GCTA: a tool for genome-wide complex trait analysis
Open accessAbstract
For most human complex diseases and traits, SNPs identified by genome-wide association studies (GWAS) explain only a small fraction of the heritability. Here we report a user-friendly software tool called genome-wide complex trait analysis (GCTA), which was developed based on a method we recently developed to address the "missing heritability" problem. GCTA estimates the variance explained by all the SNPs on a chromosome or on the whole genome for a complex trait rather than testing the association of any particular SNP to the trait. We introduce GCTA's five main functions: data management, estimation of the genetic relationships from SNPs, mixed linear model analysis of variance explained by the SNPs, estimation of the linkage disequilibrium structure, and GWAS simulation. We focus on the function of estimating the variance explained by all the SNPs on the X chromosome and testing the hypotheses of dosage compensation. The GCTA software is a versatile tool to estimate and partition complex trait variation with large GWAS data sets.
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SNP array data from each donor was merged with HapMap phase 3 data, and genetic principal components were calculated with GCTA to confirm the ethnicity of each sample.
“SNP array data from each donor was merged with HapMap phase 3 data, and genetic principal components (PCs) were calculated with GCTA (Yang et al., 2011) to further confirm the ethnicity of each sample”
From DNA methylation meta-analysis reveals cellular alterations in psychosis and markers of treatment-resistant schizophrenia (2021) · cites this paper as Yang et al., 2011
Several studies have demonstrated that non-genome-wide effects explain an important proportion of the effect of genetic variation on disease susceptibility.
“The commonly used threshold of genome-wide significance (p-value = 5×10−8) has played an important role in making human genetic study results robust; however, several studies have demonstrated that non-genome-wide effects explain an important proportion of the effect of genetic variation on disease susceptibility. (18, 19)”
From Multiple sclerosis genomic map implicates peripheral immune cells and microglia in susceptibility (2019) · cites this paper as [19]
SNP-heritability methods calculate heritability from SNP array data and provide assessments based on genome-wide genotypes, improving on traditional heritability measurements.
“Second, several methods can calculate the heritability of a trait using SNP array data (Bulik-Sullivan et al., 2015; Yang et al., 2011). These provide assessments of heritability based on genome-wide genotypes, and improves upon traditional heritability measurements given their basis in direct genetic measurements.”
From Defining the genetic, genomic, cellular, and diagnostic architectures of psychiatric disorders (2019) · cites this paper as Yang et al., 2011
The SNP heritability estimates were consistent with estimates using individual-level data, largest contributing studies, and prior work.
“consistent with estimates using individual-level data18, SNP heritabilities calculated from the largest individual contributing studies (Supplementary Table 13), and prior work19.”
From Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use (2019) · cites this paper as [18]
GCTA conditional analysis identified five secondary signals: rs77294520 near GREB1 on 2p25.1; rs2206949 in ESR1 and rs17803970 in SYNE1 on 6q25.1; and rs10757272 in CDKN2B-AS1 and rs1448792 on 9p21.3.
“GCTA identified five secondary signals including one (rs77294520 near GREB1) on 2p25.1; two (rs2206949 in ESR1 and rs17803970 in SYNE1) on 6q25.1, and two (rs10757272 in CDKN2B-AS1 and rs1448792) on 9p21.3 (Table 2).”
From Meta-analysis identifies five novel loci associated with endometriosis highlighting key genes involved in hormone metabolism (2017) · cites this paper as [18]
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- Authors
- Yang, J., Lee, S. H., Goddard, M. E., & Visscher, P. M.
- Affiliations
- QIMR Berghofer Medical Research Institute (AU)
- The University of Melbourne (AU)
- Corresponding author
- Yang, Jian
- Venue
- The American Journal of Human Genetics
- Publisher
- Elsevier BV
- Published
- 2011-01
- Volume
- 88
- Issue
- 1
- Pages
- 76-82
- ISSN
- 0002-9297 (ISSN-L)
- PMC
- PMC3014363
- OpenAlex
- W2033151532
- Cited by
- 8,279
- References
- 36
- Citations by year
- 2026: 646 · 2025: 847 · 2024: 973 · 2023: 808 · 2022: 856 · 2021: 847 · 2020: 735 · 2019: 560 · 2018: 460 · 2017: 393 · 2016: 367 · 2015: 291 · 2014: 231 · 2013: 146 · 2012: 97
- FWCI
- 46.12
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- The American Journal of Human Genetics (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- EPubDev Repository (submittedVersion)
- Adelaide Research & Scholarship (AR&S) (University of Adelaide) (submittedVersion)
- RUNE (Research UNE) (submittedVersion)
- Funders
- Australian Research Council
- National Health and Medical Research Council
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Keywords
- GCTA, missing heritability, linear mixed models, X chromosome, dosage compensation, complex traits
- MeSH terms
- Computer Simulation, Dosage Compensation, Genetic, Female, Humans, Genetic Linkage, Male, Models, Genetic, Software, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Chromosomes, Human, X, Genome-Wide Association Study
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