Genome-Wide Complex Trait Analysis (GCTA): Methods, Data Analyses, and Interpretations

  • Jian Yang
  • Sang Hong Lee
  • Michael E. Goddard
  • Peter M. Visscher

Abstract

Estimating genetic variance is traditionally performed using pedigree analysis. Using high-throughput DNA marker data measured across the entire genome it is now possible to estimate and partition genetic variation from population samples. In this chapter, we introduce methods and a software tool called Genome-wide Complex Trait Analysis (GCTA) to estimate genomic relationships between pairs of conventionally unrelated individuals using genome-wide single nucleotide polymorphism (SNP) data, to estimate variance explained by all SNPs simultaneously on genomic or chromosomal segments or over the whole genome, and to perform a joint and conditional multiple SNPs association analysis using summary statistics from a meta-analysis of genome-wide association studies and linkage disequilibrium between SNPs estimated from a reference sample.

Key words

GWAS SNP Complex trait Missing heritability Variance explained Genomic relationship REML 

References

  1. 1.
    Hindorff LA, Sethupathy P, Junkins HA et al (2009) Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Natl Acad Sci U S A 106(23):9362–9367PubMedCrossRefGoogle Scholar
  2. 2.
    Maher B (2008) Personal genomes: the case of the missing heritability. Nature 456(7218):18–21PubMedCrossRefGoogle Scholar
  3. 3.
    Yang J, Benyamin B, McEvoy BP et al (2010) Common SNPs explain a large proportion of the heritability for human height. Nat Genet 42(7):565–569PubMedCrossRefGoogle Scholar
  4. 4.
    Yang J, Manolio TA, Pasquale LR et al (2011) Genome partitioning of genetic variation for complex traits using common SNPs. Nat Genet 43(6):519–525PubMedCrossRefGoogle Scholar
  5. 5.
    Davies G, Tenesa A, Payton A et al (2011) Genome-wide association studies establish that human intelligence is highly heritable and polygenic. Mol Psychiatry 16(10):996–1005PubMedCrossRefGoogle Scholar
  6. 6.
    Deary IJ, Yang J, Davies G et al (2012) Genetic contributions to stability and change in intelligence from childhood to old age. Nature 482(7384):212–215PubMedGoogle Scholar
  7. 7.
    Lee SH, Decandia TR, Ripke S et al (2012) Estimating the proportion of variation in susceptibility to schizophrenia captured by common SNPs. Nat Genet 44(3):247–250PubMedCrossRefGoogle Scholar
  8. 8.
    Gibson G (2010) Hints of hidden heritability in GWAS. Nat Genet 42(7):558–560PubMedCrossRefGoogle Scholar
  9. 9.
    Visscher PM, Brown MA, McCarthy MI, Yang J (2012) Five years of GWAS discovery. Am J Hum Genet 90(1):7–24PubMedCrossRefGoogle Scholar
  10. 10.
    Teslovich TM, Musunuru K, Smith AV et al (2010) Biological, clinical and population relevance of 95 loci for blood lipids. Nature 466(7307):707–713PubMedCrossRefGoogle Scholar
  11. 11.
    Heid IM, Jackson AU, Randall JC et al (2010) Meta-analysis identifies 13 new loci associated with waist-hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution. Nat Genet 42(11):949–960PubMedCrossRefGoogle Scholar
  12. 12.
    Lango Allen H, Estrada K, Lettre G et al (2010) Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature 467(7317):832–838PubMedCrossRefGoogle Scholar
  13. 13.
    Speliotes EK, Willer CJ, Berndt SI et al (2010) Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet 42(11):937–948PubMedCrossRefGoogle Scholar
  14. 14.
    Ripke S, Sanders AR, Kendler KS et al (2011) Genome-wide association study identifies five new schizophrenia loci. Nat Genet 43(10):969–976CrossRefGoogle Scholar
  15. 15.
    Yang J, Ferreira T, Morris AP et al (2012) Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat Genet 44(4):369–375PubMedCrossRefGoogle Scholar
  16. 16.
    Yang J, Lee SH, Goddard ME, Visscher PM (2011) GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet 88(1):76–82PubMedCrossRefGoogle Scholar
  17. 17.
    Hayes BJ, Visscher PM, Goddard ME (2009) Increased accuracy of artificial selection by using the realized relationship matrix. Genet Res 91(1):47–60CrossRefGoogle Scholar
  18. 18.
    Strandén I, Garrick DJ (2009) Technical note: derivation of equivalent computing algorithms for genomic predictions and reliabilities of animal merit. J Dairy Sci 92(6):2971–2975PubMedCrossRefGoogle Scholar
  19. 19.
    VanRaden PM (2008) Efficient methods to compute genomic predictions. J Dairy Sci 91(11):4414–4423PubMedCrossRefGoogle Scholar
  20. 20.
    Patterson HD, Thompson R (1971) Recovery of inter-block information when block sizes are unequal. Biometrika 58(3):545–554CrossRefGoogle Scholar
  21. 21.
    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):559–575PubMedCrossRefGoogle Scholar
  22. 22.
    Lee SH, van der Werf JH (2006) An efficient variance component approach implementing an average information REML suitable for combined LD and linkage mapping with a general complex pedigree. Genet Sel Evol 38(1):25–43PubMedCrossRefGoogle Scholar
  23. 23.
    Jorjani H, Klei L, Emanuelson U (2003) A simple method for weighted bending of genetic (co)variance matrices. J Dairy Sci 86(2):677–679PubMedCrossRefGoogle Scholar
  24. 24.
    Hill WG, Thompson R (1978) Probabilities of non-positive definite between-group or genetic covariance matrices. Biometrics 34:429–439CrossRefGoogle Scholar
  25. 25.
    Haseman JK, Elston RC (1972) The investigation of linkage between a quantitative trait and a marker locus. Behav Genet 2:2–19CrossRefGoogle Scholar
  26. 26.
    Lynch M, Walsh B (1998) Genetics and analysis of quantitative traits. Sinauer Associates, Sunderland, MAGoogle Scholar
  27. 27.
    Falconer DS (1965) The inheritance of liability to certain diseases, estimated from the incidence among relatives. Ann Hum Genet 29:51–71CrossRefGoogle Scholar
  28. 28.
    Dempster ER, Lerner IM (1950) Heritability of threshold characters. Genetics 35(2):212–236PubMedGoogle Scholar
  29. 29.
    Lee SH, Wray NR, Goddard ME, Visscher PM (2011) Estimating missing heritability for disease from genome-wide association studies. Am J Hum Genet 88(3):294–305PubMedCrossRefGoogle Scholar
  30. 30.
    Price AL, Weale ME, Patterson N et al (2008) Long-range LD can confound genome scans in admixed populations. Am J Hum Genet 83(1):132–135PubMedCrossRefGoogle Scholar
  31. 31.
    Gilmour AR, Thompson R, Cullis BR (1995) Average information REML: an efficient algorithm for variance parameters estimation in linear mixed models. Biometrics 51:1440–1450CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2013

Authors and Affiliations

  • Jian Yang
    • 1
  • Sang Hong Lee
    • 2
  • Michael E. Goddard
    • 3
    • 4
  • Peter M. Visscher
    • 1
    • 2
  1. 1.University of Queensland Diamantina Institute, Princess Alexandra HospitalUniversity of QueenslandBrisbaneAustralia
  2. 2.The Queensland Brain InstituteThe University of QueenslandBrisbaneAustralia
  3. 3.Department of Food and Agricultural SystemsUniversity of MelbourneMelbourneAustralia
  4. 4.Biosciences Research Division, Department of Primary IndustriesBundooraAustralia

Personalised recommendations