Cited by 657Genetic Associations and EpidemiologyGenetic and phenotypic traits in livestockGenetic Mapping and Diversity in Plants and Animals

The DOI matches its Crossref record; 71 of the 73 references it cites were found in a registry or an index; 21 of the 24 sources its claims cite were checked for support.

Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals

Open access
Okbay, Aysu, Wu, Yeda, Wang, Nancy, Jayashankar, Hariharan, Bennett, Michael, Nehzati, Seyed Moeen, Sidorenko, Julia, Kweon, Hyeokmoon, Goldman, Grant, Gjorgjieva, Tamara, Jiang, Yunxuan, Hicks, Barry, Tian, Chao, Hinds, David A., Ahlskog, Rafael, Magnusson, Patrik K. E., Oskarsson, Sven, Hayward, Caroline, Campbell, Archie, Porteous, David J., Freese, Jeremy, Herd, Pamela, 23andMe Research Team, Agee, Michelle, Alipanahi, Babak, Auton, Adam, Bell, Robert K., Bryc, Katarzyna, Elson, Sarah L., Fontanillas, Pierre, Furlotte, Nicholas A., Hinds, David A., Huber, Karen E., Kleinman, Aaron, Litterman, Nadia K., McCreight, Jennifer C., McIntyre, Matthew H., Mountain, Joanna L., Northover, Carrie A. M., Pitts, Steven J., Sathirapongsasuti, J. Fah, Sazonova, Olga V., Shelton, Janie F., Shringarpure, Suyash, Tung, Joyce Y., Vacic, Vladimir, Wilson, Catherine H., Social Science Genetic Association Consortium, Fontana, Mark Alan, Pers, Tune H., Rietveld, Cornelius A., Chen, Guo-Bo, Emilsson, Valur, Meddens, S. Fleur W., Pickrell, Joseph K., Thom, Kevin, Timshel, Pascal, de Vlaming, Ronald, Abdellaoui, Abdel, Ahluwalia, Tarunveer S., Bacelis, Jonas, Baumbach, Clemens, Bjornsdottir, Gyda, Brandsma, Johannes H., Concas, Maria Pina, Derringer, Jaime, Galesloot, Tessel E., Girotto, Giorgia, Gupta, Richa, Hall, Leanne M., Harris, Sarah E., Hofer, Edith, Horikoshi, Momoko, Huffman, Jennifer E., Kaasik, Kadri, Kalafati, Ioanna P., Karlsson, Robert, Lahti, Jari, van der Lee, Sven J., de Leeuw, Christiaan, Lind, Penelope A., Lindgren, Karl-Oskar, Liu, Tian, Mangino, Massimo, Marten, Jonathan, Mihailov, Evelin, Miller, Michael B., van der Most, Peter J., Oldmeadow, Christopher, Payton, Antony, Pervjakova, Natalia, Peyrot, Wouter J., Qian, Yong, Raitakari, Olli, Rueedi, Rico, Salvi, Erika, Schmidt, Börge, Schraut, Katharina E., Shi, Jianxin, Smith, Albert V., Poot, Raymond A., Pourcain, Beate St, Teumer, Alexander, Thorleifsson, Gudmar, Verweij, Niek, Vuckovic, Dragana, Wellmann, Juergen, Westra, Harm-Jan, Yang, Jingyun, Zhao, Wei, Zhu, Zhihong, Alizadeh, Behrooz Z., Amin, Najaf, Bakshi, Andrew, Baumeister, Sebastian E., Biino, Ginevra, Bønnelykke, Klaus, Boyle, Patricia A., Campbell, Harry, Cappuccio, Francesco P., Davies, Gail, De Neve, Jan-Emmanuel, Deloukas, Panos, Demuth, Ilja, Ding, Jun, Eibich, Peter, Eisele, Lewin, Eklund, Niina, Evans, David M., Faul, Jessica D., Feitosa, Mary F., Forstner, Andreas J., Gandin, Ilaria, Gunnarsson, Bjarni, Halldórsson, Bjarni V., Harris, Tamara B., Heath, Andrew C., Hocking, Lynne J., Holliday, Elizabeth G., Homuth, Georg, Horan, Michael A., Hottenga, Jouke-Jan, de Jager, Philip L., Joshi, Peter K., Jugessur, Astanand, Kaakinen, Marika A., Kähönen, Mika, Kanoni, Stavroula, Keltigangas-Järvinen, Liisa, Kiemeney, Lambertus A. L. M., Kolcic, Ivana, Koskinen, Seppo, Kraja, Aldi T., Kroh, Martin, Kutalik, Zoltan, Latvala, Antti, Launer, Lenore J., Lebreton, Maël P., Levinson, Douglas F., Lichtenstein, Paul, Lichtner, Peter, Liewald, David C. M., LifeLines Cohort Study, Loukola, Anu, Madden, Pamela A., Mägi, Reedik, Mäki-Opas, Tomi, Marioni, Riccardo E., Marques-Vidal, Pedro, Meddens, Gerardus A., McMahon, George, Meisinger, Christa, Meitinger, Thomas, Milaneschi, Yusplitri, Milani, Lili, Montgomery, Grant W., Myhre, Ronny, Nelson, Christopher P., Nyholt, Dale R., Ollier, William E. R., Palotie, Aarno, Paternoster, Lavinia, Pedersen, Nancy L., Petrovic, Katja E., Räikkönen, Katri, Ring, Susan M., Robino, Antonietta, Rostapshova, Olga, Rudan, Igor, Rustichini, Aldo, Salomaa, Veikko, Sanders, Alan R., Sarin, Antti-Pekka, Schmidt, Helena, Scott, Rodney J., Smith, Blair H., Smith, Jennifer A., Staessen, Jan A., Steinhagen-Thiessen, Elisabeth, Strauch, Konstantin, Terracciano, Antonio, Tobin, Martin D., Ulivi, Sheila, Vaccargiu, Simona, Quaye, Lydia, van Rooij, Frank J. A., Venturini, Cristina, Vinkhuyzen, Anna A. E., Völker, Uwe, Völzke, Henry, Vonk, Judith M., Vozzi, Diego, Waage, Johannes, Ware, Erin B., Willemsen, Gonneke, Attia, John R., Bennett, David A., Berger, Klaus, Bertram, Lars, Bisgaard, Hans, Boomsma, Dorret I., Borecki, Ingrid B., Bültmann, Ute, Chabris, Christopher F., Cucca, Francesco, Cusi, Daniele, Deary, Ian J., Dedoussis, George V., van Duijn, Cornelia M., Eriksson, Johan G., Franke, Barbara, Franke, Lude, Gasparini, Paolo, Gejman, Pablo V., Gieger, Christian, Grabe, Hans-Jörgen, Gratten, Jacob, Groenen, Patrick J. F., Gudnason, Vilmundur, van der Harst, Pim, Hoffmann, Wolfgang, Hyppönen, Elina, Iacono, William G., Jacobsson, Bo, Järvelin, Marjo-Riitta, Jöckel, Karl-Heinz, Kaprio, Jaakko, Kardia, Sharon L. R., Lehtimäki, Terho, Lehrer, Steven F., Martin, Nicholas G., McGue, Matt, Metspalu, Andres, Pendleton, Neil, Penninx, Brenda W. J. H., Perola, Markus, Pirastu, Nicola, Pirastu, Mario, Polasek, Ozren, Posthuma, Danielle, Power, Christine, Province, Michael A., Samani, Nilesh J., Schlessinger, David, Schmidt, Reinhold, Sørensen, Thorkild I. A., Spector, Tim D., Stefansson, Kari, Thorsteinsdottir, Unnur, Thurik, A. Roy, Timpson, Nicholas J., Tiemeier, Henning, Uitterlinden, André G., Vitart, Veronique, Vollenweider, Peter, Weir, David R., Wilson, James F., Wright, Alan F., Conley, Dalton C., Krueger, Robert F., Smith, George Davey, Hofman, Albert, Laibson, David I., Medland, Sarah E., Yang, Jian, Esko, Tõnu, Watson, Chelsea, Jala, Jonathan, Conley, Dalton, Koellinger, Philipp D., Johannesson, Magnus, Laibson, David, Meyer, Michelle N., Lee, James J., Kong, Augustine, Yengo, Loic, Cesarini, David, Turley, Patrick, Visscher, Peter M., Beauchamp, Jonathan P., Benjamin, Daniel J., Young, Alexander I.·2022·Nature Genetics

Abstract

We conduct a genome-wide association study (GWAS) of educational attainment (EA) in a sample of ~3 million individuals and identify 3,952 approximately uncorrelated genome-wide-significant single-nucleotide polymorphisms (SNPs). A genome-wide polygenic predictor, or polygenic index (PGI), explains 12–16% of EA variance and contributes to risk prediction for ten diseases. Direct effects (i.e., controlling for parental PGIs) explain roughly half the PGI’s magnitude of association with EA and other phenotypes. The correlation between mate-pair PGIs is far too large to be consistent with phenotypic assortment alone, implying additional assortment on PGI-associated factors. In an additional GWAS of dominance deviations from the additive model, we identify no genome-wide-significant SNPs, and a separate X-chromosome additive GWAS identifies 57.

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

This GWAS meta-analysis of educational attainment in ~3.0 million individuals identified 3,952 approximately uncorrelated autosomal SNPs and a polygenic index explaining 12–16% of EA variance. It shows that the EA PGI’s predictive power partly reflects direct effects, predicts ten diseases, and has mate-pair PGI correlations too large to be explained by phenotypic assortment alone.

Key findings

  • The additive EduYears GWAS (N = 3,037,499) identified 3,952 lead SNPs (pairwise r² < 0.1, P < 5×10⁻⁸); the median SNP effect was 1.4 weeks more schooling per reference allele. The LDpred PGI explained 15.8% of EduYears variance in Add Health and 12.0% in HRS (sample-size-weighted mean 13.3%); an SBayesR PGI explained 17.0% and 12.9%, respectively (mean 14.3%).
  • The X-chromosome GWAS (N = 2,713,033) identified 57 lead SNPs, with X-chromosome SNP heritability of 0.4% and male-female genetic correlation rg = 0.94 (S.E. = 0.03). The dominance GWAS (N = 2,574,253) identified no genome-wide-significant SNPs; dominance SNP heritability was 0.00015 (S.E. = 0.00024, P = 0.54), and the inbreeding-depression estimate implied offspring of first cousins have ~1.0 fewer month of EA (P = 0.04).
  • The EA PGI significantly predicted all ten diseases examined; mean incremental R² was 0.63%, compared with 1.19% for disease-specific PGIs. For EA, the ratio of direct to population effect was 0.556 (S.E. = 0.020), implying 30.9% of the PGI’s R² was due to direct effects; corresponding ratios were 0.910 for height, 0.962 for BMI, and 0.824 for cognitive performance.
  • Mate-pair EA PGI correlation was 0.175 (S.E. = 0.020), versus 0.031 (S.E. = 0.004) predicted by phenotypic assortment; for height, observed 0.106 (S.E. = 0.020) versus predicted 0.087 (S.E. = 0.007). Residualizing EA PGI on EA reduced the mate-pair correlation by only 37%, to 0.110 (S.E. = 0.021); further adjustment for 40 PCs reduced it to 0.091 (S.E. = 0.021).

Methods

  • Meta-analysis of EduYears combined prior public results from 69 cohorts (N = 324,162, excluding UKB and 23andMe), new 23andMe results (N = 2,272,216), and new UKB results (N = 441,121), totaling N = 3,037,499, all in European genetic ancestries, with controls for sex, year of birth, their interaction, and genetic principal components; METAL sample-size-weighted meta-analysis and LD score intercept 1.663.
  • Separate X-chromosome meta-analysis (N = 2,713,033) and dominance-deviation GWAS meta-analysis (N = 2,574,253) were conducted; dominance analyses used adapted LD score regression and ldscdom for inbreeding depression.
  • Polygenic prediction used Add Health, HRS, and WLS holdout samples, with LDpred HapMap3 PGIs as main and SBayesR post hoc; disease prediction used UKB and Nagelkerke incremental R². Within-family analyses used ~53,000 individuals with genotyped siblings and ~3,500 with both parents genotyped from UKB, Generation Scotland, and the Swedish Twin Registry; assortative-mating analyses used 862 UKB and 1,603 Generation Scotland mate pairs.

Limitations

  • Stated: all main GWAS analyses were restricted to European genetic ancestries, and the PGI had much lower predictive power in African-genetic-ancestry samples (incremental R² 1.3% in HRS and 2.3% in Add Health; relative accuracies 11% and 15%). The paper also states the PGI cannot be used to meaningfully predict an individual’s EA, that dominance results do not rule out rare SNPs with substantial dominance effects, and that family samples lacked power for the ten diseases.
  • Evident: the dominance GWAS addresses common SNPs and found no genome-wide-significant dominance effects; family/direct-effect analyses are limited to ~53,000 siblings, ~3,500 parent-genotyped individuals, and 23 phenotypes.
  • Evident: assortative-mating analyses rely on UKB and Generation Scotland mate pairs and leave a substantial mate-pair PGI correlation unexplained after adjustment for EA, PCs, cognitive performance, and vocabulary.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1038/s41588-022-01016-z

Cites

71 of 73 references verified0 citation errors · 2 not checkable

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Verification

Claims the paper makes

42 Own result · 16 Own interpretation · 14 Own method · 8 Supported · 3 Partly supported · 3 No basis given · 2 Sources not found · 1 Mixed support · 1 No full text of the sources · accuracy not yet measured

Show all 39 claims

  • It may state two separate claims as one. 32
  • Probably not a substantial claim. 19
  • The quote only partly states this claim. 1
51 of 51
  • The paper relies on this without support.

    Cognitive performance and vocabulary measures in Generation Scotland are proxies for the composite phenotype on which mate pairs assort.

    “The Generation Scotland cohort contains high quality measures of cognitive performance and vocabulary, proxies for plausible candidates of such a composite.”
  • The paper relies on this without support.

    The top 40 principal components capture the genetic ancestry relevant to assortative mating.

    “After residualizing the EA PGI on 40 principal components (PCs) of the genomic relatedness matrix in addition to EA, we find that the mate-pair PGI correlation falls to 0.091 (S.E. = 0.021).”
  • The authors state this as an assumption.

    The SBayesR finite mixture model has 4 components with initial mixture probabilities π = (0.95, 0.02, 0.02, 0.01) and fixed γ = (0.0, 0.01, 0.1, 1).

    “We assume 4 components in the finite mixture model, with initial mixture probabilities π = (0.95, 0.02, 0.02, 0.01) and fixed γ = (0.0, 0.01, 0.1, 1), where γ is a parameter that constrains how the SNP-effect-size variance scales in each of the four distributions.”
  • The paper relies on this without support.

    In the expected prediction accuracy model, A may vary by prediction sample but B does not.

    “While A may vary by prediction sample, B does not.”
  • The paper relies on this without support.

    The relationship between phenotype and PGI is linear.

    “The correlation between the mate pairs’ PGIs should be equal to ryrprm if the correlation is explained by assortative mating on the phenotype alone, and the relationship between the PGI and the phenotype is linear.”
5 of 5

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Citations

Cites

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Research works printed but not linked: 1

  1. Bulmer, M. G. The Mathematical Theory of Quantitative Genetics (Clarendon Press, 1980).

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Metadata

Authors
Okbay, A., Wu, Y., Wang, N., Jayashankar, H., Bennett, M., Nehzati, S. M., Sidorenko, J., Kweon, H., Goldman, G., Gjorgjieva, T., Jiang, Y., Hicks, B., Tian, C., Hinds, D. A., Ahlskog, R., Magnusson, P. K. E., Oskarsson, S., Hayward, C., Campbell, A., Porteous, D. J., Freese, J., Herd, P., 23andMe Research Team, Agee, M., Alipanahi, B., Auton, A., Bell, R. K., Bryc, K., Elson, S. L., Fontanillas, P., Furlotte, N. A., Hinds, D. A., Huber, K. E., Kleinman, A., Litterman, N. K., McCreight, J. C., McIntyre, M. H., Mountain, J. L., Northover, C. A. M., Pitts, S. J., Sathirapongsasuti, J. F., Sazonova, O. V., Shelton, J. F., Shringarpure, S., Tung, J. Y., Vacic, V., Wilson, C. H., Social Science Genetic Association Consortium, Fontana, M. A., Pers, T. H., Rietveld, C. A., Chen, G.-B., Emilsson, V., Meddens, S. F. W., Pickrell, J. K., Thom, K., Timshel, P., de Vlaming, R., Abdellaoui, A., Ahluwalia, T. S., Bacelis, J., Baumbach, C., Bjornsdottir, G., Brandsma, J. H., Concas, M. P., Derringer, J., Galesloot, T. E., Girotto, G., Gupta, R., Hall, L. M., Harris, S. E., Hofer, E., Horikoshi, M., Huffman, J. E., Kaasik, K., Kalafati, I. P., Karlsson, R., Lahti, J., van der Lee, S. J., de Leeuw, C., Lind, P. A., Lindgren, K.-O., Liu, T., Mangino, M., Marten, J., Mihailov, E., Miller, M. B., van der Most, P. J., Oldmeadow, C., Payton, A., Pervjakova, N., Peyrot, W. J., Qian, Y., Raitakari, O., Rueedi, R., Salvi, E., Schmidt, B., Schraut, K. E., Shi, J., Smith, A. V., Poot, R. A., Pourcain, B. S., Teumer, A., Thorleifsson, G., Verweij, N., Vuckovic, D., Wellmann, J., Westra, H.-J., Yang, J., Zhao, W., Zhu, Z., Alizadeh, B. Z., Amin, N., Bakshi, A., Baumeister, S. E., Biino, G., Bønnelykke, K., Boyle, P. A., Campbell, H., Cappuccio, F. P., Davies, G., De Neve, J.-E., Deloukas, P., Demuth, I., Ding, J., Eibich, P., Eisele, L., Eklund, N., Evans, D. M., Faul, J. D., Feitosa, M. F., Forstner, A. J., Gandin, I., Gunnarsson, B., Halldórsson, B. V., Harris, T. B., Heath, A. C., Hocking, L. J., Holliday, E. G., Homuth, G., Horan, M. A., Hottenga, J.-J., de Jager, P. L., Joshi, P. K., Jugessur, A., Kaakinen, M. A., Kähönen, M., Kanoni, S., Keltigangas-Järvinen, L., Kiemeney, L. A. L. M., Kolcic, I., Koskinen, S., Kraja, A. T., Kroh, M., Kutalik, Z., Latvala, A., Launer, L. J., Lebreton, M. P., Levinson, D. F., Lichtenstein, P., Lichtner, P., Liewald, D. C. M., Study, L. C., Loukola, A., Madden, P. A., Mägi, R., Mäki-Opas, T., Marioni, R. E., Marques-Vidal, P., Meddens, G. A., McMahon, G., Meisinger, C., Meitinger, T., Milaneschi, Y., Milani, L., Montgomery, G. W., Myhre, R., Nelson, C. P., Nyholt, D. R., Ollier, W. E. R., Palotie, A., Paternoster, L., Pedersen, N. L., Petrovic, K. E., Räikkönen, K., Ring, S. M., Robino, A., Rostapshova, O., Rudan, I., Rustichini, A., Salomaa, V., Sanders, A. R., Sarin, A.-P., Schmidt, H., Scott, R. J., Smith, B. H., Smith, J. A., Staessen, J. A., Steinhagen-Thiessen, E., Strauch, K., Terracciano, A., Tobin, M. D., Ulivi, S., Vaccargiu, S., Quaye, L., van Rooij, F. J. A., Venturini, C., Vinkhuyzen, A. A. E., Völker, U., Völzke, H., Vonk, J. M., Vozzi, D., Waage, J., Ware, E. B., Willemsen, G., Attia, J. R., Bennett, D. A., Berger, K., Bertram, L., Bisgaard, H., Boomsma, D. I., Borecki, I. B., Bültmann, U., Chabris, C. F., Cucca, F., Cusi, D., Deary, I. J., Dedoussis, G. V., van Duijn, C. M., Eriksson, J. G., Franke, B., Franke, L., Gasparini, P., Gejman, P. V., Gieger, C., Grabe, H.-J., Gratten, J., Groenen, P. J. F., Gudnason, V., van der Harst, P., Hoffmann, W., Hyppönen, E., Iacono, W. G., Jacobsson, B., Järvelin, M.-R., Jöckel, K.-H., Kaprio, J., Kardia, S. L. R., Lehtimäki, T., Lehrer, S. F., Martin, N. G., McGue, M., Metspalu, A., Pendleton, N., Penninx, B. W. J. H., Perola, M., Pirastu, N., Pirastu, M., Polasek, O., Posthuma, D., Power, C., Province, M. A., Samani, N. J., Schlessinger, D., Schmidt, R., Sørensen, T. I. A., Spector, T. D., Stefansson, K., Thorsteinsdottir, U., Thurik, A. R., Timpson, N. J., Tiemeier, H., Uitterlinden, A. G., Vitart, V., Vollenweider, P., Weir, D. R., Wilson, J. F., Wright, A. F., Conley, D. C., Krueger, R. F., Smith, G. D., Hofman, A., Laibson, D. I., Medland, S. E., Yang, J., Esko, T., Watson, C., Jala, J., Conley, D., Koellinger, P. D., Johannesson, M., Laibson, D., Meyer, M. N., Lee, J. J., Kong, A., Yengo, L., Cesarini, D., Turley, P., Visscher, P. M., Beauchamp, J. P., Benjamin, D. J., & Young, A. I.
Affiliations
  • Vrije Universiteit Amsterdam (NL)
  • The University of Queensland (AU)
  • National Bureau of Economic Research (US)
  • University of California, Los Angeles (US)
  • 23andMe (United States) (US)
  • Uppsala University (SE)
  • Karolinska Institutet (SE)
  • Western General Hospital (GB)
  • Georgetown University (US)
  • University of Southern California (US)
  • Statens Serum Institut (DK)
  • Erasmus MC (NL)
  • University of Iceland (IS)
  • New York Genome Center (US)
  • New York University (US)
  • University of Copenhagen (DK)
  • Helmholtz Munich (DE)
  • Institute of Genetic and Biomedical Research (IT)
  • University of Illinois Urbana-Champaign (US)
  • Radboud University Nijmegen (NL)
  • University of Trieste (IT)
  • University of Helsinki (FI)
  • University of Leicester (GB)
  • University of Edinburgh (GB)
  • University of Graz (AT)
  • Centre for Human Genetics (GB)
  • Harokopio University of Athens (GR)
  • QIMR Berghofer Medical Research Institute (AU)
  • Max Planck Institute for Human Development (DE)
  • King's College London (GB)
  • University of Tartu (EE)
  • University of Minnesota (US)
  • Hunter Medical Research Institute (AU)
  • University of Manchester (GB)
  • Finnish Institute for Health and Welfare (FI)
  • GGZ inGeest (NL)
  • National Institute on Aging (US)
  • University of Turku (FI)
  • SIB Swiss Institute of Bioinformatics (CH)
  • University of Milan (IT)
  • Essen University Hospital (DE)
  • National Cancer Institute (US)
  • Universitätsmedizin Greifswald (DE)
  • University Medical Center Groningen (NL)
  • University of Münster (DE)
  • Broad Institute (US)
  • Rush University Medical Center (US)
  • University of Michigan (US)
  • Istituto di Genetica Molecolare (IT)
  • University of Warwick (GB)
  • Queen Mary University of London (GB)
  • Charité – Universitätsmedizin Berlin (DE)
  • German Institute for Economic Research (DE)
  • Translational Research Institute (AU)
  • Washington University in St. Louis (US)
  • University of Bonn (DE)
  • deCODE Genetics (Iceland) (IS)
  • Reykjavík University (IS)
  • National Institutes of Health (US)
  • University of Aberdeen (GB)
  • Centre Hospitalier Universitaire Vaudois (CH)
  • Norwegian Institute of Public Health (NO)
  • Imperial College London (GB)
  • Tampere University (FI)
  • University of Amsterdam (NL)
  • Stanford Medicine (US)
  • University of Toronto (CA)
  • University of Bristol (GB)
  • Queensland University of Technology (AU)
  • Medical University of Graz (AT)
  • IRCCS Materno Infantile Burlo Garofolo (IT)
  • Harvard University (US)
  • Endeavor Health (US)
  • University of Dundee (GB)
  • Maastricht University (NL)
  • Florida State University (US)
  • Autism & Developmental Medicine Institute (US)
  • University of South Australia (AU)
  • Oulu University Hospital (FI)
  • Princeton University (US)
  • University of Wisconsin–Madison (US)
  • Geisinger Medical Center (US)
  • George Mason University (US)
Corresponding author
  • Okbay, Aysu
  • Visscher, Peter M.
  • Benjamin, Daniel J.
  • Young, Alexander I.
ORCID
Venue
Nature Genetics
Published
2022-03-31
Volume
54
Issue
4
Pages
437-449
ISSN
  • 1061-4036 (ISSN-L)
  • 1546-1718
OpenAlex
W4220711675
Cited by
654
References
69
Citations by year
2026: 103 · 2025: 183 · 2024: 179 · 2023: 144 · 2022: 43
FWCI
83.18
Type
journal article
Language
en
Open access
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Funders
  • U.S. Department of Health & Human Services | NIH | National Institute on Aging (R56-AG058726, R24-AG065184, R01-AG042568, K99-AG062787-01)
  • Nederlandse Organisatie voor Wetenschappelijk Onderzoek (016.Veni.198.058)
  • F.G. Meade Scholarship and UQ Research Training Scholarship from the University of Queensland Senate
  • Riksbankens Jubileumsfond (P18-0782:18)
  • Vetenskapsrådet (2019-00244, 421-2013-1061)
  • RCUK | Medical Research Council (MC_UU_00007/10)
  • ERC Consolidator Grant
  • Pershing Square Foundation
  • Li Ka Shing Foundation
  • Department of Education and Training | Australian Research Council (DE200100425, FL180100072)
  • Ragnar Söderbergs stiftelse (E42/15)
  • Department of Health | National Health and Medical Research Council (GNT113400)
  • Genome Canada (OGI-152)
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
educational attainment, polygenic index, polygenic prediction, single nucleotide polymorphisms, direct genetic effects, parental effects, assortative mating, dominance effects, X chromosome, genetic risk prediction, disease risk prediction
MeSH terms
Humans, Multifactorial Inheritance, Polymorphism, Single Nucleotide, 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.

A language model drew the claims from the paper on 2026-10-29, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).

A second model labelled each claim on 2026-10-29 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).

Cited sources are checked with a language model’s help (gpt-6-sol).

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 ›

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