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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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Genetic mapping across autoimmune diseases reveals shared associations and mechanisms
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“Most pleiot-ropy mapping methods also assume that effects are shared across diseases 19 ; however, it is unclear whether this approach will work at loci that contain multiple associations that vary across diseases (for example, IL2RA ).”
Combining genome-wide association studies highlight novel loci involved in human facial variation
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InfluentialLong-term impact of digital media on brain development in children
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“These SNPs were discovered by large genome-wide association studies (GWAS) on educational attainment, mathematical ability, and general cognitive ability 72 .”
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“Following the pipeline developed by Allegrini et al. (2019), multivariate polygenic scores were constructed using MTAG (68) and Genomic SEM (69), and combined the IQ3 and EA3 GPS with summary statistics of three additional educationally relevant traits: household income (N= 96,900; (70)), age at completion of full-tim…”
Wendt, F. R., Pathak, G. A., Deak, J. D., Angelis, F. D., Koller, D., Cabrera‐Mendoza, B., Lebovitch, D. S., Levey, D. F., Stein, M. B., Kranzler, H. R., et al. (2022) · Molecular Psychiatry · doi:10.1038/s41380-022-01469-y · Cited by 35
“As controls, we included GWASs of neuroticism (expected positive rg) and subjective well-being (expected negative rg) [19].”
BackgroundMethodMultivariate genetic analysis of personality and cognitive traits reveals abundant pleiotropy
Hindley, G., Shadrin, A. A., van der Meer, D., Parker, N., Cheng, W., O’Connell, K. S., Bahrami, S., Lin, A., Karadag, N., Holen, B., et al. (2023) · Nature Human Behaviour · doi:10.1038/s41562-023-01630-9 · Cited by 37
Low-frequency variation in TP53 has large effects on head circumference and intracranial volume
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Multi-trait analysis of rare-variant association summary statistics using MTAR
Luo, L., Shen, J., Zhang, H., Chhibber, A., Mehrotra, D. V., & Tang, Z.-Z. (2020) · Nature Communications · doi:10.1038/s41467-020-16591-0 · Cited by 40
“Many multi-trait methods are designed for testing the single-variant association 3 – 8 .”
MethodThorwarth, P., Liu, G., Ebmeyer, E., Schacht, J., Schachschneider, R., Kazman, E., Reif, J. C., Würschum, T., & Longin, C. F. H. (2018) · Theoretical and Applied Genetics · doi:10.1007/s00122-018-3236-x · Cited by 37
Human brain anatomy reflects separable genetic and environmental components of socioeconomic status
Kweon, H., Aydogan, G., Dagher, A., Bzdok, D., Ruff, C. C., Nave, G., Farah, M. J., & Koellinger, P. D. (2022) · Science Advances · doi:10.1126/sciadv.abm2923 · Cited by 36
Jung, H., Jung, H., Baek, E. J., Kwon, S. Y., Kang, J., Lim, J. E., & Oh, B. (2024) · Communications Biology · doi:10.1038/s42003-024-05874-7 · Cited by 37
“2, and the difference in OR per 1 SD PRS between RFDiseasemetaPRS and disease PRS varied among the diseases, ranging from 6.33E − 03 (breast cancer) to 0.22 (chronic renal failure) (Supplementary Table 9).”
InfluentialKudinov, A. A., Dementieva, N. V., Mitrofanova, O. V., Игоревна, С. О., Федорова, Е. С., Ларкина, Т. А., Mishina, A. I., Plemyashov, K. V., Griffin, D. K., & Romanov, M. N. (2019) · BMC Genomics · doi:10.1186/s12864-019-5605-5 · Cited by 36
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“24 Genotypic prediction of psychiatric disorders and related phenotypes has been shown to improve by exploiting genetic correlations among multiple related traits, 7 25 and methods have been extended to incorporate polygenic scores for multiple traits.”
Genetic predictors of lifelong medication-use patterns in cardiometabolic diseases
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The Genetic Determinants of Aortic Distention
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Genome-wide meta-analysis of alcohol use disorder in East Asians
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“Multi-trait analysis [40] was performed, which combined data from this study with excessive alcohol consumption defined as weekly intake >150ml of alcohol for ≥6 months from the Taiwan Biobank [41].”
MethodJiang, X., Xu, Z., Soorma, T., Tariq, A., Bhatti, T., Baneke, A., Pontikos, N., Leo, S. M., Webster, A., Williams, K. M., et al. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2119675119 · Cited by 35
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“As illustrated by the findings from a series of recent studies from multiple research groups showcasing a new generation of biostatistical tools, leveraging genetic overlap has the potential to substantially enhance both locus discovery and genetics-based prediction [98, 129, 130].”
BackgroundDimensional and transdiagnostic phenotypes in psychiatric genome-wide association studies
Waszczuk, M. A., Jonas, K. G., Bornovalova, M. A., Breen, G. D., Bulik, C. M., Docherty, A. R., Eley, T. C., Hettema, J. M., Kotov, R., Krueger, R. F., et al. (2023) · Molecular Psychiatry · doi:10.1038/s41380-023-02142-8 · Cited by 35
“While genomic SEM and other meta-analytic approaches such as MTAG [119] infer empirically-based transdiagnostic phenotypes, Box 1.”
Spence, J. P., Sinnott-Armstrong, N., Assimes, T. L., & Pritchard, J. K. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.04.18.488696 · Cited by 34
“Fortunately, this can be relatively easily fixed as has been done in other contexts [58].”
BackgroundGorman, B. R., Ji, S., Francis, M., Sendamarai, A. K., Shi, Y., Devineni, P., Saxena, U., Partan, E. S., DeVito, A. K., Byun, J., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-52129-4 · Cited by 38
“Similar odds ratios at rs61494113 were observed across LUSC and breast cancer, and MTAG enhanced the GWAS signal at this locus (Fig.”
InfluentialJoint Association Analysis Identified 18 New Loci for Bone Mineral Density
Pei, Y., Liu, L., Liu, T.-L., Yang, X., Zhang, H., Wei, X., Feng, G.-J., Hai, R., Ran, S., & Zhang, L. (2019) · Journal of Bone and Mineral Research · doi:10.1002/jbmr.3681 · Cited by 34
“We applied the multi-trait analysis of GWAS (MTAG) method to the two studies for a joint analysis.((10)) One of the assumptions underlying MTAG is that the variant affects multiple traits jointly; ie, the pleiotropic effect.”
BackgroundMethodInfluentialNovel Gene-Based Analysis of ASD GWAS: Insight Into the Biological Role of Associated Genes
Alonso-González, A., Calaza, M., Rodriguez‐Fontenla, C., & Carracedo, Á. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00733 · Cited by 33
A genome-wide association study identifies genetic loci associated with specific lobar brain volumes
van der Lee, S. J., Knol, M. J., Chauhan, G., Satizábal, C. L., Smith, A. V., Hofer, E., Bis, J. C., Hibar, D. P., Hilal, S., van den Akker, E. B., et al. (2019) · Communications Biology · doi:10.1038/s42003-019-0537-9 · Cited by 33
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“Subsequently, MTAG [43] (multi-trait analyses of GWAS) was run to include summary statistics from all eight theta EEG coherences phenotypes, both low and high theta for the four pairs described in the “EEG recording and processing” section.”
BackgroundMethodInfluentialAdvances and Applications of Polygenic Scores for Coronary Artery Disease
Patel, A. P., & Khera, A. V. (2022) · Annual Review of Medicine · doi:10.1146/annurev-med-042921-112629 · Cited by 35
Smeland, O. B., Frei, O., Fan, C.-C., Shadrin, A. A., Dale, A. M., & Andreassen, O. A. (2019) · Psychiatric Genetics · doi:10.1097/ypg.0000000000000234 · Cited by 34
Idiopathic pulmonary fibrosis and the role of genetics in the era of precision medicine
Alonso-González, A., Tosco-Herrera, E., Molina‐Molina, M., & Flores, C. (2023) · Frontiers in Medicine · doi:10.3389/fmed.2023.1152211 · Cited by 36
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Bayesian meta‐analysis across genome‐wide association studies of diverse phenotypes
Trochet, H., Pirinen, M., Band, G., Jostins, L., McVean, G., & Spencer, C. C. A. (2019) · Genetic Epidemiology · doi:10.1002/gepi.22202 · Cited by 33
“When estimating the correlation directly from the genome‐wide β̂‐statistics, we used several approaches: taking the intercept from LD score regression (LDSC; B. Bulik‐Sullivan et al., 2015; B. K. Bulik‐Sullivan et al., 2015; Turley et al., 2018), which provides an estimate of trait covariance due to undetected shared…”
BackgroundMethodInfluentialPolygenic risk associated with post-traumatic stress disorder onset and severity
Misganaw, B., Guffanti, G., Lori, A., Amara, D. A., Flory, J. D., SBPBC, Hammamieh, R., Gautam, A., Yang, R., Daigle, B. J., et al. (2019) · Translational Psychiatry · doi:10.1038/s41398-019-0497-3 · Cited by 34
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“As described in detail below, the multi-trait analysis of GWAS (MTAG) software 48 was used to meta-analyze GWAS summary statistics for four non-overlapping samples of European ancestry: (1) a GWAS of avSER in 101,523 adult UK Biobank participants; (2) a GWAS of age-of-onset-of-spectacle-wear ( AOSW…”
Chen, L. M., Pokhvisneva, I., Lahti‐Pulkkinen, M., Kvist, T., Baldwin, J. R., Parent, C. I., Silveira, P. P., Lahti, J. M. T., Räikkönen, K., Glover, V. A. S., et al. (2023) · Journal of the American Academy of Child & Adolescent Psychiatry · doi:10.1016/j.jaac.2023.08.018 · Cited by 38
“29 PRSs for ADHD and schizophrenia focused on the top 10,000 SNPs identi fi ed from the GWASs of ADHD and schizophrenia.”
BackgroundHan, X., Qassim, A., An, J., Marshall, H. N., Zhou, T., Ong, J., Hassall, M., Hysi, P. G., Foster, P. J., Khaw, P. T., et al. (2019) · Human Molecular Genetics · doi:10.1093/hmg/ddz193 · Cited by 30
Warrington, N. M., Hwang, L., Nivard, M. G., & Evans, D. M. (2021) · Nature Communications · doi:10.1038/s41467-021-25723-z · Cited by 40
He, Y., Qian, D. C., Diao, J. A., Cho, M. H., Silverman, E. K., Gusev, A., Manrai, A. K., Martin, A. R., & Patel, C. J. (2023) · Nature Communications · doi:10.1038/s41467-023-44047-8 · Cited by 36
Reynolds, T., Johnson, E. C., Huggett, S. B., Bubier, J. A., Palmer, R. H. C., Arpana Agrawal, A., Baker, E. J., & Chesler, E. J. (2020) · Neuropsychopharmacology · doi:10.1038/s41386-020-00795-5 · Cited by 32
“…disorder can transfer across species and be easily captured with experimental data, and increasingly, GWAS of psychiatric disorders are providing corroborating support for variants that in fl uence both disorders and their trait-like manifestations that may be recapitulated in model organisms [46].”
BackgroundPredicting the Future of Genetic Risk Profiling of Glaucoma
Han, X., Hewitt, A. W., & MacGregor, S. (2020) · JAMA Ophthalmology · doi:10.1001/jamaophthalmol.2020.5404 · Cited by 31
Multitrait GWAS to connect disease variants and biological mechanisms
Julienne, H., Laville, V., McCaw, Z. R., He, Z., Guillemot, V., Lasry, C., Ziyatdinov, A., Nerin, C., Vaysse, A., Lechat, P., et al. (2021) · PLoS Genetics · doi:10.1371/journal.pgen.1009713 · Cited by 35
Temprano‐Sagrera, G., Sitlani, C. M., Bone, W. P., Martín‐Bórnez, M., Voight, B. F., Morrison, A. C., Damrauer, S. M., de Vries, P. S., Smith, N. L., Sabater‐Lleal, M., et al. (2022) · Journal of Thrombosis and Haemostasis · doi:10.1111/jth.15698 · Cited by 30
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Multi-Ancestry Meta-Analysis yields novel genetic discoveries and ancestry-specific associations
Turley, P., Martin, A. R., Goldman, G., Li, H., Kanai, M., Walters, R. K., Jala, J. B., Lin, K., Millwood, I. Y., Carey, C. E., et al. (2021) · doi:10.1101/2021.04.23.441003 · Cited by 35
Self-citationKweon, H., Burik, C. A. P., Ning, Y., Ahlskog, R., Xia, C., Abner, E., Bao, Y., Bhatta, L., Faquih, T. O., de Feijter, M., et al. (2025) · Nature Human Behaviour · doi:10.1038/s41562-024-02080-7 · Cited by 40
“We then meta-analysed the male and female results for each income measure using the multi-trait analysis of genome-wide association summary statistics method (MTAG) 28 , which accounts for any potential genetic relatedness between the male and female samples.”
InfluentialSelf-citationYou, D., Wu, Y., Lu, M., Shao, F., Tang, Y., Liu, S., Liu, L., Zhou, Z., Zhang, R., Shen, S., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-58248-w · Cited by 35
Hwang, L., Gharahkhani, P., Breslin, P. A. S., Gordon, S. D., Zhu, G., Martin, N. G., Martin, N. G., Reed, D. R., & Wright, M. J. (2018) · BMC Genomics · doi:10.1186/s12864-018-5058-2 · Cited by 30
“Recent advancement in the methodology of multivariate GWAS (i.e. MTAG43) could make multivariate analysis easier to apply because it uses individual summary level results from different studies and does not require correlated phenotypes to be collected from the same sample.”
BackgroundTan, V. Y., & Timpson, N. J. (2022) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-121321-093606 · Cited by 28
du Pont, A., Rhee, S. H., Corley, R. P., Hewitt, J. K., & Friedman, N. P. (2019) · Journal of Abnormal Psychology · doi:10.1037/abn0000430 · Cited by 32
“Common genetic influences on neuroticism, rumination, and psychopathology also indicate that rumination may be a potential candidate for inclusion in a multi-trait analysis of genome-wide association studies (MTAG; Turley et al., 2018).”
BackgroundJASS: command line and web interface for the joint analysis of GWAS results
Julienne, H., Lechat, P., Guillemot, V., Lasry, C., Yao, C., Araud, R., Laville, V., Vilhjálmsson, B. J., Ménager, H., & Aschard, H. (2020) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqaa003 · Cited by 29
“Finally, to compare the execution time of JASS with the HIPO (10) and MTAG (3) methods on a lipid example containing four traits and 1 818 293 SNPs, HIPO, MTAG and JASS ran, respectively, in 329, 212 and 33 s.”
BackgroundMethodGWAS for genetics of complex quantitative traits: Genome to pangenome and SNPs to SVs and k‐mers
Gupta, P. K. (2021) · BioEssays · doi:10.1002/bies.202100109 · Cited by 31
“Amethod called “Multi-TraitAnalysis ofGWAS (MTAG)was also developed for joint analysis of summary statistics from GWAS of different traits[44].”
MethodKoel, M., Võsa, U., Jõeloo, M., Läll, K., Pujol‐Gualdo, N., Laivuori, H. M., Lemmelä, S. M., Daly, M. J., FinnGen, Palta, P., et al. (2023) · Human Molecular Genetics · doi:10.1093/hmg/ddad043 · Cited by 32
“8 (50) (Multi-Trait Analysis of GWAS) to jointly analyse the summary statistics from dysplasia and CC EUR analyses and thus increase the power to detect additional associations.”
BackgroundSchwabe, I., Milaneschi, Y., Gerring, Z. F., Sullivan, P. F., Schulte, E. C., Suppli, N. P., Thorp, J. G., Derks, E. M., & Middeldorp, C. M. (2019) · Psychological Medicine · doi:10.1017/s0033291719002502 · Cited by 34
“…genetic liability across different phenotypes, the newly developed multi-trait analysis of GWAS (MTAG) has been shown to substantially increase statistical power to detect genetic association for each trait in a joint analysis of multiple traits using GWAS summary statistics (Turley et al., 2018).”
BackgroundPerforming post-genome-wide association study analysis: overview, challenges and recommendations
Adam, Y., Samtal, C., Brandenburg, J., Falola, O., & Adebiyi, E. F. (2021) · F1000Research · doi:10.12688/f1000research.53962.1 · Cited by 31
Kim, M. S., Song, M., Kim, B., Shim, I., Kim, D. S., Natarajan, P., Do, R., & Won, H. (2023) · Cell Reports Medicine · doi:10.1016/j.xcrm.2023.101112 · Cited by 31
“34 We identified proteins that were significantly associated with the top SNPs (p < 1 3 10 (cid”
Cai, Z., Iso‐Touru, T., Sanchez, M.-P., Kadri, N. K., Bouwman, A. C., Chitneedi, P., MacLeod, I. M., Jagt, C. J. V., Chamberlain, A. J., Gredler, B., et al. (2024) · Genetics Selection Evolution · doi:10.1186/s12711-024-00920-8 · Cited by 42
“Moreover, multi-trait analysis of highly correlated traits can boost power by using information from multiple traits to prioritize common genetic mechanisms [40].”
BackgroundMethodInfluentialThe phenotypic and genetic association between endometriosis and immunological diseases
Shigesi, N., Harris, H. R., Fang, H., Ndungu, A., Lincoln, M. R., Cotsapas, C. J., Knight, J. C., Missmer, S. A., Morris, A. P., Becker, C. M., et al. (2025) · Human Reproduction · doi:10.1093/humrep/deaf062 · Cited by 33
Bonelli, R., Ansell, B. R. E., Lotta, L. A., Scerri, T. S., Clemons, T. E., Leung, I., Pető, T., Bird, A. C., Sallo, F. B., Langenberg, C., et al. (2021) · Genome Medicine · doi:10.1186/s13073-021-00848-4 · Cited by 27
He, W., Han, X., Ong, J., Hewitt, A. W., Mackey, D. A., Gharahkhani, P., & MacGregor, S. (2022) · JAMA Ophthalmology · doi:10.1001/jamaophthalmol.2022.0891 · Cited by 28
de la Fuente, J., Grotzinger, A. D., Marioni, R. E., Nivard, M. G., & Tucker–Drob, E. M. (2022) · PLoS Genetics · doi:10.1371/journal.pgen.1010208 · Cited by 30
Siewert, K. M., & Voight, B. F. (2018) · Circulation Genomic and Precision Medicine · doi:10.1161/circgen.118.002239 · Cited by 28
Jamshidi, J., Williams, L. M., Schofield, P. R., Park, H. R. P., Montalto, A., Chilver, M. R., Bryant, R. A., Claudio Toma, A., Fullerton, J. M., & Gatt, J. M. (2020) · Genes Brain & Behavior · doi:10.1111/gbb.12694 · Cited by 31
“Novel statistical methods such as MTAG and GWAMA have made it possible to meta-analyze the GWAS of different correlated phenotypes to improve the power of detecting genetic variants with very small effect size.(8) This approach has advanced previous wellbeing”
MethodConstruction and Application of Polygenic Risk Scores in Autoimmune Diseases
Khunsriraksakul, C., Markus, H., Olsen, N. J., Carrel, L., Jiang, B., & Liu, D. J. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.889296 · Cited by 31
“Multi-trait GWAS methods - MTAG (34) - wMT-GWAS (35) - Genomic SEM (36)”
MethodInfluentialCárcel‐Márquez, J., Muiño, E., Gallego-Fábrega, C., Cullell, N., Lledós, M., Llucià‐Carol, L., Sobrino, T., Campos, F., Castillo, J., Freijo, M. D. M., et al. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.940696 · Cited by 27
Self-citationYao, M., Huang, X., Guo, Y., Zhao, J. V., & Liu, Z. (2023) · Journal of Medical Virology · doi:10.1002/jmv.28570 · Cited by 28
“We conducted a meta‐analysis with multiple GWASs to identify novel SNPs with strong signals using the MTAG software.(19) MTAG re‐estimates the effect sizes for each COVID‐19 trait by incorpo-”
MethodOptimizing and benchmarking polygenic risk scores with GWAS summary statistics
Zhao, Z., Gruenloh, T., Yan, M., Wu, Y., Sun, Z., Miao, J., Wu, Y., Song, J., & Lu, Q. (2024) · Genome biology · doi:10.1186/s13059-024-03400-w · Cited by 28
“…to support the superiority of ensemble PRS as a proof of concept, more genetic variants and more PRS methods need to be jointly modeled and evaluated in the future, including scores that leverage auxiliary information including functional annotation [13, 14] or multiple phenotypes [15, 17, 63].”
MethodGenetic Nature or Genetic Nurture? Quantifying Bias in Analyses Using Polygenic ScoresPreprint
Trejo, S., & Domingue, B. W. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/524850 · Cited by 27
“…Study of Adolescent to Adult Health (Harris 2013) for six phenotypes: educational attainment (Lee et al. 2018), cognitive ability (Lee et al. 2018), depressive symptoms (Turley et al. 2018), birth weight (Warrington et al. 2019), body mass index (Locke et al. 2015), and height (Wood et al. 2014).”
BackgroundZhuang, Z., Yao, M., Wong, J. Y. Y., Liu, Z., & Huang, T. (2021) · BMC Medicine · doi:10.1186/s12916-021-01972-z · Cited by 28
“Out of the 16 independent loci reported for BF% using MTAG, 6 were novel associations and 2 of these fell within proteincoding gene bodies (PLA2G6, RPTOR) (Table S4).”
BackgroundMethodInfluentialXPXP: improving polygenic prediction by cross-population and cross-phenotype analysis
Xiao, J., Cai, M., Hu, X., Wan, X., Chen, G., & Yang, C. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac029 · Cited by 24
“We have also been aware of several other methods that construct PRS in a single population, such as lassosum [Mak, 2017], MTAG [Turley, 2018].”
MethodA systematic review of the neural correlates of well-being reveals no consistent associations
de Vries, L. P., van de Weijer, M. P., & Bartels, M. (2023) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2023.105036 · Cited by 29
Wang, J., Campos, A. I., Rentería, M. E., & Xu, L. (2023) · European Journal of Preventive Cardiology · doi:10.1093/eurjpc/zwad005 · Cited by 24
Correlation of mutated gene and signalling pathways in ASD
Apte, M., & Kumar, A. B. (2023) · IBRO Neuroscience Reports · doi:10.1016/j.ibneur.2023.03.011 · Cited by 30
MRBEE: A bias-corrected multivariable Mendelian randomization method
Lorincz‐Comi, N. J., Yang, Y., Li, G., & Zhu, X. (2024) · Human Genetics and Genomics Advances · doi:10.1016/j.xhgg.2024.100290 · Cited by 31
“We also compared GWPT with cross-phenotype association analysis (CPASSOC), 37 multi-trait analysis of GWAS (MTAG), 38 which are joint tests of association between all exposures in the outcome.”
García‐Marín, L. M., Campos, A. I., Díaz‐Torres, S., Rabinowitz, J. A., Ceja, Z., Mitchell, B. L., Grasby, K. L., Thorp, J. G., Agartz, I., Alhusaini, S., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01951-z · Cited by 35
“0.8) 69 .”
InfluentialAnalysis of common genetic variation and rare CNVs in the Australian Autism Biobank
Yap, C. X., Alvares, G. A., Henders, A. K., Lin, T., Wallace, L. M., Farrelly, A., McLaren, T., Berry, J., Vinkhuyzen, A. A. E., Trzaskowski, M., et al. (2021) · Molecular Autism · doi:10.1186/s13229-020-00407-5 · Cited by 29
“The largest published ASD genome-wide association study (GWAS) [4] identified five genomewide significant loci in an analysis of 18,381 cases and 27,969 controls, and an additional seven loci were identified using multi-trait analysis of GWAS [5] methodology leveraging power from correlated traits.”
BackgroundSelf-citationIdentifying nootropic drug targets via large-scale cognitive GWAS and transcriptomics
Lam, M., Chen, C., Ge, T., Xia, Y., Hill, D. W., Trampush, J. W., Yu, J., Knowles, E., Davies, G., Stahl, E. A., et al. (2021) · Neuropsychopharmacology · doi:10.1038/s41386-021-01023-4 · Cited by 23
“, [30], which used height to show that MTAG was robust to 50% sample overlap.”
Wang, H., Yi, Z., & Shi, T. (2021) · Science China Life Sciences · doi:10.1007/s11427-020-1934-x · Cited by 26
Jamshidi, J., Schofield, P. R., Gatt, J. M., & Fullerton, J. M. (2022) · Translational Psychiatry · doi:10.1038/s41398-022-01874-5 · Cited by 25
“As wellbeing is genetically and phenotypically correlated to negative mental health constructs including neuroticism and depressive symptoms [8, 10], other analytic approaches have used multivariate GWAS meta-analysis to include additional correlated phenotypes, as part of a ‘wellbeing spectrum’ [10, 11] that spans co…”
BackgroundShared components of heritability across genetically correlated traits
Ballard, J. L., & O’Connor, L. J. (2022) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2022.04.003 · Cited by 22
Associations between diet and incidence risk of lung cancer: A Mendelian randomization study
Yan, H., Jin, X., Zhang, C., Zhu, C., He, Y., Du, X., & Feng, G. (2023) · Frontiers in Nutrition · doi:10.3389/fnut.2023.1149317 · Cited by 28
“To select valid IVs, we included single nucleotide polymorphisms (SNPs) at the genomewide significant level (p < 5 × 10−8) (21) and used strict cutoff values (R2 < 0.”
MethodGenetic Association of Phosphodiesterases With Human Cognitive Performance
Gurney, M. E. (2019) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2019.00022 · Cited by 25
RAISS: robust and accurate imputation from summary statistics
Julienne, H., Shi, H., Paşaniuc, B., & Aschard, H. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz466 · Cited by 25
Minimal phenotyping yields GWAS hits of reduced specificity for major depressionPreprint
Cai, N., Revez, J. A., Adams, M. J., Andlauer, T. F. M., Breen, G. D., Byrne, E. M., Clarke, T., Forstner, A. J., Grabe, H. J., Hamilton, S. P., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/440735 · Cited by 25
Valid inference for machine learning-assisted genome-wide association studies
Miao, J., Wu, Y., Sun, Z., Miao, X., Lu, T., Zhao, J., & Lu, Q. (2024) · Nature Genetics · doi:10.1038/s41588-024-01934-0 · Cited by 28
“The validity of the results of these approaches is often justified based on ad hoc analyses that show comparable effect sizes between GWAS of imputed and observed phenotypes 2,4,5,8 or include efforts to account for phenotypic heterogeneity during meta-analyses 2,20,21 .”
Multimorbidity, comorbidity, frailty, and venous thromboembolism
Zöller, B., & Connors, J. M. (2024) · Haematologica · doi:10.3324/haematol.2023.284579 · Cited by 24
Shared genetic architecture and bidirectional clinical risks within the psycho-metabolic nexus
Guo, X., Feng, Y., Ji, X., Jia, N., Maimaiti, A., Lai, J., Wang, Z., Yang, S., & Hu, S. (2024) · EBioMedicine · doi:10.1016/j.ebiom.2024.105530 · Cited by 31
Stuttering: Our Current Knowledge, Research Opportunities, and Ways to Address Critical Gaps
Chang, S., Below, J. E., Chow, H. M., Guenther, F. H., Wray, A. H., Jackson, E. S., Max, L., Neef, N. E., SheikhBahaei, S., Shekim, L., et al. (2025) · Neurobiology of Language · doi:10.1162/nol_a_00162 · Cited by 28
Li, X., & Zhao, H. (2020) · PLoS Genetics · doi:10.1371/journal.pgen.1009089 · Cited by 22
Georgakis, M. K., Parodi, L., Frerich, S., Mayerhofer, E., Tsivgoulis, G. K., Pirruccello, J. P., Słowik, A., Rundek, T., Malik, R., Dichgans, M., et al. (2022) · Annals of Neurology · doi:10.1002/ana.26332 · Cited by 25
“We did not perform alternative methods, such as MTAG 21 or genomic SEM, 22 because they are based on the assumption that the examined pairs of traits represent different manifestations of a common underlying mechanism, which in our analysis remains unclear.”
Currant, H., Fitzgerald, T. W., Patel, P. J., Khawaja, A. P., Webster, A. R., Mahroo, O. A., & Birney, E. (2023) · PLoS Genetics · doi:10.1371/journal.pgen.1010587 · Cited by 22
“The summary statistics from the GWAS of the ONL, IS and OS were meta-analysed using MTAG [21].”
MethodInfluentialmtPGS: Leverage multiple correlated traits for accurate polygenic score construction
Xu, C., Ganesh, S. K., & Zhou, X. (2023) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2023.08.016 · Cited by 21
Cardiovascular Predictive Value and Genetic Basis of Ventricular Repolarization Dynamics
Ramírez, J., van Duijvenboden, S., Aung, N., Laguna, P., Pueyo, E., Tinker, A., Lambiase, P. D., Orini, M., & Munroe, P. B. (2019) · Circulation Arrhythmia and Electrophysiology · doi:10.1161/circep.119.007549 · Cited by 25
Glubb, D. M., Thompson, D., Aben, K., Alsulimani, A., Amant, F., Annibali, D., Attia, J., Barricarte, A., Beckmann, M. W., Berchuck, A., et al. (2020) · Cancer Epidemiology, Biomarkers and Prevention · doi:10.1158/1055-9965.epi-20-0739 · Cited by 20
“Cross-cancer GWAS meta-analyses To identify joint endometrial and ovarian cancer genetic risk variants, summary statistics from ECAC and OCAC were combined by inverse-variance meta-analysis, adjusting for unknown sample overlap using MTAG (26).”
MethodRestuadi, R., Garton, F. C., Benyamin, B., Lin, T., Williams, K. L., Vinkhuyzen, A. A. E., van Rheenen, W., Zhu, Z., Laing, N. G., Mather, K. A., et al. (2021) · European Journal of Human Genetics · doi:10.1038/s41431-021-00885-y · Cited by 25
“MTAG and SMTpred use similar methodologies to develop a multi-trait predictor.”
BackgroundInfluentialSelf-citationGiovannetti, M., Göschl, C., Dietzen, C., Andersen, S. U., Kopřiva, S., & Busch, W. (2019) · PLoS Genetics · doi:10.1371/journal.pgen.1008126 · Cited by 22
Xu, H. Y., Toikumo, S., Crist, R. C., Glogowska, K., Jinwala, Z., Deak, J. D., Justice, A. C., Gelernter, J. E., Johnson, E. C., Kranzler, H. R., et al. (2023) · Addiction · doi:10.1111/add.16229 · Cited by 23
“We calculated maximum False Discovery Rate (maxFDR) [11] as the upper bound of False Discovery Rate (FDR) for”
MethodAdams, M. J., Thorp, J. G., Jermy, B. S., Kwong, A. S. F., Kõiv, K., Grotzinger, A. D., Nivard, M. G., Marshall, S., Milaneschi, Y., Baune, B. T., et al. (2024) · Psychological Medicine · doi:10.1017/s0033291724001880 · Cited by 31
“An upstream variant (rs6884321, p = 4.27 × 10 − 8 ) for an uncharacterized long intergenic non-protein coding RNA (LINC01938) was associated with Community Anhedonia while this region was previously associated with neuroticism and MDD (Turley et al., 2018).”
BackgroundInfluentialSelf-citationGarcía‐Marín, L. M., Campos, A. I., Martin, N. G., Cuéllar-Partida, G., & Rentería, M. E. (2021) · The Journal of Headache and Pain · doi:10.1186/s10194-021-01284-w · Cited by 21
Jiang, X., Zhang, M. J., Zhang, Y., Durvasula, A., Inouye, M., Holmes, C. C., Price, A. L., & McVean, G. (2023) · Nature Genetics · doi:10.1038/s41588-023-01522-8 · Cited by 23
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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