a tool for Genome-wide Complex Trait Analysis

Latest release v1.26.0 (22 June 2016)

Last release v1.25.3 (27 April 2015)


Please visit GCTA forum for the latest documentation

GCTA Forum http://gcta.freeforums.net


GCTA (Genome-wide Complex Trait Analysis) was originally designed to estimate the proportion of phenotypic variance explained by genome- or chromosome-wide SNPs for complex traits (the GREML method), and has subsequently extended for many other analyses to better understand the genetic architecture of complex traits. GCTA currently supports the following functionalities:


Jian Yang developed the software with methodological support from Hong Lee, Mike Goddard and Peter Visscher. Andrew Bakshi and Jian Yang developed the fastBAT module.

Questions and Help Requests
If you have any bug reports or questions please send an email to Jian Yang at jian.yang@uq.edu.au


Software tool:
Yang J, Lee SH, Goddard ME and Visscher PM. GCTA: a tool for Genome-wide Complex Trait Analysis. Am J Hum Genet. 2011 Jan 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 J, Benyamin B, McEvoy BP, Gordon S, Henders AK, Nyholt DR, Madden PA, Heath AC, Martin NG, Montgomery GW, Goddard ME, Visscher PM. Common SNPs explain a large proportion of the heritability for human height. Nat Genet. 2010 Jul 42(7): 565-9. [PubMed ID: 20562875]

GREML method being extended for case-control design with its application to the WTCCC data:
Lee SH, Wray NR, Goddard ME and Visscher PM. Estimating Missing Heritability for Disease from Genome-wide Association Studies. Am J Hum Genet. 2011 Mar 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 J, Manolio TA, Pasquale LR, Boerwinkle E, Caporaso N, Cunningham JM, de Andrade M, Feenstra B, Feingold E, Hayes MG, Hill WG, Landi MT, Alonso A, Lettre G, Lin P, Ling H, Lowe W, Mathias RA, Melbye M, Pugh E, Cornelis MC, Weir BS, Goddard ME, Visscher PM: Genome partitioning of genetic variation for complex traits using common SNPs. Nat Genet. 2011 Jun 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 J, Ferreira T, Morris AP, Medland SE; Genetic Investigation of ANthropometric Traits (GIANT) Consortium; DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium, Madden PA, Heath AC, Martin NG, Montgomery GW, Weedon MN, Loos RJ, Frayling TM, McCarthy MI, Hirschhorn JN, Goddard ME, Visscher PM (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 SH, Yang J, Goddard ME, Visscher PM Wray NR (2012) Estimation of pleiotropy between complex diseases using SNP-derived genomic relationships and restricted maximum likelihood. Bioinformatics. 2012 Oct 28(19): 2540-2542. [PubMed ID: 22843982]


Mixed linear model based association analysis:
Yang J, Zaitlen NA, Goddard ME, Visscher PM and Price AL (2013) Mixed model association methods: advantages and pitfalls.
Nat Genet. 2014 Feb;46(2):100-6. [Pubmed ID: 24473328]


GREML-LDMS method:
Yang et al. (2015) Estimation of genetic variance from imputed sequence variants reveals negligible missing heritability for human height and body mass index. Nat Genet, doi: 10.1038/ng.3390.

Last update: 22 June 2016







1. Input and output

2. Data management

3. Estimation of the genetic relationships

4. Manipulation of the genetic relationship matrix

5. Principal component analysis

6. Estimation of the variance explained by all the SNPs

7. Estimation of the LD structure

8. GWAS Simulation

9. Raw genotype data

10. Conditional & joint GWAS analysis

11. Bivariate REML analysis

12. Mixed Linear Model Association Analysis

13. Multi-thread computing

14. GREML Power Calculator


16. Fast gene-based test