Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Meta-Analysis
. 2010 Sep 1;26(17):2190-1.
doi: 10.1093/bioinformatics/btq340. Epub 2010 Jul 8.

METAL: fast and efficient meta-analysis of genomewide association scans

Cristen J Willer  1 , Yun Li, Gonçalo R Abecasis
Affiliations
Meta-Analysis

METAL: fast and efficient meta-analysis of genomewide association scans

Cristen J Willer et al. Bioinformatics. .

Abstract

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/.

PubMed Disclaimer

Similar articles

See all similar articles

Cited by

  • Causal relationship between beta-2 microglobulin and B-cell malignancies: genome-wide meta-analysis and a bidirectional two-sample Mendelian randomization study.
    Li J, Wu Y, Zhang X, Wang X. Li J, et al. Front Immunol. 2024 Oct 7;15:1448476. doi: 10.3389/fimmu.2024.1448476. eCollection 2024. Front Immunol. 2024. PMID: 39434879 Free PMC article.
  • DCAF4, a novel gene associated with leucocyte telomere length.
    Mangino M, Christiansen L, Stone R, Hunt SC, Horvath K, Eisenberg DT, Kimura M, Petersen I, Kark JD, Herbig U, Reiner AP, Benetos A, Codd V, Nyholt DR, Sinnreich R, Christensen K, Nassar H, Hwang SJ, Levy D, Bataille V, Fitzpatrick AL, Chen W, Berenson GS, Samani NJ, Martin NG, Tishkoff S, Schork NJ, Kyvik KO, Dalgård C, Spector TD, Aviv A. Mangino M, et al. J Med Genet. 2015 Mar;52(3):157-62. doi: 10.1136/jmedgenet-2014-102681. Epub 2015 Jan 26. J Med Genet. 2015. PMID: 25624462 Free PMC article.
  • Pleiotropic effects of obesity-susceptibility loci on metabolic traits: a meta-analysis of up to 37,874 individuals.
    van Vliet-Ostaptchouk JV, den Hoed M, Luan J, Zhao JH, Ong KK, van der Most PJ, Wong A, Hardy R, Kuh D, van der Klauw MM, Bruinenberg M, Khaw KT, Wolffenbuttel BH, Wareham NJ, Snieder H, Loos RJ. van Vliet-Ostaptchouk JV, et al. Diabetologia. 2013 Oct;56(10):2134-46. doi: 10.1007/s00125-013-2985-y. Epub 2013 Jul 5. Diabetologia. 2013. PMID: 23827965
  • Leveraging both individual-level genetic data and GWAS summary statistics increases polygenic prediction.
    Albiñana C, Grove J, McGrath JJ, Agerbo E, Wray NR, Bulik CM, Nordentoft M, Hougaard DM, Werge T, Børglum AD, Mortensen PB, Privé F, Vilhjálmsson BJ. Albiñana C, et al. Am J Hum Genet. 2021 Jun 3;108(6):1001-1011. doi: 10.1016/j.ajhg.2021.04.014. Epub 2021 May 7. Am J Hum Genet. 2021. PMID: 33964208 Free PMC article.
  • A meta-analysis of thyroid-related traits reveals novel loci and gender-specific differences in the regulation of thyroid function.
    Porcu E, Medici M, Pistis G, Volpato CB, Wilson SG, Cappola AR, Bos SD, Deelen J, den Heijer M, Freathy RM, Lahti J, Liu C, Lopez LM, Nolte IM, O'Connell JR, Tanaka T, Trompet S, Arnold A, Bandinelli S, Beekman M, Böhringer S, Brown SJ, Buckley BM, Camaschella C, de Craen AJ, Davies G, de Visser MC, Ford I, Forsen T, Frayling TM, Fugazzola L, Gögele M, Hattersley AT, Hermus AR, Hofman A, Houwing-Duistermaat JJ, Jensen RA, Kajantie E, Kloppenburg M, Lim EM, Masciullo C, Mariotti S, Minelli C, Mitchell BD, Nagaraja R, Netea-Maier RT, Palotie A, Persani L, Piras MG, Psaty BM, Räikkönen K, Richards JB, Rivadeneira F, Sala C, Sabra MM, Sattar N, Shields BM, Soranzo N, Starr JM, Stott DJ, Sweep FC, Usala G, van der Klauw MM, van Heemst D, van Mullem A, Vermeulen SH, Visser WE, Walsh JP, Westendorp RG, Widen E, Zhai G, Cucca F, Deary IJ, Eriksson JG, Ferrucci L, Fox CS, Jukema JW, Kiemeney LA, Pramstaller PP, Schlessinger D, Shuldiner AR, Slagboom EP, Uitterlinden AG, Vaidya B, Visser TJ, Wolffenbuttel BH, Meulenbelt I, Rotter JI, Spector TD, Hicks AA, Toniolo D, Sanna S, Peeters RP, Naitza S. Porcu E, et al. PLoS Genet. 2013;9(2):e1003266. doi: 10.1371/journal.pgen.1003266. Epub 2013 Feb 7. PLoS Genet. 2013. PMID: 23408906 Free PMC article.
See all "Cited by" articles

References

    1. Cochran WG. The combination of estimates from different experiments. Biometrics. 1954;10:101–129.
    1. de Bakker PI, et al. Practical aspects of imputation-driven meta-analysis of genome-wide association studies. Hum. Mol. Genet. 2008;17:R122–R128. - PMC - PubMed
    1. Devlin B, Roeder K. Genomic control for association studies. Biometrics. 1999;55:997–1004. - PubMed
    1. Kathiresan S, et al. Six new loci associated with blood low-density lipoprotein cholesterol, high-density lipoprotein cholesterol or triglycerides in humans. Nat. Genet. 2008;40:189–197. - PMC - PubMed
    1. Kathiresan S, et al. Common variants at 30 loci contribute to polygenic dyslipidemia. Nat. Genet. 2009;41:56–65. - PMC - PubMed

Publication types

  NODES
Association 8
twitter 2