Common genetic variants associated with cognitive performance identified using the proxy-phenotype method
- aDepartment of Applied Economics, Erasmus School of Economics, Erasmus University, 3000 DR, Rotterdam, The Netherlands;
- Departments of bEpidemiology,
- ddNeurology, and
- jjInternal Medicine,
- wGenetic Epidemiology Unit, Department of Epidemiology and Biostatistics, and
- eeGeneration R Study Group, Erasmus Medical Center, 3000 CA, Rotterdam, The Netherlands;
- cDivision of Genetics and Endocrinology, Boston Children's Hospital, Boston, MA 02115;
- dProgram in Medical and Population Genetics, Broad Institute of the Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142;
- eDepartment of Genetics, Harvard Medical School, Boston, MA 02115;
- fEstonian Genome Center, University of Tartu, 51010 Tartu, Estonia;
- gCentre for Cognitive Ageing and Cognitive Epidemiology,
- hDepartment of Psychology, and
- llAlzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh EH8 9JZ, United Kingdom;
- Departments of iEconomics and
- oPsychology, Harvard University, Cambridge, MA 02138;
- jQueensland Brain Institute, University of Queensland, Brisbane, QLD 4072, Australia;
- kDepartment of Psychology, Union College, Schenectady, NY 12308;
- lIcelandic Heart Association, 201 Kopavogur, Iceland;
- mFaculty of Pharmaceutical Sciences, University of Iceland, 107 Reykjavík, Iceland;
- nFramingham Heart Study, National Heart, Lung, and Blood Institute, Framingham, MA 01702;
- pDepartment of Psychology, University of Minnesota, Minneapolis, MN 55455-0344;
- qDepartment of Complex Trait Genetics, VU University Amsterdam and VU Medical Center, 1081 HV, Amsterdam, The Netherlands;
- rMachine Learning Group, Intelligent Systems, Institute for Computing and Information Sciences, Faculty of Science, Radboud University, 6500 GL, Nijmegen, The Netherlands;
- sCentre for Genomic and Experimental Medicine and
- ccMedical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XU, United Kingdom;
- tQuantitative Genetics Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4029, Australia;
- uGenetic Epidemiology Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4029, Australia;
- vHarvard Kennedy School, Harvard University, Cambridge, MA 02139;
- xDepartment of Sociology and
- rrCenter for Experimental Social Science, Department of Economics, New York University, New York, NY 10012;
- yDepartment of Psychology, University of Illinois, Urbana–Champaign, IL 61820;
- zCentre for Medical Systems Biology, Leiden University Medical Center, 2300 RC, Leiden, The Netherlands;
- aaDepartment of Genetics, University Medical Center Groningen, University of Groningen, 9713 GZ, Groningen, The Netherlands;
- bbNeuroimaging Genetics Group, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4029, Australia;
- ffDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet, 171 77 Stockholm, Sweden;
- ggSchool of Social and Community Medicine, University of Bristol, Bristol BS8 2PR, United Kingdom;
- hhDepartment of Child and Adolescent Psychiatry, Erasmus Medical Center, 3000 CB, Rotterdam, The Netherlands;
- iiDepartment of Clinical Genetics, VU University Medical Center, 1081 BT, Amsterdam, The Netherlands;
- kkMedical Research Institute, University of Dundee, Dundee DD2 4RB, United Kingdom;
- mmMedical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2PR, United Kingdom;
- nnMedical Research Council Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, King's College London, London SE5 8AF, United Kingdom;
- ooDepartment of Economics, Stockholm School of Economics, 113 83 Stockholm, Sweden;
- ppUniversity of Queensland Diamantina Institute, University of Queensland, Princess Alexandra Hospital, Brisbane, QLD 4102, Australia;
- qqDepartment of Economics, Cornell University, Ithaca, NY 14853;
- ssInstitute for the Interdisciplinary Study of Decision Making, New York University, New York, NY 10012; and
- ttFaculty of Economics and Business, University of Amsterdam, 1012 WX, Amsterdam, The Netherlands
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Edited by Michael S. Gazzaniga, University of California, Santa Barbara, CA, and approved August 14, 2014 (received for review March 12, 2014)

Significance
We identify several common genetic variants associated with cognitive performance using a two-stage approach: we conduct a genome-wide association study of educational attainment to generate a set of candidates, and then we estimate the association of these variants with cognitive performance. In older Americans, we find that these variants are jointly associated with cognitive health. Bioinformatics analyses implicate a set of genes that is associated with a particular neurotransmitter pathway involved in synaptic plasticity, the main cellular mechanism for learning and memory. In addition to the substantive contribution, this work also serves to show a proxy-phenotype approach to discovering common genetic variants that is likely to be useful for many phenotypes of interest to social scientists (such as personality traits).
Abstract
We identify common genetic variants associated with cognitive performance using a two-stage approach, which we call the proxy-phenotype method. First, we conduct a genome-wide association study of educational attainment in a large sample (n = 106,736), which produces a set of 69 education-associated SNPs. Second, using independent samples (n = 24,189), we measure the association of these education-associated SNPs with cognitive performance. Three SNPs (rs1487441, rs7923609, and rs2721173) are significantly associated with cognitive performance after correction for multiple hypothesis testing. In an independent sample of older Americans (n = 8,652), we also show that a polygenic score derived from the education-associated SNPs is associated with memory and absence of dementia. Convergent evidence from a set of bioinformatics analyses implicates four specific genes (KNCMA1, NRXN1, POU2F3, and SCRT). All of these genes are associated with a particular neurotransmitter pathway involved in synaptic plasticity, the main cellular mechanism for learning and memory.
Footnotes
↵1D.J.B., D. Cesarini, and P.D.K. contributed equally to this work.
- ↵2To whom correspondence may be addressed. Email: daniel.benjamin{at}gmail.com or p.d.koellinger{at}uva.nl.
Author contributions: D.J.B., D. Cesarini, and P.D.K. designed research; C.A.R., T.E., G.D., T.H.P., P.T., B.B., V.E., A.D.J., J.J.L., C.d.L., R.E.M., S.E.M., M.B.M., O.R., S.J.v.d.L., A.A.E.V., N.A., D. Conley, J.D., R.F., L.F., C.H., C.I.-V., J.K., D.C.L., P.K.E.M., G.M., D.P., M.T., M.E.W., M.J., P.M.V., and D. Cesarini analyzed data; C.A.R., T.E., P.T., C.F.C., D.L., D.J.B., D. Cesarini, and P.D.K. wrote the paper; C.F.C., C.M.v.D., E.L.G., W.G.I., V.J., D.L., P.L., N.G.M., M.M., N.L.P., S.P., D.P., J.M.S., H.T., F.C.V., M.J.W., G.D.S., I.J.D., M.J., and R.P. performed data collection; J.J.L., M.B.M., C.M.v.D., N.K.H., P.K.E.M., D.J.P., B.H.S., J.M.S., H.T., N.J.T., M.J.W., I.J.D., and M.J. performed phenotyping; and G.D., M.B.M., C.M.v.D., C.H., V.J., D.C.L., P.K.E.M., N.G.M., D.J.P., F.R., N.J.T., and A.G.U. performed genotyping.
See SI Appendix for further details.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
Data deposition: Genetic summary data on which our work is based are posted on the website of our research consortium (www.ssgac.org).
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1404623111/-/DCSupplemental.
Freely available online through the PNAS open access option.
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