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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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“We also re-estimated and leveraged the genetic correlation between the traits to amplify statistical power to discover IPF associations by performing multi-trait meta-analysis (MTAG) 24 .”
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InfluentialLyu, Y., Tong, S., Huang, W., Ma, Y., Zeng, R., Jiang, R., Luo, R., Leung, F. W., Lian, Q., Sha, W., et al. (2025) · PubMed Central · doi:10.1093/gigascience/giaf023
“To meet the assumption, we rigor ousl y filter ed the MTAG SNP with MAF ≥ 1% and sample size ≥ 75% of the 90th percentile and dropped the outliers [ 37 ].”
Exploring causal relationships and shared genomic signatures of coronary artery disease and strokePreprint
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“29 As input, it takes the GWAS summary statistics of correlated traits and incorporates the LDSC method to estimate the genetic covariance matrix.”
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“36,37 Shared genetic signals were considered signi fi cant if they reached genome-wide signi fi cance in the joint analysis ( P 5 (cid:1) 10 (cid”
Sun, F., Hui, S., Gong, B., Gu, J., & Huang, P. (2025) · PLoS ONE · doi:10.1371/journal.pone.0336109
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Lyu, Q., Wang, X.-Y., Chen, E., Sun, C., Qiu, Z., Xie, Y., & He, P. (2025) · iScience · doi:10.1016/j.isci.2025.114129
“54 MTAG provides increased statistical strength and reduces the inflation of the false discovery rate for trait pairs, particularly when there are strong correlations.”
Kousathanas, A., Odhams, C. A., Cook, J. P., Hu, Y., Klee, S., Erzurumluoglu, A. M., Ramírez, F., Mayr, C. H., Schäfer, D., Beck, L., et al. (2025) · EBioMedicine · doi:10.1016/j.ebiom.2025.106048
“34 Our analysis identified five putatively novel IPF locus associations of which one was robustly replicated at 2p16.”
InfluentialTeng, C.-S., Wang, X., Liu, C., Wang, Q., Cui, Y., & Xu, S. (2026) · Genetics · doi:10.1093/genetics/iyag143
“…correlations by using multivariate linear mixed models, im - proving power and reducing false-positive associations in identifying pleiotropic genetic variants, which affect multiple traits simultaneously ( Korte et al. 2012; Porter and O’Reilly 2017; Turley et al. 2018; Zhou and Stephens 2014).”
Hu, Y., He, M., & Yu, C. (2025) · Archives of Dermatological Research · doi:10.1007/s00403-025-04449-8
“To identify pleiotropic loci shared between HS and CVDs or metabolic traits, we performed a cross-trait meta-analysis using MTAG [25].”
Shui, W., Wu, X., Liu, H., Li, Z., & Zheng, G. (2025) · Human Genomics · doi:10.1186/s40246-025-00888-6
“By integrating multi-source GWAS datasets through the MTAG method, we enhanced the detection power of genetic variants while reducing false-positive or false-negative risks caused by genetic correlations [24].”
Zhang, W., van der Most, P. J., Wang, S., Kamali, Z., Giontella, A., Enhörning, S., Gansevoort, R. T., van der Harst, P., Bakker, S. J. L., Melander, O., et al. (2025) · EBioMedicine · doi:10.1016/j.ebiom.2025.106014 · Cited by 1
“The MTAG results indicated that despite the genetic correlation with the CALCA protein, the regulatory variants influencing PCT levels may be more closely linked to the CALCB locus.”
InfluentialAdditional file 14 of A novel candidate gene CLN8 regulates fat deposition in avian
Li, X., Zhang, F., Sun, Y., Sun, D., Yang, F., Liu, Y., & Hou, Z. (2024) · Figshare · doi:10.6084/m9.figshare.26584473
Kim, K., Jun, T.-H., Ha, B.-K., Wang, S., & Sun, H. (2023) · Figshare · doi:10.6084/m9.figshare.24286486
Additional file 5 of A novel candidate gene CLN8 regulates fat deposition in avian
Li, X., Zhang, F., Sun, Y., Sun, D., Yang, F., Liu, Y., & Hou, Z. (2024) · Figshare · doi:10.6084/m9.figshare.26584446
Additional file 4 of Multi-trait multi-locus SEM model discriminates SNPs of different effects
Igolkina, A. A., Meshcheryakov, G. A., Gretsova, M., Nuzhdin, S. V., & Samsonova, M. G. (2020) · Figshare · doi:10.6084/m9.figshare.12731646
Yu, S., Lin, Y., Yang, Y., Jin, X., Banghua, L., Lu, D., & Huang, J. (2024) · Figshare · doi:10.6084/m9.figshare.26011845
Yu, S., Lin, Y., Yang, Y., Jin, X., Banghua, L., Lu, D., & Huang, J. (2024) · Figshare · doi:10.6084/m9.figshare.26732137
Additional file 15 of A novel candidate gene CLN8 regulates fat deposition in avian
Li, X., Zhang, F., Sun, Y., Sun, D., Yang, F., Liu, Y., & Hou, Z. (2024) · Figshare · doi:10.6084/m9.figshare.26584476
Yu, S., Lin, Y., Yang, Y., Jin, X., Banghua, L., Lu, D., & Huang, J. (2024) · Figshare · doi:10.6084/m9.figshare.26011990
Yu, S., Lin, Y., Yang, Y., Jin, X., Banghua, L., Lu, D., & Huang, J. (2024) · Figshare · doi:10.6084/m9.figshare.26732125
Additional file 16 of A novel candidate gene CLN8 regulates fat deposition in avian
Li, X., Zhang, F., Sun, Y., Sun, D., Yang, F., Liu, Y., & Hou, Z. (2024) · Figshare · doi:10.6084/m9.figshare.26584479
Cai, Z., Iso‐Touru, T., Sanchez, M.-P., Kadri, N. K., Bouwman, A. C., Chitneedi, P., MacLeod, I. M., Vander-Jagt, C. J., Chamberlain, A. J., Gredler, B., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26749229
Yu, S., Lin, Y., Yang, Y., Jin, X., Banghua, L., Lu, D., & Huang, J. (2024) · Figshare · doi:10.6084/m9.figshare.26732128
Additional file 4 of A novel candidate gene CLN8 regulates fat deposition in avian
Li, X., Zhang, F., Sun, Y., Sun, D., Yang, F., Liu, Y., & Hou, Z. (2024) · Figshare · doi:10.6084/m9.figshare.26584443
Yu, S., Lin, Y., Yang, Y., Jin, X., Banghua, L., Lu, D., & Huang, J. (2024) · Figshare · doi:10.6084/m9.figshare.26732134
Chen, S.-Y., Schenkel, F. S., de Melo, A. L. P., Oliveira, H. R., Pedrosa, V. B., Araujo, A. C., Melka, M. G., & Brito, L. F. (2022) · Figshare · doi:10.6084/m9.figshare.19681786
Alemany, S., Artigas, M. S., Domínguez, J. C., Fakhreddine, D., Llonga, N., Vilar‐Ribó, L., Rodríguez‐Urrutia, A., Palacio, J., González-Castro, A. M., Lobo, B., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26584023
Yu, S., Lin, Y., Yang, Y., Jin, X., Banghua, L., Lu, D., & Huang, J. (2024) · Figshare · doi:10.6084/m9.figshare.26732146
Nosková, A., Mehrotra, A., Kadri, N. K., Lloret‐Villas, A., Neuenschwander, S., Andreas Hofer, A., & Pausch, H. (2023) · Figshare · doi:10.6084/m9.figshare.22626415
Nosková, A., Mehrotra, A., Kadri, N. K., Lloret‐Villas, A., Neuenschwander, S., Andreas Hofer, A., & Pausch, H. (2023) · Figshare · doi:10.6084/m9.figshare.22626409
Zhang, Y., Lu, Q., Ye, Y., Huang, K., Liu, W., Wu, Y., Zhong, X., Li, B., Yu, Z., Travers, B. G., et al. (2021) · Figshare · doi:10.6084/m9.figshare.16584354
Additional file 3 of Multi-trait multi-locus SEM model discriminates SNPs of different effects
Igolkina, A. A., Meshcheryakov, G. A., Gretsova, M., Nuzhdin, S. V., & Samsonova, M. G. (2020) · Figshare · doi:10.6084/m9.figshare.12731643
Ibanez, L., Bahena, J. A., Yang, C., Dube, U., Farias, F. G., Budde, J. P., Bergmann, K., Brenner-Webster, C., Morris, J. C., Perrin, R. J., et al. (2020) · Figshare · doi:10.6084/m9.figshare.13265837
Wang, H., Pei, F., Vanyukov, M. M., Bahar, I., Wu, W., & Xing, E. P. (2021) · Figshare · doi:10.6084/m9.figshare.13725332
Yu, S., Lin, Y., Yang, Y., Jin, X., Banghua, L., Lu, D., & Huang, J. (2024) · Figshare · doi:10.6084/m9.figshare.26732131
Additional file 6 of A novel candidate gene CLN8 regulates fat deposition in avian
Li, X., Zhang, F., Sun, Y., Sun, D., Yang, F., Liu, Y., & Hou, Z. (2024) · Figshare · doi:10.6084/m9.figshare.26584449
Cai, Z., Iso‐Touru, T., Sanchez, M.-P., Kadri, N. K., Bouwman, A. C., Chitneedi, P., MacLeod, I. M., Vander-Jagt, C. J., Chamberlain, A. J., Gredler, B., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26308951
Additional file 11 of A novel candidate gene CLN8 regulates fat deposition in avian
Li, X., Zhang, F., Sun, Y., Sun, D., Yang, F., Liu, Y., & Hou, Z. (2024) · Figshare · doi:10.6084/m9.figshare.26584464
Additional file 17 of A novel candidate gene CLN8 regulates fat deposition in avian
Li, X., Zhang, F., Sun, Y., Sun, D., Yang, F., Liu, Y., & Hou, Z. (2024) · Figshare · doi:10.6084/m9.figshare.26584482
Olasege, B. S., Porto-Neto, L. R., Tahir, M. S., Gouveia, G. C., Cánovas, Á., Hayes, B. J., & Fortes, M. R. S. (2022) · Figshare · doi:10.6084/m9.figshare.21273909
Additional file 1 of A novel candidate gene CLN8 regulates fat deposition in avian
Li, X., Zhang, F., Sun, Y., Sun, D., Yang, F., Liu, Y., & Hou, Z. (2024) · Figshare · doi:10.6084/m9.figshare.26584434
Olasege, B. S., Porto-Neto, L. R., Tahir, M. S., Gouveia, G. C., Cánovas, Á., Hayes, B. J., & Fortes, M. R. S. (2022) · Figshare · doi:10.6084/m9.figshare.21273897
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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