The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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
In the citing paper
“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]
In the citing paper
“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
In the citing paper
“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]
In the citing paper
“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]
Citations
Cites
PLINK: a tool set for whole-genome association and population-based linkage analyses.
Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A. R., Bender, D., Maller, J., Sklar, P., de Bakker, P. I. W., Daly, M. J., et al. (2007) · The American Journal of Human Genetics · doi:10.1086/519795 · Cited by 34,089
Principal components analysis corrects for stratification in genome-wide association studies
Price, A. L., Patterson, N. J., Plenge, R. M., Weinblatt, M. E., Shadick, N. A., & Reich, D. (2006) · Nature Genetics · doi:10.1038/ng1847 · Cited by 10,248
Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls
The Wellcome Trust Case Control Consortium, Management Committee, Burton, P. R., Clayton, D. G., Cardon, L. R., Craddock, N., Deloukas, P., Duncanson, A., Kwiatkowski, D. P., McCarthy, M. I., et al. (2007) · Nature · doi:10.1038/nature05911 · Cited by 9,442
Finding the missing heritability of complex diseases
Manolio, T. A., Collins, F. S., Cox, N. J., Goldstein, D. B., Hindorff, L. A., Hunter, D. J., McCarthy, M. I., Ramos, E. M., Cardon, L. R., Chakravarti, A., et al. (2009) · Nature · doi:10.1038/nature08494 · Cited by 8,336
Self-citationFalush, D., Stephens, M., & Pritchard, J. K. (2003) · Genetics · doi:10.1093/genetics/164.4.1567 · Cited by 8,006
Genetics and Analysis of Quantitative Traits
(2002) · Journal of Animal Breeding and Genetics · doi:10.1046/j.1439-0388.2002.00356.x · Cited by 6,176
Efficient Methods to Compute Genomic Predictions
VanRaden, P. M. (2008) · Journal of Dairy Science · doi:10.3168/jds.2007-0980 · Cited by 6,050
Common polygenic variation contributes to risk of schizophrenia and bipolar disorder
The International Schizophrenia Consortium (2009) · Nature · doi:10.1038/nature08185 · Cited by 4,821
Common SNPs explain a large proportion of the heritability for human height
Yang, J., Benyamin, B., McEvoy, B. P., Gordon, S., Henders, A. K., Nyholt, D. R., Madden, P. A., Heath, A. C., Martin, N. G., Montgomery, G. W., et al. (2010) · Nature Genetics · doi:10.1038/ng.608 · Cited by 4,446
Self-citationRecovery of Inter-Block Information when Block Sizes are Unequal
Patterson, H. D., & Thompson, R. (1971) · Biometrika · doi:10.1093/biomet/58.3.545 · Cited by 4,073
Hindorff, L. A., Sethupathy, P., Junkins, H. A., Ramos, E. M., Mehta, J. P., Collins, F. S., & Manolio, T. A. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0903103106 · Cited by 4,013
The Wellcome Trust Case Control Consortium, U.K.
Mossman, K. (2008) · Scientific American · doi:10.1038/scientificamerican0108-42a · Cited by 2,714
Best Linear Unbiased Estimation and Prediction under a Selection Model
Henderson, C. (1975) · Biometrics · doi:10.2307/2529430 · Cited by 2,513
Hundreds of variants clustered in genomic loci and biological pathways affect human height
Lango Allen, H., Estrada, K., Lettre, G., Berndt, S. I., Weedon, M. N., Rivadeneira, F., Willer, C. J., Jackson, A. U., Vedantam, S., Raychaudhuri, S., et al. (2010) · Nature · doi:10.1038/nature09410 · Cited by 1,945
Self-citationGene genealogies and the coalescent process.
Hudson, R. R. (1990) · Medical Entomology and Zoology · Cited by 1,838
GenABEL: an R library for genome-wide association analysis
Aulchenko, Y. S., Ripke, S., Isaacs, A., & van Duijn, C. M. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm108 · Cited by 1,836
Personal genomes: The case of the missing heritability
Maher, B. (2008) · Nature · doi:10.1038/456018a · Cited by 1,737
Missing heritability and strategies for finding the underlying causes of complex disease
Eichler, E. E., Flint, J., Gibson, G. C., Kong, A., Leal, S. M., Moore, J. H., & Nadeau, J. H. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2809 · Cited by 1,728
The inheritance of liability to certain diseases, estimated from the incidence among relatives
Falconer, D. (1965) · Annals of Human Genetics · doi:10.1111/j.1469-1809.1965.tb00500.x · Cited by 1,662
Estimating genetic parameters in natural populations using the ‘animal model’
Kruuk, L. E. B. (2004) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2003.1437 · Cited by 1,218
Population stratification and spurious allelic association
Cardon, L. R., & Palmer, L. J. (2003) · The Lancet · doi:10.1016/s0140-6736(03)12520-2 · Cited by 1,189
Gilmour, A. R., Thompson, R., & Cullis, B. R. (1995) · Biometrics · doi:10.2307/2533274 · Cited by 915
HERITABILITY OF THRESHOLD CHARACTERS
Dempster, E. R., & Lerner, I. M. (1950) · Genetics · doi:10.1093/genetics/35.2.212 · Cited by 816
Increased accuracy of artificial selection by using the realized relationship matrix
Hayes, B. J., Visscher, P. M., & Goddard, M. E. (2009) · Genetics Research · doi:10.1017/s0016672308009981 · Cited by 675
Self-citationSpencer, C. C. A., Su, Z., Donnelly, P., & Marchini, J. (2009) · PLoS Genetics · doi:10.1371/journal.pgen.1000477 · Cited by 622
Demonstrating stratification in a European American population
Campbell, C. D., Ogburn, E. L., Lunetta, K. L., Lyon, H. N., Freedman, M. L., Groop, L. C., Altshuler, D., Ardlie, K. G., & Hirschhorn, J. N. (2005) · Nature Genetics · doi:10.1038/ng1607 · Cited by 444
THE HERITABILITY OF ALL-OR-NONE TRAITS: VIABILITY OF POULTRY
Robertson, A., & Lerner, I. M. (1949) · Genetics · doi:10.1093/genetics/34.4.395 · Cited by 305
Hints of hidden heritability in GWAS
Gibson, G. (2010) · Nature Genetics · doi:10.1038/ng0710-558 · Cited by 281
Strandén, I., & Garrick, D. J. (2009) · Journal of Dairy Science · doi:10.3168/jds.2008-1929 · Cited by 277
Predicting genetic predisposition in humans: the promise of whole-genome markers
Campos, G. D. L., Gianola, D., & Allison, D. B. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2898 · Cited by 240
Estimating effects and making predictions from genome-wide marker dataPreprint
Goddard, M. E., Wray, N. R., VERBYLA, K. L., & Visscher, P. M. (2009) · Cited by 138
Self-citationGENOME: a rapid coalescent-based whole genome simulator
Liang, L., Zöllner, S. K., & Abecasis, G. R. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm138 · Cited by 133
Estimating Effects and Making Predictions from Genome-Wide Marker Data
Goddard, M. E., Wray, N. R., Verbyla, K., & Visscher, P. M. (2009) · Statistical Science · doi:10.1214/09-sts306 · Cited by 121
Self-citationSequence-Level Population Simulations Over Large Genomic Regions
Hoggart, C. J., Chadeau‐Hyam, M., Clark, T. G., Lampariello, R., Whittaker, J. C., Iorio, M. D., & Balding, D. J. (2007) · Genetics · doi:10.1534/genetics.106.069088 · Cited by 114
Wilson, W. M., Dufour, D. L., Staten, L. K., Barac‐Nieto, M., Reina, J., & Spurr, G. B. (1999) · American Journal of Human Biology · doi:10.1002/(sici)1520-6300(199911/12)11:6 · Cited by 31
Estimating the additive genetic effect of the X chromosome
Kent, J. W., Dyer, T. D., & Blangero, J. (2005) · Genetic Epidemiology · doi:10.1002/gepi.20093 · Cited by 21
Research works printed but not linked: 1
- Li, 2006, Mach 1.0: Rapid Haplotype Reconstruction and Missing Genotype Inference, Am. J. Hum. Genet., S79, 2290
Cited by
Sorted by citations
Second-generation PLINK: rising to the challenge of larger and richer datasets
Chang, C. C., Chow, C. C., Tellier, L. C., Vattikuti, S., Purcell, S. M., & Lee, J. J. (2015) · Gigascience · doi:10.1186/s13742-015-0047-8 · Cited by 12,709
LD Score regression distinguishes confounding from polygenicity in genome-wide association studies
Schizophrenia Working Group of the Psychiatric Genomics Consortium, Bulik-Sullivan, B. K., Loh, P.-R., Finucane, H. K., Ripke, S., Yang, J., Patterson, N., Daly, M. J., Price, A. L., & Neale, B. M. (2015) · Nature Genetics · doi:10.1038/ng.3211 · Cited by 5,559
Self-citationGenetic studies of body mass index yield new insights for obesity biology
Study, T. L. C., Locke, A. E., The ADIPOGen Consortium, The AGEN-BMI Working Group, The CARDIOGRAMplusC4D Consortium, The CKDGen Consortium, The, G. L. G. C., The, I. C. B. P., The MAGIC Investigators, The MuTHER Consortium, et al. (2015) · Nature · doi:10.1038/nature14177 · Cited by 4,534
Self-citationAn atlas of genetic correlations across human diseases and traits
ReproGen Consortium, Bulik-Sullivan, B., Psychiatric Genomics Consortium, Genetic Consortium for Anorexia Nervosa of the Wellcome Trust Case Control Consortium 3, Finucane, H. K., Anttila, V., Gusev, A., Day, F. R., Loh, P.-R., Duncan, L., et al. (2015) · Nature Genetics · doi:10.1038/ng.3406 · Cited by 4,255
An epigenetic biomarker of aging for lifespan and healthspan
Levine, M. E., Lu, A. T., Quach, A., Chen, B. H., Assimes, T. L., Bandinelli, S., Hou, L., Baccarelli, A. A., Stewart, J. D., Li, Y., et al. (2018) · Aging · doi:10.18632/aging.101414 · Cited by 3,675
Metadata
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
