA) by GaiaAssociation, a publicly available, comprehensive software for the efficient analysis of variant enrichment across cell-specific chromatin architecture. The strength of RLEA stems from its unique consideration of the local chromatin environment at specific genomic loci, allowing for the analysis of under-explored variants. The incorporation of cell type regulatory landscape analysis is pivotal to realizing the clinical utility in WGS from non-coding variants.Chapter III integrates multiple functional datasets to assess the functionality of ≈709,200 non-coding de novo variants from 5,515 individuals with autism and 3,556 neurotypical individuals. The chapter explores the enrichment of DNVs at loci destined to be cis-regulatory, driven by C>T transitions in CG dinucleotide contexts, with the preferential occurrence of DNVs in cis-regulatory loci of brain cells near implicated autism genes, and provides an updated category-wide association studies (CWAS) approach that highlights glutamatergic neurons and cells expressed at the mid-fetal stage of development as mediative cell types in intellectual impairment. These data suggest that de novo events in regulatory regions contribute to the genomic architecture of autism and highlight the potential of this approach for understanding non-coding risks across other genomic traits.The Conclusions section discusses the advantages and limitations of these studies in the interpretation of non-coding variants. It explores how these findings may influence the clinical interpretation of non-coding events and serve as a practical tool for clinical application and prediction in the future. The data provided here offer a valuable insight for variant characterization and will facilitate the identification of novel associations between regulatory elements and non-coding variants.

English

ISBN: 9798383624296Subjects--Topical Terms:

530508
Genetics.
Subjects--Index Terms:

Autism

Non-Coding Variants in the Genomic Architecture of Autism and Intellectual Impairment /
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520 $a Non-coding variants (NCVs) represent a distinctive and understudied type of genetic variation beyond alterations in the coding regions of our DNA and an increasingly valuable area of research. The non-coding genome is significantly larger than its protein-coding counterpart and requires strategical analyses for variant prioritization. The large number of NCVs and limited knowledge of regulatory elements challenges our ability to prioritize and characterize pathogenic variants from background noise. This thesis explores the intricate interplay between de novo variants (DNVs) and chromatin through a computational genomics framework, enriching our understanding of NCVs in autism -a trait for which limited diagnostic success rates stem in part from our inability to interpret non-coding variation. This thesis reviews the parameters that best predict the contribution(s) of NCVs, introduces a toolkit for the analysis of variant enrichment across cell-specific chromatin architecture, and establishes a link between non-coding variants and intellectual impairment in autism. The timing of this thesis is critical given the evolving feasibility of whole genome sequencing (WGS) technologies for unresolved diseases in the clinical setting.Chapter I provides an overview to understand DNA sequence variation at non-coding regulatory elements and reviews the range of mechanisms by which they alter gene expression and contribute to rare disease. It highlights specific case studies that elucidate functionality in non-coding regions and discusses their interpretation for clinical application through computational methods. This chapter reviews the role of non-coding variants in autism development, the current challenges in predicting their functional consequences, and considerations for strategical prioritization.Chapter II discusses the need for computational toolkits that integrate context-specific chromatin data to identify candidate regulatory variants and introduces Regulatory Landscape Enrichment Analysis (RLEA) by GaiaAssociation, a publicly available, comprehensive software for the efficient analysis of variant enrichment across cell-specific chromatin architecture. The strength of RLEA stems from its unique consideration of the local chromatin environment at specific genomic loci, allowing for the analysis of under-explored variants. The incorporation of cell type regulatory landscape analysis is pivotal to realizing the clinical utility in WGS from non-coding variants.Chapter III integrates multiple functional datasets to assess the functionality of ≈709,200 non-coding de novo variants from 5,515 individuals with autism and 3,556 neurotypical individuals. The chapter explores the enrichment of DNVs at loci destined to be cis-regulatory, driven by C>T transitions in CG dinucleotide contexts, with the preferential occurrence of DNVs in cis-regulatory loci of brain cells near implicated autism genes, and provides an updated category-wide association studies (CWAS) approach that highlights glutamatergic neurons and cells expressed at the mid-fetal stage of development as mediative cell types in intellectual impairment. These data suggest that de novo events in regulatory regions contribute to the genomic architecture of autism and highlight the potential of this approach for understanding non-coding risks across other genomic traits.The Conclusions section discusses the advantages and limitations of these studies in the interpretation of non-coding variants. It explores how these findings may influence the clinical interpretation of non-coding events and serve as a practical tool for clinical application and prediction in the future. The data provided here offer a valuable insight for variant characterization and will facilitate the identification of novel associations between regulatory elements and non-coding variants.
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