Cited by 1,112Genetic Associations and EpidemiologyGenetic Mapping and Diversity in Plants and AnimalsGenetic and phenotypic traits in livestock

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

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23andMe Research Team, Turley, Patrick, Social Science Genetic Association Consortium, 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.·2018·Nature Genetics

DOI 10.1038/s41588-017-0009-4PMC PMC5805593

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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 (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.

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Multi-trait Analysis of GWAS (MTAG) of Substance Use Traits Identifies Novel Genetic Loci and Phenomic AssociationsPreprint
Xu, H., Toikumo, S., Crist, R. C., Glogowska, K., Deak, J. D., Gelernter, J. E., Johnson, E. C., Kranzler, H. R., & Kember, R. L. (2022) · medRxiv · doi:10.1101/2022.07.06.22277340 · Cited by 2
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Pettersson, O. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.11.02.565287 · Cited by 2
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Background
A Multivariate Genome-Wide Association Study Reveals Neural Correlates and Common Biological Mechanisms of Psychopathology SpectraPreprint
Davis, C. N., Khan, Y., Toikumo, S., Jinwala, Z., Boomsma, D. I., Levey, D. F., Gelernter, J. E., Kember, R. L., & Kranzler, H. R. (2024) · Research Square · doi:10.21203/rs.3.rs-4228593/v1 · Cited by 2
“41 Exploratory Analysis Genetic Correlations.”
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Dias, J., Chen, T., Hua, X., Wang, X., Rodriguez, A. A., Madduri, R., Kraft, P., & Zhang, H. (2025) · medRxiv · doi:10.1101/2025.03.11.25323772 · Cited by 2
“; https://doi.org/10.1101/2025.03.11.25323772 doi: medRxiv preprint methodological advancements, such as multi-trait 38 or ancestry-aware fine-mapping approaches 39 , could refine pooled analysis to better capture ancestry-specific effects.”
Influential
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“The key idea of MTAG is that, when GWAS results from different traits are correlated, the effect estimates of each trait can be improved by appropriately integrating the information from the GWAS results of other traits [25].”
Background
Novel insights into causal associations of body mass index or height with pneumothorax: a two-sample Mendelian randomization study
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Background
Genetic Predisposition of Different Social Status Indicators in Men and Women
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“…was an out-of-sample predictive power of a PGS greater than 1%; if a multivariate estimate PGS (estimated by multitrait analysis of GWAS [MTAG]; Turley et al., 2018) was provided by the WLS, we used the multivariate PGSs; if no multivariate PGS was provided, we used the univariate PGS…”
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Influential
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“…(MR) was employed to assess causal effects; (4) genetic overlap and shared loci were analyzed with the conditional/conjunctional false discovery rate (cond/conjFDR) analysis [20]; (5) comorbidity risk loci for both conditions were validated via multi-trait analysis of GWASs (MTAG) [21].”
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“14 This powerful strategy has been particularly effective for ischemic stroke (IS) sub-types, which often suffer from low statistical power due to limited sample sizes.”
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“0.8) [27].”
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“When applying MTAG to the obesity-related traits, we found that MTAG did not increase the number of discoveries and performed worse than the standard analysis at some sample sizes.”
Influential
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“Recognized as a widely utilized analytical tool, MTAG offers distinct advantages in detecting comorbidity risk loci [21].”
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“For metabolism-SNHL trait pairs with significant genetic correlations, we performed sequential cross-trait meta-analyses to identify pleiotropic loci (Turley et al. 2018).”
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“By integrating conjFDR and MTAG as dual validation approaches, several putative shared genetic loci were successfully identified.”
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“Using MTAG, a loneliness composite variable was computed using question (1) (perceived feelings of loneliness) as the primary trait and using questions (2) and (3) as related, supporting traits.”
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“Model validity was verified through maximum false discovery rate (maxFDR) evaluation to ensure methodological robustness [35].”
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“In this study, we perform four most commonly used GWAS summary-based multiple phenotype association tests to identify the association between phenotype in each network module and each of genetic variants, including minP [37], ACAT [38], MTAG [39], and SHom [40] (details in Text S3).”
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Causal evidence linking chronic pain genetics to late-onset asthma via the nervous system
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“MTAG-enhanced sum-mary statistics were then used to refine gene prioritisation 33 (Supplementary Methods).”
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“MTAG is a generalised meta-analytic framework that leverages the shared genetic architecture across traits to boost power for identifying trait-specific associations, while accounting for sample overlap and incomplete genetic correlation [46].”
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“First, we utilized the MTAG software with the -equal_h2 option to perform a meta-analysis, termed IA FinnGen, to address sample overlap, 21 considering the high genetic correlation between SAH and UIA ( [ r g = ± 0 970 0 133 .”
Influential
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Authors: 23andMe Research Team, Turley, P., Social Science Genetic Association Consortium, Walters, R. K., Maghzian, O., Okbay, A., Lee, J. J., Fontana, M. A., Nguyen-Viet, T. A., Wedow, R., Zacher, M., Furlotte, N. A., Magnusson, P., Oskarsson, S., Johannesson, M., Visscher, P. M., Laibson, D., Cesarini, D., Neale, B. M., & Benjamin, D. J.
Affiliations: Broad Institute (US)
Harvard University (US)
Vrije Universiteit Amsterdam (NL)
University of Minnesota (US)
Hospital for Special Surgery (US)
University of Southern California (US)
University of Colorado Boulder (US)
23andMe (United States) (US)
Karolinska Institutet (SE)
Uppsala University (SE)
Stockholm School of Economics (SE)
The University of Queensland (AU)
National Bureau of Economic Research (US)
Corresponding author: Turley, Patrick
Cesarini, David
Neale, Benjamin M.
Benjamin, Daniel J.
ORCID: Turley, Patrick
Walters, Raymond K.
Okbay, Aysu
Lee, James J.
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.
Venue: Nature Genetics
Publisher: Springer Science and Business Media LLC
Published: 2018-01-01
Volume: 50
Issue: 2
Pages: 229-237
ISSN: 1061-4036 (ISSN-L)
1546-1718
DOI: 10.1038/s41588-017-0009-4
PMC: PMC5805593
OpenAlex: W2780560743
Cited by: 1,112
References: 34
Citations by year: 2026: 132 · 2025: 181 · 2024: 173 · 2023: 158 · 2022: 139 · 2021: 115 · 2020: 88 · 2019: 88 · 2018: 37
FWCI: 38.75
Type: journal article
Language: en
Update notices: correction (2019-05-30)
correction (2019-06-25)
Open access: bronze
doi.org
Licence: www.springer.com/tdm (tdm)
Locations: Nature Genetics (publishedVersion)
PubMed (publishedVersion)
VU Research Portal (publishedVersion, other-oa)
Europe PMC (PubMed Central) (acceptedVersion)
PubMed Central (acceptedVersion)
eScholarship (California Digital Library) (submittedVersion)
Max Planck Digital Library (submittedVersion)
MPG.PuRe (Max Planck Society) (submittedVersion)
Data Archiving and Networked Services (DANS) (submittedVersion)
Funders: National Science Foundation (1144083)
University of Southern California
Wellcome Trust (213422/Z/18/Z)
Broad Institute
Vetenskapsrådet (421-2013-1061)
National Institutes of Health (MH107649)
Medical Research Council (MR/N01104X/1, MR/N01104X/2)
Economic and Social Research Council (ES/S008349/1)
National Institute on Aging (T32AG000186, AG005842, R01AG042568, P01AG005842, P30AG012810, P30AG034532)
National Institute of Mental Health (U01MH109539, R01MH107649, R01MH101244)
Eunice Kennedy Shriver National Institute of Child Health and Human Development (P01HD031921, R01HD073342, R01HD060726)
H2020 European Research Council (647648)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
Keywords: multi-trait analysis, MTAG, genome-wide association summary statistics, depressive symptoms, neuroticism, subjective well-being, polygenic risk score, genetic loci, personality traits, overlapping samples
MeSH terms: Neuroticism, Algorithms, Data Interpretation, Statistical, Depression, Health, Humans, Phenotype, Meta-Analysis as Topic, Multifactorial Inheritance, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Genome-Wide Association Study, Genetic Association Studies, Diagnostic Self Evaluation, Datasets as Topic

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

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