| Title: | Multi-trait analysis of genome-wide association summary statistics using MTAG |
| Journal: | Nature Genetics |
| Published: | 1 Jan 2018 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/29292387/ |
| DOI: | https://doi.org/10.1038/s41588-017-0009-4 |
| URL: | https://research.vu.nl/files/246500000/Multi_trait_analysis_of_genome_wide_association_summary_statistics_using_MTAG.pdf |
| Citations: | 790 (262 in last 2 years) as of 8 Aug 2024 |
Publication 2888
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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 (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.</p>
15 Keywords
- Algorithms
- Data Interpretation, Statistical
- Datasets as Topic
- Depression
- Diagnostic Self Evaluation
- Genetic Association Studies
- Genome-Wide Association Study
- Health
- Humans
- Meta-Analysis as Topic
- Multifactorial Inheritance
- Neuroticism
- Phenotype
- Polymorphism, Single Nucleotide
- Quantitative Trait Loci
20 Authors
- Patrick Turley
- Raymond K. Walters
- Omeed Maghzian
- Aysu Okbay
- James J. Lee
- Mark Alan Fontana
- Tuan Anh Nguyen-Viet
- Robbee Wedow
- Meghan Zacher
- Nicholas A. Furlotte
- 23andMe Research Team
- Social Science Genetic Association Consortium
- Patrik Magnusson
- Sven Oskarsson
- Magnus Johannesson
- Peter M. Visscher
- David Laibson
- David Cesarini
- Benjamin M. Neale
- Daniel J. Benjamin
1 Application
| Application ID | Title |
|---|---|
| 11425 | The Social Science Genetic Association Consortium |
Enabling scientific discoveries that improve human health