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Integrative analysis of multi-omics ...

Hu, Yiming.

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Integrative analysis of multi-omics data improves genetic risk prediction and transcriptome-wide association analysis.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Integrative analysis of multi-omics data improves genetic risk prediction and transcriptome-wide association analysis./
Author:	Hu, Yiming.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
Description:	121 p.
Notes:	Source: Dissertations Abstracts International, Volume: 80-02, Section: B.
Contained By:	Dissertations Abstracts International80-02B.
Subject:	Biostatistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10927801
ISBN:	9780438193710

Integrative analysis of multi-omics data improves genetic risk prediction and transcriptome-wide association analysis.
Hu, Yiming.

Integrative analysis of multi-omics data improves genetic risk prediction and transcriptome-wide association analysis. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 121 p.

Source: Dissertations Abstracts International, Volume: 80-02, Section: B.

Thesis (Ph.D.)--Yale University, 2018.

This item is not available from ProQuest Dissertations & Theses.

Accurate prediction of disease risk based on genetic factors is an important goal in human genetics research and precision medicine. Advanced prediction models will lead to more effective disease prevention and early treatment strategies. Despite the identification of thousands of disease-associated genetic variants through GWAS in the past decade, accuracy of genetic risk prediction remains moderate for most diseases, which is largely due to the challenges in both identifying all the functionally relevant variants and accurately estimating their effect sizes. Here, we first present a statistical framework to predict disease risk through integration of GWAS summary statistics and diverse types of data, including functional annotations and pleiotropy. Through comprehensive simulations and real data analyses of multiple complex diseases, we demonstrate that our approach can substantially increase the accuracy of polygenic risk prediction and risk population stratification, for example, our approach can significantly better separate type-II diabetes patients with early and late onset ages, illustrating its potential clinical application. We then extended the idea to jointly impute gene expression in 44 human tissues and proposed a cross-tissue transcriptome-wide association analysis framework. Compared with single-tissue methods, our approach achieved an average 39% improvement in imputation accuracy and generated effective imputation models for an average 120% (range 13%-339%) more genes in each tissue. When applying our method to analyze GWAS results for 50 complex traits (Ntotal=4.5 million), we were able to identify considerably more genes in tissues enriched for trait heritability, and cross-tissue analysis significantly outperformed single-tissue strategies (p=1.7e-8). Finally, we performed a cross-tissue genome-wide association study for late-onset Alzheimer's disease (LOAD) and replicated our findings in two independent datasets (N total=175,776). In total, we identified 69 significant genes, many of which are novel, leading to novel insights on LOAD etiologies.

ISBN: 9780438193710Subjects--Topical Terms:

1002712
Biostatistics.

Integrative analysis of multi-omics data improves genetic risk prediction and transcriptome-wide association analysis.
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520 $a Accurate prediction of disease risk based on genetic factors is an important goal in human genetics research and precision medicine. Advanced prediction models will lead to more effective disease prevention and early treatment strategies. Despite the identification of thousands of disease-associated genetic variants through GWAS in the past decade, accuracy of genetic risk prediction remains moderate for most diseases, which is largely due to the challenges in both identifying all the functionally relevant variants and accurately estimating their effect sizes. Here, we first present a statistical framework to predict disease risk through integration of GWAS summary statistics and diverse types of data, including functional annotations and pleiotropy. Through comprehensive simulations and real data analyses of multiple complex diseases, we demonstrate that our approach can substantially increase the accuracy of polygenic risk prediction and risk population stratification, for example, our approach can significantly better separate type-II diabetes patients with early and late onset ages, illustrating its potential clinical application. We then extended the idea to jointly impute gene expression in 44 human tissues and proposed a cross-tissue transcriptome-wide association analysis framework. Compared with single-tissue methods, our approach achieved an average 39% improvement in imputation accuracy and generated effective imputation models for an average 120% (range 13%-339%) more genes in each tissue. When applying our method to analyze GWAS results for 50 complex traits (Ntotal=4.5 million), we were able to identify considerably more genes in tissues enriched for trait heritability, and cross-tissue analysis significantly outperformed single-tissue strategies (p=1.7e-8). Finally, we performed a cross-tissue genome-wide association study for late-onset Alzheimer's disease (LOAD) and replicated our findings in two independent datasets (N total=175,776). In total, we identified 69 significant genes, many of which are novel, leading to novel insights on LOAD etiologies.
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