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PTWAS: investigating tissue-relevant causal molecular mechanisms of complex traits using probabilistic TWAS analysis

We propose a new computational framework, probabilistic transcriptome-wide association study (PTWAS), to investigate causal relationships between gene expressions and complex traits. PTWAS applies the established principles from instrumental variables analysis and takes advantage of probabilistic eQ...

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Autores principales: Zhang, Yuhua, Quick, Corbin, Yu, Ketian, Barbeira, Alvaro, Luca, Francesca, Pique-Regi, Roger, Kyung Im, Hae, Wen, Xiaoquan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7488550/
https://www.ncbi.nlm.nih.gov/pubmed/32912253
http://dx.doi.org/10.1186/s13059-020-02026-y
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author Zhang, Yuhua
Quick, Corbin
Yu, Ketian
Barbeira, Alvaro
Luca, Francesca
Pique-Regi, Roger
Kyung Im, Hae
Wen, Xiaoquan
author_facet Zhang, Yuhua
Quick, Corbin
Yu, Ketian
Barbeira, Alvaro
Luca, Francesca
Pique-Regi, Roger
Kyung Im, Hae
Wen, Xiaoquan
author_sort Zhang, Yuhua
collection PubMed
description We propose a new computational framework, probabilistic transcriptome-wide association study (PTWAS), to investigate causal relationships between gene expressions and complex traits. PTWAS applies the established principles from instrumental variables analysis and takes advantage of probabilistic eQTL annotations to delineate and tackle the unique challenges arising in TWAS. PTWAS not only confers higher power than the existing methods but also provides novel functionalities to evaluate the causal assumptions and estimate tissue- or cell-type-specific gene-to-trait effects. We illustrate the power of PTWAS by analyzing the eQTL data across 49 tissues from GTEx (v8) and GWAS summary statistics from 114 complex traits.
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spelling pubmed-74885502020-09-16 PTWAS: investigating tissue-relevant causal molecular mechanisms of complex traits using probabilistic TWAS analysis Zhang, Yuhua Quick, Corbin Yu, Ketian Barbeira, Alvaro Luca, Francesca Pique-Regi, Roger Kyung Im, Hae Wen, Xiaoquan Genome Biol Method We propose a new computational framework, probabilistic transcriptome-wide association study (PTWAS), to investigate causal relationships between gene expressions and complex traits. PTWAS applies the established principles from instrumental variables analysis and takes advantage of probabilistic eQTL annotations to delineate and tackle the unique challenges arising in TWAS. PTWAS not only confers higher power than the existing methods but also provides novel functionalities to evaluate the causal assumptions and estimate tissue- or cell-type-specific gene-to-trait effects. We illustrate the power of PTWAS by analyzing the eQTL data across 49 tissues from GTEx (v8) and GWAS summary statistics from 114 complex traits. BioMed Central 2020-09-11 /pmc/articles/PMC7488550/ /pubmed/32912253 http://dx.doi.org/10.1186/s13059-020-02026-y Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Method
Zhang, Yuhua
Quick, Corbin
Yu, Ketian
Barbeira, Alvaro
Luca, Francesca
Pique-Regi, Roger
Kyung Im, Hae
Wen, Xiaoquan
PTWAS: investigating tissue-relevant causal molecular mechanisms of complex traits using probabilistic TWAS analysis
title PTWAS: investigating tissue-relevant causal molecular mechanisms of complex traits using probabilistic TWAS analysis
title_full PTWAS: investigating tissue-relevant causal molecular mechanisms of complex traits using probabilistic TWAS analysis
title_fullStr PTWAS: investigating tissue-relevant causal molecular mechanisms of complex traits using probabilistic TWAS analysis
title_full_unstemmed PTWAS: investigating tissue-relevant causal molecular mechanisms of complex traits using probabilistic TWAS analysis
title_short PTWAS: investigating tissue-relevant causal molecular mechanisms of complex traits using probabilistic TWAS analysis
title_sort ptwas: investigating tissue-relevant causal molecular mechanisms of complex traits using probabilistic twas analysis
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7488550/
https://www.ncbi.nlm.nih.gov/pubmed/32912253
http://dx.doi.org/10.1186/s13059-020-02026-y
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