GCTA

a tool for Genome-wide Complex Trait Analysis

Overview

About

GCTA (Genome-wide Complex Trait Analysis) is a software package initially developed to estimate the proportion of phenotypic variance explained by all genome-wide SNPs for a complex trait but has been greatly extended for many other analyses of data from genome-wide association studies (GWASs). GCTA currently supports the following analyses.

Heritability, genetic correlation, and phenotype prediction

  • GRM: estimating genetic relationships among individuals from SNP data;
  • Estimating the inbreeding coefficients of individuals in GWAS data;
  • GREML: estimating the proportion of variance in a phenotype explained by all SNPs (i.e., the SNP-based heritability);
  • Partitioning genetic variance into contributions from different sets of SNPs stratified by chromosome location, allele frequency, or functional annotation;
  • Estimating the genetic variance attributed to the X chromosome, and testing for the effect of dosage compensation;
  • GREMLd: estimating dominance variance in unrelated individuals using GWAS data;
  • Bivariate GREML: estimating the genetic correlation between two traits (diseases) using GWAS data;
  • Haseman-Elston regression to estimate SNP-based heritability for a trait and genetic correlation between two traits;
  • sBLUP: summary-data based BLUP analysis for genomic risk prediction;

Genome-wide association analysis

  • fastGWA: ultra-fast (mixed) linear model association analysis using a sparse GRM.
  • fastGWA-GLMM: ultra-fast generalized linear mixed model-based association analysis for binary traits using a sparse GRM.
  • MLMA and MLMA-LOCO: mixed linear model association analysis using a dense GRM;
  • COJO: conditional & joint association analysis using GWAS summary statistics;
  • mtCOJO: multi-trait-based conditional & joint association analysis using GWAS summary statistics;
  • fastBAT: a gene- or set-based association test using GWAS summary statistics;
  • mBAT-combo: a gene-based association test to decipher masking effects;
  • fastGWA-BB: fastGWA-GLMM burden test;
  • ACAT-V: a fast Cauchy p-value combination test for the aggregate effect of multiple rare variants.

GWAS simulation, population genetics, and Mendelian randomisaion

  • Simulating a phenotype based on GWAS data;
  • GSMR: generalised summary-data-based Mendelian randomisaion;
  • PCA analysis and estimation of Fst in GWAS data;
  • Estimating inbreeding coefficients of individuals from SNP data;
  • Computing LD scores and searching for LD friends for a list of target SNPs;

Latest release v1.94.1, click to download or view update log (10 November 2021)

Credits

Jian Yang developed the original version of the software (with supports from Peter Visscher, Mike Goddard and Hong Lee) and currently maintains the software.

Zhili Zheng programmed the fastGWA, fastGWA-GLMM and fastGWA-BB modules, rewrote the I/O and GRM modules, improved the GREML and bivariate GREML modules, extended the PCA module, and improved the SBLUP module.

Zhihong Zhu programmed the mtCOJO and GSMR modules and improved the COJO module.

Longda Jiang and Hailing Fang developed the ACAT-V module.

Jian Zeng rewrote the GCTA-HEreg module.

Andrew Bakshi contributed to the GCTA-fastBAT module.

Angli Xue improved the GSMR module.

Robert Maier improved the GCTA-SBLUP module.

Contributions to the development of methods included in GCTA (e.g., GREML methods, COJO, mtCOJO, MLMA-LOCO, fastBAT, fastGWA and fastGWA-GLMM) can be found in the papers cited in the corresponding web pages.

Questions and Help Requests

If you have any bug reports or questions please send an email to Jian Yang (jian.yang@westlake.edu.cn).

Citations

GCTA Software tool:
Yang et al. (2011) GCTA: a tool for Genome-wide Complex Trait Analysis. Am J Hum Genet. 88(1): 76-82. [PubMed ID: 21167468]

Method for estimating the variance explained by all SNPs (GREML method) with its application in human height:
Yang et al. (2010) Common SNPs explain a large proportion of the heritability for human height. Nat Genet. 42(7): 565-9. [PubMed ID: 20562875]

GREML method being extended for case-control design with its application to the WTCCC data:
Lee et al. (2011) Estimating Missing Heritability for Disease from Genome-wide Association Studies. Am J Hum Genet. 88(3): 294-305. [PubMed ID: 21376301]

Extension of GREML method to partition the genetic variance into individual chromosomes and genomic segments with its applications in height, BMI, vWF and QT interval:
Yang et al. (2011) Genome partitioning of genetic variation for complex traits using common SNPs. Nat Genet. 43(6): 519-525. [PubMed ID: 21552263]

Method for conditional and joint analysis using summary statistics from GWAS with its application to the GIANT meta-analysis data for height and BMI:
Yang et al. (2012) Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat Genet 44(4):369-375. [PubMed ID: 22426310]

Bivariate GREML method:
Lee et al. (2012) Estimation of pleiotropy between complex diseases using SNP-derived genomic relationships and restricted maximum likelihood. Bioinformatics. 28(19): 2540-2542. [PubMed ID: 22843982]

Mixed linear model based association analysis:
Yang et al. (2014) Mixed model association methods: advantages and pitfalls. Nat Genet. 46(2): 100-106. [Pubmed ID: 24473328]

GREML-LDMS method and LD-score calculation:
Yang et al. (2015) Genetic variance estimation with imputed variants finds negligible missing heritability for human height and body mass index. Nat Genet. 47(10): 1114-1120. [PMID: 26323059]

Method to search for LD friends:
Yang et al. (2011) Genomic inflation factors under polygenic inheritance. Eur J Hum Genet. 19(7): 807-812. [Pubmed ID: 21407268]

fastBAT method:
Bakshi et al. (2016) Fast set-based association analysis using summary data from GWAS identifies novel gene loci for human complex traits. Scientific Reports 6, 32894. [PMID: 27604177]

mtCOJO and GSMR methods:
Zhu et al. (2018) Causal associations between risk factors and common diseases inferred from GWAS summary data. Nat Commun. 9, 224.[PMID: 29335400]

fastGWA method:
Jiang et al. (2019) A resource-efficient tool for mixed model association analysis of large-scale data. Nat Genet. 51(12): 1749-1755. [PMID: 31768069]

fastGWA-GLMM and fastGWA-BB methods:
Jiang et al. (2021) A generalized linear mixed model association tool for biobank-scale data. Nat Genet. 53(11): 1616-1621. [PMID: 34737426]