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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 (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
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
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Alzheimer’s disease: analyzing the missing heritability
Ridge, P. G., Mukherjee, S., Crane, P. K., Kauwe, J. S. K., & Alzheimer’s Disease Genetics Consortium (2013) · PLoS ONE · doi:10.1371/journal.pone.0079771 · Cited by 328
Zhao, B., Luo, T., Li, T., Li, Y., Zhang, J., Shan, Y., Wang, X., Yang, L., Zhou, F., Zhu, Z., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0516-6 · Cited by 295
Hu, Y., Shmygelska, A., Tran, D., Eriksson, N., Tung, J. Y., & Hinds, D. A. (2016) · Nature Communications · doi:10.1038/ncomms10448 · Cited by 321
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Speed, D., & Balding, D. J. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3821 · Cited by 316
Cordell, H. J., Han, Y., Mells, G. F., Li, Y., Hirschfield, G. M., Greene, C. S., Xie, G., Juran, B. D., Zhu, D., Qian, D. C., et al. (2015) · Nature Communications · doi:10.1038/ncomms9019 · Cited by 287
Moss, D. J. H., Pardiñas, A. F., Langbehn, D., Lo, K., Leavitt, B. R., Roos, R., Durr, A., Mead, S., Coleman, A., Santos, R. D., et al. (2017) · The Lancet Neurology · doi:10.1016/s1474-4422(17)30161-8 · Cited by 312
Self-citationLane, J. M., Vlasac, I., Anderson, S. G., Kyle, S. D., Dixon, W. G., Bechtold, D. A., Gill, S., Little, M. A., Luik, A., Loudon, A., et al. (2016) · Nature Communications · doi:10.1038/ncomms10889 · Cited by 312
Hill, W. D., Marioni, R. E., Maghzian, O., Ritchie, S. J., Hagenaars, S. P., McIntosh, A. M., Gale, C. R., Davies, G., & Deary, I. J. (2019) · Molecular Psychiatry · doi:10.1038/s41380-017-0001-5 · Cited by 311
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Duan, S.-F., Han, P.-J., Wang, Q.-M., Liu, W.-Q., Shi, J.-Y., Li, K., Zhang, X.-L., & Bai, F.-Y. (2018) · Nature Communications · doi:10.1038/s41467-018-05106-7 · Cited by 244
Haplotype Reference Consortium, Evans, L. M., Tahmasbi, R., Vrieze, S. I., Abecasis, G. R., Das, S., Gazal, S., Bjelland, D. W., de Candia, T. R., Goddard, M. E., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0108-x · Cited by 239
Self-citationThe road to precision psychiatry: translating genetics into disease mechanisms
Gandal, M. J., Leppa, V., Won, H., Parikshak, N. N., & Geschwind, D. H. (2016) · Nature Neuroscience · doi:10.1038/nn.4409 · Cited by 223
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Qanbari, S., Pausch, H., Jansen, S., Somel, M., Strom, T. M., Fries, R., Nielsen, R., & Simianer, H. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004148 · Cited by 233
(ADNI), A. D. N. I., Deming, Y., The Alzheimer Disease Genetic Consortium (ADGC), Li, Z., Kapoor, M., Harari, O., Del-Aguila, J. L., Black, K., Carrell, D., Cai, Y., et al. (2017) · Acta Neuropathologica · doi:10.1007/s00401-017-1685-y · Cited by 231
Marzi, S. J., Leung, S. K., Ribarska, T., Hannon, E., Smith, A. R., Pishva, E., Poschmann, J., Moore, K., Troakes, C., Al-Sarraj, S., et al. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0253-7 · Cited by 232
Schork, A. J., Won, H., Appadurai, V., Nudel, R., Gandal, M., Delaneau, O., Revsbech Christiansen, M., Hougaard, D. M., Bækved-Hansen, M., Bybjerg-Grauholm, J., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-018-0320-0 · Cited by 223
Doherty, J. L., & Owen, M. J. (2014) · Genome Medicine · doi:10.1186/gm546 · Cited by 228
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Zhu, Z., Bakshi, A., Vinkhuyzen, A. A. E., Hemani, G., Lee, S. H., Nolte, I. M., van Vliet-Ostaptchouk, J. V., Snieder, H., Esko, T., Milani, L., et al. (2015) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2015.01.001 · Cited by 224
Self-citationLindström, S., Wang, L., Smith, E. N., Gordon, W., van Hylckama Vlieg, A., de Andrade, M., Brody, J. A., Pattee, J. W., Haessler, J., Brumpton, B. M., et al. (2019) · Blood · doi:10.1182/blood.2019000435 · Cited by 223
Sasson, G., Kruger Ben-Shabat, S., Seroussi, E., Doron-Faigenboim, A., Shterzer, N., Yaacoby, S., Berg Miller, M. E., White, B. A., Halperin, E., & Mizrahi, I. (2017) · mBio · doi:10.1128/mbio.00703-17 · Cited by 219
Lee, S. H., Harold, D., Nyholt, D. R., ANZGene Consortium, International Endogene Consortium, the Genetic and Environmental Risk for Alzheimer's disease (GERAD1) Consortium, Goddard, M. E., Zondervan, K. T., Williams, J., Montgomery, G. W., et al. (2013) · Human Molecular Genetics · doi:10.1093/hmg/dds491 · Cited by 217
Self-citationLi, Y. R., & Keating, B. J. (2014) · Genome Medicine · doi:10.1186/s13073-014-0091-5 · Cited by 214
Adhikari, K., Fuentes-Guajardo, M., Quinto-Sánchez, M., Mendoza-Revilla, J., Camilo Chacón-Duque, J., Acuña-Alonzo, V., Jaramillo, C., Arias, W., Lozano, R. B., Pérez, G. M., et al. (2016) · Nature Communications · doi:10.1038/ncomms11616 · Cited by 200
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Effect of polygenic risk scores on depression in childhood trauma
Peyrot, W. J., Milaneschi, Y., Abdellaoui, A., Sullivan, P. F., Hottenga, J. J., Boomsma, D. I., & Penninx, B. W. J. H. (2014) · British Journal of Psychiatry · doi:10.1192/bjp.bp.113.143081 · Cited by 209
Discovery of target genes and pathways at GWAS loci by pooled single-cell CRISPR screens
Morris, J. A., Caragine, C., Daniloski, Z., Domingo, J., Barry, T., Lu, L., Davis, K., Ziosi, M., Glinos, D. A., Hao, S., et al. (2023) · Science · doi:10.1126/science.adh7699 · Cited by 208
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Keller, M. F., Saad, M., Bras, J., Bettella, F., Nicolaou, N., Simon-Sanchez, J., Mittag, F., Buchel, F., Sharma, M., Gibbs, J. R., et al. (2012) · Human Molecular Genetics · doi:10.1093/hmg/dds335 · Cited by 190
Coleman, J. R. I., Gaspar, H. A., Bryois, J., Byrne, E. M., Forstner, A. J., Holmans, P. A., de Leeuw, C. A., Mattheisen, M., McQuillin, A., Whitehead Pavlides, J. M., et al. (2020) · Biological Psychiatry · doi:10.1016/j.biopsych.2019.10.015 · Cited by 199
Self-citationTaylor, A. E., Jones, H. J., Sallis, H., Euesden, J., Stergiakouli, E., Davies, N. M., Zammit, S., Lawlor, D. A., Munafò, M. R., Davey Smith, G., et al. (2018) · International Journal of Epidemiology · doi:10.1093/ije/dyy060 · Cited by 192
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Boitard, S., Rodríguez, W., Jay, F., Mona, S., & Austerlitz, F. (2016) · PLoS Genetics · doi:10.1371/journal.pgen.1005877 · Cited by 190
Cordell, H. J., Fryett, J. J., Ueno, K., Darlay, R., Aiba, Y., Hitomi, Y., Kawashima, M., Nishida, N., Khor, S.-S., Gervais, O., et al. (2021) · Journal of Hepatology · doi:10.1016/j.jhep.2021.04.055 · Cited by 182
Krapohl, E., & Plomin, R. (2016) · Molecular Psychiatry · doi:10.1038/mp.2015.2 · Cited by 160
Taylor, M. J., Martin, J., Lu, Y., Brikell, I., Lundström, S., Larsson, H., & Lichtenstein, P. (2019) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2018.3652 · Cited by 174
Richmond, R. C., Davey Smith, G., Ness, A. R., den Hoed, M., McMahon, G., & Timpson, N. J. (2014) · PLoS Medicine · doi:10.1371/journal.pmed.1001618 · Cited by 167
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Relatedness disequilibrium regression estimates heritability without environmental bias
Young, A. I., Frigge, M. L., Gudbjartsson, D. F., Thorleifsson, G., Bjornsdottir, G., Sulem, P., Masson, G., Thorsteinsdottir, U., Stefansson, K., & Kong, A. (2018) · Nature Genetics · doi:10.1038/s41588-018-0178-9 · Cited by 173
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Hill, W. D., Arslan, R. C., Xia, C., Luciano, M., Amador, C., Navarro, P., Hayward, C., Nagy, R., Porteous, D. J., McIntosh, A. M., et al. (2018) · Molecular Psychiatry · doi:10.1038/s41380-017-0005-1 · Cited by 158
Erlangsen, A., Appadurai, V., Wang, Y., Turecki, G., Mors, O., Werge, T., Mortensen, P. B., Starnawska, A., Børglum, A. D., Schork, A., et al. (2020) · Molecular Psychiatry · doi:10.1038/s41380-018-0218-y · Cited by 157
Yang, L., Neale, B. M., Liu, L., Lee, S. H., Wray, N. R., Ji, N., Li, H., Qian, Q., Wang, D., Li, J., et al. (2013) · American Journal of Medical Genetics Part B: Neuropsychiatric Genetics · doi:10.1002/ajmg.b.32169 · Cited by 171
Self-citationZeng, J., Xue, A., Jiang, L., Lloyd-Jones, L. R., Wu, Y., Wang, H., Zheng, Z., Yengo, L., Kemper, K. E., Goddard, M. E., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-21446-3 · Cited by 144
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Emani, P. S., Warrell, J., Anticevic, A., Bekiranov, S., Gandal, M., McConnell, M. J., Sapiro, G., Aspuru-Guzik, A., Baker, J. T., Bastiani, M., et al. (2021) · Nature Methods · doi:10.1038/s41592-020-01004-3 · Cited by 163
Luo, Y., de Lange, K. M., Jostins, L., Moutsianas, L., Randall, J., Kennedy, N. A., Lamb, C. A., McCarthy, S., Ahmad, T., Edwards, C., et al. (2017) · Nature Genetics · doi:10.1038/ng.3761 · Cited by 162
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Beleza, S., Johnson, N. A., Candille, S. I., Absher, D. M., Coram, M. A., Lopes, J., Campos, J., Araújo, I. I., Anderson, T. M., Vilhjálmsson, B. J., et al. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003372 · Cited by 161
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Martin, A. R., Lin, M., Granka, J. M., Myrick, J. W., Liu, X., Sockell, A., Atkinson, E. G., Werely, C. J., Möller, M., Sandhu, M. S., et al. (2017) · Cell · doi:10.1016/j.cell.2017.11.015 · Cited by 156
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Dunn, E. C., Brown, R. C., Dai, Y., Rosand, J., Nugent, N. R., Amstadter, A. B., & Smoller, J. W. (2015) · Harvard Review of Psychiatry · doi:10.1097/hrp.0000000000000054 · Cited by 158
GWAS for executive function and processing speed suggests involvement of the CADM2 gene
Ibrahim-Verbaas, C. A., Bressler, J., Debette, S., Schuur, M., Smith, A. V., Bis, J. C., Davies, G., Trompet, S., Smith, J. A., Wolf, C., et al. (2016) · Molecular Psychiatry · doi:10.1038/mp.2015.37 · Cited by 147
Jiang, G., Luk, A. O. Y., Tam, C. H. T., Xie, F., Carstensen, B., Lau, E. S. H., Lim, C. K. P., Lee, H. M., Ng, A. C. W., Ng, M. C. Y., et al. (2019) · Kidney International · doi:10.1016/j.kint.2018.08.026 · Cited by 159
Schoech, A. P., Jordan, D. M., Loh, P.-R., Gazal, S., O’Connor, L. J., Balick, D. J., Palamara, P. F., Finucane, H. K., Sunyaev, S. R., & Price, A. L. (2019) · Nature Communications · doi:10.1038/s41467-019-08424-6 · Cited by 157
Docherty, A. R., Mullins, N., Ashley-Koch, A. E., Qin, X., Coleman, J. R. I., Shabalin, A., Kang, J., Murnyak, B., Wendt, F., Adams, M., et al. (2023) · American Journal of Psychiatry · doi:10.1176/appi.ajp.21121266 · Cited by 160
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Warrier, V., Zhang, X., Reed, P., Havdahl, A., Moore, T. M., Cliquet, F., Leblond, C. S., Rolland, T., Rosengren, A., LEAP, E.-A., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01072-5 · Cited by 152
Chaste, P., Klei, L., Sanders, S. J., Hus, V., Murtha, M. T., Lowe, J. K., Willsey, A. J., Moreno-De-Luca, D., Yu, T. W., Fombonne, E., et al. (2015) · Biological Psychiatry · doi:10.1016/j.biopsych.2014.09.017 · Cited by 144
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Yengo, L., Robinson, M. R., Keller, M. C., Kemper, K. E., Yang, Y., Trzaskowski, M., Gratten, J., Turley, P., Cesarini, D., Benjamin, D. J., et al. (2018) · Nature Human Behaviour · doi:10.1038/s41562-018-0476-3 · Cited by 148
Self-citationHannon, E., Dempster, E. L., Mansell, G., Burrage, J., Bass, N., Bohlken, M. M., Corvin, A., Curtis, C. J., Dempster, D., Di Forti, M., et al. (2021) · eLife · doi:10.7554/elife.58430 · Cited by 132
“To explore the extent to which this diversity might be influencing our results, we merged SNP array data from each donor with HapMap phase 3 data and calculated genetic PCs using GCTA ( Yang et al., 2011 ; Figure 1—figure supplement 2 ).”
Biernacka, J. M., Sangkuhl, K., Jenkins, G., Whaley, R. M., Barman, P., Batzler, A., Altman, R. B., Arolt, V, Brockmöller, J., Chen, C. H., et al. (2015) · Translational Psychiatry · doi:10.1038/tp.2015.47 · Cited by 140
The Electronic Medical Records and Genomics (eMERGE) Consortium, Wood, A. R., The MIGen Consortium, The PAGE Consortium, Study, T. L. C., Esko, T., Yang, J., Vedantam, S., Pers, T. H., Gustafsson, S., et al. (2014) · Nature Genetics · doi:10.1038/ng.3097 · Cited by 1,853
Self-citationBaselmans, B. M. L., Yengo, L., van Rheenen, W., & Wray, N. R. (2021) · Biological Psychiatry · doi:10.1016/j.biopsych.2020.05.034 · Cited by 132
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Kim, K., Kwon, T., Dessie, T., Yoo, D., Mwai, O. A., Jang, J., Sung, S., Lee, S., Salim, B., Jung, J., et al. (2020) · Nature Genetics · doi:10.1038/s41588-020-0694-2 · Cited by 140
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Plomin, R., Haworth, C. M. A., Meaburn, E. L., Price, T. S., Wellcome Trust Case Control Consortium 2, & Davis, O. S. P. (2013) · Psychological Science · doi:10.1177/0956797612457952 · Cited by 138
Ramanan, V. K., Risacher, S. L., Nho, K., Kim, S., Shen, L., McDonald, B. C., Yoder, K. K., Hutchins, G. D., West, J. D., Tallman, E. F., et al. (2015) · Brain · doi:10.1093/brain/awv231 · Cited by 133
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Jansen, I. E., van der Lee, S. J., Gomez-Fonseca, D., de Rojas, I., Dalmasso, M. C., Grenier-Boley, B., Zettergren, A., Mishra, A., Ali, M., Andrade, V., et al. (2022) · Acta Neuropathologica · doi:10.1007/s00401-022-02454-z · Cited by 132
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Neumann, A., Pappa, I., Lahey, B. B., Verhulst, F. C., Medina-Gomez, C., Jaddoe, V. W., Bakermans-Kranenburg, M. J., Moffitt, T. E., van IJzendoorn, M. H., & Tiemeier, H. (2016) · Journal of the American Academy of Child & Adolescent Psychiatry · doi:10.1016/j.jaac.2016.09.498 · Cited by 121
Wild, P. S., Felix, J. F., Schillert, A., Teumer, A., Chen, M.-H., Leening, M. J. G., Völker, U., Großmann, V., Brody, J. A., Irvin, M. R., et al. (2017) · Journal of Clinical Investigation · doi:10.1172/jci84840 · Cited by 129
the 23andMe Research Team, Sanchez-Roige, S., Fontanillas, P., Elson, S. L., Pandit, A., Schmidt, E. M., Foerster, J. R., Abecasis, G. R., Gray, J. C., de Wit, H., et al. (2018) · Nature Neuroscience · doi:10.1038/s41593-017-0032-x · Cited by 128
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Yap, C. X., Sidorenko, J., Wu, Y., Kemper, K. E., Yang, J., Wray, N. R., Robinson, M. R., & Visscher, P. M. (2018) · Nature Communications · doi:10.1038/s41467-018-07862-y · Cited by 124
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Escott-Price, V., & Hardy, J. (2022) · Brain Communications · doi:10.1093/braincomms/fcac125 · Cited by 119
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Zaidi, A. A., Mattern, B. C., Claes, P., McEcoy, B., Hughes, C., & Shriver, M. D. (2017) · PLOS Genetics · doi:10.1371/journal.pgen.1006616 · Cited by 117
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Viana, J., Hannon, E., Dempster, E., Pidsley, R., Macdonald, R., Knox, O., Spiers, H., Troakes, C., Al-Saraj, S., Turecki, G., et al. (2016) · Human Molecular Genetics · doi:10.1093/hmg/ddw373 · Cited by 105
Western, D., Timsina, J., Wang, L., Wang, C., Yang, C., Phillips, B., Wang, Y., Liu, M., Ali, M., Beric, A., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01972-8 · Cited by 111
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Rietveld, C. A., Conley, D., Eriksson, N., Esko, T., Medland, S. E., Vinkhuyzen, A. A. E., Yang, J., Boardman, J. D., Chabris, C. F., Dawes, C. T., et al. (2014) · Psychological Science · doi:10.1177/0956797614545132 · Cited by 111
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Hansen, M. E. B., Rubel, M. A., Bailey, A. G., Ranciaro, A., Thompson, S. R., Campbell, M. C., Beggs, W., Dave, J. R., Mokone, G. G., Mpoloka, S. W., et al. (2019) · Genome Biology · doi:10.1186/s13059-018-1616-9 · Cited by 110
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Yao, D., Binan, L., Bezney, J., Simonton, B., Freedman, J., Frangieh, C. J., Dey, K., Geiger-Schuller, K., Eraslan, B., Gusev, A., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-023-01964-9 · Cited by 101
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Haffner, C., Malik, R., & Dichgans, M. (2016) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1038/jcbfm.2015.71 · Cited by 109
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Adhikari, K., Reales, G., Smith, A. J. P., Konka, E., Palmen, J., Quinto-Sanchez, M., Acuña-Alonzo, V., Jaramillo, C., Arias, W., Fuentes, M., et al. (2015) · Nature Communications · doi:10.1038/ncomms8500 · Cited by 95
Li, Z., Farias, F. H. G., Dube, U., Del-Aguila, J. L., Mihindukulasuriya, K. A., Fernandez, M. V., Ibanez, L., Budde, J. P., Wang, F., Lake, A. M., et al. (2020) · Acta Neuropathologica · doi:10.1007/s00401-019-02066-0 · Cited by 85
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Renaud, G., Hanghøj, K., Korneliussen, T. S., Willerslev, E., & Orlando, L. (2019) · Genetics · doi:10.1534/genetics.119.302057 · Cited by 101
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Taylor, P. N., Porcu, E., Chew, S., Campbell, P. J., Traglia, M., Brown, S. J., Mullin, B. H., Shihab, H. A., Min, J., Walter, K., et al. (2015) · Nature Communications · doi:10.1038/ncomms6681 · Cited by 104
Self-citationSanchez‐Roige, S., Fontanillas, P., Elson, S. L., The 23andMe Research Team, Gray, J. C., de Wit, H., Davis, L. K., MacKillop, J., & Palmer, A. A. (2019) · Addiction Biology · doi:10.1111/adb.12574 · Cited by 101
Wu, Y., Zheng, Z., Visscher, P. M., & Yang, J. (2017) · Genome biology · doi:10.1186/s13059-017-1216-0 · Cited by 100
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Xiong, Z., Dankova, G., Howe, L. J., Lee, M. K., Hysi, P. G., de Jong, M. A., Zhu, G., Adhikari, K., Li, D., Li, Y., et al. (2019) · eLife · doi:10.7554/elife.49898 · Cited by 92
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Browning, S. R., & Browning, B. L. (2011) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2011.05.025 · Cited by 96
Feng, C., Lori, A., Waldman, I. D., Binder, E. B., Haroon, E., & Rilling, J. K. (2015) · Genes, Brain and Behavior · doi:10.1111/gbb.12234 · Cited by 94
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Tucci, S., Vohr, S. H., McCoy, R. C., Vernot, B., Robinson, M. R., Barbieri, C., Nelson, B. J., Fu, W., Purnomo, G. A., Sudoyo, H., et al. (2018) · Science · doi:10.1126/science.aar8486 · Cited by 97
Self-citationLu, Q., Powles, R. L., Abdallah, S., Ou, D., Wang, Q., Hu, Y., Lu, Y., Liu, W., Li, B., Mukherjee, S., et al. (2017) · PLOS Genetics · doi:10.1371/journal.pgen.1006933 · Cited by 97
Gao, X., Meng, L.-X., Ma, K.-L., Liang, J., Wang, H., Gao, Q., & Wang, T. (2019) · European Psychiatry · doi:10.1016/j.eurpsy.2019.05.004 · Cited by 94
Genetics of obsessive-compulsive disorder and related disorders
Browne, H. A., Gair, S. L., Scharf, J. M., & Grice, D. E. (2014) · Psychiatric Clinics of North America · doi:10.1016/j.psc.2014.06.002 · Cited by 92
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Hou, K., Burch, K. S., Majumdar, A., Shi, H., Mancuso, N., Wu, Y., Sankararaman, S., & Pasaniuc, B. (2019) · Nature Genetics · doi:10.1038/s41588-019-0465-0 · Cited by 91
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Koomar, T., Thomas, T. R., Pottschmidt, N. R., Lutter, M., & Michaelson, J. J. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.668297 · Cited by 92
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
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