Cited by 1,112Genetic Associations and EpidemiologyGenetic Mapping and Diversity in Plants and AnimalsGenetic and phenotypic traits in livestock

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Multi-trait analysis of genome-wide association summary statistics using MTAG

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23andMe Research Team, Turley, Patrick, Social Science Genetic Association Consortium, Walters, Raymond K., Maghzian, Omeed, Okbay, Aysu, Lee, James J., Fontana, Mark Alan, Nguyen-Viet, Tuan Anh, Wedow, Robbee, Zacher, Meghan, Furlotte, Nicholas A., Magnusson, Patrik, Oskarsson, Sven, Johannesson, Magnus, Visscher, Peter M., Laibson, David, Cesarini, David, Neale, Benjamin M., Benjamin, Daniel J.·2018·Nature Genetics

DOI 10.1038/s41588-017-0009-4PMC PMC5805593

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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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Method
Polygenic Risk Scores Driving Clinical Change in Glaucoma
Kolovos, A., Hassall, M., Siggs, O. M., Souzeau, E., & Craig, J. E. (2024) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-121222-105817 · Cited by 12
Gene × environment effects and mediation involving adverse childhood events, mood and anxiety disorders, and substance dependence
Kranzler, H. R., Davis, C. N., Feinn, R., Jinwala, Z., Khan, Y., Oikonomou, A., Silva-Lopez, D., Burton, I., Dixon, M., Milone, J., et al. (2024) · Nature Human Behaviour · doi:10.1038/s41562-024-01885-w · Cited by 18
“Using GWAS of the latent genetic factors, we generated summary statistics and then calculated ancestry-specific M/AD and SUD PRS in the Yale–Penn sample (for AA, n = 2,284; for EA, n = 2,871).”
Influential
Multi-trait multi-locus SEM model discriminates SNPs of different effects
Igolkina, A. A., Meshcheryakov, G. A., Gretsova, M., Nuzhdin, S. V., & Samsonova, M. G. (2020) · BMC Genomics · doi:10.1186/s12864-020-06833-2 · Cited by 11
“combining several single-trait GWAS of different traits was proposed [9].”
Background
Shared genetic influences on resting‐state functional networks of the brain
Guimaraes, J., Sprooten, E., Beckmann, C. F., Franke, B., & Bralten, J. (2022) · Human Brain Mapping · doi:10.1002/hbm.25712 · Cited by 9
Familial autoimmunity in patients with idiopathic inflammatory myopathies
Che, W. I., Westerlind, H., Lundberg, I. E., Hellgren, K. E., Kuja‐Halkola, R., & Holmqvist, M. E. (2022) · Journal of Internal Medicine · doi:10.1111/joim.13573 · Cited by 10
Accurate modeling of replication rates in genome-wide association studies by accounting for Winner’s Curse and study-specific heterogeneity
Zou, J., Zhou, J., Faller, S., Brown, R., Sankararaman, S., & Eskin, E. (2022) · G3 Genes Genomes Genetics · doi:10.1093/g3journal/jkac261 · Cited by 10
Causal associations of sleep apnea and snoring with type 2 diabetes and glycemic traits and the role of BMI
Wang, J., Campos, A. I., García‐Marín, L. M., Rentería, M. E., & Xu, L. (2023) · Obesity · doi:10.1002/oby.23669 · Cited by 10
“Compared with univariable MR-IVW, the MVMR results using the MTAG showed the associations of sleep apnea and snoring with type T AB L E 2 MR associations of sleep apnea with cardiovascular diseases using SNPs for sleep apnea and snoring, using published GWASa Exposure Outcomes SNP (F-statistic) Methods Odds ratio/β 95…”
MethodInfluential
Multi-trait genome-wide association analyses leveraging alcohol use disorder findings identify novel loci for smoking behaviors in the Million Veteran Program
Cheng, Y., Dao, C., Zhou, H., Li, B., Kember, R. L., Toikumo, S., Zhao, H., Gelernter, J. E., Kranzler, H. R., Justice, A. C., et al. (2023) · Translational Psychiatry · doi:10.1038/s41398-023-02409-2 · Cited by 10
“A recently developed method, multi-trait analysis of GWASs (MTAG), enables joint analysis of genetically correlated traits to boost statistical power to detect variants for each trait [15].”
BackgroundMethodResultInfluential
Cognitive Genomics: Recent Advances and Current Challenges
Fitzgerald, J. C., Morris, D. W., & Donohoe, G. J. (2020) · Current Psychiatry Reports · doi:10.1007/s11920-019-1125-x · Cited by 11
A trans-ethnic two-stage polygenetic scoring analysis detects genetic correlation between osteoporosis and schizophrenia
Liu, L., Wen, Y., Ning, Y.-J., Li, P., Cheng, B., Cheng, S.-Q., Zhang, L., Ma, M., Qi, X., Liang, C.-J., et al. (2020) · Clinical and Translational Medicine · doi:10.1186/s40169-020-00272-y · Cited by 9
“In the second stage, we performed MTAG [31] analysis to detect novel candidate genes for OP and SCZ, respectively.”
BackgroundMethodInfluential
Truncated tests for combining evidence of summary statistics
Bu, D., Yang, Q., Meng, Z., Zhang, S., & Li, Q. (2020) · Genetic Epidemiology · doi:10.1002/gepi.22330 · Cited by 11
“Multi‐trait analysis of GWAS (Turley et al., 2018) improves the estimates of single trait by incorporating information contained in the GWAS estimation for other traits.”
Rostral middle frontal gyrus thickness mediates the relationship between genetic risk and neuroticism trait
Li, S., Zhou, Z., Meng, J., Zhu, X., Wang, K., Wei, D., & Qiu, J. (2020) · Psychophysiology · doi:10.1111/psyp.13728 · Cited by 12
Interplay between genetic risk and the parent environment in adolescence and substance use in young adulthood: A TRAILS study
Pasman, J. A., Smit, K., Vollebergh, W., Nolte, I. M., Hartman, C. A., Abdellaoui, A., Verweij, K. J. H., Maciejewski, D. F., & Vink, J. M. (2021) · Development and Psychopathology · doi:10.1017/s095457942100081x · Cited by 13
“MTAG jointly analyzes two or more genetically correlated traits, aggregating their signal and boosting power to detect genetic associations (Turley et al., 2018).”
Fate or coincidence: do COPD and major depression share genetic risk factors?
Martucci, V. L., Richmond, B. W., Davis, L. K., Blackwell, T. S., Cox, N. J., Samuels, D. C., Edwards, D. R. V., & Aldrich, M. C. (2021) · Human Molecular Genetics · doi:10.1093/hmg/ddab068 · Cited by 12
Comparison of two multi-trait association testing methods and sequence-based fine mapping of six additive QTL in Swiss Large White pigs
Nosková, A., Mehrotra, A., Kadri, N. K., Lloret‐Villas, A., Neuenschwander, S., Andreas Hofer, A., & Pausch, H. (2023) · BMC Genomics · doi:10.1186/s12864-023-09295-4 · Cited by 10
“The phenomenon that associations detected by stGWAS might disappear in multi-trait analyses has been reported earlier [12, 40].”
Background
Familial aggregation of multimorbidity in Sweden: national explorative family study
Zöller, B., Pirouzifard, M., Holmquist, B., Sundquist, J., Halling, A., & Sundquist, K. (2023) · BMJ Medicine · doi:10.1136/bmjmed-2021-000070 · Cited by 10
Genetic variants for head size share genes and pathways with cancer
Knol, M. J., Poot, R. A., Evans, T. E., Satizábal, C. L., Mishra, A., Sargurupremraj, M., der Auwera, S. V., Duperron, M., Jian, X., Hostettler, I. C., et al. (2024) · Cell Reports Medicine · doi:10.1016/j.xcrm.2024.101529 · Cited by 14
Multitrait Genetic Analysis Identifies Novel Pleiotropic Loci for Depression and Schizophrenia in East Asians
Song, Y., Li, L., Jiang, Y., Peng, B., Jiang, H., Chao, Z., & Chang, X. (2024) · Schizophrenia Bulletin · doi:10.1093/schbul/sbae145 · Cited by 10
Investigating the shared genetic basis of inflammatory bowel disease and systemic lupus erythematosus using genetic overlap analysis
Yuan, W., Luo, Q., & Wu, N. (2024) · BMC Genomics · doi:10.1186/s12864-024-10787-0 · Cited by 10
“…to examine genetic overlap and loci between different traits [13]; ③ Performing genome-wide association study (GWAS) multi-trait analysis (MTAG) [14] for the two diseases to identify shared risk loci; and ④ Applying LDSC specifically expressed gene (LDSC-SEG) analysis in determining tissues…”
BackgroundMethodInfluential
DINGO: increasing the power of locus discovery in maternal and fetal genome-wide association studies of perinatal traits
Hwang, L., Cuéllar-Partida, G., Yengo, L., Zeng, J., Toivonen, J., Arvas, M., Beaumont, R. N., Freathy, R. M., Moen, G.-H., Warrington, N. M., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-53495-9 · Cited by 9
“This makes sense since the two-degree-of-freedom test (and the meta-analytic strategy) are derived by speci ﬁ cally modeling the correlation between maternal and fetal genotypes whereas other multivariate tests, e.g., MTAG 13 , use models that are agnostic to the mother-offspring relationship and do not take advantage…”
Influential
Exploring autism spectrum disorder and co-occurring trait associations to elucidate multivariate genetic mechanisms and insights
Salenius, K., Väljä, N., Thusberg, S., Iris, F., Ladd‐Acosta, C., Roos, C., Nykter, M., Fasano, A., Autio, R., Lin, J., et al. (2024) · BMC Psychiatry · doi:10.1186/s12888-024-06392-w · Cited by 11
Genetic Association of Juvenile Idiopathic Arthritis With Adult Rheumatic Disease
Fan, J., Hao, J., Fu, Y., Liu, X., Qu, H., Glessner, J., Ji, D., Liu, W. Y., Zheng, G., Ding, Z., et al. (2024) · JAMA Network Open · doi:10.1001/jamanetworkopen.2024.51341 · Cited by 10
Multiple strategies association revealed functional candidate FASN gene for fatty acid composition in cattle
Zhu, B., Wang, T., Niu, Q., Wang, Z., Hay, E. H., Xu, L., Chen, Y., Zhang, L., Gao, H., Gao, H., et al. (2025) · Communications Biology · doi:10.1038/s42003-025-07604-z · Cited by 10
“To improve the statistical power of GWASs for medium and high genetic correlationfattyacidtraits,themulti-traitanalysisofGWAS(MTAG 87 )was used to joint analysis of multiple traits with the imputed WGS data.”
Multitrait GWAS of non-suicidal self-injury and the polygenetic effects on child psychopathology and brain structures
Sun, Y., Zhao, G., Zhang, Y., Lu, Z., Kang, Z., Sun, J., Feng, X., Guo, J., Liao, Y., Guo, L., et al. (2025) · Cell Reports Medicine · doi:10.1016/j.xcrm.2025.102119 · Cited by 11
Combined genome-wide association study of facial traits in Europeans increases explained variance and improves prediction
Xiong, Z., Li, Y., Liu, X., Lu, H., Hysi, P. G., Pardo, L. M., Uitterlinden, A. G., Rivadeneira, F., Ikram, M. A., Ghanbari, M., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-61761-7 · Cited by 13
“Compared with our previous face C-GWAS of 78 traits obtained from 13 landmarks in 10,115 individuals 31 , the current C-GWAS of 946 facial traits from 44 landmarks in 11,662 individuals gained 184% extra statisticalpowerasestimatedusingtheincreaseinthemean χ 2 statistic method described in Turley et al. 32 .”
Influential
Coupled mixed model for joint genetic analysis of complex disorders with two independently collected data sets
Wang, H., Pei, F., Vanyukov, M. M., Bahar, İ., Wu, W., & Xing, E. P. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-03959-2 · Cited by 10
A novel transcriptional risk score for risk prediction of complex human diseases
Shan, N., Xie, Y., Song, S., Jiang, W., Wang, Z., & Hou, L. (2021) · Genetic Epidemiology · doi:10.1002/gepi.22424 · Cited by 9
Common Variants on FGD5 Increase Hazard of Mortality or Rehospitalization in Patients With Heart Failure From the ASCEND-HF Trial
Gui, H., Tang, W. H. W., Francke, S., Li, J., She, R., Bazeley, P. S., Pereira, N. L., Adams, K. F., Luzum, J. A., Connolly, T. M., et al. (2023) · Circulation Heart Failure · doi:10.1161/circheartfailure.122.010438 · Cited by 10
Additional Evidence for the Relationship Between Type 2 Diabetes and Stroke Through Observational and Genetic Analyses
Zhang, W., Zhang, L., Zhu, J., Xiao, C., Cui, H., Yang, C., Yan, P., Tang, M., Wang, Y., Chen, L., et al. (2023) · Diabetes · doi:10.2337/db22-0954 · Cited by 10
The contribution of whole-genome sequence data to genome-wide association studies in livestock: Outcomes and perspectives
Ros‐Freixedes, R. (2024) · Livestock Science · doi:10.1016/j.livsci.2024.105430 · Cited by 10
Improved Estimation of Phenotypic Correlations Using Summary Association Statistics
Li, T., Zheng, N., & Shen, X. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.665252 · Cited by 10
“The phenotypic correlation can be estimated by the intercept of a bivariate linkage disequilibrium score regression (LDSC) (Bulik-Sullivan et al., 2015; Turley et al., 2018; Zheng et al., 2018).”
Method
Genome-wide association study of colorectal polyps identified highly overlapping polygenic architecture with colorectal cancer
Hikino, K., Koido, M., Otomo, N., Tomizuka, K., Ikegawa, S., Matsuda, K., Momozawa, Y., The Biobank Japan Project, Yamanashi, Y., Furukawa, Y., et al. (2021) · Journal of Human Genetics · doi:10.1038/s10038-021-00980-4 · Cited by 10
Dissecting Meta-Analysis in GWAS Era: Bayesian Framework for Gene/Subnetwork-Specific Meta-Analysis
Chimusa, E. R., & Defo, J. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.838518 · Cited by 10
“In the last decade, the use of meta-analysis method has increased due to different interests from both the medical researchers and statisticians Shi and Lee (2016); Fan et al. (2016); Turley et al. (2018).”
BackgroundMethod
Multi-PGS enhances polygenic prediction: weighting 937 polygenic scoresPreprint
Albiñana, C., Zhu, Z., Schork, A. J., Ingason, A., Aschard, H., Brikell, I., Bulik, C. M., Pedersen, L. V., Agerbo, E., Grove, J., et al. (2022) · medRxiv · doi:10.1101/2022.09.14.22279940 · Cited by 10
Self-citation
Combined genome-wide association study of 136 quantitative ear morphology traits in multiple populations reveal 8 novel loci
Li, Y., Xiong, Z., Zhang, M., Hysi, P. G., Qian, Y., Adhikari, K., Weng, J., Wu, S., Du, S., Gonzalez-Jose, R., et al. (2023) · PLOS Genetics · doi:10.1371/journal.pgen.1010786 · Cited by 9
Genetic Links Between Metabolic Syndrome and Osteoarthritis: Insights From Cross-Trait Analysis
Huang, J.-X., Xu, S.-Z., Tian, T., Wang, J., Jiang, L.-Q., He, T., Meng, S., Ni, J., & Pan, H. (2024) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/clinem/dgae169 · Cited by 10
Shared genetic effect of kidney function on bipolar and major depressive disorders: a large-scale genome-wide cross-trait analysis
Yu, S., Lin, Y., Yang, Y., Jin, X., Liao, B., Lu, D., & Huang, J. (2024) · Human Genomics · doi:10.1186/s40246-024-00627-3 · Cited by 8
“Moreover, MTAG association statistics provide additional power and minimal inflation of the FDR for each trait examined with high correlation, aligning with theoretical expectations [32].”
BackgroundMethod
Uncovering the heritable components of multimorbidities and disease trajectories using a nationwide cohort
Westergaard, D., Jørgensen, F. H., Waaben, J., Jung, A. W., Lademann, M., Hansen, T. F., Cremers, J., Ostrowski, S. R., Pedersen, O. B., Reguant, R., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-51795-8 · Cited by 12
“Large studies of genetic correlations have been used to identify previously unrealized similar disorders and suggest new nosologies 8,13 .”
Integrating electronic health records and GWAS summary statistics to predict the progression of autoimmune diseases from preclinical stages
Wang, C., Markus, H., Diwadkar, A. R., Khunsriraksakul, C., Carrel, L., Li, B., Zhong, X. Y., Wang, X., Zhan, X., Foulke, G., et al. (2025) · Nature Communications · doi:10.1038/s41467-024-55636-6 · Cited by 9
The Genetics of Partnership Dissolution
Jørgensen, R. E., Cheesman, R., Andreassen, O. A., & Lyngstad, T. H. (2025) · Sociological Science · doi:10.15195/v12.a4 · Cited by 9
Exploring genetic structures and shared sites between alcohol, cheese intake, and inflammatory bowel disease
Huang, Z., & Yuan, W. (2025) · Frontiers in Nutrition · doi:10.3389/fnut.2025.1468457 · Cited by 9
Progress in Translating Glaucoma Genetics Into the Clinic: A Review
Kolovos, A., Maxwell, G., Souzeau, E., & Craig, J. E. (2025) · Clinical and Experimental Ophthalmology · doi:10.1111/ceo.14500 · Cited by 9
“The genetic power of endophenotypes can be leveraged through a method known as multi-trait analysis of GWAS (MTAG) [58].”
Whole-genome sequencing analyses suggest novel genetic factors associated with Alzheimer’s disease and a cumulative effects model for risk liability
Kim, J. P., Cho, M., Kim, C., Lee, H., Jang, B., Jung, S., Kim, Y., Koh, I. G., Kim, S., Shin, D., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-59949-y · Cited by 10
Self-citation
Shared genetic architecture contributes to risk of major cardiovascular diseases
Qiao, J., Jiang, L., Cai, L., Chang, M., Wang, C., Zhao, R., Song, S., Zhao, Y., Chen, M., Ding, S., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-62419-0 · Cited by 12
“Building on this, we conducted cross-trait GWAS meta-analyses using the MTAG method 16 to enhance the identi ﬁ cation of novel genetic associations for each CVD by pinpointing pleiotropic SNPs contributing to CVD susceptibility.”
Influential
Genome-wide analysis of heart failure yields insights into disease heterogeneity and enables prognostic prediction in the Japanese population
Enzan, N., Miyazawa, K., Koyama, S., Kurosawa, R., Ieki, H., Yoshida, H., Takechi, F., Fukuyama, M., Osako, R., Tomizuka, K., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-64659-6 · Cited by 11
“This resulted in mapping from genes to phenotypes in animals (Supplementary Data 20, 33, and 44).”
Influential
Dissecting the genetic and proteomic risk factors for delirium
Raptis, V., Bhak, Y., Cannings, T. I., MacLullich, A. M. J., & Tenesa, A. (2025) · Nature Aging · doi:10.1038/s43587-025-01018-6 · Cited by 9
“Such approach increases statistical power to detect genetic associations for each trait 34 .”
Influential
Multitrait analyses identify genetic variants associated with aortic valve function and aortic stenosis risk
Kany, S., Rämö, J. T., Hou, C. R., Jurgens, S. J., Khurshid, S. S., Nauffal, V., Cunningham, J. W., Lau, E. S., Koyama, S., FinnGen, et al. (2025) · Nature Genetics · doi:10.1038/s41588-025-02397-7 · Cited by 11
“Having established that quantitative aortic valve measurements were heritable biomarkers for aortic valve stenosis, we pursued MTAG to incorporate information from these related traits 29 .”
Influential
Topic modeling identifies novel genetic loci associated with multimorbidities in UK Biobank
Zhang, Y., Jiang, X., Mentzer, A. J., McVean, G., & Lunter, G. A. (2023) · Cell Genomics · doi:10.1016/j.xgen.2023.100371 · Cited by 13
A multitrait genetic study of hemostatic factors and hemorrhagic transformation after stroke treatment
Gallego-Fábrega, C., Temprano‐Sagrera, G., Cárcel‐Márquez, J., Muiño, E., Cullell, N., Lledós, M., Llucià‐Carol, L., Martín-Campos, J. M., Sobrino, T., Castillo, J., et al. (2023) · Journal of Thrombosis and Haemostasis · doi:10.1016/j.jtha.2023.11.027 · Cited by 9
“Multitrait analysis [26,27] is similar to meta-analysis in which 2 GWAS datasets from related phenotypes are combined to boost the power and aid the identification of new shared loci among them.”
Method

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: Turley, Patrick
Walters, Raymond K.
Okbay, Aysu
Lee, James J.
Wedow, Robbee
Zacher, Meghan
Furlotte, Nicholas A.
Magnusson, Patrik
Oskarsson, Sven
Johannesson, Magnus
Visscher, Peter M.
Laibson, David
Cesarini, David
Neale, Benjamin M.
Benjamin, Daniel J.
Venue: Nature Genetics
Publisher: Springer Science and Business Media LLC
Published: 2018-01-01
Volume: 50
Issue: 2
Pages: 229-237
ISSN: 1061-4036 (ISSN-L)
1546-1718
DOI: 10.1038/s41588-017-0009-4
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: correction (2019-05-30)
correction (2019-06-25)
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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Multi-trait analysis of genome-wide association summary statistics using MTAG

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