title: Genome-wide complex trait analysis (GCTA): methods, data analyses, and interpretations.
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.
authors.affiliation: University of Queensland Diamantina Institute, Princess Alexandra Hospital, University of Queensland, Brisbane, QLD, Australia.
authors.forename: Jian
authors.initials: J
authors.lastname: Yang
authors.forename: Sang Hong
authors.initials: SH
authors.lastname: Lee
authors.forename: Michael E
authors.initials: ME
authors.lastname: Goddard
authors.forename: Peter M
authors.initials: PM
authors.lastname: Visscher
mesh_terms: D002877:Chromosomes, Human; D003627:Data Interpretation, Statistical; D005819:Genetic Markers; D015894:Genome, Human; D055106:Genome-Wide Association Study / Q000706:statistics & numerical data*; D006801:Humans; D015810:Linkage Disequilibrium; D020412:Multifactorial Inheritance; D010375:Pedigree; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D012107:Research Design; D012984:Software
chemical_list: D005819:Genetic Markers