國立高雄大學圖資館 |

Statistical Methods for Causal Gene Implication in Integrative Genetic Association Analyses /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Statistical Methods for Causal Gene Implication in Integrative Genetic Association Analyses /Jeffrey Okamoto.
作者:	Okamoto, Jeffrey,
面頁冊數:	1 electronic resource (218 pages)
附註:	Source: Dissertations Abstracts International, Volume: 86-03, Section: B.
附註:	Advisors: Wen, Xiaoquan Committee members: Parker, Stephen; Baladandayuthapani, Veerabhadran; Morrison, Jean.
Contained By:	Dissertations Abstracts International86-03B.
標題:	Statistics.
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31631086
ISBN:	9798384042266

Statistical Methods for Causal Gene Implication in Integrative Genetic Association Analyses /
Okamoto, Jeffrey,

Statistical Methods for Causal Gene Implication in Integrative Genetic Association Analyses /Jeffrey Okamoto. - 1 electronic resource (218 pages)

Source: Dissertations Abstracts International, Volume: 86-03, Section: B.

Improvements in sequencing technologies over the past several years have led to numerous successful genome-wide association studies (GWAS) that reveal variant-level genetic associations underlying complex diseases. However, a lack of understanding of the molecular mechanisms through which these variants act remains a significant barrier to understanding disease etiology and the advancement of treatments. Integrative genetic association methods have shown great promise in post-GWAS analyses, in which one of the most challenging tasks is identifying putative causal genes (PCGs) and uncovering molecular mechanisms of complex traits. Recent studies suggest that prevailing computational approaches, including transcriptome-wide association studies (TWAS) and colocalization analysis, are individually imperfect, but their joint usage can yield robust and powerful inference results. In Chapter 2, we present INTACT, a computational framework to integrate probabilistic evidence from these distinct types of analyses and implicate PCGs. This procedure is flexible and can work with a wide range of existing integrative analysis approaches. It has the unique ability to quantify the uncertainty of implicated genes, enabling rigorous control of false-positive discoveries. We further propose an efficient algorithm, INTACT-GSE, for gene set enrichment analysis based on the integrated probabilistic evidence. We illustrate their improved performance over the existing approaches through simulation studies. We apply the proposed methods to analyze the multi-tissue eQTL data from the GTEx project and eight large-scale complex and molecular trait GWAS datasets from multiple consortia and the UK Biobank. Overall, we find that the proposed methods markedly improve the existing PCG implication methods and are particularly advantageous in evaluating and identifying key gene sets and biological pathways underlying complex traits. In Chapter 3, we introduce Multi-INTACT, a statistical framework that integrates GWAS results and data for multiple molecular gene products (e.g. encoded RNA transcript levels and protein levels) to implicate putative causal genes and assess the plausibility that each gene product exerts a direct effect on the complex trait. We employ a Bayesian procedure to compare possible underlying molecular mechanisms using probabilistic evidence. Multi-INTACT is computationally efficient and provides probabilistic uncertainty quantification for causal gene nomination. In simulations, Multi-INTACT maintains proper false discovery rate control and greater power for detecting causal genes than methods that consider only one molecular gene product at a time. Additionally, Multi-INTACT consistently provides quantitative evidence that points towards the true underlying molecular mechanism. Finally, we apply Multi-INTACT to GWAS for 1,408 plasma metabolites measured on 10,188 Finnish men from the METSIM Metabolon metabolite study, integrating the GTEx multi-tissue expression QTL and UK Biobank plasma protein QTL data sets to nominate putative causal genes and mechanisms. We identify many known causal genes underlying metabolite levels and provide insights on each causal gene's mediating gene product(s). In Chapter 4, we propose integration of transcriptome-wide association studies and colocalization-sum of single effects (ISuSiE), a method for causal gene fine-mapping and effect size estimation that efficiently estimates informative gene-to-trait effect priors. Through extensive simulations, we perform a systematic comparison of existing causal gene fine-mapping methods and demonstrate the critical importance of informative prior specification to the performance of the method. Additionally, we display the advantages of ISuSiE over existing approaches in both inference and effect estimation. We apply ISuSiE to the metabolite GWASs and the pQTL data to identify causal genes underlying metabolite levels and to showcase the insights into biological mechanisms uniquely provided by ISuSiE.

English

ISBN: 9798384042266Subjects--Topical Terms:

182057
Statistics.
Subjects--Index Terms:

Multi-omics

Statistical Methods for Causal Gene Implication in Integrative Genetic Association Analyses /
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