METAL: fast and efficient meta-analysis of genomewide association scans
By Cristen J. Willer, Yun Li, Gonçalo R. Abecasis. Bioinform., 2010
5,095 citations recorded by the source corpus.
Summary: METAL provides a computationally efficient tool for meta-analysis of genome-wide association scans, which is a commonly used approach for improving power complex traits gene mapping studies. METAL provides a rich scripting interface and implements efficient memory management to allow analyses of very large data sets and to support a variety of input file formats. Availability and implementation: METAL, including source code, documentation, examples, and executables, is available at http://www.sph.umich.edu/csg/abecasis/metal/ Contact: goncalo@umich.edu
Subfields: Genetics
View Paper: https://www.semanticscholar.org/paper/971f47ca15436c758d6ddd12a13bac23abc9d088
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In Genetics
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Citations in the corpus
- Meta-analysis of genome-wide association studies for height and body mass index in ∼700,000 individuals of European ancestry (2018, methodology): “Before meta-analysis with UKB, we filtered out SNPs which reported pairs of alleles did not match the pairs of alleles in the HRS and UKB and also which had reported allele frequency too different (absolute difference > 0.15) from that calculated using unrelated participants of HRS. Fixed-effect inv”
- Distribution and Medical Impact of Loss-of-Function Variants in the Finnish Founder Population (2014, methodology): “The results for the LPA with cardiovascular disease association from MIGen ExA and the Estonian Biobank were meta-analyzed using METAL [33] and the combined results with FINRISK were obtained using the Fisher’s Combined P method with 4 degrees of freedom.”
- Genome-wide association study identifies multiple susceptibility loci for pulmonary fibrosis (2013, methodology): “Willer, C.J., Li, Y. & Abecasis, G.R. METAL: fast and efficient meta-analysis of genomewide association scans.”