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Identifying Human GWAS Effector Genes Using Model Organism Data.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Identifying Human GWAS Effector Genes Using Model Organism Data./
作者:	Dong, Chenyang.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
面頁冊數:	246 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-12, Section: B.
Contained By:	Dissertations Abstracts International83-12B.
標題:	Bioinformatics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29215028
ISBN:	9798802751152

Identifying Human GWAS Effector Genes Using Model Organism Data.
Dong, Chenyang.

Identifying Human GWAS Effector Genes Using Model Organism Data. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 246 p.

Source: Dissertations Abstracts International, Volume: 83-12, Section: B.

Thesis (Ph.D.)--The University of Wisconsin - Madison, 2022.

This item must not be sold to any third party vendors.

Genome-wide association studies (GWAS) have revealed many non-coding single nucleotide variants that are statistically associated with complex traits and diseases. However, model organism studies have largely remained an untapped resource for unveiling the effector genes of non-coding variants. A recent well-powered expression quantitative locus (eQTL) study in islets from Diversity Outbred (DO) mice identified thousands of eQTLs; however, it lacked the resolution to pinpoint causal single nucleotide variants and the regulatory mechanisms responsible for the wide range in susceptibility to diabetes due to high linkage disequilibrium. To address this bottleneck and leverage eQTLs derived from DO mice for unraveling effector genes of human GWAS variants, we propose a statistical data integration model, INFIMA for Integrative Fine-Mapping with Model Organism Multi-Omics Data. INFIMA capitalizes on multi-omics data modalities such as chromatin accessibility and transcriptome from the eight DO mice founder strains to fine-map DO islet eQTLs. In addition, INFIMA employs footprinting and in silico mutation analysis to reveal regulatory genetic variants that mediate strain-specific expression differences. We applied INFIMA to identify novel effector genes for GWAS variants associated with diabetes. Our results demonstrate INFIMA's superior performance compared to alternatives with human and mouse high-resolution chromatin conformation capture datasets.Elucidating orthologous regulatory regions for human and model organism genomes is critical for exploiting model organism research and advancing our understanding of GWAS results. Sequence conservation is the de facto approach for finding orthologous non-coding regions between human and model organism genomes. However, existing methods for mapping non-coding genomic regions across species are challenged by the multi-mapping, low precision, and low mapping rate issues. We develop Adaptive liftOver (AdaLiftOver), a large-scale computational tool for identifying orthologous non-coding regions across species. AdaLiftOver builds on the UCSC liftOver framework to extend the query regions and prioritizes the resulting candidate target regions based on the conservation of the epigenomic signals and the sequence grammar. Evaluations of AdaLiftOver with multiple case studies, spanning both genomic intervals from epigenome datasets and GWAS SNPs yield AdaLiftOver as a robust and accurate method for deriving hard-to-obtain human epigenome datasets as well as reliably identifying orthologous loci for GWAS SNPs.

ISBN: 9798802751152Subjects--Topical Terms:

553671
Bioinformatics.
Subjects--Index Terms:

Diversity outbred mice

Identifying Human GWAS Effector Genes Using Model Organism Data.
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500 $a Source: Dissertations Abstracts International, Volume: 83-12, Section: B.
500 $a Advisor: Keles, Sunduz .
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506 $a This item must not be sold to any third party vendors.
520 $a Genome-wide association studies (GWAS) have revealed many non-coding single nucleotide variants that are statistically associated with complex traits and diseases. However, model organism studies have largely remained an untapped resource for unveiling the effector genes of non-coding variants. A recent well-powered expression quantitative locus (eQTL) study in islets from Diversity Outbred (DO) mice identified thousands of eQTLs; however, it lacked the resolution to pinpoint causal single nucleotide variants and the regulatory mechanisms responsible for the wide range in susceptibility to diabetes due to high linkage disequilibrium. To address this bottleneck and leverage eQTLs derived from DO mice for unraveling effector genes of human GWAS variants, we propose a statistical data integration model, INFIMA for Integrative Fine-Mapping with Model Organism Multi-Omics Data. INFIMA capitalizes on multi-omics data modalities such as chromatin accessibility and transcriptome from the eight DO mice founder strains to fine-map DO islet eQTLs. In addition, INFIMA employs footprinting and in silico mutation analysis to reveal regulatory genetic variants that mediate strain-specific expression differences. We applied INFIMA to identify novel effector genes for GWAS variants associated with diabetes. Our results demonstrate INFIMA's superior performance compared to alternatives with human and mouse high-resolution chromatin conformation capture datasets.Elucidating orthologous regulatory regions for human and model organism genomes is critical for exploiting model organism research and advancing our understanding of GWAS results. Sequence conservation is the de facto approach for finding orthologous non-coding regions between human and model organism genomes. However, existing methods for mapping non-coding genomic regions across species are challenged by the multi-mapping, low precision, and low mapping rate issues. We develop Adaptive liftOver (AdaLiftOver), a large-scale computational tool for identifying orthologous non-coding regions across species. AdaLiftOver builds on the UCSC liftOver framework to extend the query regions and prioritizes the resulting candidate target regions based on the conservation of the epigenomic signals and the sequence grammar. Evaluations of AdaLiftOver with multiple case studies, spanning both genomic intervals from epigenome datasets and GWAS SNPs yield AdaLiftOver as a robust and accurate method for deriving hard-to-obtain human epigenome datasets as well as reliably identifying orthologous loci for GWAS SNPs.
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653 $a Model organism
653 $a Multi-omics data
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