Using Collaborative Mixed Models to Account for Imputation Uncertainty in Transcriptome-Wide Association Studies

Xingjie Shi; Can Yang; Jin Liu

doi:10.1007/978-1-0716-0947-7_7

Using Collaborative Mixed Models to Account for Imputation Uncertainty in Transcriptome-Wide Association Studies

Xingjie Shi, Can Yang, Jin Liu^*

^*Corresponding author for this work

Department of Mathematics

Research output: Chapter in Book/Conference Proceeding/Report › Book Chapter › peer-review

1 Citation (Scopus)

Abstract

Transcriptome-wide association studies (TWASs) integrate expression quantitative trait loci (eQTLs) studies with genome-wide association studies (GWASs) to prioritize candidate target genes for complex traits. TWASs have become increasingly popular. They have been used to analyze many complex traits with expression profiles from different tissues, successfully enhancing the discovery of genetic risk loci for complex traits. Though conceptually straightforward, some steps are required to perform the TWAS properly. Here we provide a step-by-step guide to integrate eQTL data with both GWAS individual-level data and GWAS summary statistics from complex traits.

Original language	English
Title of host publication	Methods in Molecular Biology
Publisher	Humana Press Inc.
Pages	93-103
Number of pages	11
DOIs	https://doi.org/10.1007/978-1-0716-0947-7_7
Publication status	Published - 2021

Publication series

Name	Methods in Molecular Biology
Volume	2212
ISSN (Print)	1064-3745
ISSN (Electronic)	1940-6029

Bibliographical note

Publisher Copyright:
© 2021, Springer Science+Business Media, LLC, part of Springer Nature.

Keywords

Associate studies
Collaborative mixed model
Data imputation
TWAS
Transcriptome
Uncertainty

Access to Document

10.1007/978-1-0716-0947-7_7

Cite this

@inbook{36d9caac271f44dcafc34d4a452c0084,

title = "Using Collaborative Mixed Models to Account for Imputation Uncertainty in Transcriptome-Wide Association Studies",

abstract = "Transcriptome-wide association studies (TWASs) integrate expression quantitative trait loci (eQTLs) studies with genome-wide association studies (GWASs) to prioritize candidate target genes for complex traits. TWASs have become increasingly popular. They have been used to analyze many complex traits with expression profiles from different tissues, successfully enhancing the discovery of genetic risk loci for complex traits. Though conceptually straightforward, some steps are required to perform the TWAS properly. Here we provide a step-by-step guide to integrate eQTL data with both GWAS individual-level data and GWAS summary statistics from complex traits.",

keywords = "Associate studies, Collaborative mixed model, Data imputation, TWAS, Transcriptome, Uncertainty",

author = "Xingjie Shi and Can Yang and Jin Liu",

note = "Publisher Copyright: {\textcopyright} 2021, Springer Science+Business Media, LLC, part of Springer Nature.",

year = "2021",

doi = "10.1007/978-1-0716-0947-7\_7",

language = "English",

series = "Methods in Molecular Biology",

publisher = "Humana Press Inc.",

pages = "93--103",

booktitle = "Methods in Molecular Biology",

}

TY - CHAP

T1 - Using Collaborative Mixed Models to Account for Imputation Uncertainty in Transcriptome-Wide Association Studies

AU - Shi, Xingjie

AU - Yang, Can

AU - Liu, Jin

PY - 2021

Y1 - 2021

N2 - Transcriptome-wide association studies (TWASs) integrate expression quantitative trait loci (eQTLs) studies with genome-wide association studies (GWASs) to prioritize candidate target genes for complex traits. TWASs have become increasingly popular. They have been used to analyze many complex traits with expression profiles from different tissues, successfully enhancing the discovery of genetic risk loci for complex traits. Though conceptually straightforward, some steps are required to perform the TWAS properly. Here we provide a step-by-step guide to integrate eQTL data with both GWAS individual-level data and GWAS summary statistics from complex traits.

AB - Transcriptome-wide association studies (TWASs) integrate expression quantitative trait loci (eQTLs) studies with genome-wide association studies (GWASs) to prioritize candidate target genes for complex traits. TWASs have become increasingly popular. They have been used to analyze many complex traits with expression profiles from different tissues, successfully enhancing the discovery of genetic risk loci for complex traits. Though conceptually straightforward, some steps are required to perform the TWAS properly. Here we provide a step-by-step guide to integrate eQTL data with both GWAS individual-level data and GWAS summary statistics from complex traits.

KW - Associate studies

KW - Collaborative mixed model

KW - Data imputation

KW - TWAS

KW - Transcriptome

KW - Uncertainty

UR - https://www.webofscience.com/wos/woscc/full-record/WOS:000707157200008

UR - https://openalex.org/W3138461635

UR - https://www.scopus.com/pages/publications/85102896984

U2 - 10.1007/978-1-0716-0947-7_7

DO - 10.1007/978-1-0716-0947-7_7

M3 - Book Chapter

C2 - 33733352

T3 - Methods in Molecular Biology

SP - 93

EP - 103

BT - Methods in Molecular Biology

PB - Humana Press Inc.

ER -

Using Collaborative Mixed Models to Account for Imputation Uncertainty in Transcriptome-Wide Association Studies

Abstract

Publication series

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this