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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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Cites
GCTA: a tool for genome-wide complex trait analysis
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“Using the GWAS results, we obtain an estimate of h 2 using LD score regression 14 and an estimate of R E( ) k 2 from the predictive power of the GWAS-based polygenic score.”
Self-citationGenetic studies of body mass index yield new insights for obesity biology
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“Next, bivariate LD score regressions 6 are run using each pair of GWAS results, and the estimated intercepts are used to construct the off-diagonal elements of Σ (cid:31) j .”
Self-citationGlutamate Receptor Ion Channels: Structure, Regulation, and Function
Traynelis, S. F., Wollmuth, L. P., McBain, C. J., Menniti, F. S., Vance, K. M., Ogden, K. K., Hansen, K. B., Yuan, H., Myers, S. J., & Dingledine, R. (2010) · Pharmacological Reviews · doi:10.1124/pr.109.002451 · Cited by 3,568 · cited 2×
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“We again used the HRS and Add Health cohorts as our prediction samples (and obtained the SNP effect estimates for SWB from the analyses that omitted HRS from the discovery sample).”
InfluentialThe Electronic Medical Records and Genomics (eMERGE) Consortium, Wood, A. R., The MIGen Consortium, The PAGE Consortium, Study, T. L. C., Esko, T., Yang, J., Vedantam, S., Pers, T. H., Gustafsson, S., et al. (2014) · Nature Genetics · doi:10.1038/ng.3097 · Cited by 1,853
New genetic loci link adipose and insulin biology to body fat distribution
The ADIPOGen Consortium, Shungin, D., The CARDIOGRAMplusC4D Consortium, The CKDGen Consortium, The GEFOS Consortium, The GENIE Consortium, The, G. L. G. C., The, I. C. B. P., The International Endogene Consortium, Study, T. L. C., et al. (2015) · Nature · doi:10.1038/nature14132 · Cited by 1,580
Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores
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Items 301–400 of 1,000
Akimova, E. T., Wolfram, T., Ding, X., Tropf, F. C., & Mills, M. C. (2024) · Nature Human Behaviour · doi:10.1038/s41562-024-02076-3 · Cited by 37
“MTAG (in Python v.2.7) 44 was used to meta-analyse all three occupational status measures with a secondary GWAS on household income in UKB and a secondary GWAS on educational attainment in UKB (for the validation subsample of siblings in UKB) or the third GWAS meta-analysis for education 6…”
InfluentialGenetics, leadership position, and well-being: An investigation with a large-scale GWAS
Song, Z., Li, W., Jin, X., Ying, J., Zhang, X., Song, Y., Li, H., & Fan, Q. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2114271119 · Cited by 22
Judd, N., Sauce, B., & Klingberg, T. (2022) · npj Science of Learning · doi:10.1038/s41539-022-00148-5 · Cited by 27
Estimation and implications of the genetic architecture of fasting and non-fasting blood glucose
Qiao, Z., Sidorenko, J., Revez, J. A., Xue, A., Lu, X., Pärna, K., Snieder, H., Study, L. C., Visscher, P. M., Wray, N. R., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-36013-1 · Cited by 23
“(95,199), 3 h (108,222), 4 h (80,645), 5 h (43,938) and 6-24h (39,423).”
Self-citationMocci, E., Ward, K., Perry, J. A., Starkweather, A. R., Stone, L. S., Schabrun, S. M., Renn, C. L., Dorsey, S. G., & Ament, S. A. (2023) · PLoS Genetics · doi:10.1371/journal.pgen.1010977 · Cited by 29
“As well as PLEIO [28], MTAG [66] uses LD score regression [65] to adjust for sample overlap between the traits.”
The goldmine of GWAS summary statistics: a systematic review of methods and tools
Kontou, P. I., & Bagos, P. G. (2024) · BioData Mining · doi:10.1186/s13040-024-00385-x · Cited by 23
“We also need to mention methods like MTAG [247] and PLEIO [248] which use LDSC and apart from sample overlap also allow data from multiple studies, something that can be considered meta-analysis and methods like MSKAT [249], multiSKAT [250], MGAS [251], MAIUP [252] and MTAR (multi-trait analysis of…”
MethodGWAS Identifies Multiple Genetic Loci for Skin Color in Korean Women
Seo, J. Y., You, S. W., Shin, J., Kim, Y., Park, S. G., Won, H., & Kang, N. G. (2021) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2021.08.440 · Cited by 21
Qiu, S., Zheng, K., Hu, Y., & Liu, G. (2023) · Journal of Medical Virology · doi:10.1002/jmv.28780 · Cited by 20
“Based on the possibility of a significant association between vitamin D and COVID‐19, we used cross‐trait GWAS meta‐analysis to identify the risk SNPs associated with both vitamin D and COVID‐19 (joint phenotype).(37) Here, we used two complementary cross‐trait GWAS meta‐analysis methods, that is, MTAG (multitrait ana…”
MethodCohort profile: Genetic data in the German Socio-Economic Panel Innovation Sample (SOEP-G)
Koellinger, P., Okbay, A., Kweon, H., Schweinert, A., Linnér, R. K., Goebel, J., Richter, D., Reiber, L., Zweck, B. M., Belsky, D. W., et al. (2023) · PLoS ONE · doi:10.1371/journal.pone.0294896 · Cited by 21
“The single-trait PGIs were based on univariate GWAS summary statistics (Table 3), whereas the multi-trait PGIs were based on multivariate MTAG analyses that exploit genetic correlations between several traits to improve predictive accuracy (S3 Table) [45].”
Self-citationGenetic and non-genetic predictors of risk for opioid dependence
Na, P. J., Deak, J. D., Kranzler, H. R., Pietrzak, R. H., & Gelernter, J. E. (2024) · Psychological Medicine · doi:10.1017/s0033291723003732 · Cited by 21
Dissecting shared genetic architecture between depression and body mass index
Zhang, H., Zheng, R., Yu, B., Yu, Y., Luo, X., Yin, S., Zheng, Y., Shi, J., & Ai, S. (2024) · BMC Medicine · doi:10.1186/s12916-024-03681-9 · Cited by 23
“To identify common SNPs and gene regions for DEP and BMI, we conducted a cross-trait meta-analysis using multi-trait analysis of GWAS (MTAG) and cross-phenotype association (CPASSOC) (Additional file 2: Supplementary Methods) [31, 32].”
MethodKim, K., Kim, S., Myung, W., Shim, I., Lee, H., Kim, B., Cho, S. K., Yoon, J., Kim, D. K., & Won, H. (2021) · Brain Sciences · doi:10.3390/brainsci11081042 · Cited by 21
“Suicide risk in PD patients is also higher than in the general population [4,5].”
BackgroundAlemany, S., Soler-Artigas, M., Cabana-Domínguez, J., Fakhreddine, D., Llonga, N., Vilar-Ribó, L., Rodríguez-Urrutia, A., Palacio, J., González-Castro, A. M., Lobo, B., et al. (2023) · Journal of Translational Medicine · doi:10.1186/s12967-023-04107-5 · Cited by 20
“MTAG estimates trait-specific effects from GWAS summary statistics of several traits genetically correlated while accounting for sample overlap across the discovery samples [24].”
BackgroundMethodResultInfluentialCai, N., Verhulst, B., Andreassen, O. A., Buitelaar, J. K., Edenberg, H. J., Hettema, J. M., Gandal, M. J., Grotzinger, A. D., Jonas, K. G., Lee, P., et al. (2024) · Molecular Psychiatry · doi:10.1038/s41380-024-02878-x · Cited by 18
“These methods include LT-FH [134] (which models family history-based liability to disease), MTAG [135] (a meta-analytical approach leveraging information from collateral GWAS phenotypes with high rG to target GWAS), and Genomic SEM [136] (a framework for modeling genetic covariance structure that…”
Genomics of drug target prioritization for complex diseases
Chen, R. K., Duffy, Á., & Do, R. (2025) · Nature Reviews Genetics · doi:10.1038/s41576-025-00904-4 · Cited by 28
“Statistical methods such as multi-trait analysis of GWAS (MTAG) are primarily intended to increase power in genetic association testing and are useful for diseases with relatively few cases 81 .”
mTADA is a framework for identifying risk genes from de novo mutations in multiple traits
Nguyen, T.-H., Dobbyn, A. L., Brown, R. C., Riley, B. P., Buxbaum, J. D., Pinto, D., Purcell, S., Sullivan, P. F., He, X., & Stahl, E. A. (2020) · Nature Communications · doi:10.1038/s41467-020-16487-z · Cited by 20
“In addition, additional risk loci have been identi fi ed using these approaches 7,9 .”
MethodIbáñez, L., Bahena, J. A., Yang, C., Dube, U., Farias, F., Budde, J., Bergmann, K., Brenner-Webster, C., Morris, J. C., Perrin, R. J., et al. (2020) · Acta Neuropathologica Communications · doi:10.1186/s40478-020-01072-8 · Cited by 20
Zhang, X., Lucas, A., Veturi, Y., Drivas, T. G., Bone, W. P., Verma, A., Chung, W. K., Crosslin, D. R., Denny, J. C., Hebbring, S. J., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-30678-w · Cited by 21
Sex-Specific Genetic and Transcriptomic Liability to Neuroticism
Wendt, F. R., Pathak, G. A., Singh, K., Stein, M. B., Koenen, K. C., Krystal, J. H., Gelernter, J. E., Davis, L. K., & Polimanti, R. A. (2022) · Biological Psychiatry · doi:10.1016/j.biopsych.2022.07.019 · Cited by 21
Xiu, X., Zhang, H., Xue, A., Cooper, D. N., Li, Y., Yang, Y., Yang, Y., & Zhao, H. (2022) · BMC Medicine · doi:10.1186/s12916-022-02476-0 · Cited by 24
“Multi‐trait analysis of T2D and PAD To detect risk SNPs shared between T2D and PAD, we performed an inverse variance-weighted cross-trait meta-analysis using MTAG [60], which has an advantage over other meta-analysis tools because MTAG can account for potential sample overlap between GWAS summary data via cross-trait…”
MethodInfluentialChangalidis, A. I., Maksiutenko, E. M., Barbitoff, Y. A., Tkachenko, A. A., Vashukova, E. S., Pachuliia, O. V., Nasykhova, Y. A., & Glotov, A. S. (2022) · Genes · doi:10.3390/genes13122255 · Cited by 20
“We used 2 different software tools for meta-analysis: METAL [18] and MTAG (MultiTrait Analysis of GWAS) [19].”
MethodTirozzi, A., Quiccione, M. S., Cerletti, C., Donati, M. B., de Gaetano, G., Iacoviello, L., & Gialluisi, A. (2023) · Cells · doi:10.3390/cells12020245 · Cited by 21
“Second, in multi-trait association approaches, association significance is often driven by the largest source GWAS involved in the MTAG analysis, which may have biased the analyses towards the largest studies.”
BackgroundMethodInfluentialWang, X., Walker, A., Revez, J. A., Ni, G., Adams, M. J., McIntosh, A. M., Wray, N. R., Ripke, S., Mattheisen, M., Trzaskowski, M., et al. (2023) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2023.06.006 · Cited by 19
Shared Genetics and Comorbid Genes of Amyotrophic Lateral Sclerosis and Parkinson's Disease
Tian, Y., Ma, G., Li, H., Zeng, Y., Zhou, S., Wang, X., Shan, S., Xu, Y., Xiong, J., & Cheng, G. (2023) · Movement Disorders · doi:10.1002/mds.29572 · Cited by 20
“15 By taking GWAS summary data for multiple traits as input and applying a generalized IVW meta-analysis to improve statistical power, MTAG detects novel genetic associations for each trait.”
MethodApplying polygenic risk score methods to pharmacogenomics GWAS: challenges and opportunities
Zhai, S., Mehrotra, D. V., & Shen, J. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad470 · Cited by 23
“With the success of leveraging information from multiple related traits for signal detection in GWAS [129] and WES (SNP-set analysis)[130],itisalsoappealingtousemultipletraitstoincrease the power of PRS analysis for drug response prediction and patient stratification in PGx GWAS.”
Qi, G., Chhetri, S. B., Ray, D., Dutta, D., Battle, A. J., Bhattacharjee, S., & Chatterjee, N. (2024) · Nature Communications · doi:10.1038/s41467-024-51075-5 · Cited by 20
“MTAG 10 uses multi-trait summary statistics to estimate SNP effects on each trait but does not account for multiple testing across traits which is critical for the analysis of many traits simultaneously. fastASSET returns both multiple-testing adjusted p -values for association testing and a subset of selected traits,…”
Beyond heritability: improving discoverability in imaging genetics
Fan, C. C., Smeland, O. B., Schork, A. J., Chen, C., Holland, D., Lo, M., Sundar, V. S., Frei, O., Jernigan, T. L., Andreassen, O. A., et al. (2018) · Human Molecular Genetics · doi:10.1093/hmg/ddy082 · Cited by 20
“Multi-trait analysis of GWAS summary statistics (MTAG) 2018 (64) No R25 Human Molecular Genetics, 2018, Vol.”
MethodLiang, Z., Qiu, Y., & Schnable, J. C. (2020) · Molecular Plant · doi:10.1016/j.molp.2020.03.003 · Cited by 20
Jian, X., Sofer, T., Tarraf, W., Bressler, J., Faul, J. D., Zhao, W., Ratliff, S. M., Lamar, M., Launer, L. J., Laurie, C. C., et al. (2020) · Translational Psychiatry · doi:10.1038/s41398-020-00930-2 · Cited by 22
Díaz‐Torres, S., He, W., Yu, R., Han, X., Hamel, A. R., Young, T. L., Lotery, A. J., Jorgenson, E., Choquet, H., Hauser, M. A., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-54301-2 · Cited by 19
“…of GWAS (MTAG), version 2020080, a method for the joint analysis of summary statistics from genome-wide association studies (GWAS) of correlated traits 36 , and an inverse variance weighting meta-analysis approach through METAL, version 20211102, to enhance the power for discovering NTG risk loci.”
metaGE: Investigating genotype x environment interactions through GWAS meta-analysis
Walsche, A. D., Vergne, A., Rincent, R., Roux, F., Nicolas, S., Welcker, C., Mezmouk, S., Charcosset, A., & Mary‐Huard, T. (2025) · PLoS Genetics · doi:10.1371/journal.pgen.1011553 · Cited by 27
Gonda, X., Eszlári, N., Török, D., Gál, Z., Bokor, J., Millinghoffer, A., Baksa, D., Petschner, P., Antal, P., Breen, G. D., et al. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01436-1 · Cited by 19
Investigating the shared genetic architecture between schizophrenia and body mass index
Yu, Y., Fu, Y., Yu, Y., Tang, M., Sun, Y., Wang, Y., Zhang, K., Li, H., Guo, H., Wang, B., et al. (2023) · Molecular Psychiatry · doi:10.1038/s41380-023-02104-0 · Cited by 22
“Most shared loci between SCZ and BMI colocalized at the same candidate causal SNPs (PPH4 > 0.95) (20/ 27) (Supplementary Table S9).”
BackgroundInfluentialMulti-trait discovery and fine-mapping of lipid loci in 125,000 individuals of African ancestry
Kamiza, A. B., Touré, S. M., Zhou, F., Soremekun, O. S., Cissé, C., Wélé, M., Touré, A. M., Nashiru, O., Corpas, M., Nyirenda, M., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-41271-0 · Cited by 19
“We then proceeded to perform a multi-trait analysis of GWAS (MTAG) 17 to increase the statistical power to identify additional shared genetic variants associated with lipid traits.”
MethodZhang, C., Zhang, Y., Zhang, Y., & Zhao, H. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad407 · Cited by 22
Ishikawa, Y., Tanaka, N., Asano, Y., Kodera, M., Shirai, Y., Akahoshi, M., Hasegawa, M., Matsushita, T., Saito, K., Motegi, S., et al. (2024) · Nature Communications · doi:10.1038/s41467-023-44541-z · Cited by 21
“Multi-trait analysis of GWAS (MTAG) jointly analyzes summary statistics from GWAS of different traits 68 , and the recent multi-ancestry MTAG for SLE incorporating 10 genetically correlated AIDs success-fullyidenti fi ed16novelloci 69 .”
McCaw, Z. R., Gao, J., Lin, X., & Gronsbell, J. L. (2024) · Nature Genetics · doi:10.1038/s41588-024-01793-9 · Cited by 20
“MTAG augments the results from a standard GWAS, for a given level of missingness, with those from the proxy GWAS.”
InfluentialSong, Z., Cheng, Z. J., Yuan, H., Chang, Z., Lv, Y., Huang, X., Li, H., Liang, Z., Cao, W., Chen, Y., et al. (2024) · Journal of Translational Medicine · doi:10.1186/s12967-024-05604-x · Cited by 24
Three Novel Loci for Infant Head Circumference Identified by a Joint Association Analysis
Yang, X., Zhang, S.-Y., Zhang, H., Wei, X., Feng, G.-J., Pei, Y., & Zhang, L. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00947 · Cited by 17
Chen, T.-H., Chatterjee, N., Landi, M. T., & Shi, J. (2020) · Journal of the American Statistical Association · doi:10.1080/01621459.2020.1764849 · Cited by 19
“A variety of association tests have been proposed to identify SNPs that are modestly associated with multiple traits by modeling pleiotropy (Bhattacharjee et al. 2012; Qi and Chatterjee 2018; Turley et al. 2018).”
BackgroundPatel, P. C., Rietveld, C. A., Wolfe, M. T., & Wiklund, J. (2020) · Entrepreneurship Theory and Practice · doi:10.1177/1042258720936984 · Cited by 20
“…Variables It is standard in genetic studies to include these components to control for subtle population stratification in the sample (Rietveld et al., 2014).3 Therefore, in addition to the PRS of SWB, we include 10 principal components of a genetic relationship matrix (Turley et al., 2018).”
BackgroundMethodResultInfluentialZhou, Y., Kusmec, A., Mirnezami, S. V., Attigala, L., Srinivasan, S., Jubery, T. Z., Schnable, J. C., Fernandez, M. G. S., Ganapathysubramanian, B., & Schnable, P. S. (2021) · The Plant Cell · doi:10.1093/plcell/koab134 · Cited by 17
“To overcome this issue, several studies have attempted to flatten objects prior to image collection to compress 3D structures into 2D structures to increase phenotyping accuracy (AL-Tam et al., 2013; Crowell et al., 2014; Vasseur et al., 2018).”
BackgroundIdentification of pleiotropy at the gene level between psychiatric disorders and related traits
Polushina, T. V., Banerjee, N., Giddaluru, S., Bettella, F., Espeseth, T., Lundervold, A. J., Djurovic, S., Cichon, S., Hoffmann, P., Nöthen, M. M., et al. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01530-4 · Cited by 19
“One of these 23 genes, SHANK3, had been reported in another study [10, 72].”
BackgroundConsidering hormone-sensitive cancers as a single disease in the UK biobank reveals shared aetiology
Ahmed, M. B., Mäkinen, V., Mulugeta, A., Shin, J., Boyle, T., Hyppönen, E., & Lee, S. (2022) · Communications Biology · doi:10.1038/s42003-022-03554-y · Cited by 17
Trauma Matters: Integrating Genetic and Environmental Components of PTSD
Marchese, S., & Huckins, L. M. (2022) · Advanced Genetics · doi:10.1002/ggn2.202200017 · Cited by 21
Defo, J., Awany, D., & Ramesar, R. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac600 · Cited by 20
Lin, Z., Xue, H., & Pan, W. (2023) · PLoS Genetics · doi:10.1371/journal.pgen.1010762 · Cited by 18
“Two approaches generally gave very similar results with only slight differences, and we used the LDSC estimates in the subsequent analysis.”
MethodInfluentialGedik, H., Nguyen, T. H., Peterson, R. E., Chatzinakos, C., Riley, B. P., Vladimirov, V. I., & Bacanu, S. (2023) · Frontiers in Genetics · doi:10.3389/fgene.2023.1191264 · Cited by 16
“This supports the joint analysis of NPSUDs to potentially increase detection power, especially for underpowered disorders (Turley et al., 2018; Gleason et al., 2020; Taraszka et al., 2022).”
MethodPsychiatric disorders and cardiovascular diseases: A mendelian randomization study
Sui, X., Liu, T., Liang, Y., & Zhang, B. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e20754 · Cited by 17
“In addition, heterogeneity exhibited by the Cochrane ’ s Q test may have reduced the potency of the present study [41].”
Huang, W., Jiang, R., Li, S., Zeng, R., Yang, L., Zhang, Y., Tong, S., Lyu, Y., Wang, J., Lian, Q., et al. (2024) · JHEP Reports · doi:10.1016/j.jhepr.2024.101037 · Cited by 20
“46 Cross-trait meta-analysis Two independent cross-trait meta-analyses of GWAS summary statistics were performed, including cross-phenotype association test (CPASSOC) 47 and multi-trait analysis of GWAS (MTAG), 48 to identify shared risk SNPs between PBC and each of IBD, CD, and UC.”
MethodPolygenic risk of social isolation behavior and its influence on psychopathology and personality
Socrates, 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. (2024) · Molecular Psychiatry · doi:10.1038/s41380-024-02617-2 · Cited by 26
“Multi-Trait Analysis of GWAS (MTAG) [56] was used to meta-analyze the individual “ Frequency of family/friend visits ” , “ Being able to con fi de in others ” , “ Number of social activities a week ” , and “ Number in household ” outcomes to form a single, composite SIB GWAS.”
MethodGeneralized genetic liability to substance use disorders
Miller, A. P., Bogdan, R., Arpana Agrawal, A., & Hatoum, A. S. (2024) · Journal of Clinical Investigation · doi:10.1172/jci172881 · Cited by 20
“In adolescent populations, there are also ethical considerations to using PGS to prognosticate the likelihood of outcomes such as SUDs.”
InfluentialSergouniotis, P. I., Diakite, A., Gaurav, K., Allen, N., Aslam, T. M., Atan, D., Barman, S. A., Barrett, J., Bishop, P., Black, G. C. M., et al. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae732 · Cited by 15
“Under certain assumptions, the generated estimates would be expected to be more precise than those obtained from the input GWAS (Turley et al. 2018).”
InfluentialBrčić, L., Barić, A., Gračan, S., Torlak, V., Brekalo, M., Škrabić, V., Zemunik, T., Barbalić, M., Punda, A., & Perica, V. B. (2019) · Scientific Reports · doi:10.1038/s41598-019-41850-6 · Cited by 18
Effect of non-normality and low count variants on cross-phenotype association tests in GWAS
Ray, D., & Chatterjee, N. (2019) · European Journal of Human Genetics · doi:10.1038/s41431-019-0514-2 · Cited by 18
Piasecki, T. M., Gizer, I. R., & Slutske, W. S. (2019) · Twin Research and Human Genetics · doi:10.1017/thg.2019.90 · Cited by 18
“Multitrait GWAS approaches that allow joint modeling of DG and SCZ might improve detection of DG-related loci and generate more informative PRSs for use in future research (Andreassen et al., 2013; Turley et al., 2018).”
BackgroundMulti-trait Genome-Wide Analyses of the Brain Imaging Phenotypes in UK Biobank
Wu, C. (2020) · Genetics · doi:10.1534/genetics.120.303242 · Cited by 18
Sun, X., Chen, L., Wang, Z., Lu, Y., Chen, M., He, Y., Xu, H., & Zheng, L. (2021) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2021.676030 · Cited by 20
“Subjective well-being (16) UKB, 23andMe, SSGAC 388,538 European”
BackgroundTwelve New Genomic Loci Associated With Bone Mineral Density
Liu, L., Zhao, M., Xie, Z., Liu, J., Peng, H., Pei, Y., Sun, H., & Zhang, L. (2020) · Frontiers in Endocrinology · doi:10.3389/fendo.2020.00243 · Cited by 19
“Joint Association Analysis The recently developed multi-trait analysis of GWAS (MTAG) method was used for joint association analysis, which accounts for sample overlap and trait heterogeneity between studies (20).”
BackgroundMethodYengo, L., Vedantam, S., Marouli, E., Sidorenko, J., Bartell, E., Sakaue, S., Graff, M., Eliasen, A. U., Jiang, Y., Raghavan, S., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.01.07.475305 · Cited by 18
Kelemen, M., Danesh, J., Angelantonio, E. D., Inouye, M., O’Sullivan, J. W., Pennells, L. A. M., Roychowdhury, T., Sweeting, M. J., Wood, A., Harrison, S. C., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-52452-w · Cited by 19
“We then evaluated two PRS pre-processing methods that exploit genetic overlaps between the aetiologies of different traits, shaPRS 16 , and MTAG 40 , to additionally integrate information from CAD, stroke, and AAA-related traits.”
MethodGenetic and environmental architecture of conscientiousness in adolescence
Takahashi, Y., Zheng, A., Yamagata, S., & Ando, J. (2021) · Scientific Reports · doi:10.1038/s41598-021-82781-5 · Cited by 21
Genome-Wide Studies in Ischaemic Stroke: Are Genetics Only Useful for Finding Genes?
Gallego-Fábrega, C., Muiño, E., Cárcel‐Márquez, J., Llucià‐Carol, L., Lledós, M., Martín-Campos, J. M., Cullell, N., & Fernández‐Cadenas, I. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23126840 · Cited by 17
“Another major advance in the field was the use of traits with known or unknown sample overlap, which can be the case for multiple studies in different biobanks [27].”
BackgroundNostalgia and Satisfaction with Life: A Behavioral Genetic Analysis
Luo, Y. L. L., Wildschut, T., Sedikides, C., & Cai, H. (2022) · Journal of Happiness Studies · doi:10.1007/s10902-022-00548-9 · Cited by 18
“For another, SWL and neuroticism share a substantial portion of genetic sources, which function in opposite directions for the two traits (e.g., promoting SWL but deflating neuroticism; Baselmans et al., 2019; Turley et al., 2018).”
BackgroundH3AGWAS: a portable workflow for genome wide association studies
Brandenburg, J., Clark, L. V., Botha, G., Panji, S., Baichoo, S., Fields, C. J., & Hazelhurst, S. (2022) · BMC Bioinformatics · doi:10.1186/s12859-022-05034-w · Cited by 17
“6) Plot and extraction data [15] Association testing GEMMA Association testing / Heritability [9] Fast-LMM (binary version) Association testing [8] BOLT-LMM Association testing / Heritability [10] SAIGE Association testing [11] GCTA : fastGWA Association testing [12,16] Post-association analysis GCTA : COJO, simulatio…”
MethodKosciuszko, M., Steptoe, A., & Ajnakina, O. (2023) · Translational Psychiatry · doi:10.1038/s41398-023-02367-9 · Cited by 23
“Even though a multi-trait polygenic score maximises the predictive power for depression [23], comparing the effect sizes (the beta coefficients and standard errors from the models) our results suggest that a polygenic score for depressive symptoms that encompass genetic information from the related traits, such as sub…”
BackgroundMethodResultInfluentialFeng, K., Yang, J., & Liu, K. (2024) · European Journal of Preventive Cardiology · doi:10.1093/eurjpc/zwae347 · Cited by 17
Methods for the Analysis and Interpretation for Rare Variants Associated with Complex Traits
Weissenkampen, J. D., Jiang, Y., Eckert, S., Jiang, B., Li, B., & Liu, D. J. (2019) · Current Protocols in Human Genetics · doi:10.1002/cphg.83 · Cited by 20
“The meta-analysis was performed using rareG-WAMA (Liu et al., 2014), and MTAG (Turley et al., 2018) was used to increase the power for locus discovery.”
MethodZhou, F., Guo, Y., Wang, Z., Liu, S., & Xu, H. (2021) · Sleep Medicine · doi:10.1016/j.sleep.2021.08.025 · Cited by 17
Yang, Y., Bartz, T. M., Brown, M. R., Guo, X., Zilhão, N. R., Trompet, S., Weiss, S., Yao, J., Brody, J. A., Defilippi, C. R., et al. (2021) · Circulation: Genomic and Precision Medicine · doi:10.1161/circgen.121.003460 · Cited by 16
Kim, S., Kim, K., Hwang, M. Y., Ko, H., Jung, S.-H., Shim, I., Cha, S., Lee, H., Kim, B., Yoon, J., et al. (2022) · Nature Human Behaviour · doi:10.1038/s41562-022-01343-5 · Cited by 18
Self-citationA framework for conducting GWAS using repeated measures data with an application to childhood BMI
Burrows, K., Heiskala, A., Bradfield, J. P., Balkhiyarova, Z., Ning, L., Boissel, M., Chan, Y.-M., Froguel, P., Bonnefond, A., Hakonarson, H. H., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-53687-3 · Cited by 16
“It may also increase the power to detect a genetic locus by leveraging information from the other correlated phenotypes, as seen in analyses using the MTAG software 44 .”
Sex-heterogeneous SNPs disproportionately influence gene expression and health
Traglia, M., Bout, M., & Weiss, L. A. (2022) · PLoS Genetics · doi:10.1371/journal.pgen.1010147 · Cited by 17
Leveraging the local genetic structure for trans-ancestry association mapping
Xiao, J., Cai, M., Yu, X., Hu, X., Chen, G., Wan, X., & Yang, C. (2022) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2022.05.013 · Cited by 15
Leveraging pleiotropy to discover and interpret GWAS results for sleep-associated traits
Chun, S., Akle, S., Teodosiadis, A., Cade, B. E., Wang, H., Sofer, T., Evans, D. S., Stone, K. L., Gharib, S. A., Mukherjee, S., et al. (2022) · PLoS Genetics · doi:10.1371/journal.pgen.1010557 · Cited by 16
Microbial Dynamics in Newly Diagnosed and Treatment Naïve IBD Patients in the Mediterranean
Rausch, P., Ellul, S., Pisani, A., Bang, C., Tabone, T., Cordina, C. M., Zahra, G., Franke, A., & Ellul, P. (2023) · Inflammatory Bowel Diseases · doi:10.1093/ibd/izad004 · Cited by 17
Zhai, S., Guo, B., Wu, B., Mehrotra, D. V., & Shen, J. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad181 · Cited by 15
Genetic Determinants of the Acute Respiratory Distress Syndrome
Suarez-Pajes, E., Tosco-Herrera, E., Ramirez-Falcon, M., González-Barbuzano, S., Hernández-Beeftink, T., Guillén‐Guío, B., Villar, J., & Flores, C. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12113713 · Cited by 17
“Multi-trait analyses increase the statistical power for identifying genetic associations on correlated complex traits [64,65] (Figure 1c).”
BackgroundVerhoef, E., Allegrini, A. G., Jansen, P. R., Lange, K., Wang, C. A., Morgan, A. T., Ahluwalia, T. S., Symeonides, C., Andreassen, O. A., Bartels, M., et al. (2023) · Biological Psychiatry · doi:10.1016/j.biopsych.2023.11.025 · Cited by 22
A genome-wide association study of 24-hour urinary excretion of endocrine disrupting chemicals
Lu, X., van der Meer, T. P., Kamali, Z., van Faassen, M. H. J. R., Kema, I. P., van Beek, A. P., Xu, X., Huo, X., Ani, A., Nolte, I. M., et al. (2023) · Environment International · doi:10.1016/j.envint.2023.108396 · Cited by 17
Kelemen, M., Vigorito, E., Fachal, L., Anderson, C. A., & Wallace, C. (2024) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2024.04.009 · Cited by 14
“In recent years, several methodsthatexploitthe geneticcorrelationbetween related traits to improve association or prediction accuracies have been proposed, including SMTPred, 20 MTAG, 19 and CTPR. 56 We choose SMTPred and MTAG as comparison methods because they also rely on genome-wide summary statisticsandthushaveasi…”
InfluentialKim, B., Kim, D. S., Shin, J., Leem, S., Cho, M., Kim, H., Gu, K., Seo, J. Y., You, S. W., Martin, A. R., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-49031-4 · Cited by 18
“Therefore, we have revised the sentence that imply a strong correlation and have included the P -value of the Mantel test in the revised manuscript.”
Leveraging pleiotropy for the improved treatment of psychiatric disorders
Woodward, D. J., Thorp, J. G., Middeldorp, C. M., Akóṣílè, W., Derks, E. M., & Gerring, Z. F. (2024) · Molecular Psychiatry · doi:10.1038/s41380-024-02771-7 · Cited by 15
Recent advances in data-driven fusion of multi-modal imaging and genomics for precision medicine
Wang, S., Liu, M., Li, Y., Zhang, X., Sun, M., Wang, Z., Li, R., Li, Q., Li, Q., He, Y., et al. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102738 · Cited by 19
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Dutta, D., VandeHaar, P., Fritsche, L. G., Zöllner, S. K., Boehnke, M., Scott, L. J., & Lee, S. (2021) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2021.02.016 · Cited by 16
Pervasive biases in proxy GWAS based on parental history of Alzheimer’s diseasePreprint
Wu, Y., Sun, Z., Zheng, Q., Miao, J., Dorn, S., Mukherjee, S., Fletcher, J. M., & Lu, Q. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.13.562272 · Cited by 16
Polygenic Risk Scores in Dilated Cardiomyopathy: Towards the Future
Kramarenko, D. R., Jurgens, S. J., Pinto, Y. M., Bezzina, C. R., & Amin, A. S. (2025) · Current Cardiology Reports · doi:10.1007/s11886-025-02239-2 · Cited by 15
Preconception paternal mental disorders and child health: Mechanisms and interventions
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Statistical models and computational tools for predicting complex traits and diseases
Chung, W. (2021) · Genomics & Informatics · doi:10.5808/gi.21053 · Cited by 16
“Recent studies have shown that GWAS of related phenotypes further improve the accuracy of polygenic predictions [23,25,51].”
BackgroundStatistical Methods in Genome-Wide Association Studies
Sun, N., & Zhao, H. (2020) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-030320-041026 · Cited by 16
Lin, J., Tabassum, R., Ripatti, S., & Pirinen, M. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.00431 · Cited by 12
“The statistical power in these discovery efforts can be boosted considerably by multivariate tests, which have become more practical through recent implementations that require only univariate summary statistics, such as MultiPhen (O’Reilly et al., 2012), TATES (van der Sluis et al., 2013), CONFIT (Gai and Eskin, 2018)…”
BackgroundInfluentialMa, X. R., Conley, S. D., Chen, M. S., Kosicki, M., Lu, Y., Bredikhin, D., Cui, R., Sheth, M. U., Qiu, W., Amgalan, D., et al. (2024) · medRxiv · doi:10.1101/2024.11.20.24317557 · Cited by 18
A Re-evaluation of Candidate Gene Studies for Well-Being in Light of Genome-Wide Evidence
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“(Turley et al., 2018) to study the association between subjective well-being and self-employment.”
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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.
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