Multi-trait analysis of genome-wide association summary statistics using MTAG

Turley, Patrick, Walters, Raymond K, Maghzian, Omeed, Okbay, Aysu, Lee, James J, Fontana, Mark Alan, Nguyen-Viet, Tuan Anh, Wedow, Robbee, Zacher, Meghan, Furlotte, Nicholas A, Magnusson, Patrik, Oskarsson, Sven, Johannesson, Magnus, Visscher, Peter M, Laibson, David, Cesarini, David, Neale, Benjamin M, Benjamin, Daniel J, 23andMe Research Team and Social Science Genetic Association Consortium (2018) Multi-trait analysis of genome-wide association summary statistics using MTAG. Nature genetics, . doi:10.1038/s41588-017-0009-4

Author Turley, Patrick
Walters, Raymond K
Maghzian, Omeed
Okbay, Aysu
Lee, James J
Fontana, Mark Alan
Nguyen-Viet, Tuan Anh
Wedow, Robbee
Zacher, Meghan
Furlotte, Nicholas A
Magnusson, Patrik
Oskarsson, Sven
Johannesson, Magnus
Visscher, Peter M
Laibson, David
Cesarini, David
Neale, Benjamin M
Benjamin, Daniel J
23andMe Research Team
Social Science Genetic Association Consortium
Title Multi-trait analysis of genome-wide association summary statistics using MTAG
Journal name Nature genetics   Check publisher's open access policy
ISSN 1546-1718
Publication date 2018-01-01
Sub-type Article (original research)
DOI 10.1038/s41588-017-0009-4
Open Access Status Not yet assessed
Abstract We introduce multi-trait analysis of GWAS (MTAG), a method for joint analysis of summary statistics from genome-wide association studies (GWAS) of different traits, possibly from overlapping samples. We apply MTAG to summary statistics for depressive symptoms (N eff = 354,862), neuroticism (N = 168,105), and subjective well-being (N = 388,538). As compared to the 32, 9, and 13 genome-wide significant loci identified in the single-trait GWAS (most of which are themselves novel), MTAG increases the number of associated loci to 64, 37, and 49, respectively. Moreover, association statistics from MTAG yield more informative bioinformatics analyses and increase the variance explained by polygenic scores by approximately 25%, matching theoretical expectations.
Q-Index Code C1
Q-Index Status Provisional Code
Grant ID P01 HD031921
R01 HD060726
R01 HD073342
R01 MH107649
U01 MH109539
R01 MH101244
P01 AG005842
P30 AG012810
R01 AG042568
T32 AG000186
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: Pubmed Import
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Created: Wed, 10 Jan 2018, 12:17:22 EST