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

doi:10.1093/bioinformatics/btq340

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 Willer¹, Yun Li, Gonçalo R Abecasis

Affiliations

PMID: 20616382
PMCID: PMC2922887
DOI: 10.1093/bioinformatics/btq340

Meta-Analysis

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

Cristen J Willer et al. Bioinformatics. 2010.

. 2010 Sep 1;26(17):2190-1.

doi: 10.1093/bioinformatics/btq340. Epub 2010 Jul 8.

Authors

Cristen J Willer¹, Yun Li, Gonçalo R Abecasis

Affiliation

¹ Department of Biostatistics, University of Michigan, 1420 Washington Heights, Ann Arbor, Michigan 48109, USA.

PMID: 20616382
PMCID: PMC2922887
DOI: 10.1093/bioinformatics/btq340

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/.

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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

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[1] Cochran WG. The combination of estimates from different experiments. Biometrics. 1954;10:101–129.

[2] Cochran WG. The combination of estimates from different experiments. Biometrics. 1954;10:101–129.

[3] 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

[4] 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

[5] Devlin B, Roeder K. Genomic control for association studies. Biometrics. 1999;55:997–1004. - PubMed

[6] Devlin B, Roeder K. Genomic control for association studies. Biometrics. 1999;55:997–1004. - PubMed

[7] 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

[8] 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

[9] Kathiresan S, et al. Common variants at 30 loci contribute to polygenic dyslipidemia. Nat. Genet. 2009;41:56–65. - PMC - PubMed

[10] Kathiresan S, et al. Common variants at 30 loci contribute to polygenic dyslipidemia. Nat. Genet. 2009;41:56–65. - PMC - PubMed

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

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

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