Integrating Predicted Transcriptome From Multiple Tissues Improves Association Detection

Alvaro N Barbeira; Milton D Pividori; Jiamao Zheng; Heather E Wheeler; Dan L Nicolae; Hae Kyung Im

doi:10.1101/292649

Integrating Predicted Transcriptome From Multiple Tissues Improves Association Detection

Alvaro N Barbeira
, Milton D Pividori
, Jiamao Zheng
, Heather E Wheeler
, Dan L Nicolae
, Hae Kyung Im

Department of Biology

University of Chicago

Research output: Contribution to journal › Article › peer-review

Original language	American English
Journal	PLOS Genetics
Volume	15
Issue number	1
DOIs	https://doi.org/10.1101/292649 https://doi.org/10.1371/journal.pgen.1007889
State	Published - Jan 22 2019

Disciplines

Biology
Computational Biology
Genetics

Access to Document

10.1101/292649License: Unspecified
10.1371/journal.pgen.1007889License: Unspecified

Cite this

@article{71bfd0015a1d49b4aee78eced095f64e,

title = "Integrating Predicted Transcriptome From Multiple Tissues Improves Association Detection",

author = "Barbeira, \{Alvaro N\} and Pividori, \{Milton D\} and Jiamao Zheng and Wheeler, \{Heather E\} and Nicolae, \{Dan L\} and Im, \{Hae Kyung\}",

note = "Author summary We develop a new method, MultiXcan, to test the mediating role of gene expression variation on complex traits, integrating information available across multiple tissue studies. We show this approach has higher power than traditional single-tissue methods. We extend this method to use only summary-statistics from public GWAS.",

year = "2019",

month = jan,

day = "22",

doi = "10.1101/292649",

language = "American English",

volume = "15",

journal = "PLOS Genetics",

issn = "1553-7404",

number = "1",

}

TY - JOUR

T1 - Integrating Predicted Transcriptome From Multiple Tissues Improves Association Detection

AU - Barbeira, Alvaro N

AU - Pividori, Milton D

AU - Zheng, Jiamao

AU - Wheeler, Heather E

AU - Nicolae, Dan L

AU - Im, Hae Kyung

N1 - Author summary We develop a new method, MultiXcan, to test the mediating role of gene expression variation on complex traits, integrating information available across multiple tissue studies. We show this approach has higher power than traditional single-tissue methods. We extend this method to use only summary-statistics from public GWAS.

PY - 2019/1/22

Y1 - 2019/1/22

UR - http://dx.plos.org/10.1371/journal.pgen.1007889

U2 - 10.1101/292649

DO - 10.1101/292649

M3 - Article

SN - 1553-7404

VL - 15

JO - PLOS Genetics

JF - PLOS Genetics

IS - 1

ER -

Integrating Predicted Transcriptome From Multiple Tissues Improves Association Detection

Disciplines

Access to Document

Other files and links

Cite this