Multi-trait analysis of genome-wide association summary statistics using MTAG
By P. Turley, R. Walters, Omeed Maghzian, A. Okbay, James J. Lee, M. Fontana, Tuan Anh Nguyen-Viet, Robbee Wedow, Meghan Zacher, Nicholas A. Furlotte and 32 more. Nature Genetics, 2018
1,002 citations recorded by the source corpus.
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 (Neff = 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. MTAG is a new method for joint analysis of summary statistics from genome-wide association studies of different traits. Applying MTAG to summary statistics for depressive symptoms, neuroticism and subjective well-being increased discovery of associated loci as compared to single-trait analyses.
Subfields: Genetics
View Paper: https://www.semanticscholar.org/paper/30538f0b833c963604095d4fac4c967540452ae8
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In Genetics
Citations in the corpus
- Genomic relationships, novel loci, and pleiotropic mechanisms across eight psychiatric disorders (2019, background): “This region showed association with all eight psychiatric disorders, and has been previously associated with both MD and neuroticism (Turley et al., 2018; Wray et al., 2018).”
- Polygenic prediction via Bayesian regression and continuous shrinkage priors (2018, methodology): “Third, jointly modeling multiple genetically correlated traits and including functional annotations in polygenic modeling are expected to increase the predictive performance of PRS, as shown by recent studies [Marquez-Luna et al., 2018; Shi et al., 2016b; Turley et al., 2018].”