DESCRIPTION: METAL is a tool for meta-analysis genomewide association scans. METAL can combine either (a) test statistics and standard errors or (b) p-values across studies (taking sample size and direction of effect into account). METAL analysis is a convenient alternative to a direct analysis of merged data from multiple studies. It is especially appropriate when data from the individual studies cannot be analyzed together because of differences in ethnicity, phenotype distribution, gender or constraints in sharing of individual level data imposed. Meta-analysis results in little or no loss of efficiency compared to analysis of a combined dataset including data from all individual studies.
CITATION: Willer, C. J., Li, Y., & Abecasis, G. R. (2010). METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics, 26(17), 2190-2191.
MANTRA
SHORT NAME: MANTRA
FULL NAME: Meta-ANalysis of Transethnic Association studies
YEAR: 2011
CITATION: Morris, A. P. (2011). Transethnic meta‐analysis of genomewide association studies. Genetic epidemiology, 35(8), 809-822.
KEY WORDS: cross-population
MR-MEGA
SHORT NAME: MR-MEGA
FULL NAME: Meta-Regression of Multi-AncEstry Genetic Association
YEAR: 2017
DESCRIPTION: MR-MEGA (Meta-Regression of Multi-AncEstry Genetic Association) is a tool to detect and fine-map complex trait association signals via multi-ancestry meta-regression. This approach uses genome-wide metrics of diversity between populations to derive axes of genetic variation via multi-dimensional scaling [Purcell 2007]. Allelic effects of a variant across GWAS, weighted by their corresponding standard errors, can then be modelled in a linear regression framework, including the axes of genetic variation as covariates. The flexibility of this model enables partitioning of the heterogeneity into components due to ancestry and residual variation, which would be expected to improve fine-mapping resolution.
URL : https://genomics.ut.ee/en/tools
CITATION: Mägi, R., Horikoshi, M., Sofer, T., Mahajan, A., Kitajima, H., Franceschini, N., ... & Morris, A. P. (2017). Trans-ethnic meta-regression of genome-wide association studies accounting for ancestry increases power for discovery and improves fine-mapping resolution. Human molecular genetics, 26(18), 3639-3650.
KEY WORDS: cross-population, Meta-Regression
GWAMA
SHORT NAME: GWAMA
FULL NAME: Genome-Wide Association Meta-Analysis
YEAR: 2010
DESCRIPTION: Software tool for meta analysis of whole genome association data
URL : https://genomics.ut.ee/en/tools
CITATION: Mägi, R., & Morris, A. P. (2010). GWAMA: software for genome-wide association meta-analysis. BMC bioinformatics, 11(1), 1-6.
mvGWAMA
SHORT NAME: mvGWAMA
FULL NAME: Multivariate Genome-Wide Association Meta-Analysis
YEAR: 2019
DESCRIPTION: mvGWAMA is a python script to perform a GWAS meta-analysis when there are sample overlap.
URL : https://github.com/Kyoko-wtnb/mvGWAMA
CITATION: Jansen, Iris E., et al. "Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk." Nature genetics 51.3 (2019): 404-413.
MTAG
SHORT NAME: MTAG
FULL NAME: Multi-Trait Analysis of GWAS
YEAR: 2018
DESCRIPTION: mtag is a Python-based command line tool for jointly analyzing multiple sets of GWAS summary statistics as described by Turley et. al. (2018). It can also be used as a tool to meta-analyze GWAS results.
URL : https://github.com/JonJala/mtag
CITATION: Turley, P., Walters, R. K., Maghzian, O., Okbay, A., Lee, J. J., Fontana, M. A., ... & Benjamin, D. J. (2018). Multi-trait analysis of genome-wide association summary statistics using MTAG. Nature genetics, 50(2), 229-237.
KEY WORDS: Multi-trait
Genomic-SEM
SHORT NAME: Genomic-SEM
FULL NAME: genomic structural equation modelling
YEAR: 2019
DESCRIPTION: R-package which allows the user to fit structural equation models based on the summary statistics obtained from genome wide association studies (GWAS).
URL : https://github.com/GenomicSEM/GenomicSEM
CITATION: Grotzinger, A. D., Rhemtulla, M., de Vlaming, R., Ritchie, S. J., Mallard, T. T., Hill, W. D., ... & Tucker-Drob, E. M. (2019). Genomic structural equation modelling provides insights into the multivariate genetic architecture of complex traits. Nature human behaviour, 3(5), 513-525.
KEY WORDS: SEM
RareMETAL
SHORT NAME: RareMETAL
FULL NAME: RareMETAL
YEAR: 2014
DESCRIPTION: RAREMETAL is a program that facilitates the meta-analysis of rare variants from genotype arrays or sequencing (manuscript in preparation).
URL : https://genome.sph.umich.edu/wiki/RAREMETAL
CITATION: Feng, S., Liu, D., Zhan, X., Wing, M. K., & Abecasis, G. R. (2014). RAREMETAL: fast and powerful meta-analysis for rare variants. Bioinformatics, 30(19), 2828-2829.
KEY WORDS: rare variants
MetaSKAT
SHORT NAME: MetaSKAT
FULL NAME: MetaSKAT
YEAR: 2013
DESCRIPTION: MetaSKAT is a R package for multiple marker meta-analysis. It can carry out meta-analysis of SKAT, SKAT-O and burden tests with individual level genotype data or gene level summary statistics.
URL : https://www.hsph.harvard.edu/skat/metaskat/
CITATION: Lee, S., Teslovich, T. M., Boehnke, M., & Lin, X. (2013). General framework for meta-analysis of rare variants in sequencing association studies. The American Journal of Human Genetics, 93(1), 42-53.
KEY WORDS:
SMMAT
SHORT NAME: SMMAT
FULL NAME: variant set mixed model association tests
YEAR: 2019
DESCRIPTION: For rare variant analysis from sequencing association studies, GMMAT performs the variant Set Mixed Model Association Tests (SMMAT) as proposed in Chen et al. (2019), including the burden test, the sequence kernel association test (SKAT), SKAT-O and an efficient hybrid test of the burden test and SKAT, based on user-defined variant sets.
URL : https://github.com/hanchenphd/GMMAT
CITATION: Chen, H., Huffman, J. E., Brody, J. A., Wang, C., Lee, S., Li, Z., ... & Hemostasis Working Group. (2019). Efficient variant set mixed model association tests for continuous and binary traits in large-scale whole-genome sequencing studies. The American Journal of Human Genetics, 104(2), 260-274.
MetaSTAAR
SHORT NAME: MetaSTAAR
FULL NAME: MetaSTAAR
YEAR: 2022
DESCRIPTION: MetaSTAAR is an R package for performing Meta-analysis of variant-Set Test for Association using Annotation infoRmation (MetaSTAAR) procedure in whole-genome sequencing (WGS) studies. MetaSTAAR enables functionally-informed rare variant meta-analysis of large WGS studies using an efficient, sparse matrix approach for storing summary statistic, while protecting data privacy of study participants and avoiding sharing subject-level data. MetaSTAAR accounts for relatedness and population structure of continuous and dichotomous traits, and boosts the power of rare variant meta-analysis by incorporating multiple variant functional annotations.
URL : https://github.com/xihaoli/MetaSTAAR
CITATION: Li, X., Quick, C., Zhou, H. et al. Powerful, scalable and resource-efficient meta-analysis of rare variant associations in large whole genome sequencing studies. Nat Genet (2022). https://doi.org/10.1038/s41588-022-01225-6
FactorGO
SHORT NAME: FactorGO
FULL NAME: Factor analysis model in Genetic assOciation
YEAR: 2023
DESCRIPTION: FactorGo is a scalable variational factor analysis model that learns pleiotropic factors using GWAS summary statistics.
CITATION: Zhang, Z., Jung, J., Kim, A., Suboc, N., Gazal, S., & Mancuso, N. (2023). A scalable variational approach to characterize pleiotropic components across thousands of human diseases and complex traits using GWAS summary statistics. medRxiv, 2023-03.