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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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Citations
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Common and distinct genetic features of three atherosclerotic cardiovascular diseases
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Gao, X. (2026) · Frontiers in Genetics · doi:10.3389/fgene.2026.1842316 · Cited by 1
“…increase power to detect associations by jointly analyzing genetically correlated traits; however, it assumes that all SNPs share the same variance-covariance matrix across traits, potentially leading to biased effect size estimates and an increased rate of false positives (Turley et al., 2018).”
Multi-omics uncovers the pleiotropic genetic mechanisms linking MASLD and cardiometabolic syndromes
Yin, K., Zhang, C., Liu, B., Xu, R., Zeng, J., Eslam, M., & Ni, J. (2026) · Cardiovascular Diabetology · doi:10.1186/s12933-026-03180-6 · Cited by 1
“…variants shared between MASLD and these CKM traits that showed genetic correlation with MASLD, we conducted cross-trait meta-analysis of GWAS summary statistics for MASLD and each CKM trait using multi-trait analysis of GWAS (MTAG) [25] and cross-phenotype association test (CPASSOC) [26] methods.”
Ye, W., Ding, H., Peng, B., Wang, X., Cheng, Y., Zhang, G., Yu, H., Ullah, K., Chang, X., Wang, X., et al. (2026) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2026.1795532 · Cited by 1
“MTAG was applied to explore the genetic overlap between irritable bowel syndrome (IBS) and psychiatric traits by leveraging joint GWAS summary statistics (38).”
Yang, Q., Tang, H., Fan, Y., Liu, B., Zhang, Y., & Wu, J. I. A. H. U. I. (2026) · Breast Cancer · doi:10.1007/s12282-026-01861-4 · Cited by 1
“To identify the risk SNPs associated with joint phenotypes of TC and BC overall, ER(+) BC, and ER(−) BC, we performed cross-trait meta-analysis of GWAS summary statistics using Multi-trait Analysis of GWAS (MTAG) [23 ] and Cross Phenotype Association (CPASSOC) [24].”
Pleiotropic shared heritability quantifies the shared genetic variance of common diseases
Zhao, Y., Strober, B. J., Hou, K., Kerner, G., Danesh, J., Gazal, S., Cheng, W., Inouye, M., Price, A. L., & Jiang, X. (2026) · Nature Genetics · doi:10.1038/s41588-026-02607-w · Cited by 4
“First, our results can inform the incorporation of genome-wide priors in polygenic risk scores that leverage pleiotropic diseases/traits 26,44–48 .”
Parasuraman, A., Eltayib, R., Pandeya, N., Olsen, C. M., Law, M. H., Whiteman, D. C., MacGregor, S., & Seviiri, M. (2026) · American Journal of Transplantation · doi:10.1016/j.ajt.2026.08.006 · Cited by 1
A multi-trait evaluation of network propagation for GWAS results
Bruncsics, B., & Antal, P. (2019) · IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) · doi:10.1109/cibcb.2019.8791453 · Cited by 1
Detecting genetic heterogeneities in response to trauma: The case of 9/11
Furuya, S., Fletcher, J. M., Zhao, Z., Sun, Z., & Lu, Q. (2021) · SSM - Mental Health · doi:10.1016/j.ssmmh.2021.100044 · Cited by 1
Xie, T., Schweren, L., Larsson, H., Li, L., Rietz, E. D., Haavik, J., Kvalvik, L. G., Solberg, B. S., Klungsøyr, K., Snieder, H., et al. (2023) · Nutrients · doi:10.3390/nu15071625 · Cited by 1
“Larger GWASs for ADHD or other impulsivityrelated traits and improved approaches to calculating genetic predictors (e.g., MTAG [44], GenomicSEM [45]) promise to enhance the genetic prediction of impulsivity in the future.”
BackgroundGenome-wide association study on coordination and agility in 461 Chinese Han males
Wang, Y., Li, H., Hou, L., Wang, S., Kang, X., Yu, J., Tian, F., Ni, W., Deng, X., Liu, T., et al. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e19268 · Cited by 1
“Examples include the sharing effect between depressive symptoms, neuroticism, and subjective well-being [28], as well as among different facial traits [29] and periodontal disease, diabetes, and bone density [30].”
BackgroundBayesian multivariate genetic analysis improves translational insights
Urbut, S., Koyama, S., Hornsby, W., Bhukar, R., Kheterpal, S., Truong, B. M. T., Selvaraj, M. S., Neale, B. M., O’Donnell, C. J., Peloso, G. M., et al. (2023) · iScience · doi:10.1016/j.isci.2023.107854 · Cited by 1
Self-citationGenetic analyses identify evidence for a causal relationship between Ewing sarcoma and hernias
Yang, T., Mills, L. J., Hubbard, A. K., Cao, R., Raduski, A. R., Machiela, M. J., & Spector, L. G. (2023) · Human Genetics and Genomics Advances · doi:10.1016/j.xhgg.2023.100254 · Cited by 1
Variance‐components tests for genetic association with multiple interval‐censored outcomes
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“7 Many other similar approaches have been developed 8,17 for the GLM setting and are known to perform better as the correlation between jointly modeled outcomes increases.”
BackgroundMbatchou, J. S., & McPeek, M. S. (2024) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2024.06.010 · Cited by 1
Using Genetics to Investigate Relationships between Phenotypes: Application to Endometrial Cancer
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“Several methods have been developed for cross-trait variant identification (Table 1), with the most commonly used being Multi-Trait Analysis of GWAS (MTAG) due to its robustness to sample overlap and its ability to leverage GWAS summary statistics from multiple traits simultaneously [33].”
BackgroundMethodInfluentialMTML: An Efficient Multitrait Multilocus GWAS Method Based on the Cauchy Combination Test
Guo, H., Li, T., Shi, Y., & Wang, X. (2024) · Biometrical Journal · doi:10.1002/bimj.202300130 · Cited by 2
“…meta-analysis methods that integrate summary statistics from multiple single-trait GWAS (Evangelou and Ioannidis, 2013; Turley et al. 2018; Ray and Boehnke 2018; Bu, Wang, and Li 2023), dimension-reductionmethodssuchascanonicalcorrelationanal-ysis or principal…”
Investigating the Shared Genetic Architecture Between Leukocyte Telomere Length and Prostate Cancer
Li, Z., Wang, M., Zeng, S., Wang, Z., Ying, Y., Chen, Q., Zhang, C., He, W., Sheng, C., Wang, Y., et al. (2024) · The World Journal of Men s Health · doi:10.5534/wjmh.240062 · Cited by 1
“We conducted two cross-trait meta-analysis to identify shared SNPs associated with LTL and PCa, including multi-trait analysis of GWAS (MTAG) and cross-phenotype association test (CPASSOC) [21,22].”
Exploring the shared genetic architecture of type 2 diabetes mellitus and bone mineral density
Feng, X., Xu, H., Guo, Q., Wang, Z., Ba, R., Xuan, K., & Ban, J. (2025) · Archives of Osteoporosis · doi:10.1007/s11657-025-01568-7 · Cited by 1
“The multi-trait analysis of GWAS (MTAG) [22] and cross-phenotype association (CPASSOC) [23] tests were employed to identify shared risk SNPs associated with both T2DM and BMD. MTAG is a method that enables the joint analysis of multiple phenotypes using GWAS to summarize new data.”
A one-shot, lossless algorithm for cross-cohort learning in mixed-outcomes analysis
Li, R., Benz, L., Duan, R., Denny, J. C., Hakonarson, H. H., Mosley, J. D., Smoller, J. W., Wei, W., Lumley, T., Ritchie, M. D., et al. (2025) · Patterns · doi:10.1016/j.patter.2025.101321 · Cited by 1
“36 Alternative approaches such as ASSET, 37 GPN, 38 MTAG, 33 MultiPhen, 39 and subSETs 40 offer more efficient multi-trait testing strategies, but they are typically limited to either binary or continuous phenotypes and often only applicable to individual datasets.”
Shared heritability among psychiatric disorders and traits
O’Connell, K. S., Hindley, G., Smeland, O. B., Shadrin, A. A., Wang, Y., Frei, O., & Andreassen, O. A. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-819602-1.00019-x · Cited by 1
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Maternal depression and child human capital: A genetic instrumental-variable approachPreprint
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“In the ALSPAC analysis that we undertake here, DM is maternal depression between child birth and child age nine, YC the adolescent-child’s human capital, and GM the mother’s PGS for depression, based on the 88 most-relevant SNPs (p-value threshold of 10–6) derived from the single-trait meta-analysis in Turley et al. (…”
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“Furthermore, iPheGWAS also assists the process of selecting traits for a multi-trait analysis genome-wide association studies (MTAG) (24) to improve power for detecting genetic variants contributing to disease risk.”
MethodMeta‐Analysis in Genetic Association Studies
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“We conducted two independent Cross-trait meta-analysis of GWAS summary statistics including cross-phenotype association test (CPASSOC) 62 and multi-trait analysis of GWAS (MTAG) 63 to identify shared risk SNPs between PBC and each of IBD, CD and UC.”
MethodInfluentialShared Genetic Architecture Contributes to Risk of Major Cardiovascular DiseasesPreprint
Pauklin, S., Qiao, J., Cai, L., Chang, M., Wang, C., Zhao, R., Song, S., Tan, N., He, P., Jiang, L., et al. (2023) · Research Square · doi:10.21203/rs.3.rs-3261702/v1 · Cited by 1
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A multivariate outcome test of covariancePreprint
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Aging of the Spine: Characterizing genetic and physiological determinants of spinal curvaturePreprint
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Weine, E., Smith, S. P., Knowlton, R. K., & Harpak, A. (2024) · eLife · doi:10.7554/elife.99210.1 · Cited by 1
Gene-Environment Interplay for Well-Being
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Leveraging pleiotropy to improve genetic risk prediction across diseasesPreprint
Hu, J., Zhou, G., Zhao, H., & DeWan, A. T. (2025) · medRxiv · doi:10.1101/2025.06.16.25329688 · Cited by 1
Almost Free Enhancement of Multi-Population PRS: From Data-Fission to Pseudo-GWAS SubsamplingPreprint
Xu, L., Dong, Y., Zeng, X., Bian, Z., Zhou, G., Guan, L., & Zhao, H. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.16.659952 · Cited by 1
“Future research should explore the potential benefits of integrating PRS derived from additional information sources, such as PRS for related traits, to further enhance predictive accuracy [51, 52, 53, 54].”
Genomic dimensions deconstruct the clinical heterogeneity of bipolar disorderPreprint
van der Veen, T., Tesfaye, M., Yang, J. M. K., Boltz, T. A., David, F. S., Crinion, S., Koromina, M., Andlauer, T. F. M., Bigdeli, T. B., Coombes, B. J., et al. (2025) · medRxiv · doi:10.1101/2025.06.23.25330155 · Cited by 3
Virkkala, J., FINNGEN, Kettunen, J., & Kaikkonen, M. U. (2025) · medRxiv · doi:10.1101/2025.06.27.25330395 · Cited by 1
Genetic risk effects on psychiatric disorders act in setsPreprint
Rietkerk, J., Krebs, M. D., Mefford, J., Huang, L., Hellberg, K.-L. G., Børglum, A. D., Werge, T. M., Kendler, K. S., Flint, J., Schork, A. J., et al. (2025) · medRxiv · doi:10.1101/2025.07.23.25332043 · Cited by 1
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ML-MAGES enables multivariate genetic association analyses with genes and effect size shrinkagePreprint
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“The results neither shows shrinkage nor distinguishes trait-specific versus shared associations, which was expected as these tasks are not the target of MTAG, so we also excluded it from all downstream analyses.”
InfluentialZhong, H., Ma, S., Lillback, V., Gao, M., Zhang, W., Johari, M., Oghabian, A., Jonson, P. H., Xi, J., Zhu, W., et al. (2026) · medRxiv · doi:10.64898/2026.05.11.26352204 · Cited by 1
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Genetics of Eating and Weight Disorders
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Genomics of major depressive disorder
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Making sense of different measures of skin ageing
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Beyond GWAS in Atrial Fibrillation Genetics
van der Laan, S. W., & Asselbergs, F. W. (2020) · Circulation Research · doi:10.1161/circresaha.119.316479
Genetic Architecture of Common Diseases and Complex Traits
O’Connor, L. J. (2019) · Digital Access to Scholarship at Harvard (DASH) (Harvard University)
Taraszka, K., Zaitlen, N., & Eskin, E. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.17.100172
“…found associated with all traits B , D , H , S (body mass index, diastolic blood pressure, height, and systolic blood pressure). methods are compared: Single Trait GWAS (how the z-scores and p-values were derived), HIPO, MTAG, MI GWAS, and PAT [Qi and Chatterjee, 2018, Turley et al., 2018].”
BackgroundMethodInfluentialElucidating the Joint Genetic Architecture of Mood Disorder and SchizophreniaPreprint
Lam, M., Thompson, M., Li, B., Edwards, A. C., Chen, C., Ge, T., Cai, N., Bigdeli, T. B., Lencz, T., Kendler, K. S., et al. (2020) · medRxiv · doi:10.1101/2020.09.14.20193870
“This is where the Multi-Trait Analysis of GWAS (MTAG; (15)) framework could bring benefits to cross-trait, cross ancestry analysis.”
BackgroundMultiple-trait Adaptive Fisher's Method for Genome-wide Association StudiesPreprint
Deng, Q., & Song, C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.09002
Identification of Novel Genome-Wide Associations for Oral Inflammatory TraitsPreprint
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Genetic epidemiology of endometrial cancer
Kho, P. F. (2021) · Queensland University of Technology · doi:10.5204/thesis.eprints.211383
Zhang, H., Xiu, X., Yang, Y., Yang, Y., & Zhao, H. (2021) · medRxiv · doi:10.1101/2021.08.05.21261684
Estimating genetic correlation jointly using individual-level and summary-level GWAS dataPreprint
Zhao, H., Zhang, Y., Cheng, Y., Ye, Y., Jiang, W., & Lu, Q. (2021) · Research Square · doi:10.21203/rs.3.rs-830770/v1
Nürnberger, J. I. (2019) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.735152734.793563005 · Cited by 1
Faculty Opinions recommendation of Novel hypotheses emerging from GWAS in migraine?
Liu, M. (2020) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.734849754.793578340
Gu, J., Fuller, C., Carbonetto, P., He, X., Zheng, J., & Li, H. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.02.09.430368
“(𝑃 " (𝑡)) as the vector component or using Manhattan distance as a distance metric gives very similar results (see Figure S3, 4).”
ResultNürnberger, J. I. (2019) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.735562872.793563011
Bayesian Multivariate Genetic Analysis Boosts Translational InsightsPreprint
Urbut, S., Koyama, S., Selvaraj, S., Neale, B. M., O’Donnell, C. J., Peloso, G. M., & Natarajan, P. (2022) · Research Square · doi:10.21203/rs.3.rs-1319235/v1
Self-citationThe road towards precision therapy for genetic epilepsies
Stevelink, R. (2022) · Utrecht University Repository (Utrecht University) · doi:10.33540/1102
A novel candidate gene CLN8 regulates fat deposition in avianPreprint
Li, X., Zhang, F., Sun, Y.-X., Sun, D., Yang, F., Liu, Y., & Hou, Z. (2022) · Research Square · doi:10.21203/rs.3.rs-2237425/v1
Investigating shared genetic architecture between obesity and multiple sclerosisPreprint
Zeng, R., Jiang, R., Huang, W., Wang, J., Zhang, L., Ma, Y., Wu, Y., Meng, M., Leung, F. W., Lian, Q., et al. (2022) · medRxiv · doi:10.1101/2022.12.07.22283195
Nosková, A., Mehrotra, A., Kadri, N. K., Lloret‐Villas, A., Neuenschwander, S., Andreas Hofer, A., & Pausch, H. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.12.13.520268
Seviiri, M. (2022) · Queensland University of Technology · doi:10.5204/thesis.eprints.236969
Alemany, S., Artigas, M. S., Cabana‐Domínguez, J., Fakhreddine, D., Llonga, N., Vilar‐Ribó, L., Rodríguez‐Urrutia, A., Palacio, J., González-Castro, A. M., Lobo, B., et al. (2023) · medRxiv · doi:10.1101/2023.01.30.23285080
Miller, A. P. (2022) · MOspace Institutional Repository (University of Missouri) · doi:10.32469/10355/94082
Mandal, S., Kim, D. H., Hua, X., Li, S., & Shi, J. (2023) · Biostatistics · doi:10.1093/biostatistics/kxad001
Statistical methods for gene selection and genetic association studies
Cao, X. (2023) · Digital Commons - Michigan Tech (Michigan Technological University) · doi:10.37099/mtu.dc.etdr/1575
Gilchrist, L., Spargo, T. P., Green, R., Coleman, J. R. I., Howard, D. M., Thorp, J. G., Adey, B. N., Lord, J., Davies, H. L., Mundy, J., et al. (2023) · medRxiv · doi:10.1101/2023.06.05.23290588
some reason prevalence of anxiety and depression among people of different ages: a review
Shaalan, S. H., Atiya, R. N., Mubarak, S. M. H., & Hussain, S. A. (2023) · Maaen Journal for Medical Sciences · doi:10.55810/2789-9128.1026
Cerebellum Connects the Genetic Architecture of Site-Specific Osteoarthritis and Body Mass IndexPreprint
Yu, Y., Tang, M., Hong, D., Lei, X., & Zhang, L. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4603258
Wang, Y., Li, C., Liu, F., & Sun, F. (2023) · Research Square · doi:10.21203/rs.3.rs-3448139/v1
some reason prevalence of anxiety and depression among people of different ages: a review
Shaalan, S. H., Atiya, R. N., Mubarak, S. M. H., & Hussain, S. A. (2023) · Maaen Journal for Medical Sciences · doi:10.55810/2789-9136.1026
Genome-wide risk prediction of primary open-angle glaucoma across multiple ancestriesPreprint
Gharahkhani, P., He, W.-X., Han, X.-K., Ong, J.-S., Rentería, M. E., Wiggs, J., Khawaja, A., Trzaskowski, M., Mackey, D. A., Craig, J., et al. (2023) · medRxiv · doi:10.1101/2023.11.08.23298255
“…EUR ancestry-based GWAS meta-analysis summary statistics for IOP and VCDR respectively and then performed a multi-trait GWAS meta-analysis, taking POAG as the focal trait and leveraging the genetic correlation of IOP and VCDR to produce an effect size estimate for each SNP on POAG risk 15 .”
BackgroundMethodInfluentialCao, X., Zhu, L., Liang, X., Zhang, S., & Sha, Q. (2023) · medRxiv · doi:10.1101/2023.11.14.23297400
Yeung, M. W., van de Leur, R. R., Benjamins, J. W., Vessies, M. B., Ruijsink, B., Anton, E. P., van Tintelen, J. P., Verweij, N., van Es, R., & van der Harst, P. (2023) · medRxiv · doi:10.1101/2023.12.05.23299459
“…with significant genetic correlations r g >0.4 after Bonferroni correction (p 0.05/496=1.01 ×10 -4 ) using Multi-Trait Analysis of GWAS (MTAG) 19 with the objective of identifying additional genetic signals embedded in the ECG. MTAG is a tool for analysis of multiple GWAS summary statistics…”
MethodInfluentialShared Genetic Susceptibility between Abdominal Aortic Aneurysm and Cardiometabolic TraitsPreprint
Zheng, S., Tsao, P. S., & Pan, C. (2023) · medRxiv · doi:10.1101/2023.12.05.23299523
“As the assumptions in MTAG, i.e., equal SNV heritability for each trait and the same genetic covariance between traits, could be violated, we performed cross-phenotype association analysis (CPASSOC) 23 across traits as a sensitivity analysis.”
BackgroundMethodResultInfluentialXia, C., & Hill, W. D. (2023) · Molecular Psychiatry · doi:10.1038/s41380-023-02374-8
“These data were then meta-analysed by Lee et al. [5] using Multi-Trait Analysis of GWAS (MTAG) [6] to capture the genetically correlated variance from three other cognitive performance related traits (educational attainment, highest mathematics quali fi cation, and self-rated math ability).”
MethodSalenius, K., Väljä, N., Thusberg, S., Iris, F., Ladd‐Acosta, C., Roos, C., Nykter, M., Fasano, A., Autio, R., & Lin, J. (2024) · medRxiv · doi:10.1101/2024.01.07.24300940
Rao, S., Qiu, J.-H., Zhi, Y., Lin, Y., Zhang, R., Chen, X., Xu, D., & So, H. (2024) · medRxiv · doi:10.1101/2024.01.29.24301912
“This hampers the necessary validation step for the associated signals [12].”
BackgroundMethodInfluentialLarge-scale composite hypothesis testing for omics data analysesPreprint
Walsche, A. D., Gauthier, F., Nathalie, B., Charcosset, A., & Mary‐Huard, T. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.03.17.585412
Uncovering the genetic drivers of cutaneous melanoma and its risk factors
Ingold, N. (2024) · Queensland University of Technology · doi:10.5204/thesis.eprints.247498
Feng, Y., Jia, N., Gu, X., Xiao, C., & Shen, J. (2024) · Research Square · doi:10.21203/rs.3.rs-4193051/v1
Zhang, Y., Lu, Q., Ye, Y., Huang, K., Liu, W., Wu, Y., Zhong, X., Li, B., Yu, Z., Travers, B. G., et al. (2021) · Figshare · doi:10.6084/m9.figshare.16584360
Xiu, X., Zhang, H., Xue, A., Cooper, D. N., Li, Y., Yang, Y., Yang, Y., & Zhao, H. (2022) · Figshare · doi:10.6084/m9.figshare.20745245
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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