Genome-wide complex trait analysis (GCTA): Methods, data analyses, and interpretations

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

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

102 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationGenome-Wide Association Studies and Genomic Prediction
PublisherHumana Press Inc.
Pages215-236
Number of pages22
ISBN (Print)9781627034463
DOIs
Publication statusPublished or Issued - 2013
Externally publishedYes

Publication series

NameMethods in Molecular Biology
Volume1019
ISSN (Print)1064-3745

Keywords

  • Complex trait
  • GWAS
  • Genomic relationship
  • Missing heritability
  • REML
  • SNP
  • Variance explained

ASJC Scopus subject areas

  • Molecular Biology
  • Genetics

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