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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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Background
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Ma, R., Guo, Z., Cai, T. T., & Li, H. (2022) · Statistica Sinica · doi:10.5705/ss.202021.0386 · Cited by 8
Expanded genomic analyses for male voice-breaking highlights a shared phenotypic and genetic basis between puberty timing and hair colourPreprint
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The more the merrier? Multivariate approaches to genome-wide association analysisPreprint
Vroom, C.-R., de Leeuw, C., Posthuma, D., Dolan, C. V., & van der Sluis, S. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/610287 · Cited by 4
“…(Loh et al., 2015), LD Score Regression (Bulik-Sullivan et al., 2015)), alongside multivariate methods like Multi-Trait Analysis of GWAS (MTAG: Turley et al., 2018) and genomic SEM (Grotzinger et al., in press), which use this genetic covariance among traits to boost the statistical power to…”
Method
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de la Fuente, J., Davies, G., Grotzinger, A. D., Tucker–Drob, E. M., & Deary, I. J. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/766600 · Cited by 4
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“Past work involving GWAS of multiple related phenotypes has explored improving power for genetic discovery (Turley et al. 2018), identifying loci whose effects overlap between phenotypes (Shi et al.”
BackgroundSelf-citation
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Kranzler, H. R., Davis, C. N., Feinn, R., Jinwala, Z., Khan, Y., Oikonomou, A., Silva-Lopez, D., Burton, I., Dixon, M., Milone, J., et al. (2023) · medRxiv · doi:10.21203/rs.3.rs-3483320/v1 · Cited by 4
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“Modeling context dependency of genetic effects may hold the potential for constructing polygenic scores that are more accurate or improve their portability across contexts ( Patel et al., 2022 ; Miao et al., 2022 ; Turley et al., 2018 ; Spence et al., 2022 ; Wang et al., 2024 ; Smith et al., 2025 ).”
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“It is designed to enhance the statistical power of each trait in utilizing the connections among correlated traits to identify new genetic associations [7].”
Transformer-based deep learning enhances discovery in migraine GWAS
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“Open Access This Peer Review File is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a…”
Influential
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“While Multi-Trait Analysis of GWAS (MTAG) also leverages data from multiple traits to increase power, it primarily focuses on the discovery of genetic variants without explicitly modeling the underlying shared genetic structure [14].”
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Xie, T., Ani, A., Vaez, A., Nolte, I. M., Su, S., Ishikuro, M., Wang, S., Zhang, W., Soares, A. G., Motazedi, E., et al. (2026) · European Heart Journal · doi:10.1093/eurheartj/ehag313 · Cited by 4
Multivariate extension of penalized regression on summary statistics to construct polygenic risk scores for correlated traits
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“from cross-trait and trait-specific Linkage Disequilibrium Score Regression fit of SZ and BD summary statistics, an approach inspired by Turley et al.(25) The cross-trait intercept term estimate was bb12 1⁄4 0:18 leading to a between-trait summary-statistic correlationcro 1⁄4 0:16.”
Method
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Method
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“For PRS adjusted by genetic correlation, we applied similar methods in Multi-Trait Analysis of GWAS 51 and Multi-Ancestry Meta-Analysis 52 to adjust the effect sizes of SNPs.”
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Background
Pleiotropic architectures of porcine immune and growth trait pairs revealed by a self‐product‐based transcriptome method
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“Sum-mary statistics were downloaded from the NHGRI-EBI GWAS Catalog [82] for the 13 studies [31, 33, 35–39, 54, 83–87].”
Method
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“They suggest that application of meta-analysis methods to jointly model shared phenotypes may be a way to increase power to detect genetic variants in both SjD and SLE [66–69].”
Background
Causality between COVID-19 and multiple myeloma: a two-sample Mendelian randomization study and Bayesian co-localization
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“MTAG re-estimated the effect size for each trait by combining the weighted sum of the GWAS estimates for different traits to identify novel SNPs with strong signals [30].”
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“Furthermore, compared to other multi-trait whole-genome analysis methods, it has a wider range of applicability (20).”
Method
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Background
Roles of statistical modeling in characterizing the genetic basis of human diseases and traits
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Background
Genome‐wide association study reveals candidate genes for pollution excreta traits in pigs
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Distributed genetic effects of the corpus callosum subregions suggest links to neuropsychiatric disorders and related traits
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“…identified for the anterior, mid-anterior and central subregion overlapped significantly with previous GWAS of BD, alcohol use disorder, Alzheimer ’ s dementia, loneliness and neuroticism (Nagel et al ., 2018; Turley et al ., 2018; Jansen et al ., 2019; Kranzler et al ., 2019; Stahl et al ., 2019).”
Bivariate causal mixture model quantifies polygenic overlap between complex traits beyond genetic correlationPreprint
Frei, O., Holland, D., Smeland, O. B., Shadrin, A. A., Fan, C. C., Maeland, S., O’Connell, K. S., Wang, Y., Djurovic, S., Thompson, W. K., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/240275 · Cited by 5
Multi-trait genome-wide association meta-analysis of dietary intake identifies new loci and genetic and functional links with metabolic traitsPreprint
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GWAS meta-analysis and gene expression data link reproductive tract development, immune response and cellular proliferation/apoptosis with cervical cancer and clarify overlap with other cervical phenotypesPreprint
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“Regional plot for locus on chromosome 19 identified in MTAG joint analysis of cervical dysplasia and cancer.”
MethodInfluential
Evaluation of methods incorporating biological function and GWAS summary statistics to accelerate discoveryPreprint
Moore, A. L., Marks, J. A., Quach, B. C., Guo, Y., Bierut, L. J., Gaddis, N., Hancock, D. B., Page, G. P., & Johnson, E. O. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.01.10.475153 · Cited by 3
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“Multivariate analyses can increase power because multivariate records can effectively increase population size relative to univariate records [4].”
Background

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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