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MTAG: multi-trait analysis of GWAS implicates novel loci for depressive symptoms, neuroticism, and subjective well-being
16 auth. R. Walters, P. Turley, Omeed Maghzian, A. Okbay, James J. Lee, M. Fontana, Tuan Anh Nguyen-Viet, N. Furlotte, P. Magnusson, Sven Oskarsson, ...
The standard approach in genome-wide association studies (GWAS) is to meta-analyze association statistics from cohort-level GWAS of a single trait. Such single-trait analyses do not exploit information that may be available from GWAS of other correl…
The standard approach in genome-wide association studies (GWAS) is to meta-analyze association statistics from cohort-level GWAS of a single trait. Such single-trait analyses do not exploit information that may be available from GWAS of other correlated traits. We introduce a method, Multi-Trait Analysis of GWAS (MTAG), which jointly analyzes GWAS results for several related traits, thereby boosting statistical power to detect genetic associations for each trait1.
MTAG is a generalization of standard, inverse-variance-weighted meta-analysis. The estimator takes summary statistics from at least two single-trait GWASs and, after jointly analyzing these, outputs trait-specific association statistics. The set of results for each trait represents an optimal combination of the information from the single-trait summary statistics, accounting for sample overlap and correlated genetic effects between the GWAS. The resulting p values can be interpreted like GWAS p values and used in the usual ways, for example, to prioritize SNPs for subsequent analyses including biological annotation.
We demonstrate MTAG using data on depressive symptoms (DEP; effective N = 354,862), neuroticism (NEUR; N = 168,105), and subjective well-being (SWB; N = 388,538). Comparing single-trait GWAS to MTAG, the number of genome-wide significant loci increases from 32 to 74 for DEP, from 9 to 66 for NEUR, and from 13 to 60 for SWB. These gains in statistical power from MTAG analyses are equivalent to increasing the sample size of the original, single-trait GWASs by 37% (DEP), 96% (NEUR), and 85% (SWB).
The association statistics from MTAG are replicated in a large, well-phenotyped cohort (N=6,857-8,307 for the three phenotypes) and improve prediction accuracy of polygenic risk scores by approximately 25%, consistent with theoretical calculations. Moreover, the MTAG results yield more informative bioinformatics analyses, suggesting a role of glutamatergic neurotransmission in depression.
References:
1 Turley, P. et al. MTAG: Multi-Trait Analysis of GWAS. bioRxiv (2017).
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