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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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“In this situation, MTAG was analytically equivalent to METAL.(6) Because the T p values of MTAG are identical in this scenario, the multiple testing correction is not needed.”
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“S27), partitioning h 2 g by functional annotations, identifying disease-relevant tissue and cell types and multi-trait analysis (6,9,54,55).”
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“In our study, for TYK2 and IL2RA , the most likely risk genes were identi fi ed through four methods (PLACO_FUMA, MTAG, Bayesian colocalization analysis, and TWAS) and three methods (PLACO_FUMA, MTAG, and Bayesian colocalization analysis), respectively.”
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“MTAG relies on the key homogeneous assumption that all SNPs across traits share the same variance-covariance matrix of effect sizes, but the estimator of MTAG can be still consistent even if this assumption is violated when some SNPs influence only a subset of the traits [19].”
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Combined analysis of keratinocyte cancers identifies novel genome-wide loci
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“The classical methods of multiple-trait GWAS [40, 41] are difficult to implement in practice, especially on large-scale datasets due to computing requirements.”
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“We decided to use an additive construct, preferring this over the multi-trait design because our data did not satisfy the assumptions on, amongst others, the extent of the genetic correlation values required for performing MTAG analyses [62].”
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“Next, using multitrait GWAS analysis (MTAG, see Methods) on ACD and the prioritized traits (CAD, stroke, WMH), we identified intronic SNPs in SMG6 and ABCG8 to be GW significant (pMTAG 1.67E-08, for three phenotypes) for ACD (Table S10).”
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“Finally, incorporating training data from auxiliary traits has substantial potential to improve polygenic prediction accuracy, 39,40 but this was not implemented in our study; incorporating auxiliary traits into PRS-FH is straightforward under the PRS-FH framework, and remains a future research…”
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BackgroundMethodInfluentialMulti-trait genome-wide association study identifies new loci associated with optic disc parameters
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Detecting local genetic correlations with scan statistics
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Genetic analysis of dietary intake identifies new loci and functional links with metabolic traits
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Rayner, C., Coleman, J. R. I., Purves, K. L., Hodsoll, J. P., Goldsmith, K., Alpers, G. W., Andersson, E., Arolt, V., Boberg, J., Bögels, S. M., et al. (2019) · Translational Psychiatry · doi:10.1038/s41398-019-0481-y · Cited by 57
Age-of-onset information helps identify 76 genetic variants associated with allergic disease
Ferreira, M. A. R., Vonk, J. M., Baurecht, H., Marenholz, I., Tian, C., Hoffman, J., Helmer, Q., Tillander, A., Ullemar, V., Lu, Y., et al. (2020) · PLoS Genetics · doi:10.1371/journal.pgen.1008725 · Cited by 55
“This increases the effective sample size of each analysis and so it improves the power to detect associations with variants that are shared between the two phenotypes [34].”
BackgroundMethodInfluentialSelf-citationGWAS of five gynecologic diseases and cross-trait analysis in Japanese
Masuda, T., Low, S., Akiyama, M., Hirata, M., Ueda, Y., Matsuda, K., Kimura, T., Murakami, Y., Kubo, M., Kamatani, Y., et al. (2019) · European Journal of Human Genetics · doi:10.1038/s41431-019-0495-1 · Cited by 54
Multi-trait transcriptome-wide association studies with probabilistic Mendelian randomization
Liu, L., Zeng, P., Xue, F., Yuan, Z., & Zhou, X. (2021) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2020.12.006 · Cited by 52
Campos, A. I., Ingold, N., Huang, Y., Mitchell, B. L., Kho, P. F., Han, X., García‐Marín, L. M., Ong, J., Agee, M., Aslibekyan, S., et al. (2022) · SLEEP · doi:10.1093/sleep/zsac308 · Cited by 49
“Although MTAG has proven successful in boosting the discovery of loci associations, even in the presence of known or unknown sample overlap [29], combining traits with extreme power differences might inflate signals related to the most powered phenotype [29].”
BackgroundMethodInfluentialHu, Y., Zhang, Y., Zhang, H., Gao, S., Wang, L., Wang, T., Han, Z., Sun, B., & Liu, G. (2022) · Molecular Psychiatry · doi:10.1038/s41380-022-01695-4 · Cited by 48
Polygenic risk and pleiotropy in neurodegenerative diseases
Bellou, E., Stevenson‐Hoare, J. O., & Escott‐Price, V. (2020) · Neurobiology of Disease · doi:10.1016/j.nbd.2020.104953 · Cited by 59
Multivariate genome-wide analysis of education, socioeconomic status and brain phenome
Wendt, F. R., Pathak, G. A., Lencz, T., Krystal, J. H., Gelernter, J. E., & Polimanti, R. A. (2020) · Nature Human Behaviour · doi:10.1038/s41562-020-00980-y · Cited by 48
Migraine, Stroke, and Cervical Arterial Dissection
Daghlas, I., Sargurupremraj, M., Danning, R., Gormley, P., Malik, R., Amouyel, P., Metso, T. M., Pezzini, A., Kurth, T., Debette, S., et al. (2022) · Neurology Genetics · doi:10.1212/nxg.0000000000000653 · Cited by 49
“7), which leverages the pairwise genome-wide trait genetic correlation to boost power in association testing.(20) In MTAG, LDSc provided the estimates of pairwise genetic correlation.”
BackgroundGenomic SEM Provides Insights into the Multivariate Genetic Architecture of Complex TraitsPreprint
Grotzinger, A. D., Rhemtulla, M., de Vlaming, R., Ritchie, S. J., Mallard, T. T., Hill, W. D., Ip, H. F., McIntosh, A. M., Deary, I. J., Koellinger, P., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/305029 · Cited by 49
Sahana, G., Cai, Z., Sanchez, M.-P., Bouwman, A. C., & Boichard, D. (2023) · Journal of Dairy Science · doi:10.3168/jds.2022-22694 · Cited by 64
Sinnott-Armstrong, N., Sousa, I. S., Laber, S., Rendina-Ruedy, E., Dankel, S. N., Ferreira, T. C., Mellgren, G., Karasik, D., Rivas, M. A., Pritchard, J. K., et al. (2021) · Cell Metabolism · doi:10.1016/j.cmet.2021.01.001 · Cited by 55
“MTAG MTAG (Multi-Trait Analysis of GWAS) is a method to combine summary statistics from related traits in a flexible framework which takes into account the genetic correlation of the traits (Turley et al., 2017, 2018).”
MethodNath, A. P., Ritchie, S. C., Grinberg, N. F., Tang, H. H. F., Huang, Q., Teo, S. M., Ahola‐Olli, A., Würtz, P., Havulinna, A. S., Santalahti, K., et al. (2019) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2019.10.001 · Cited by 50
Zhu, Z., Anttila, V., Smoller, J. W., & Lee, P. H. (2018) · PLOS ONE · doi:10.1371/journal.pone.0193256 · Cited by 47
Is genetic liability to ADHD and ASD causally linked to educational attainment?
Dardani, C., Riglin, L., Leppert, B., Sanderson, E. C. M., Rai, D., Howe, L. D., Smith, G. D., Tilling, K. M., Thapar, A., Davies, N. M., et al. (2021) · International Journal of Epidemiology · doi:10.1093/ije/dyab107 · Cited by 46
Lee, P. H., Anttila, V., Won, H., Feng, Y. A., Rosenthal, J., Zhu, Z., Tucker–Drob, E. M., Nivard, M. G., Grotzinger, A. D., Posthuma, D., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/528117 · Cited by 51
“This region showed association with all eight psychiatric disorders, and has been previously associated with both MD and neuroticism (Turley et al., 2018; Wray et al., 2018).”
BackgroundSelf-citationXiao, L., Liu, S., Wu, Y., Huang, Y., Tao, S., Liu, Y., Tang, Y., Xie, M., Ma, Q., Yin, Y., et al. (2023) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2023.08.003 · Cited by 46
“From GWAS to MTAG, the number of lead SNPs increased from 15 to 33 for Bifidobacterium longum and from 29 to 34 for Bifidobacterium ruminantium in FR02 cohort; from 7 to 8 for Bifidobacterium catenulatum and from 12 to 11 for Bifidobacterium longum in DMP cohort; and from 32 to 46 for Bifidobacterium adolescentis and…”
MethodInfluentialAccounting for age of onset and family history improves power in genome-wide association studies
Pedersen, E. M., Agerbo, E., Plana‐Ripoll, O., Grove, J., Dreier, J. W., Musliner, K. L., Bækvad‐Hansen, M., Athanasiadis, G. I., Schork, A. J., Bybjerg‐Grauholm, J., et al. (2022) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2022.01.009 · Cited by 40
Association of Essential Tremor With Novel Risk Loci
Liao, C., Castonguay, C.-E., Heilbron, K., Vuokila, V., Medeiros, M., Houle, G., Akçimen, F., Ross, J. P., Catoire, H., Díez-Fairén, M., et al. (2022) · JAMA Neurology · doi:10.1001/jamaneurol.2021.4781 · Cited by 47
Zhu, X., Li, X., Xu, R., & Wang, T. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa985 · Cited by 41
Shared and distinct genetic etiologies for different types of clonal hematopoiesis
Brown, D. W., Cato, L. D., Zhao, Y., Nandakumar, S. K., Bao, E. L., Gardner, E. J., Hubbard, A. K., DePaulis, A., Rehling, T., Song, L., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-41315-5 · Cited by 47
“MTAG and colocalization analysis among TL, LOY, and MPN GWAS summary statistics for LOY 13 , TL 27 , and MPN 22 were utilized to conduct a meta-analysis by implementing the multi-trait analysis of GWAS (MTAG) 67,68 .”
InfluentialA multi-trait Bayesian method for mapping QTL and genomic prediction
Kemper, K. E., Bowman, P. J., Hayes, B. J., Visscher, P. M., & Goddard, M. E. (2018) · Genetics Selection Evolution · doi:10.1186/s12711-018-0377-y · Cited by 44
“Similar conclusions have been drawn for multi-trait versus univariate GWAS models [9] and for prediction accuracies of non-linear multivariate versus univariate models [17].”
BackgroundSelf-citationCade, B. E., Chen, H., Stilp, A. M., Louie, T., Ancoli-Israel, S., Arens, R., Barfield, R., Below, J. E., Cai, J., Conomos, M. P., et al. (2019) · PLOS Genetics · doi:10.1371/journal.pgen.1007739 · Cited by 39
Self-citationPolygenic contribution to the relationship of loneliness and social isolation with schizophrenia
Andreu-Bernabeu, Á., Díaz‐Caneja, C. M., Costas, J., de Hoyos, L., Stella, C., Gurriarán, X., Alloza, C., Fañanás, L., Bobes, J., González-Pinto, A. M., et al. (2022) · Nature Communications · doi:10.1038/s41467-021-27598-6 · Cited by 43
Multiethnic polygenic risk prediction in diverse populations through transfer learning
Tian, P., Chan, T. H., Wang, Y., Yang, W., Yin, G., & Zhang, Y. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.906965 · Cited by 40
Koskeridis, F., Fancy, N. N., Tan, P. F., Meena, D., Εvangelou, Ε., Elliott, P., Wang, D. Y. Q., Matthews, P. M., Dehghan, A., & Tzoulaki, I. (2024) · Nature Communications · doi:10.1038/s41467-024-53452-6 · Cited by 46
“SBP-DBP)using MTAG 17 .”
Polygenic Risk Scores in Neurodegenerative Diseases: a Review
Ibáñez, L., Farias, F., Dube, U., Mihindukulasuriya, K. A., & Harari, O. (2019) · Current Genetic Medicine Reports · doi:10.1007/s40142-019-0158-0 · Cited by 42
Novel hypotheses emerging from GWAS in migraine?
van den Maagdenberg, A. M. J. M., Nyholt, D. R., & Anttila, V. (2019) · The Journal of Headache and Pain · doi:10.1186/s10194-018-0956-x · Cited by 46
“Efforts at combining information across phenotypes either directly at the summary statistic phase [29] or by comparative analysis of correlated phenotypes [30] as well as increasing the size of the migraine GWAS itself (leading to more implicated loci) will yield improvements on the locus side of the analysis; concurr…”
BackgroundGenome-wide association study and its applications in the non-model crop Sesamum indicum
Berhe, M., Dossa, K., You, J., Mboup, P. A., Diallo, I. N., Diouf, D., Zhang, X., & Wang, L. (2021) · BMC Plant Biology · doi:10.1186/s12870-021-03046-x · Cited by 46
Deak, J. D., Levey, D. F., Wendt, F. R., Zhou, H., Galimberti, M., Kranzler, H. R., Gaziano, J. M., Stein, M. B., Polimanti, R. A., Gelernter, J. E., et al. (2022) · JAMA Network Open · doi:10.1001/jamanetworkopen.2022.38880 · Cited by 45
Galván‐Femenía, I., Obón‐Santacana, M., Piñeyro, D., Guindo-Martínez, M., Duran, X., Carreras, A., Pluvinet, R., Velasco, J., Ramos, L., Aussó, S., et al. (2018) · Journal of Medical Genetics · doi:10.1136/jmedgenet-2018-105437 · Cited by 43
Program, T. V. M. V., Bone, W. P., Siewert, K. M., Jha, A., Klarin, D., Damrauer, S. M., Chang, K.-M., Tsao, P. S., Assimes, T. L., Ritchie, M. D., et al. (2021) · Alzheimer's Research & Therapy · doi:10.1186/s13195-021-00773-z · Cited by 41
Seviiri, M., Law, M. H., Ong, J., Gharahkhani, P., Fontanillas, P., Aslibekyan, S. W., Auton, A., Babalola, E., Bell, R. K., Bielenberg, J., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-35345-8 · Cited by 44
Seligowski, A. V., Misganaw, B., Duffy, L., Ressler, K. J., & Guffanti, G. (2022) · American Journal of Psychiatry · doi:10.1176/appi.ajp.21111113 · Cited by 42
Research Review: A guide to computing and implementing polygenic scores in developmental research
Allegrini, A. G., Baldwin, J. R., Barkhuizen, W., & Pingault, J. (2022) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.13611 · Cited by 45
“…can be GWAS-based methods where the focus is on detection of trait-associated variants (e.g. MTAG and GenomicSEM, Grotzinger et al., 2019; Turley et al., 2018), and prediction-based methods (e.g. SMTpred, Maier et al., 2018) where either GWAS summary statistics or PGS are combined in a…”
How Real-World Data Can Facilitate the Development of Precision Medicine Treatment in Psychiatry
Koch, E. M., Pardiñas, A. F., O’Connell, K. S., Selvaggi, P., Camacho-Collados, J., Babić, A., Marshall, S., der Eycken, E. V., Angulo, C., Lu, Y., et al. (2024) · Biological Psychiatry · doi:10.1016/j.biopsych.2024.01.001 · Cited by 48
“While current GWASs on psychotropic drug treatment outcomes have not yielded genomic predictors that can be integrated into strati fi cation and prediction of treatment outcomes, data from clozapine clinics in the United Kingdom and Norway have been used to conduct analyses that link genomic liability to SCZ with antip…”
MethodInfluentialKho, P. F., Mortlock, S., Amant, F. C., Annibali, D., Ashton, K. A., Attia, J., Auer, P. L., Beckmann, M. W., Black, A., Brinton, L. A., et al. (2021) · Human Genetics · doi:10.1007/s00439-021-02312-0 · Cited by 41
Liu, N., Xu, J., Liu, H., Zhang, S., Li, M., Zhou, Y., Qin, W., Li, M. J., Yu, C., & Initiative, A. D. N. (2021) · PLoS Genetics · doi:10.1371/journal.pgen.1009363 · Cited by 39
Examining the shared etiology of psychopathology with genome-wide association studies
Mallard, T. T., Grotzinger, A. D., & Smoller, J. W. (2023) · Physiological Reviews · doi:10.1152/physrev.00016.2022 · Cited by 38
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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