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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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Self-citationGenetic studies of body mass index yield new insights for obesity biology
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Self-citationGlutamate Receptor Ion Channels: Structure, Regulation, and Function
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Genome-Wide Gene-Based Multi-Trait Analysis
Deng, Y., He, T., Fang, R., Li, S., Cao, H., & Cui, Y. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.00437 · Cited by 9
Facal, F., Flórez, G., Blanco, V., Rodríguez, J., Pereiro, C., Fernández, J. M., Fariñas, E., Estévez, V., Gómez-Trigo, J., Gurriarán, X., et al. (2021) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2021.108556 · Cited by 9
Multivariate Analysis and Modelling of multiple Brain endOphenotypes: Let’s MAMBO!
Vilor‐Tejedor, N., Garrido-Martín, D., Rodríguez‐Fernández, B., Lamballais, S., Guigó, R., & Gispert, J. D. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.10.019 · Cited by 9
“Some examples include SHom/SHet [78], metaCCA [79], MTAG [80] or MTAR [81].”
BackgroundAjnakina, O., Shamsutdinova, D., Wimberley, T., Dalsgaard, S., & Steptoe, A. (2022) · BMC Medicine · doi:10.1186/s12916-022-02279-3 · Cited by 7
“To calculate PGS for ADHD based on multiple traits that have an overlapping genetic make-up with ADHD, the Multi-Trait Analysis of GWAS (MTAG) approach was employed [19].”
BackgroundMethodInfluentialA Novel Framework for Analysis of the Shared Genetic Background of Correlated Traits
Svishcheva, G. R., Tiys, E., Elgaeva, E. E., Feoktistova, S. G., Timmers, P. R. H. J., Sharapov, S., Axenovich, T. I., & Tsepilov, Y. A. (2022) · Genes · doi:10.3390/genes13101694 · Cited by 10
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A genome-wide association study of mammographic texture variation
Liu, Y., Chen, H., Heine, J., Lindström, S., Turman, C., Warner, E. T., Winham, S. J., Vachon, C. M., Tamimi, R. M., Kraft, P. L., et al. (2022) · Breast Cancer Research · doi:10.1186/s13058-022-01570-8 · Cited by 13
“Studies have shown that jointly analyzing GWAS data of multiple traits can boost power to detect genetic associations for each trait and improve the prediction performance [44, 45].”
BackgroundConditional GWAS analysis identifies putative disorder-specific SNPs for psychiatric disordersPreprint
Byrne, E. M., Zhu, Z., Qi, T., Skene, N., Bryois, J., Pardiñas, A. F., Stahl, E. A., Smoller, J. W., Rietschel, M., Owen, M. J., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/592899 · Cited by 9
Self-citationYang, Z., Wu, H., Lee, P. H., Tsetsos, F., Davis, L. K., Yu, D., Lee, S., Dalsgaard, S., Haavik, J., Barta, C., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/770222 · Cited by 9
Self-citationIntegrative multi-omics QTL colocalization maps regulatory architecture in aging human brainPreprint
Cao, X., Sun, H., Feng, R., Mazumder, R., Najar, C. F. B. A., Li, Y., Jager, P. L. D., Bennett, D. A., Dey, K. K., & Wang, G. (2025) · medRxiv · doi:10.1101/2025.04.17.25326042 · Cited by 10
Buser, T., Ahlskog, R., Johannesson, M., Koellinger, P., & Oskarsson, S. (2024) · Labour Economics · doi:10.1016/j.labeco.2024.102544 · Cited by 10
Self-citationExploring the Shared Genetic Architecture Between Obstructive Sleep Apnea and Body Mass Index
Zhou, P., Li, L., Lin, Z., Ming, X., Feng, Y., Hu, Y., & Chen, X. (2024) · Nature and Science of Sleep · doi:10.2147/nss.s459136 · Cited by 9
Yan, Z., Xu, Y., Li, K., & Liu, L. (2024) · BMC Genomics · doi:10.1186/s12864-024-10536-3 · Cited by 8
“MTAG is a generalized meta-analysis method that enhances the statistical power to estimate genotype–phenotype variance–covariance matrices, thereby generating trait-specific estimates for each SNP [20].”
MethodMa, Y., Jiang, D., Li, J., Zheng, G., Deng, Y. J., Gou, X., Gao, S., Cheng Ann, C. H. E. N., Zhou, Y., Zhang, Y., et al. (2025) · Translational Psychiatry · doi:10.1038/s41398-025-03243-4 · Cited by 9
“Following established recommendations [7, 15], m -values > 0.9 indicated SNP association with a given phenotype, while m values 0.1 indicated no association.”
InfluentialAhn, Y., Kim, J., Jung, K., Lee, D. J., Jung, J. Y., Eom, Y., Park, S., Kim, J., Kim, H., Jo, H., et al. (2025) · American Journal of Psychiatry · doi:10.1176/appi.ajp.20240095 · Cited by 10
Lyu, Q., Huang, L., Liu, G., & Shen, W. (2025) · Med Research · doi:10.1002/mdr2.70008 · Cited by 8
Dissecting pleiotropy to gain mechanistic insights into human disease
Jee, Y. H., He, Y., Lu, W., Shi, Y., Lazarev, D., Daly, M. J., Reeve, M. P., & Martin, A. R. (2025) · Nature Reviews Genetics · doi:10.1038/s41576-025-00908-0 · Cited by 12
Kendler, K. S., & Aggen, S. H. (2023) · Psychological Medicine · doi:10.1017/s0033291723001241 · Cited by 8
“…genome-wide association study-derived polygenic risk scores, between lifetime MD and various forms of current depressive symptoms with representative estimates from three studies of 0.70 (Turley et al., 2018), 1.00 (Direk et al., 2017) and 0.72 – 0.88 across different cohorts (Levey et al., 2021).”
BackgroundInfluentialWorldwide Well-Being: Simulated Twins Reveal Genetic and (Hidden) Environmental Influences
Røysamb, E., Moffitt, T. E., Caspi, A., Ystrøm, E., & Nes, R. B. (2023) · Perspectives on Psychological Science · doi:10.1177/17456916231178716 · Cited by 9
“…studies have shown exciting progress, also in the field of well-being (Baselmans et al., 2019; Rietveld et al., 2013), and genome-wide association studies have started to identify specific variants involved, currently explaining 1% to 2% of the variance (Okbay et al., 2016; Turley et al., 2018).”
BackgroundCai, Z., Wu, X., Thomsen, B., Lund, M. S., & Sahana, G. (2023) · Journal of Dairy Science · doi:10.3168/jds.2023-23252 · Cited by 8
Han, Q.-J., Zhu, Y.-P., Sun, J. M., Ding, X., Wang, X., & Zhang, Q.-Z. (2024) · Frontiers in Pharmacology · doi:10.3389/fphar.2024.1418560 · Cited by 8
“…of BCC, we used MR-PheWAS based on inverse-variance-weight (IVW) and Wald Ratio (Ku fl ik and Gage, 1991) to minimize reverse bias and residual confounding bias, as it does not affect results through pathways other than exposure (GWAS to the people, 2018; Turley et al., 2018; Eldjarn et al., 2023).”
BackgroundMethodLee, H., Fernandes, M., Lee, J., Merino, J., & Kwak, S. H. (2025) · Diabetologia · doi:10.1007/s00125-025-06403-9 · Cited by 9
“When searching for shared causal variants where sample overlap may exist, multi-trait GWAS can be applied [86].”
Self-citationYang, X., Zhang, S., & Sha, Q. (2019) · Scientific Reports · doi:10.1038/s41598-018-37538-y · Cited by 8
Genetic cross-disorder analysis in psychiatry: from methodology to clinical utility
Schijven, D., Veldink, J. H., & Luykx, J. J. (2019) · The British Journal of Psychiatry · doi:10.1192/bjp.2019.72 · Cited by 8
Castellini, G., Merola, G. P., Boy, O. B., Pecoraro, V., Bozza, B., Cassioli, E., Rossi, E., Bessi, V., Sorbi, S., Nacmias, B., et al. (2022) · Psychiatric Genetics · doi:10.1097/ypg.0000000000000335 · Cited by 11
Kazantseva, A. V., Davydova, Y. D., Enikeeva, R. F., Mustafin, R. N., Malykh, S. B., Lobaskova, M. M., Kanapin, A. A., Prokopenko, I., & Хуснутдинова, Э. К. (2023) · Genes · doi:10.3390/genes14071355 · Cited by 10
“15 for weighted and unweighted effects based on [20]) (Table S4, Figure S1).”
BackgroundMethodInfluentialMolecular Genetic Studies of Cognitive Ability
Malykh, S. B., Сергеевич, М. А., Karunas, A. S., Enikeeva, R. F., Davydova, Y. D., & Хуснутдинова, Э. К. (2019) · Russian Journal of Genetics · doi:10.1134/s1022795419070111 · Cited by 8
“Subsequently, the results obtained in GWAS of cognitive abilities were combined with the data from the GWAS of educational attainment (number of years of schooling) using MTAG analysis [22].”
MethodNontrivial Replication of Loci Detected by Multi-Trait Methods
Zheng, N., Tsepilov, Y. A., Sharapov, S., Wang, Z., Grishenko, A. K., Feng, X., Shirali, M., Joshi, P. K., Wilson, J. F., Yudi Pawitan, Y., et al. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.627989 · Cited by 7
“MTAG (Multi-trait analysis of GWAS) was developed to integrate GWAS summary results of multiple related traits and improve the inference in each single-trait GWAS (Turley et al., 2018).”
MethodPolygenic regulation of PTSD severity and outcomes among World Trade Center respondersPreprint
Huckins, L. M., Johnson, J., Cancelmo, L. M., Diab, O., Schaffer, J., Cahn, L., Aaronson, C. J., Horn, S. R. G.-V., Schechter, C. B., Marchese, S., et al. (2020) · medRxiv · doi:10.1101/2020.12.06.20244772 · Cited by 9
Identification of 370 genetic loci for age at first sex and birth linked to externalising behaviourPreprint
Mills, M. C., Tropf, F. C., Brazel, D. M., van Zuydam, N. R., Vaez, A., Pers, T. H., Snieder, H., Perry, J. R. B., Ong, K. K., den Hoed, M., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.06.081273 · Cited by 8
Xu, J., Liu, N., Polemiti, E., Garcia-Mondragon, L., Tang, J., Liu, X., Lett, T. A. P., Yu, L., Nöethen, M. M., Yu, C., et al. (2022) · medRxiv · doi:10.1101/2022.09.08.22279549 · Cited by 8
Genome-wide association study identifies new loci associated with OCDPreprint
Strom, N. I., Halvorsen, M., Tian, C., Rück, C., Kvale, G., Hansen, B. K. A., Bybjerg‐Grauholm, J., Grove, J., Boberg, J., Nissen, J. B., et al. (2024) · medRxiv · doi:10.1101/2024.03.06.24303776 · Cited by 10
“We used MTAG 45 to conduct multivariable GWAS analyses for the OCD samples.”
MethodBiomarker-Guided Tailored Therapy in Major Depression
Perna, G., Spiti, A., Torti, T., Daccò, S., & Caldirola, D. (2024) · Advances in experimental medicine and biology · doi:10.1007/978-981-97-4402-2_19 · Cited by 8
Li, Y., Xie, T., Vos, M., Snieder, H., & Hartman, C. A. (2024) · Translational Psychiatry · doi:10.1038/s41398-024-03184-4 · Cited by 8
Goovaerts, S., Naqvi, S., Hoskens, H., Herrick, N., Yuan, M., Shriver, M. D., Shaffer, J. R., Walsh, S., Weinberg, S. M., Wysocka, J., et al. (2025) · Communications Biology · doi:10.1038/s42003-025-07875-6 · Cited by 9
“We are also happy that our previous efforts improved the clarity of the work.”
Mollon, J., Schultz, L. M., Knowles, E. E. M., Jacquemont, S., Glahn, D. C., & Almasy, L. A. (2025) · Biological Psychiatry · doi:10.1016/j.biopsych.2025.03.006 · Cited by 7
Khan, Y., Davis, C. N., Jinwala, Z., Feuer, K. L., Toikumo, S., Hartwell, E. E., Sanchez‐Roige, S., Peterson, R. E., Hatoum, A. S., Kranzler, H. R., et al. (2025) · Biological Psychiatry · doi:10.1016/j.biopsych.2025.04.021 · Cited by 14
Liability threshold model-based disease risk prediction based on electronic health record phenotypes
Lee, C. H., Khan, A., Wang, C., Weng, C., Buxbaum, J. D., Kiryluk, K., & Ionita‐Laza, I. (2025) · Nature Genetics · doi:10.1038/s41588-025-02370-4 · Cited by 7
“Another potential concern with methods that incorporate information from nontarget traits is the risk of genetic discoveries that are not specific to the target trait 15,44 .”
Hill, W. D. (2018) · Twin Research and Human Genetics · doi:10.1017/thg.2018.12 · Cited by 7
“Using MTAG (Turley et al., 2018) to combine GWAS data sets on the correlated phenotypes of cognitive ability and education, Lam et al.”
InfluentialGenetic Correlations between Migraine and Carpal Tunnel Syndrome
Wiberg, A., Lucey, M., Kleeman, S. O., Kang, Y., Ng, M., & Furniss, D. (2023) · Plastic & Reconstructive Surgery · doi:10.1097/prs.0000000000010976 · Cited by 7
Genetic influences on depression and selection into adverse life experiences
Rauf, T., & Jeremy Freese, A. (2024) · Social Science & Medicine · doi:10.1016/j.socscimed.2024.116633 · Cited by 9
Davis, C. N., Khan, Y., Toikumo, S., Jinwala, Z., Boomsma, D. I., Levey, D. F., Gelernter, J. E., Kember, R. L., & Kranzler, H. R. (2025) · Psychological Medicine · doi:10.1017/s0033291725000856 · Cited by 11
“…(i.e. panic disorder, agoraphobia, generalized anxiety disorder [GAD], specific phobia, and social phobia) and subclinical anxiety (i.e. GAD-2 scores), we combined three GWAS (Levey et al., 2020; Otowa et al., 2016; Purves et al., 2020) via multi-trait analysis of GWAS (Turley et al., 2018).”
A Combined Analysis of Genetically Correlated Traits Identifies Genes and Brain Regions for Insomnia
Liu, K., Zhu, L., Yu, M., Liang, X., Zhang, J., Tan, Y., Huang, C., He, W., Lei, W., Chen, J., et al. (2020) · The Canadian Journal of Psychiatry · doi:10.1177/0706743720940547 · Cited by 7
Is Genetic Risk for Sleep Apnea Causally Linked With Glaucoma Susceptibility?
Ingold, N., Campos, A. I., Han, X., Ong, J., Gharahkhani, P., Mackey, D. A., Rentería, M. E., Law, M. H., & MacGregor, S. (2022) · Investigative Ophthalmology & Visual Science · doi:10.1167/iovs.63.1.25 · Cited by 7
Wang, B., Giannakopoulou, O., Austin-Zimmerman, I., Irizar, H., Harju‐Seppänen, J., Zartaloudi, E., Bhat, A., McQuillin, A., Kuchenbaecker, K. B., & Bramon, E. (2022) · Genes · doi:10.3390/genes13010106 · Cited by 8
“Future research employing novel approaches, including multi-trait analysis of GWAS summary statistics and pathway-specific polygenic risk scores [84,85], will be beneficial in revealing the shared biological pathways between verbal memory, psychosis, and other neuropsychiatric traits.”
Polygenic risk scores: effect estimation and model optimization
Zhao, Z., Song, J., Wang, T., & Lu, Q. (2021) · Quantitative Biology · doi:10.15302/j-qb-021-0238 · Cited by 7
Gao, J., Espin‐Garcia, O., Paterson, A. D., & Sun, L. (2022) · Scientific Reports · doi:10.1038/s41598-022-14924-1 · Cited by 7
Unraveling the Relation Between Personality and Well-Being in a Genetically Informative Design
Pelt, D. H. M., de Vries, L. P., & Bartels, M. (2022) · European Journal of Personality · doi:10.1177/08902070221134878 · Cited by 12
“Several GWASs on personality (De Moor et al., 2015; Lo et al., 2017; van den Berg et al., 2016; Weiss et al., 2016) and well-being (Baselmans et al., 2019a; Okbay et al., 2016; Turley et al., 2018) have been published in recent years.”
BackgroundCarlos, J., Holuka, C., Menta, G., Turner, J. D., Vögele, C., & D’Ambrosio, C. (2022) · Biological Psychology · doi:10.1016/j.biopsycho.2022.108463 · Cited by 8
Chung, J., Vig, V., Sun, X., Han, X., O’Connor, G. T., Chen, X., DeAngelis, M. M., Farrer, L. A., & Subramanian, M. L. (2022) · Journal of Clinical Medicine · doi:10.3390/jcm12010109 · Cited by 7
“We conducted a cross-phenotype meta-analysis to identify genetic factors shared between AMD and COVID-19 outcomes (critical illness, hospitalizations, and infections) using the Multi-Trait Analysis in GWAS (MTAG) software [13].”
BackgroundMethodA novel candidate gene CLN8 regulates fat deposition in avian
Li, X., Zhang, F., Sun, Y.-X., Sun, D., Yang, F., Liu, Y., & Hou, Z. (2023) · Journal of Animal Science and Biotechnology/Journal of animal science and biotechnology · doi:10.1186/s40104-023-00864-x · Cited by 7
“3) to perform association analysis with the Wald test [23, 24], an additive genetic model incorporating birthday, sex, batch, and 5 genetic principal components (PCs) as covariates.”
MethodTrade-offs in modeling context dependency in complex trait genetics
Weine, E., Smith, S. P., Knowlton, R. K., & Harpak, A. (2025) · eLife · doi:10.7554/elife.99210.3 · Cited by 8
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“No association was detected between rs2168518 and the combined GA and CNV phenotype.”
BackgroundPhenotypic and genetic markers of psychopathology in a population-based sample of older adults
Gard, A. M., Ware, E. B., Hyde, L. W., Schmitz, L., Faul, J. D., & Mitchell, C. M. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01354-2 · Cited by 7
“As with other molecular and behavioral genetic approaches, PGS analyses have revealed widespread cross-phenotype correlations (for a review, see [24]), yet no studies have evaluated”
Distinct characteristics of genes associated with phenome-wide variation in maize ( Zea mays )Preprint
Liang, Z., Qiu, Y., & Schnable, J. C. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/534503 · Cited by 6
MegaLMM: Mega-scale linear mixed models for genomic predictions with thousands of traitsPreprint
Runcie, D. E., Qu, J., Cheng, H., & Crawford, L. A. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.26.116814 · Cited by 7
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Buser, T., Ahlskog, R., Johannesson, M., Koellinger, P., & Oskarsson, S. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3938154 · Cited by 7
Self-citationSocrates, A., Mullins, N., Gur, R. C., Gur, R. E., Stahl, E. A., O’Reilly, P. F., Reichenberg, A., Jones, H. J., Zammit, S., & Velthorst, E. (2023) · Research Square · doi:10.21203/rs.3.rs-2583059/v1 · Cited by 7
Lyu, X., Peng, L., Xu, X., Yang, F., Yang, Y. J., Chen, J., Liu, M., Chen, Y., Zhang, C., Yang, S., et al. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1417899 · Cited by 6
Dinkelbach, L., Peters, T., Grasemann, C., Hinney, A., & Hirtz, R. (2025) · Psychological Medicine · doi:10.1017/s0033291725000352 · Cited by 6
“Separate GWAS for the five phenotypes (age at VB, early/late VB, and early/late facial hair growth) were calculated, standardized, and then meta-analyzed using Multi-Trait Analysis of GWAS (MTAG) to derive the composite phenotype ‘ Male Puberty Timing ’ (Hollis et al., 2020; Turley et al., 2018).”
Galimberti, M., Levey, D. F., Deak, J. D., Adhikari, K., Overstreet, C. M., Gupta, P. D., Nitin, R., Zhou, H., Lake, N. J., Harrington, K. M., et al. (2025) · Nature Genetics · doi:10.1038/s41588-025-02260-9 · Cited by 8
“We also conducted MTAG analysis with leisure screen time and liking of PA (‘Supplementary Results—MTAG analyses of EUR All-PA–leisure + SSOE’; Supplementary Tables 12 and 13).”
Gu, S. C., Welton, T., Sun, Q.-Y., Wu, Y., Tan, E. K., & Zhou, Z. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf350 · Cited by 7
“Multitrait frameworks like Multi-Trait Analysis of GWAS enhance detection power through cross-phenotype genetic correlation analysis, whereas gene-basedassociationmethodssuchasMulti-markerAnalysisof GenoMic Annotation a ggregate SNP-level signals to provide gene or pathway level insights [10, 11].”
Advances and Challenges in Depression Marker Research
Jin, W., Chen, S., Li, D., Chen, Q., Zhu, M., Wang, M., Fu, X., & Lin, P. C. (2025) · Neuropsychiatric Disease and Treatment · doi:10.2147/ndt.s527897 · Cited by 7
Yan, R., Li, W., Zhao, D., Zhao, J., Ying, S.-Q., Xiang, L., Wang, H., He, T., Zhao, C., Cui, W., et al. (2025) · Bioactive Materials · doi:10.1016/j.bioactmat.2025.10.032 · Cited by 7
GWAS for primary angle-closure glaucoma identifies loci related to ocular biometry and morphology
Luben, R., Biradar, M. I., Stuart, K. V., Hu, R., Sun, Z., Li, Z., Wang, N., Do, T., Pang, C. P., Nakano, M., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-64949-z · Cited by 7
“In order to increase power to discover novel associations with PACG, Multi-trait Analysis for GWAS (MTAG) was used by combining the PACG European ancestry meta-analysis with refractive error.”
InfluentialCross-trait genomic modeling reveals the polygenic architecture and systemic impact of MASLD
Du, M., Yuan, H., Wu, T., Jiang, Y., Suo, C., Li, J., Zhang, T., Liu, Z., & Chen, X. (2026) · Science Advances · doi:10.1126/sciadv.aeb5665 · Cited by 7
Feng, Y., Jia, N., Huang, P., Hu, S., & Yang, S. (2026) · Molecular Psychiatry · doi:10.1038/s41380-026-03541-3 · Cited by 7
“Emerging multivariate approaches offer powerful frameworks for modeling latent cross-phenotype liability, identifying pleiotropic loci, and enhancing statistical power across genetically correlated traits [13 – 16].”
Mental Health Effects of Income over the Adult Life Course
Rauf, T. (2023) · Socius Sociological Research for a Dynamic World · doi:10.1177/23780231231186072 · Cited by 9
Ingold, N., Seviiri, M., Ong, J., Gordon, S. D., Neale, R. Ε., Whiteman, D. C., Olsen, C. M., MacGregor, S., & Law, M. H. (2024) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2024.02.019 · Cited by 7
Mendelian randomization analysis using multiple biomarkers of an underlying common exposure
Jin, J., Qi, G., Yu, Z., & Chatterjee, N. (2024) · Biostatistics · doi:10.1093/biostatistics/kxae006 · Cited by 6
Jeong, R., & Bulyk, M. L. (2025) · eLife · doi:10.7554/elife.98289.3 · Cited by 13
Jighly, A. (2024) · Plant Science · doi:10.1016/j.plantsci.2024.112110 · Cited by 7
Kunkel, D., Sørensen, P., Shankar, V., & Morgante, F. (2025) · PLoS Genetics · doi:10.1371/journal.pgen.1011519 · Cited by 5
Polygenic risk score prediction accuracy convergence
Henches, L., Kim, J., Yang, Z., Rubinacci, S., Pires, G., Albiñana, C., Boetto, C., Julienne, H., Frouin, A., Auvergne, A., et al. (2025) · Human Genetics and Genomics Advances · doi:10.1016/j.xhgg.2025.100457 · Cited by 8
Hill, W. D. (2018) · Twin Research and Human Genetics · doi:10.1017/thg.2018.55 · Cited by 5
“…synapse, calcium channel, potassium channel, cerebellum Lam et al. (2018) assert that their use of multi-trait analysis of genome-wide association study (GWAS) (MTAG; Turley et al., 2018) resulted in the generation of associations that were specific to the trait of cognitive ability.”
BackgroundMethodBig data and the goal of personalized health interventions
Hindley, G., Smeland, O. B., Frei, O., & Andreassen, O. A. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-822201-0.00021-6 · Cited by 6
Gillett, A. C., Jermy, B. S., Lee, S., Pain, O., Howard, D. M., Hagenaars, S. P., Hanscombe, K. B., Coleman, J. R. I., & Lewis, C. M. (2022) · Genetic Epidemiology · doi:10.1002/gepi.22449 · Cited by 5
Wang, B.-R., Wang, J., Tian, T., Zhang, S., Zhao, Y.-Q., Meng, S., Wu, Z.-Y., Huang, F., Zeng, J., & Ni, J. (2023) · Journal of Affective Disorders · doi:10.1016/j.jad.2023.12.042 · Cited by 6
Investigating the shared genetic architecture between frailty and insomnia
Song, Z., Li, W., Han, Y., Xu, Y., & Wang, Y. (2024) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2024.1358996 · Cited by 6
Predictive Power of Polygenic Risk Scores for Intraocular Pressure or Vertical Cup-Disc Ratio
He, W., Lee, S. S., Díaz‐Torres, S., Han, X., Gharahkhani, P., Hunter, M. L., Balartnasingam, C., Craig, J. E., Hewitt, A. W., Mackey, D. A., et al. (2024) · JAMA Ophthalmology · doi:10.1001/jamaophthalmol.2024.4856 · Cited by 6
Adjustment for covariates using summary statistics of genome‐wide association studies
Wang, T., Xue, X., Xie, X., Ye, K. Q., Zhu, X., & Elston, R. C. (2018) · Genetic Epidemiology · doi:10.1002/gepi.22148 · Cited by 6
“…data or GWAS summary statistics of single traits, have been developed (Bhattacharjee et al., 2012; Chung, Yang, Li, Gelernter, & Zhao, 2014; Cotsapas et al., 2011; Hackinger & Zeggini, 2017; Huang, Johnson, & O'Donnell, 2011; Solovieff et al., 2013; Turley et al., 2018; Zhu et al., 2015).”
BackgroundJoint analysis of individual-level and summary-level GWAS data by leveraging pleiotropy
Dai, M., Wan, X., Peng, H. F., Wang, Y., Liu, Y., Liu, J., Xu, Z., & Yang, C. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty870 · Cited by 6
“…of summary statistics from GWASs increases, the methods in the second group (e.g., [Chung et al. , 2014, Liu et al. , 2016, 2017, Mak et al. , 2017, Turley et al. , 2018, Zhu et al. , 2015]) working with summary statistics are more convenient to be applied because the summary-level data sets are…”
MethodGenetic variations in evolutionary accelerated regions disrupt cognition in schizophrenia
Bhattacharyya, U., Kukshal, P., Triptish Bhatia, A., Deshpande, S. N., & Thelma, B. K. (2022) · Psychiatry Research · doi:10.1016/j.psychres.2022.114586 · Cited by 6
Quantifying concordant genetic effects of de novo mutations on multiple disorders
Guo, H., Hou, L., Shi, Y., Jin, S. C., Zeng, X. Q., Li, B., Lifton, R. P., Brueckner, M. K., Zhao, H., & Lu, Q. (2022) · eLife · doi:10.7554/elife.75551 · Cited by 6
Maternal genetic risk for depression and child human capital
Menta, G., Lepinteur, A., Clark, A. E., Ghislandi, S., & D’Ambrosio, C. (2022) · Journal of Health Economics · doi:10.1016/j.jhealeco.2022.102718 · Cited by 7
“…SNPs) in large samples, has brought about significant advances in the understanding of the genetic architecture of complex traits such as education (Lee et al. , 2018; Demange et al. , 2021), depression (Okbay et al. , 2016; Turley et al. , 2018) and risk behaviour (Karlsson Linnér et al. , 2019).”
InfluentialLarge-scale GWAS reveals insights into the genetic architecture of same-sex sexual behavior
Ganna, A., Verweij, K. J. H., Nivard, M. G., Maier, R., Wedow, R., Busch, A. S., Abdellaoui, A., Guo, S., Sathirapongsasuti, J. F., Lichtenstein, P., et al. (2020) · Yearbook of pediatric endocrinology · doi:10.1530/ey.17.15.15 · Cited by 6
Self-citationGenetic underpinnings of sociability in the UK BiobankPreprint
Bralten, J., Klemann, C. J. H. M., Mota, N. R., Witte, W. D., Arango, C., Fabbri, C., Kas, M. J., van der Wee, N. J. A., Penninx, B. W. J. H., Serretti, A., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/781195 · Cited by 6
“We did not perform a multi-trait design because our data did not satisfy the assumptions required for performing MTAG analyses, including the genetic correlations between the single questions being higher than 0.7 and a high value of the mean χ2-statistic of our trait (Turley et al., 2018).”
MethodInfluentialFongang, B., Sargurupremraj, M., Jian, X., Mishra, A., Damotte, V., Bis, J. C., Kang-Hsien, F., Li, G., Yang, J., Hilal, S., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.10.11.509802 · Cited by 6
Khan, Y., Davis, C. N., Jinwala, Z., Feuer, K. L., Toikumo, S., Hartwell, E. E., Sanchez‐Roige, S., Peterson, R. E., Hatoum, A. S., Kranzler, H. R., et al. (2024) · medRxiv · doi:10.1101/2024.05.09.24307111 · Cited by 6
Meng, Z., Zhang, C., Liu, S., Li, W., Wang, Y., Zhang, Q., Peng, B., Ye, W., Jiang, Y., Song, Y., et al. (2025) · Frontiers in Genetics · doi:10.3389/fgene.2025.1502839 · Cited by 6
“It begins by fi ltering variants to remove non-common SNPs, duplicated SNPs, or SNPs with strand ambiguity (Turley et al., 2018).”
Zhou, F., Astle, W. J., Butterworth, A. S., & Asimit, J. L. (2025) · Cell Genomics · doi:10.1016/j.xgen.2025.100847 · Cited by 4
“Applied to 99 blood cell traits, it improves the detection of genetic signals and provides more precise results than traditional methods. flashfmZero offers insights into the genetic basis of complex health traits.”
The Genetic Associations Between Kidney Stones, Obesity, Type 2 Diabetes, and Hypertension
Zhou, L., Stark, I. W., Ali, A. E., Jayachandran, M., Haskic, Z., Harris, P. C., Koo, K., McDonnell, S. K., Larson, N. B., & Lieske, J. C. (2025) · Mayo Clinic Proceedings · doi:10.1016/j.mayocp.2025.04.030 · Cited by 5
Dai, J., Chen, K., Zhu, Y., Xia, L., Wang, T., Yuan, Z., & Zeng, P. (2023) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2023.110906 · Cited by 5
Zhang, J., Wen, J., Dai, Z., Zhang, H., Zhang, N., Lei, R., Liu, Z., Luo, P., & Cheng, Q. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.05.012 · Cited by 5
“To ascertain risk SNPs linked to the composite phenotype comprising LTL and COVID-19 outcomes, we conducted a cross-trait meta-analysis using MTAG with GWAS summary statistics [27].”
Facal, F., & Costas, J. (2024) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2024.111183 · Cited by 6
Identification of shared genetic loci for asthma, allergic rhinitis, and pollinosis in East Asians
Wang, D., Liu, S., Wu, Q., Jiang, Y., Zhang, C., Ye, W., Peng, B., Xie, H., Li, W., Wang, Y., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-90443-z · Cited by 5
“The multi-trait analysis of GWAS (MTAG) framework, a generalized meta-analysis method that outputs trait-specific SNP associations, was employed in this study 5 .”
Network Interactions of Circulating FGF23, HRG-HMGB1, and Cardiac Disease in CKD
Perwad, F., Akwo, E. A., Singhal, A., Vartanian, N., Suva, L. J., Friedman, P. A., & Robinson‐Cohen, C. (2025) · Journal of the American Society of Nephrology · doi:10.1681/asn.0000000710 · Cited by 5
Yuan, L., Su, Y., Zhao, J., Cho, M., Wang, D., Yuan, L. P., Li, M., Zheng, D., Piao, H., Wang, Y., et al. (2025) · Frontiers in Endocrinology · doi:10.3389/fendo.2025.1578944 · Cited by 5
“The MTAG method improves the ef fi ciency of identifying associated genes by integrating the genetic correlation structure of several similar traits into a single “ meta-analysis, ” thereby enabling joint analysis of multiple traits (24).”
A data-adaptive Bayesian regression approach for polygenic risk prediction
Song, S., Hou, L., & Liu, J. S. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac024 · Cited by 5
“Vach (2013) commented that a calibration can look perfect even if all regression coefficients are underestimated, and introduced the concept of ‘bias slope’ (b̂bias), which is the slope for regressing the predicted scores against the true phenotype.”
Background
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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