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Leveraging Large-Scale Genetic Data ...

Rao, Abhiram.

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Leveraging Large-Scale Genetic Data for Drug Discovery and Mechanistic Understanding.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Leveraging Large-Scale Genetic Data for Drug Discovery and Mechanistic Understanding./
Author:	Rao, Abhiram.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	193 p.
Notes:	Source: Dissertations Abstracts International, Volume: 82-05, Section: B.
Contained By:	Dissertations Abstracts International82-05B.
Subject:	Bioengineering. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28241801
ISBN:	9798684639678

Leveraging Large-Scale Genetic Data for Drug Discovery and Mechanistic Understanding.
Rao, Abhiram.

Leveraging Large-Scale Genetic Data for Drug Discovery and Mechanistic Understanding. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 193 p.

Source: Dissertations Abstracts International, Volume: 82-05, Section: B.

Thesis (Ph.D.)--Stanford University, 2020.

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

Genotype-phenotype associations, which have been discovered in abundance via genome-wide association studies (GWAS) during the past 15 years, offer a valuable roadmap to elucidate the mechanistic underpinnings of disease. One of the primary objectives of this "post-GWAS era" is to identify causal genes that mediate these associations with the broader aim of developing effective therapies. Large-scale biobank datasets offer the possibility of conducting data experiments with human data in addition to in vitro and in vivo data to identify causal genes and evaluate the long term impact of therapies targeting them. Individuals who harbor genetic variants that alter the function of disease-causing genes form a valuable cohort that can be used to evaluate these long term effects. In this thesis, I demonstrate the utility of genetic evidence to predict therapeutic effects for metabolic diseases and liver diseases with drugs currently in clinical trials. I present methodological advances to evaluate genetic evidence, and present results that were subsequently confirmed in clinical trials. In addition, I identify the causal gene at a GWAS locus for metabolic disease and delineate its mechanism of action using multi-omic data from humans and in vitro/in vivo knockout models. Identifying the causal gene is quite challenging in such cases, where associations occur in non-coding regions and phenotypes are complex combinations of biological effects in different tissues. I demonstrate a tissue specific mechanism of action for variants in the locus that confer carriers with a predisposition to a complex normal-weight "metabolically obese" phenotype. I present lessons learned from integrating multi-omic data to discover causal genes and develop a method for conducting these data integration studies.

ISBN: 9798684639678Subjects--Topical Terms:

657580
Bioengineering.
Subjects--Index Terms:

Genotype-phenotype associations

Leveraging Large-Scale Genetic Data for Drug Discovery and Mechanistic Understanding.
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500 $a Advisor: Ingelsson, Erik;Montgomery, Stephen;Altman, Russ;Fordyce, Polly.
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520 $a Genotype-phenotype associations, which have been discovered in abundance via genome-wide association studies (GWAS) during the past 15 years, offer a valuable roadmap to elucidate the mechanistic underpinnings of disease. One of the primary objectives of this "post-GWAS era" is to identify causal genes that mediate these associations with the broader aim of developing effective therapies. Large-scale biobank datasets offer the possibility of conducting data experiments with human data in addition to in vitro and in vivo data to identify causal genes and evaluate the long term impact of therapies targeting them. Individuals who harbor genetic variants that alter the function of disease-causing genes form a valuable cohort that can be used to evaluate these long term effects. In this thesis, I demonstrate the utility of genetic evidence to predict therapeutic effects for metabolic diseases and liver diseases with drugs currently in clinical trials. I present methodological advances to evaluate genetic evidence, and present results that were subsequently confirmed in clinical trials. In addition, I identify the causal gene at a GWAS locus for metabolic disease and delineate its mechanism of action using multi-omic data from humans and in vitro/in vivo knockout models. Identifying the causal gene is quite challenging in such cases, where associations occur in non-coding regions and phenotypes are complex combinations of biological effects in different tissues. I demonstrate a tissue specific mechanism of action for variants in the locus that confer carriers with a predisposition to a complex normal-weight "metabolically obese" phenotype. I present lessons learned from integrating multi-omic data to discover causal genes and develop a method for conducting these data integration studies.
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