@inbook{09e0468a45134c37bf2bbe499ead2b0e,
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 = "英语",
series = "Methods in Molecular Biology",
publisher = "Humana Press Inc.",
pages = "93--103",
booktitle = "Methods in Molecular Biology",
}