Abstract
Meta-analysis is the use of statistical methods to synthesize results of individual studies examining the same trait. A genome-wide meta-analysis primarily serves the purpose of combining data to increase power to obtain statistical evidence of association between disease and a single variant that would have otherwise escaped detection, for example because of its small effect size. Furthermore, in the era ofwhole-exome and whole-genome sequencing, aggregate tests that combine information from low frequency/rare variants within a region are typically used to test association of a gene or other chromosomal unit with the trait of interest, as single-point analysis of variants tends to have low power for variants at the low end of the MAF spectrum. Here, we discuss key principles and practical considerations when conducting meta-analysis of single-point and aggregate tests in the presence or absence of modest levels of heterogeneity and across a range of different allelic architectures.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Asimit J, Zeggini E (2010) Rare variant association analysis methods for complex traits. Annu Rev Genet 44:293–308
Asimit J, Day-Williams A, Zgaga L, Rudan I, Boraska V, Zeggini E (2012) An evaluation of different meta-analysis approaches in the presence of allelic heterogeneity. Eur J Hum Genet 20:709–712
Chapman K, Ferreira T, Morris A, Asimit J, Zeggini E (2011) Defining the power limits of genome-wide association scan meta-analyses. Genet Epidemiol 35:781–789
Chen H, Hendricks AE, Cheng Y, Cupples AL, Dupuis J, Liu CT (2011) Comparison of statistical approaches to rare variant analysis for quantitative traits. BMC Proc 5:S113
Cirulli ET, Goldstein DB (2010) Uncovering the roles of rare variants in common disease through whole-genome sequencing. Nat Rev Genet 11:415–425
Cox DR, Hinkley DV (1974) Theoretical statistics. Chapman and Hall, London
de Bakker PIW, Ferreira MAR, Jia X, Neale BM, Raychaudhuri S, Voight B (2008) Practical aspects of imputation-driven meta-analysis of genome-wide association studies. Hum Mol Genet 17:122–128
De Iorio M, Newcombe PJ, Tachmazidou I, Verzilli CJ, Whittaker JC (2011) Bayesian semiparametric meta-analysis for genetic association studies. Genet Epidemiol 35:333–340
Devlin B, Roeder K, Wassermanb L (2001) Genomic control, a new approach to genetic-based association studies. Theor Popul Biol 60:155–166
Eichler EE, Flint J, Gibson G, Kong A, Leal SM, Moore JH, Nadeau JH (2010) Missing heritability and strategies for finding the underlying causes of complex disease. Nat Rev Genet 11:446–450
Evangelou E, Ioannidis JPA (2013) Meta-analysis methods for genome-wide association studies and beyond. Nat Genet Rev 14:379–389
Firth D (1993) Bias reduction of maximum-likelihood-estimates. Biometrika 80:27–38
Fisher RA (1932) Statistical methods for research workers. Oliver and Boyd, Edinburgh
Han E, Eskin JP (2011) Random-effects model aimed at discovering associations in meta-analysis of genome-wide association studies. Am J Hum Genet 88:586–598
Han E, Eskin JP (2012) Interpreting meta-analysis of genome-wide association studies. PLoS Genet 8:e1002555
Ladouceur M, Dastani Z, Aulchenko YS, Greenwood CMT, Brent RJ (2011) The empirical power of rare variant association methods: results from Sanger sequencing in 1,998 individuals. PLoS Genet 8:e1002496
Lee S, Emond MJ, Bamshad MJ, Barnes KC, Rieder MJ, Nickerson DA, Christiani DC, Wurfel MM, Lin X (2012) Optimal unified approach for rare-variant association testing with application to small-sample case-control whole-exome sequencing studies. Am J Hum Genet 91:224–237
Lee S, Teslovich TM, Boehnke M, Lin X (2013) General framework for meta-analysis of rare variants in sequencing association studies. Am J Hum Genet 93:1–12
Li B, Leal SM (2008) Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data. Am J Hum Genet 83:311–321
Lin DY, Tang ZZ (2011) A general framework for detecting disease associations with rare variants in sequencing studies. Am J Hum Genet 89:354–367
Liu JZ et al (2010) Meta-analysis and imputation refines the association of 15q25 with smoking quantity. Nat Genet 42:436–440
Liu L, Sabo A, Neale BM, Nagaswamy U, Stevens C, Lim E, Bodea CA, Muzny D, Reid JG et al (2013a) Analysis of rare, exonic variation amongst subjects with autism spectrum disorders and population controls. PLoS Genet 9:e1003443
Liu DJ, Peloso GM, Zhan X, Holmen O, Zawistowski M, Feng S, Nikpay M, Auer PL, Goel A, Zhang H et al (2013b) Meta-analysis of gene level association tests. http://arxiv.org/abs/1305.1318
Lumley T, Brody J, Dupuis J, Cupples A (2013) Meta-analysis of a rare variant association test. http://stattech.wordpress.fos.auckland.ac.nz/files/2012/11/skat-meta-paper.pdf
Ma C, Blackwell T, Boehnke M, Scott LJ, The GoT2D Investigators (2013) Recommended joint and meta-analysis strategies for case-control association testing of single low-count variants. Genet Epidemiol. doi:10.1002/gepi.21742
Magi R, Morris AP (2010) GWAMA: software for genome-wide association meta-analysis. BMC Bioinf 11:288–294
Marchini J, Howie B, Myers S, McVean G, Donnelly P (2007) A new multipoint method for genome-wide association studies via imputation of genotypes. Nat Genet 39:906–913
Mathieson I, McVean G (2012) Differential confounding of rare and common variants in spatially structured populations. Nat Genet 44:243–246
Morris AP, Zeggini E (2010) An evaluation of statistical approaches to rare variant analysis in genetic association studies. Genet Epidemiol 34:188–193
Mukhopadhyay I, Feingold E, Weeks DE, Thalamuthu A (2010) Association tests using kernel-based measures of multi-locus genotype similarity between individuals. Genet Epidemiol 34:213–221
Neale BM, Rivas MA, Voight BF, Altshuler D, Devlin B, Orho-Melander M, Kathiresan S, Purcell SM, Roeder K, Daly MJ (2011) Testing for an unusual distribution of rare variants. PLoS Genet 7:e1001322
Newcombe PJ, Verzilli C, Casas JP, Hingorani AD, Smeeth L, Whittaker JC (2009) Multilocus Bayesian meta-analysis of gene-disease associations. Am J Hum Genet 84:567–580
Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D (2006) Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 38:904–909
Price AL, Kryukov GV, de Bakker PIW, Purcell SM, Staples J, Wei LJ, Sunyaev SR (2010) Pooled association tests for rare variants in exon-resequencing studies. Am J Hum Genet 86:832–838
Stouffer SA, Suchman EA, DeVinney LC, Williams JRM (1949) The American soldier, volume I: adjustment during army life. Princeton University Press, Princeton
Tang ZZ, Lin DY (2013) MASS: meta-analysis of score statistics for sequencing studies. Bioinformatics 29:1803–1805
Thompson JR, Attia J, Minelli C (2011) The meta-analysis of genome-wide association studies. Brief Bioinform 12:259–269
Verzilli C, Shah T, Casas JP, Chapman J, Sandhu M, Debenham SL, Boekholdt MS, Khaw KT, Wareham NJ, Judson R, Benjamin EJ, Kathiresan S, Larson MJ, Rong J, Sofat R, Humphries SE, Smeeth L, Cavaller G, Whittaker JC, Hingorani AD (2008) Bayesian meta-analysis of genetic association studies with different sets of markers. Am J Hum Genet 82:859–872
Wu MC, Lee S, Cai T, Li Y, Boehnke M, Lin X (2011) Rare-variant association testing for sequencing data with the sequence kernel association test. Am J Hum Genet 89:82–93
Yang J, Ferreira T, Morris AP, Medland SE, Genetic Investigation of ANthropometric Traits (GIANT) Consortium, DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium, Madden PA, Heath AC, Martin NG, Montgomery GW, Weedon MN, Loos RJ, Frayling TM, McCarthy MI, Hirschhorn JN, Goddard ME, Visscher PM (2012) Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat Genet 44:369–375
Zeggini E, Ioannidis JP (2009) Meta-analysis in genome-wide association studies. Pharmacogenomics 10:191–201
Zhang Y, Guan W, Pan W (2012) Adjustment for population stratification via principal components in association analysis of rare variants. Genet Epidemiol 37:99–109
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer Science+Business Media New York
About this chapter
Cite this chapter
Tachmazidou, I., Zeggini, E. (2015). Meta-Analysis of Rare Variants. In: Zeggini, E., Morris, A. (eds) Assessing Rare Variation in Complex Traits. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-2824-8_15
Download citation
DOI: https://doi.org/10.1007/978-1-4939-2824-8_15
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4939-2823-1
Online ISBN: 978-1-4939-2824-8
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)