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

  • Jian Yang
  • Sang Hong Lee
  • Michael E. Goddard
  • Peter M. Visscher
Part of the Methods in Molecular Biology book series (MIMB, volume 1019)


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 


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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

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