東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

FindBook

Google Book

Amazon

博客來

Statistical Strategies and Resources for Deciphering Mechanisms of Diabetes Risk Loci.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Statistical Strategies and Resources for Deciphering Mechanisms of Diabetes Risk Loci./
作者:	Aylward, Anthony.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	158 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-04, Section: B.
Contained By:	Dissertations Abstracts International83-04B.
標題:	Bioinformatics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28647029
ISBN:	9798460432653

Statistical Strategies and Resources for Deciphering Mechanisms of Diabetes Risk Loci.
Aylward, Anthony.

Statistical Strategies and Resources for Deciphering Mechanisms of Diabetes Risk Loci. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 158 p.

Source: Dissertations Abstracts International, Volume: 83-04, Section: B.

Thesis (Ph.D.)--University of California, San Diego, 2021.

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

The two most common forms of diabetes are type 1 diabetes (T1D) which is an autoimmune disorder and type 2 diabetes (T2D) which is a metabolic disorder. Large-scale genome-wide association studies (GWAS) have identified hundreds of loci associated with disease risk, but determining the molecular mechanisms of these loci is not trivial. One major challenge in interpreting GWAS loci is that there is extensive linkage disequilibrium in the human genome where many variants will show association through linkage with the causal variant but not be causal themselves. A second challenge is that most GWAS loci map to non-coding regions of the genome and have no immediately obvious function or affected gene. Together these challenges motivate research to fine-map causal variants at diabetes risk loci and leverage epigenomic and functional genomic data to determine the mechanisms of fine-mapped variants.In my work I developed strategies and created resources for fine-mapping diabetes risk signals identified in GWAS and determining their molecular mechanisms. In the first chapter, we identify genetic risk shared by T1D and T2D. We then fine-map causal variants at specific shared loci and perform molecular characterization of candidate causal variants at the shared risk loci GLIS3 and CTRB1/2 in pancreatic islets. In the second chapter, we use ATAC-seq and RNA-seq on dexamethasone-treated and untreated pancreatic islets to generate a map of glucocorticoid- responsive islet chromatin sites and gene expression, as well as genetic variants that interact with glucocorticoid signaling to affect islet regulation. We identify enrichment of T2D-associated variants in glucocorticoid-responsive islet chromatin and characterize a fine-mapped T2D risk variant with glucocorticoid-dependent effects on islet accessible chromatin and SIX2/3 expression. Finally, in the third chapter we develop a novel framework for allelic imbalance mapping using ChIP-seq and ATAC-seq data. We quantify the allelic effects of variants on epigenomic sequencing data in islets and liver cells and demonstrate that these effects can help predict likely causal variants for expression QTLs and T2D risk loci. At the HMG20A locus, we identify a fine-mapped T2D risk variant with allelic imbalance in pancreatic islet accessible chromatin and validate allelic effects on pancreatic islets.

ISBN: 9798460432653Subjects--Topical Terms:

553671
Bioinformatics.
Subjects--Index Terms:

Chromatin

Statistical Strategies and Resources for Deciphering Mechanisms of Diabetes Risk Loci.
LDR:03703nmm a2200433 4500 001 2346235
005 20220620110452.5
008 241004s2021 ||||||||||||||||| ||eng d
020 $a 9798460432653
035 $a (MiAaPQ)AAI28647029
035 $a AAI28647029
040 $a MiAaPQ $c MiAaPQ
100 1 $a Aylward, Anthony. $3 3685304
245 1 0 $a Statistical Strategies and Resources for Deciphering Mechanisms of Diabetes Risk Loci.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 158 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-04, Section: B.
500 $a Advisor: Gaulton, Kyle J.;Sears, Dorothy D.
502 $a Thesis (Ph.D.)--University of California, San Diego, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a The two most common forms of diabetes are type 1 diabetes (T1D) which is an autoimmune disorder and type 2 diabetes (T2D) which is a metabolic disorder. Large-scale genome-wide association studies (GWAS) have identified hundreds of loci associated with disease risk, but determining the molecular mechanisms of these loci is not trivial. One major challenge in interpreting GWAS loci is that there is extensive linkage disequilibrium in the human genome where many variants will show association through linkage with the causal variant but not be causal themselves. A second challenge is that most GWAS loci map to non-coding regions of the genome and have no immediately obvious function or affected gene. Together these challenges motivate research to fine-map causal variants at diabetes risk loci and leverage epigenomic and functional genomic data to determine the mechanisms of fine-mapped variants.In my work I developed strategies and created resources for fine-mapping diabetes risk signals identified in GWAS and determining their molecular mechanisms. In the first chapter, we identify genetic risk shared by T1D and T2D. We then fine-map causal variants at specific shared loci and perform molecular characterization of candidate causal variants at the shared risk loci GLIS3 and CTRB1/2 in pancreatic islets. In the second chapter, we use ATAC-seq and RNA-seq on dexamethasone-treated and untreated pancreatic islets to generate a map of glucocorticoid- responsive islet chromatin sites and gene expression, as well as genetic variants that interact with glucocorticoid signaling to affect islet regulation. We identify enrichment of T2D-associated variants in glucocorticoid-responsive islet chromatin and characterize a fine-mapped T2D risk variant with glucocorticoid-dependent effects on islet accessible chromatin and SIX2/3 expression. Finally, in the third chapter we develop a novel framework for allelic imbalance mapping using ChIP-seq and ATAC-seq data. We quantify the allelic effects of variants on epigenomic sequencing data in islets and liver cells and demonstrate that these effects can help predict likely causal variants for expression QTLs and T2D risk loci. At the HMG20A locus, we identify a fine-mapped T2D risk variant with allelic imbalance in pancreatic islet accessible chromatin and validate allelic effects on pancreatic islets.
590 $a School code: 0033.
650 4 $a Bioinformatics. $3 553671
650 4 $a Biostatistics. $3 1002712
650 4 $a Molecular biology. $3 517296
650 4 $a Biochemistry. $3 518028
650 4 $a Endocrinology. $3 610914
650 4 $a Genetics. $3 530508
653 $a Chromatin
653 $a Dexamethasone
653 $a Diabetes
653 $a Epigenetics
653 $a Fine-mapping diabetes risk signals
653 $a Molecular mechanisms
653 $a Allelic imbalance
690 $a 0715
690 $a 0487
690 $a 0369
690 $a 0409
690 $a 0307
690 $a 0308
710 2 $a University of California, San Diego. $b Bioinformatics and Systems Biology. $3 3285993
773 0 $t Dissertations Abstracts International $g 83-04B.
790 $a 0033
791 $a Ph.D.
792 $a 2021
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28647029