Methods for Meta‐Analysis of Genetic Data

Kathryn L. Lunetta1

1 Boston University School of Public Health, Boston, Massachusetts
Publication Name:  Current Protocols in Human Genetics
Unit Number:  Unit 1.24
DOI:  10.1002/0471142905.hg0124s77
Online Posting Date:  April, 2013
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Modern genetic association studies, using genome‐wide genotype data, are often underpowered. Meta‐analyses of multiple studies performing genome‐wide genotyping improve power and have led to the identification of thousands of genotype‐trait associations. This unit provides an overview of the key concepts required for genetic meta‐analyses, and presents strategic approaches and key decisions that must be made in the process of performing genome‐wide association study (GWAS) meta‐analyses. The commentary discusses the interpretation of GWAS meta‐analysis results, complications, and some of the possible next steps once a GWAS meta‐analysis has successfully identified regions associated with a trait. Curr. Protoc. Hum. Genet. 77:1.24.1‐1.24.8. © 2013 by John Wiley & Sons, Inc.

Keywords: genome‐wide association; GWAS; genetic association analysis; meta‐analysis; common variants

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Table of Contents

  • Introduction
  • Key Concepts
  • Discussion
  • Literature Cited
  • Figures
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Literature Cited

Literature Cited
   Aulchenko, Y.S., Ripke, S., Isaacs, A., and van Duijn, C.M. 2007. GenABEL: An R library for genome‐wide association analysis. Bioinformatics 23:1294‐1296.
   Cochran, W.G. 1954. The combination of estimates from different experiments. Biometrics 10:101‐129.
   de Bakker, P.I., Ferreira, M.A., Jia, X., Neale, B.M., Raychaudhuri, S., and Voight, B.F. 2008. Practical aspects of imputation‐driven meta‐analysis of genome‐wide association studies. Hum. Mol. Genet. 17:R122‐R128.
   Devlin, B. and Roeder, K. 1999. Genomic control for association studies. Biometrics 55:997‐1004.
   Fisher, R.A. 1925. Statistical Methods for Research Workers. Oliver and Boyd, Edinburgh.
   Han, B. and Eskin, E. 2011. Random‐effects model aimed at discovering associations in meta‐analysis of genome‐wide association studies. Am. J. Hum. Genet. 88:586‐598.
   Han, B. and Eskin, E. 2012. Interpreting meta‐analyses of genome‐wide association studies. PLoS Genet. 8:e1002555.
   Hardy, R.J. and Thompson, S.G. 1998. Detecting and describing heterogeneity in meta‐analysis. Stat. Med. 17:841‐856.
   Higgins, J.P. and Thompson, S.G. 2002. Quantifying heterogeneity in a meta‐analysis. Stat. Med. 21:1539‐1558.
   Ioannidis, J.P., Patsopoulos, N.A., and Evangelou, E. 2007. Heterogeneity in meta‐analyses of genome wide association investigations. PLoS One 2:e841.
   Li, Y., Willer, C.J., Ding, J., Scheet, P., and Abecasis, G.R. 2010. MaCH: Using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genet. Epidemiol. 34:816‐834.
   Lin, D.Y. and Zeng, D. 2010. Meta‐analysis of genome‐wide association studies: No efficiency gain in using individual participant data. Genet. Epidemiol. 34:60‐66.
   Magi, R. and Morris, A.P. 2010. GWAMA: Software for genome‐wide association meta‐analysis.BMC Bioinformatics 11:288.
   Manning, A.K., LaValley, M., Liu, C.T., Rice, K., An, P., Liu, Y., Miljkovic, I., Rasmussen‐Torvik, L., Harris, T.B., Province, M.A., Borecki, I.B., Florez, J.C., Meigs, J.B., Cupples, L.A., and Dupuis, J. 2010. Meta‐analysis of gene‐environment interaction: Joint estimation of SNP and SNP x environment regression coefficients. Genet. Epidemiol. 35:11‐18.
   Morris, A.P. 2011. Transethnic meta‐analysis of genomewide association studies. Genet. Epidemiol. 35:809‐822.
   Purcell, S., Neale, B., Todd‐Brown, K., Thomas, L., Ferreira, M.A., Bender, D., Maller, J., Sklar, P., de Bakker, P.I., Daly, M.J., and Sham, P.C. 2007. PLINK: A tool set for whole‐genome association and population‐based linkage analyses. Am. J. Hum. Genet. 81:559‐575.
   Stouffer, S.A., Suchman, E.A., Devinney, L.C., Star, S.A., and Williams, R.M. Jr. 1949. The American Soldier: Adjustment During Army Life. Princeton University Press, Princeton, New Jersey.
   Willer, C.J., Li, Y., and Abecasis, G.R. 2010. METAL: Fast and efficient meta‐analysis of genomewide association scans. Bioinformatics 26:2190‐2191.
   Yang, J., Ferreira, T., Morris, A.P., Medland, S.E., Madden, P.A., Heath, A.C., Martin, N.G., Montgomery, G.W., Weedon, M.N., Loos, R.J., Frayling, T.M., McCarthy, M.I., Hirschhorn, J.N., Goddard, M.E., and Visscher, P.M. 2012. Conditional and joint multiple‐SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat. Genet. 44:369‐375.
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