Advertisement

Meta-Analysis of Common and Rare Variants

  • Kyriaki MichailidouEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1793)

Abstract

Meta-analysis is a statistical technique that is widely used for improving the power to detect associations, by synthesizing data from independent studies, and is extensively used in the genomic analyses of complex traits. Estimates from different studies are combined and the results effectively provide the power of a much larger study. Meta-analysis also has the potential of discovering heterogeneity in the effects among the different studies. This chapter provides an overview of the methods used for meta-analysis of common and rare single variants and also for gene/region-based analyses; common variants are mainly identified via genome-wide association studies (GWAS) and rare variants through various types of sequencing experiments.

Key words

Meta-analysis Common variants Rare variants Aggregation analysis Single variant analysis GWAS NGS 

References

  1. 1.
    Zeggini E, Ioannidis JP (2009) Meta-analysis in genome-wide association studies. Pharmacogenomics 10(2):191–201.  https://doi.org/10.2217/14622416.10.2.191 CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Evangelou E, Ioannidis JP (2013) Meta-analysis methods for genome-wide association studies and beyond. Nat Rev Genet 14(6):379–389.  https://doi.org/10.1038/nrg3472 CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Manolio TA (2010) Genomewide association studies and assessment of the risk of disease. N Engl J Med 363(2):166–176.  https://doi.org/10.1056/NEJMra0905980 CrossRefPubMedGoogle Scholar
  4. 4.
    Pearson TA, Manolio TA (2008) How to interpret a genome-wide association study. JAMA 299(11):1335–1344.  https://doi.org/10.1001/jama.299.11.1335 CrossRefPubMedGoogle Scholar
  5. 5.
    International HapMap Consortium (2005) A haplotype map of the human genome. Nature 437(7063):1299–1320.  https://doi.org/10.1038/nature04226 CrossRefGoogle Scholar
  6. 6.
    1000 Genomes Project Concortium, Auton A, Brooks LD et al (2016) A reference panel of 64,976 haplotypes for genotype imputation. Nat Genet 48(10):1279–1283.  https://doi.org/10.1038/ng.3643 CrossRefGoogle Scholar
  7. 7.
    Haplotype Reference, C (2016) A reference panel of 64,976 haplotypes for genotype imputation. Nat Genet 48:1279Google Scholar
  8. 8.
    Visscher PM, Brown MA, McCarthy MI, Yang J (2012) Five years of GWAS discovery. Am J Hum Genet 90(1):7–24.  https://doi.org/10.1016/j.ajhg.2011.11.029 CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Welter D, MacArthur J, Morales J et al (2014) The NHGRI GWAS catalog, a curated resource of SNP-trait associations. Nucleic Acids Res 42(Database issue):D1001–D1006.  https://doi.org/10.1093/nar/gkt1229 CrossRefPubMedGoogle Scholar
  10. 10.
    Do R, Kathiresan S, Abecasis GR (2012) Exome sequencing and complex disease: practical aspects of rare variant association studies. Hum Mol Genet 21(R1):R1–R9.  https://doi.org/10.1093/hmg/dds387 CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Lee S, Abecasis GR, Boehnke M et al (2014) Rare-variant association analysis: study designs and statistical tests. Am J Hum Genet 95(1):5–23.  https://doi.org/10.1016/j.ajhg.2014.06.009 CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Ansorge WJ (2009) Next-generation DNA sequencing techniques. New Biotechnol 25(4):195–203.  https://doi.org/10.1016/j.nbt.2008.12.009 CrossRefGoogle Scholar
  13. 13.
    Price AL, Patterson NJ, Plenge RM et al (2006) Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 38(8):904–909.  https://doi.org/10.1038/ng1847 CrossRefPubMedGoogle Scholar
  14. 14.
    Devlin B, Roeder K (1999) Genomic control for association studies. Biometrics 55(4):997–1004CrossRefPubMedGoogle Scholar
  15. 15.
    de Bakker PI, Ferreira MA, Jia X et al (2008) Practical aspects of imputation-driven meta-analysis of genome-wide association studies. Hum Mol Genet 17(R2):R122–R128.  https://doi.org/10.1093/hmg/ddn288 CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Howie BN, Donnelly P, Marchini J (2009) A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet 5(6):e1000529.  https://doi.org/10.1371/journal.pgen.1000529 CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Howie B, Fuchsberger C, Stephens M et al (2012) Fast and accurate genotype imputation in genome-wide association studies through pre-phasing. Nat Genet 44(8):955–959.  https://doi.org/10.1038/ng.2354 CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Browning BL, Browning SR (2009) A unified approach to genotype imputation and haplotype-phase inference for large data sets of trios and unrelated individuals. Am J Hum Genet 84(2):210–223.  https://doi.org/10.1016/j.ajhg.2009.01.005 CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Lin DY, Zeng D (2010) Meta-analysis of genome-wide association studies: no efficiency gain in using individual participant data. Genet Epidemiol 34(1):60–66.  https://doi.org/10.1002/gepi.20435 CrossRefPubMedGoogle Scholar
  20. 20.
    Cochran WG (1954) The combination of estimates from different experiments. Biometrics 10:101–129CrossRefGoogle Scholar
  21. 21.
    Mantel N, Haenszel W (1959) Statistical aspects of the analysis of data from retrospective studies of disease. J Natl Cancer Inst 22(4):719–748PubMedGoogle Scholar
  22. 22.
    Borenstein M, Hedges LV, Higgins JP et al (2010) A basic introduction to fixed-effect and random-effects models for meta-analysis. Res Synth Methods 1(2):97–111.  https://doi.org/10.1002/jrsm.12 CrossRefPubMedGoogle Scholar
  23. 23.
    DerSimonian R, Laird N (1986) Meta-analysis in clinical trials. Control Clin Trials 7(3):177–188CrossRefGoogle Scholar
  24. 24.
    Hardy RJ, Thompson SG (1996) A likelihood approach to meta-analysis with random effects. Stat Med 15:619–629CrossRefPubMedGoogle Scholar
  25. 25.
    Han B, Eskin E (2011) Random-effects model aimed at discovering associations in meta-analysis of genome-wide association studies. Am J Hum Genet 88(5):586–598.  https://doi.org/10.1016/j.ajhg.2011.04.014 CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Han B, Eskin E (2012) Interpreting meta-analyses of genome-wide association studies. PLoS Genet 8(3):e1002555.  https://doi.org/10.1371/journal.pgen.1002555 CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Shi J, Lee S (2016) A novel random effect model for GWAS meta-analysis and its application to trans-ethnic meta-analysis. Biometrics 72(3):945–954.  https://doi.org/10.1111/biom.12481 CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Fisher RA (1925) Statistical methods for research workers. Oliver and Boyd, EdinburghGoogle Scholar
  29. 29.
    Stouffer SA (1949) Adjustment during army life. Princeton University Press, Princeton, NJGoogle Scholar
  30. 30.
    Stephens M, Balding DJ (2009) Bayesian statistical methods for genetic association studies. Nat Rev Genet 10(10):681–690.  https://doi.org/10.1038/nrg2615 CrossRefPubMedGoogle Scholar
  31. 31.
    Morris AP (2011) Transethnic meta-analysis of genomewide association studies. Genet Epidemiol 35(8):809–822.  https://doi.org/10.1002/gepi.20630 CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Lek M, Karczewski KJ, Minikel EV et al (2016) The OncoArray consortium: a network for understanding the genetic architecture of common cancers. Cancer Epidemiol Biomark Prev 26:126.  https://doi.org/10.1158/1055-9965.EPI-16-0106 CrossRefGoogle Scholar
  33. 33.
    Amos CI, Dennis J et al (2017) The OncoArray Consortium: A Network for Understanding the Genetic Architecture of Common Cancers. Cancer Epidemiol Biomark Prev 26(1):126–135Google Scholar
  34. 34.
    Lin DY, Tang ZZ (2011) A general framework for detecting disease associations with rare variants in sequencing studies. Am J Hum Genet 89(3):354–367.  https://doi.org/10.1016/j.ajhg.2011.07.015 CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Wang K, Li M, Hakonarson H (2010) ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res 38(16):e164.  https://doi.org/10.1093/nar/gkq603 CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    Morgenthaler S, Thilly WG (2007) A strategy to discover genes that carry multi-allelic or mono-allelic risk for common diseases: a cohort allelic sums test (CAST). Mutat Res 615(1–2):28–56.  https://doi.org/10.1016/j.mrfmmm.2006.09.003 CrossRefPubMedGoogle Scholar
  37. 37.
    Morris AP, Zeggini E (2010) An evaluation of statistical approaches to rare variant analysis in genetic association studies. Genet Epidemiol 34(2):188–193.  https://doi.org/10.1002/gepi.20450 CrossRefPubMedGoogle Scholar
  38. 38.
    Madsen BE, Browning SR (2009) A groupwise association test for rare mutations using a weighted sum statistic. PLoS Genet 5(2):e1000384.  https://doi.org/10.1371/journal.pgen.1000384 CrossRefPubMedPubMedCentralGoogle Scholar
  39. 39.
    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(3):311–321.  https://doi.org/10.1016/j.ajhg.2008.06.024 CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    Price AL, Kryukov GV, de Bakker PI et al (2010) Pooled association tests for rare variants in exon-resequencing studies. Am J Hum Genet 86(6):832–838.  https://doi.org/10.1016/j.ajhg.2010.04.005 CrossRefPubMedPubMedCentralGoogle Scholar
  41. 41.
    Wu MC, Lee S, Cai T et al (2011) Rare-variant association testing for sequencing data with the sequence kernel association test. Am J Hum Genet 89(1):82–93.  https://doi.org/10.1016/j.ajhg.2011.05.029 CrossRefPubMedPubMedCentralGoogle Scholar
  42. 42.
    Neale BM, Rivas MA, Voight BF et al (2011) Testing for an unusual distribution of rare variants. PLoS Genet 7(3):e1001322.  https://doi.org/10.1371/journal.pgen.1001322 CrossRefPubMedPubMedCentralGoogle Scholar
  43. 43.
    Lee S, Wu MC, Lin X (2012) Optimal tests for rare variant effects in sequencing association studies. Biostatistics 13(4):762–775.  https://doi.org/10.1093/biostatistics/kxs014 CrossRefPubMedPubMedCentralGoogle Scholar
  44. 44.
    Liu DJ, Peloso GM, Zhan X et al (2014) Meta-analysis of gene-level tests for rare variant association. Nat Genet 46(2):200–204.  https://doi.org/10.1038/ng.2852 CrossRefPubMedGoogle Scholar
  45. 45.
    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
  46. 46.
    Tang ZZ, Lin DY (2015) Meta-analysis for discovering rare-variant associations: statistical methods and software programs. Am J Hum Genet 97(1):35–53.  https://doi.org/10.1016/j.ajhg.2015.05.001 CrossRefPubMedPubMedCentralGoogle Scholar
  47. 47.
    Lee S, Teslovich TM, Boehnke M et al (2013) General framework for meta-analysis of rare variants in sequencing association studies. Am J Hum Genet 93(1):42–53.  https://doi.org/10.1016/j.ajhg.2013.05.010 CrossRefPubMedPubMedCentralGoogle Scholar
  48. 48.
    Voorman A, Brody J, Chen H, Lumley T, Davis B (2017) seqMeta: Meta-analysis of region-based tests of rare DNA variants. R package version 1.6.7. https://CRAN.R-project.org/package=seqMeta
  49. 49.
    Hu YJ, Berndt SI, Gustafsson S et al (2013) Meta-analysis of gene-level associations for rare variants based on single-variant statistics. Am J Hum Genet 93(2):236–248.  https://doi.org/10.1016/j.ajhg.2013.06.011 CrossRefPubMedPubMedCentralGoogle Scholar
  50. 50.
    Tang ZZ, Lin DY (2013) MASS: meta-analysis of score statistics for sequencing studies. Bioinformatics 29(14):1803–1805.  https://doi.org/10.1093/bioinformatics/btt280 CrossRefPubMedPubMedCentralGoogle Scholar
  51. 51.
    Tang ZZ, Lin DY (2014) Meta-analysis of sequencing studies with heterogeneous genetic associations. Genet Epidemiol 38(5):389–401.  https://doi.org/10.1002/gepi.21798 CrossRefPubMedPubMedCentralGoogle Scholar
  52. 52.
    Higgins JP, Thompson SG (2002) Quantifying heterogeneity in a meta-analysis. Stat Med 21(11):1539–1558.  https://doi.org/10.1002/sim.1186 CrossRefPubMedGoogle Scholar
  53. 53.
    Nelson MR, Wegmann D, Ehm MG et al (2012) An abundance of rare functional variants in 202 drug target genes sequenced in 14,002 people. Science 337(6090):100–104.  https://doi.org/10.1126/science.1217876 CrossRefPubMedPubMedCentralGoogle Scholar
  54. 54.
    Willer CJ, Li Y, Abecasis GR (2010) METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26(17):2190–2191.  https://doi.org/10.1093/bioinformatics/btq340 CrossRefPubMedPubMedCentralGoogle Scholar
  55. 55.
    Liu JZ, Tozzi F, Waterworth DM et al (2010) Meta-analysis and imputation refines the association of 15q25 with smoking quantity. Nat Genet 42(5):436–440.  https://doi.org/10.1038/ng.572 CrossRefPubMedPubMedCentralGoogle Scholar
  56. 56.
    Magi R, Morris AP (2010) GWAMA: software for genome-wide association meta-analysis. BMC Bioinformatics 11:288.  https://doi.org/10.1186/1471-2105-11-288 CrossRefPubMedPubMedCentralGoogle Scholar
  57. 57.
    Purcell S, Neale B, Todd-Brown K et al (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81(3):559–575.  https://doi.org/10.1086/519795 CrossRefPubMedPubMedCentralGoogle Scholar
  58. 58.
    R Core Team (2015) R: a language and environment for statistical computing. R Foundation for Statistical Computing, ViennaGoogle Scholar
  59. 59.
    Aulchenko YS, Ripke S, Isaacs A et al (2007) GenABEL: an R library for genome-wide association analysis. Bioinformatics 23(10):1294–1296.  https://doi.org/10.1093/bioinformatics/btm108 CrossRefPubMedGoogle Scholar
  60. 60.
    Feng S, Liu D, Zhan X et al (2014) RAREMETAL: fast and powerful meta-analysis for rare variants. Bioinformatics 30(19):2828–2829.  https://doi.org/10.1093/bioinformatics/btu367 CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electron Microscopy/Molecular PathologyThe Cyprus Institute of Neurology and GeneticsNicosiaCyprus

Personalised recommendations