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Multi-trait analysis of genome-wide association summary statistics using MTAG
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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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In the citing paper
“C-GWAS has been shown to have increased statistical power compared to traditional methods such as minimal p-values of multiple single-trait GWASs (MinGWAS) and MTAG [5]”
From Combined genome-wide association study of 136 quantitative ear morphology traits in multiple populations reveal 8 novel loci (2023) · cites this paper as [5]
Experimental — accuracy not yet measuredIn the citing paper
“and has been previously associated with both MD and neuroticism (Turley et al., 2018; Wray et al., 2018).”
From Genomic Relationships, Novel Loci, and Pleiotropic Mechanisms across Eight Psychiatric Disorders (2019) · cites this paper as Turley et al., 2018
In the citing paper
“Using this method, we discovered 1,193 independent, genome-wide significantly associated common variants (MAF > 1%; 173 for AgeSmk, 89 CigDay, 83 SmkCes, 692 SmkInit, and 156 DrnkWk) listed in Supplementary Table 12”
From Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use (2019) · cites this paper as [16]
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Improving genetic prediction by leveraging genetic correlations among human diseases and traits
Maier, R. M., Zhu, Z., Lee, S., Trzaskowski, M., Ruderfer, D. M., Stahl, E. A., Ripke, S., Wray, N. R., Yang, J., Visscher, P. M., et al. (2018) · Nature Communications · doi:10.1038/s41467-017-02769-6 · Cited by 157
“Equations (16), (17), (18), (19), (20), (21), (22), (23), (24), (25), (26), (27), (28), (29), (30), (31), (32),”
Self-citationEducation can reduce health differences related to genetic risk of obesity
Barcellos, S., Carvalho, L., & Turley, P. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1802909115 · Cited by 161
“We used UKB data to augment the published GWASs in a way that avoids over-fitting (SI Appendix) and followed a standard set of quality-control protocols (27).”
MethodSelf-citationCai, N., Choi, K. W., & Fried, E. I. (2020) · Human Molecular Genetics · doi:10.1093/hmg/ddaa115 · Cited by 160
Mills, M. C., Tropf, F. C., Brazel, D. M., van Zuydam, N. R., Vaez, A., Agbessi, M. D. T., Ahsan, H., Alves, I., Andiappan, A. K., Arindrarto, W., et al. (2021) · Nature Human Behaviour · doi:10.1038/s41562-021-01135-3 · Cited by 166
“MTAG results 70 were calculated using GWA meta-analysis results of the following related phenotypes: AFS, AFB, number of children ever born, childlessness.”
Self-citationGenetic variation, brain, and intelligence differences
Deary, I. J., Cox, S. R., & Hill, W. D. (2022) · Molecular Psychiatry · doi:10.1038/s41380-021-01027-y · Cited by 137
Sociology, Genetics, and the Coming of Age of Sociogenomics
Mills, M. C., & Tropf, F. C. (2020) · Annual Review of Sociology · doi:10.1146/annurev-soc-121919-054756 · Cited by 162
Levin, M. G., Tsao, N. L., Singhal, P., Liu, C., Vy, H. M. T., Paranjpe, I., Backman, J., Bellomo, T. R., Bone, W. P., Biddinger, K. J., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-34216-6 · Cited by 155
Maina, J. G., Balkhiyarova, Z., Nouwen, A., Pupko, I., Ulrich, A., Boissel, M., Bonnefond, A., Froguel, P., Khamis, A., Prokopenko, I., et al. (2023) · Diabetes Care · doi:10.2337/dc22-2373 · Cited by 154
“In UKBB, two MTAG models were tested, one with type 2 diabetes andMDD and the other with type 2 diabetes and total PHQ-9 scores, to assess the consequence of using two different depression definition criteria (disease diagnosis vs. disease symptoms) in an MP-GWAS approach.”
BackgroundMethodInfluentialJiang, J., Cole, J. B., Freebern, E., Da, Y., VanRaden, P. M., & Ma, L. (2019) · Communications Biology · doi:10.1038/s42003-019-0454-y · Cited by 149
Large-scale multitrait genome-wide association analyses identify hundreds of glaucoma risk loci
Han, X., Gharahkhani, P., Hamel, A. R., Ong, J., Rentería, M. E., Mehta, P. A., Dong, X., Pasutto, F., Hammond, C. J., Young, T. L., et al. (2023) · Nature Genetics · doi:10.1038/s41588-023-01428-5 · Cited by 158
Cross-trait assortative mating is widespread and inflates genetic correlation estimates
Border, R., Athanasiadis, G. I., Buil, A., Schork, A. J., Cai, N., Young, A. I., Werge, T. M., Flint, J., Kendler, K. S., Sankararaman, S., et al. (2022) · Science · doi:10.1126/science.abo2059 · Cited by 137
“The consequences for methods such as MTAG [38] are not obvious as xAM will not necessarily decrease prediction R2 (e.g., see Figure S5b).”
MethodDeak, J. D., Zhou, H., Galimberti, M., Levey, D. F., Wendt, F. R., Sanchez‐Roige, S., Hatoum, A. S., Johnson, E. C., Nuñez, Y. Z., Demontis, D., et al. (2022) · Molecular Psychiatry · doi:10.1038/s41380-022-01709-1 · Cited by 133
“Multi-trait analysis of GWAS summary statistics (MTAG) Based on LDSC estimates of genetic correlations with OUD, a joint-analysis that included the EUR OUD GWAS and GWAS summary statistics for AUD [11] and CanUD [12] was conducted using MTAG [13].”
BackgroundMethodInfluentialChallenges and future directions for studying effects of host genetics on the gut microbiome
Sanna, S., Kurilshikov, A., van der Graaf, A., Fu, J., & Zhernakova, A. (2022) · Nature Genetics · doi:10.1038/s41588-021-00983-z · Cited by 140
Polygenic prediction of preeclampsia and gestational hypertension
Honigberg, M. C., Truong, B. M. T., Khan, R. R., Xiao, B., Bhatta, L., Vy, H. M. T., Guerrero, R. F., Schuermans, A., Selvaraj, M. S., Patel, A. P., et al. (2023) · Nature Medicine · doi:10.1038/s41591-023-02374-9 · Cited by 143
Eising, E., Mirza‐Schreiber, N., de Zeeuw, E. L., Wang, C. A., Truong, D. T., Allegrini, A. G., Shapland, C. Y., Zhu, G., Wigg, K. G., Gerritse, M., et al. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2202764119 · Cited by 150
Genomics of perivascular space burden unravels early mechanisms of cerebral small vessel disease
Duperron, M., Knol, M. J., Grand, Q. L., Evans, T. E., Mishra, A., Tsuchida, A., Roshchupkin, G. V., Konuma, T., Trégouët, D., Romero, J. R., et al. (2023) · Nature Medicine · doi:10.1038/s41591-023-02268-w · Cited by 138
Emdin, C. A., Haas, M., Ajmera, V. H., Simon, T. G., Homburger, J. R., Neben, C. L., Jiang, L., Wei, W., Feng, Q., Zhou, A. Y., et al. (2020) · Gastroenterology · doi:10.1053/j.gastro.2020.12.011 · Cited by 131
Erzurumluoglu, A. M., Liu, M., Jackson, V. E., Barnes, D. R., Datta, G., Melbourne, C., Young, R., Batini, C., Surendran, P., Jiang, T., et al. (2019) · Molecular Psychiatry · doi:10.1038/s41380-018-0313-0 · Cited by 126
The pathogenesis of cerebral small vessel disease and vascular cognitive impairment
Markus, H. S., & Joutel, A. (2025) · Physiological Reviews · doi:10.1152/physrev.00028.2024 · Cited by 140
Shi, H., Gazal, S., Kanai, M., Koch, E. M., Schoech, A. P., Siewert, K. M., Kim, S. S., Luo, Y., Amariuta, T., Huang, H., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-21286-1 · Cited by 119
Meier, S. M., & Deckert, J. (2019) · Current Psychiatry Reports · doi:10.1007/s11920-019-1002-7 · Cited by 182
Complex Trait Prediction from Genome Data: Contrasting EBV in Livestock to PRS in Humans
Wray, N. R., Kemper, K. E., Hayes, B. J., Goddard, M. E., & Visscher, P. M. (2019) · Genetics · doi:10.1534/genetics.119.301859 · Cited by 113
“Other methodologies for estimating PRS, such as those commonly used for GEBVs, have been investigated (de los Campos et al. 2013; Abraham et al. 2014; Golan and Rosset 2014; Moser et al. 2015; Vilhjálmsson et al. 2015).”
MethodSelf-citationLevey, D. F., Galimberti, M., Deak, J. D., Wendt, F. R., Bhattacharya, A., Koller, D., Harrington, K. M., Quaden, R. M., Johnson, E. C., Gupta, P., et al. (2023) · Nature Genetics · doi:10.1038/s41588-023-01563-z · Cited by 120
Lam, M., Hill, W. D., Trampush, J. W., Yu, J., Knowles, E., Davies, G., Stahl, E. A., Huckins, L. M., Liewald, D. C. M., Djurovic, S., et al. (2019) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2019.06.012 · Cited by 115
Genome-wide association study of cerebral small vessel disease reveals established and novel loci
Chung, J., Marini, S., Pera, J., Norrving, B., Jiménez-Conde, J., Roquer, J., Fernández‐Cadenas, I., Tirschwell, D., Selim, M. H., Brown, D. L., et al. (2019) · Brain · doi:10.1093/brain/awz233 · Cited by 117
“Multi-trait analysis of GWAS (MTAG) requires GWAS summary statistics for traits, rather than individual-level data (Turley et al., 2018).”
Currant, H., Hysi, P. G., Fitzgerald, T. W., Gharahkhani, P., Bonnemaijer, P. W. M., Senabouth, A., Hewitt, A. W., Atan, D., Aung, T., Charng, J., et al. (2021) · PLoS Genetics · doi:10.1371/journal.pgen.1009497 · Cited by 119
“Given that several of the significant loci were common between the two studies, we combined the two GWAS using meta-analysis methods implemented in MTAG [33] (Fig 2).”
MethodInfluentialYang, Y., Musco, H., Simpson, S., Zhu, Z., Wang, Y., Lin, X., Zhang, J., Taylor, B. V., Gratten, J., & Zhou, Y. (2021) · Nature Communications · doi:10.1038/s41467-021-25768-0 · Cited by 123
Hatoum, A. S., Johnson, E. C., Colbert, S. M. C., Polimanti, R. A., Zhou, H., Walters, R. K., Gelernter, J. E., Edenberg, H. J., Bogdan, R., & Arpana Agrawal, A. (2021) · Neuropsychopharmacology · doi:10.1038/s41386-021-01209-w · Cited by 122
“We applied Multi-Trait Analysis of GWAS summary statistics (MTAG [30]) to summary statistics generated from the GWAS and Sequencing Consortium of Alcohol and Nicotine Use (GSCAN) GWAS of cigarettes per day to create the combined problematic tobacco use (PTU) phenotype [25].”
MethodSelf-citationGrotzinger, A. D., de la Fuente, J., Privé, F., Nivard, M. G., & Tucker–Drob, E. M. (2021) · Biological Psychiatry · doi:10.1016/j.biopsych.2022.05.029 · Cited by 103
Symptom-level modelling unravels the shared genetic architecture of anxiety and depression
Thorp, J. G., Campos, A. I., Grotzinger, A. D., Gerring, Z. F., An, J., Ong, J., Wang, W., Shringarpure, S., Byrne, E. M., MacGregor, S., et al. (2021) · Nature Human Behaviour · doi:10.1038/s41562-021-01094-9 · Cited by 110
Transferability of genetic risk scores in African populations
Kamiza, A. B., Touré, S. M., Vujković, M., Machipisa, T., Soremekun, O. S., Kintu, C., Corpas, M., Pirie, F. J., Young, E. H., Gill, D., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-01835-x · Cited by 114
Peyrot, W. J., & Price, A. L. (2021) · Nature Genetics · doi:10.1038/s41588-021-00787-1 · Cited by 107
Cai, M., Xiao, J., Zhang, S., Wan, X., Zhao, H., Chen, G., & Yang, C. (2021) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2021.03.002 · Cited by 102
“When MTAG was applied, a covariance matrix of the estimation error should be first constructed and provided as an input.(31) Because”
MethodA general dimension of genetic sharing across diverse cognitive traits inferred from molecular data
de la Fuente, J., Davies, G., Grotzinger, A. D., Tucker–Drob, E. M., & Deary, I. J. (2020) · Nature Human Behaviour · doi:10.1038/s41562-020-00936-2 · Cited by 112
Genomics and psychological resilience: a research agenda
Choi, K. W., Stein, M. B., Dunn, E. C., Koenen, K. C., & Smoller, J. W. (2019) · Molecular Psychiatry · doi:10.1038/s41380-019-0457-6 · Cited by 108
“A larger meta-analytic GWAS (N=388,538) leveraging genome-wide information from correlated traits (34) identified 49 independent loci associated with subjective wellbeing and supported a role for genes expressed in central nervous system tissues and related to dopaminergic neurons, startle response, and exploration of…”
BackgroundBeesley, L. J., Salvatore, M., Fritsche, L. G., Pandit, A., Rao, A., Brummett, C. M., Willer, C. J., Lisabeth, L. D., & Mukherjee, B. (2019) · Statistics in Medicine · doi:10.1002/sim.8445 · Cited by 109
Zhu, Z., Hasegawa, K., Camargo, C. A., & Liang, L. (2020) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2020.07.004 · Cited by 105
“However, this method assumes that all single-nucleotide polymorphisms (SNPs) share the same variance-covariance matrix among all traits, which could be violated when some SNPs are associated only with a subset of traits.(44) In a previous cross-trait analysis of allergic diseases and asthma,(22) ASSET was used to dete…”
Dissecting polygenic signals from genome-wide association studies on human behaviour
Abdellaoui, A., & Verweij, K. J. H. (2021) · Nature Human Behaviour · doi:10.1038/s41562-021-01110-y · Cited by 104
Genetics of common cerebral small vessel disease
Bordes, C., Sargurupremraj, M., Mishra, A., & Debette, S. (2022) · Nature Reviews Neurology · doi:10.1038/s41582-021-00592-8 · Cited by 108
Belsky, D. W., & Harden, K. P. (2019) · Current Directions in Psychological Science · doi:10.1177/0963721418807729 · Cited by 99
“For example, methods are now available to combine results from multiple GWASs of related traits to refine polygenic prediction (Grotzinger et al., 2018; Turley et al., 2018).”
MethodHujoel, M. L. A., Gazal, S., Loh, P., Patterson, N. J., & Price, A. L. (2020) · Nature Genetics · doi:10.1038/s41588-020-0613-6 · Cited by 91
Centromeric Satellite DNAs: Hidden Sequence Variation in the Human Population
Miga, K. H. (2019) · Genes · doi:10.3390/genes10050352 · Cited by 92
Metadata
- 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
- Venue
- Nature Genetics
- Published
- 2018-01-01
- Volume
- 50
- Issue
- 2
- Pages
- 229-237
- ISSN
- 1061-4036 (ISSN-L)
- 1546-1718
- 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
- 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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