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Molecular Genetic Contributions to Socioeconomic Status and Intelligence Edinburgh Research Explorer Molecular genetic contributions to socioeconomic status and intelligence Citation for published version: Marioni, RE, Davies, G, Hayward, C, Liewald, D, Kerr, SM, Campbell, A, Luciano, M, Smith, BH, Padmanabhan, S, Hocking, LJ, Hastie, ND, Wright, AF, Porteous, DJ, Visscher, PM & Deary, IJ 2014, 'Molecular genetic contributions to socioeconomic status and intelligence', Intelligence, vol. 44, no. 100, pp. 26-32. https://doi.org/10.1016/j.intell.2014.02.006 Digital Object Identifier (DOI): 10.1016/j.intell.2014.02.006 Link: Link to publication record in Edinburgh Research Explorer Document Version: Publisher's PDF, also known as Version of record Published In: Intelligence Publisher Rights Statement: Open Access General rights Copyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights. Take down policy The University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please contact [email protected] providing details, and we will remove access to the work immediately and investigate your claim. Download date: 02. Oct. 2021 Intelligence 44 (2014) 26–32 Contents lists available at ScienceDirect Intelligence Molecular genetic contributions to socioeconomic status and intelligence Riccardo E. Marioni a,b,⁎, Gail Davies a,b,c, Caroline Hayward d, Dave Liewald a, Shona M. Kerr d, Archie Campbell c, Michelle Luciano a,b, Blair H. Smith e, Sandosh Padmanabhan f, Lynne J. Hocking g, Nicholas D. Hastie d, Alan F. Wright d, David J. Porteous a,c, Peter M. Visscher a,h,i, Ian J. Deary a,b,⁎ a Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, 7 George Square, Edinburgh EH8 9JZ, UK b Department of Psychology, University of Edinburgh, Edinburgh EH8 9JZ, UK c Medical Genetics Section, Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XU, UK d Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XU, UK e Medical Research Institute, University of Dundee, Dundee DD2 4RB, UK f British Heart Foundation Glasgow Cardiovascular Research Centre, Institute of Cardiovascular and Medical Sciences, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow G12 8TA, UK g Musculoskeletal Research Programme, Division of Applied Medicine, University of Aberdeen, Aberdeen AB25 2ZD, UK h University of Queensland Diamantina Institute, The University of Queensland, Princess Alexandra Hospital, Brisbane 4072, QLD, Australia i Queensland Brain Institute, The University of Queensland, Brisbane 4072, QLD, Australia article info abstract Article history: Education, socioeconomic status, and intelligence are commonly used as predictors of health Received 23 October 2013 outcomes, social environment, and mortality. Education and socioeconomic status are typically Received in revised form 10 January 2014 viewed as environmental variables although both correlate with intelligence, which has a substantial Accepted 10 February 2014 genetic basis. Using data from 6815 unrelated subjects from the Generation Scotland study, we Available online xxxx examined the genetic contributions to these variables and their genetic correlations. Subjects underwent genome-wide testing for common single nucleotide polymorphisms (SNPs). Keywords: DNA-derived heritability estimates and genetic correlations were calculated using the Generation Scotland ‘Genome-wide Complex Trait Analyses’ (GCTA) procedures. 21% of the variation in education, 18% Intelligence of the variation in socioeconomic status, and 29% of the variation in general cognitive ability was Education explained by variation in common SNPs (SEs ~ 5%). The SNP-based genetic correlations of education Socioeconomic status Genetics and socioeconomic status with general intelligence were 0.95 (SE 0.13) and 0.26 (0.16), respectively. There are genetic contributions to intelligence and education with near-complete overlap between common additive SNP effects on these traits (genetic correlation ~ 1). Genetic influences on socioeconomic status are also associated with the genetic foundations of intelligence. The results are also compatible with substantial environmental contributions to socioeconomic status. © 2014 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/3.0/). ⁎ Corresponding authors at: Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, 7 George Square, Edinburgh EH8 9JZ, UK. Tel.: +44 131 650 3422; fax: +44 131 651 1771. E-mail addresses: [email protected] (R.E. Marioni), [email protected] (G. Davies), [email protected] (C. Hayward), [email protected] (D. Liewald), [email protected] (S.M. Kerr), [email protected] (A. Campbell), [email protected] (B.H. Smith), [email protected] (S. Padmanabhan), [email protected] (L.J. Hocking), [email protected] (N.D. Hastie), [email protected] (A.F. Wright), [email protected] (D.J. Porteous), [email protected] (P.M. Visscher), [email protected] (I.J. Deary). http://dx.doi.org/10.1016/j.intell.2014.02.006 0160-2896/© 2014 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/3.0/). R.E. Marioni et al. / Intelligence 44 (2014) 26–32 27 1. Introduction Here, we perform univariate and bivariate genetic analyses for education, socioeconomic status, and intelligence. Previous Three important, inter-correlated variables that psycho- univariate and bivariate heritability studies of these variables social scientists bring to bear on social and health-related have mostly been based on twin and family studies. These topics are: intelligence, education, and socioeconomic status. models assume random mating of parents and identical envi- The causes of the associations between these three variables ronmental exposure for the twins. Such assumptions have been are poorly understood (Deary & Johnson, 2010). All three are questioned, and the samples used in such analyses are not used as predictors of people's health outcomes, social environ- necessarily population-representative. We do not wish to ment, and mortality (Batty, Kivimaki, & Deary, 2010; Calvin, suggest that the conclusions based on these samples and Batty, Lowe, & Deary, 2011; Calvin, Deary, et al., 2011; Deary & models are incorrect, but there is now a new and independent Batty, 2007; Marmot et al., 1991; Richards et al., 2009). There- way to study genetic contributions to, and genetic correlations fore, disentangling their interplay is important in human life- between, complex traits that requires neither these assump- course research (Deary & Johnson, 2010; Richards & Sacker, tions nor such non-representative samples. Molecular (SNP) 2003; Singh-Manoux, Richards, & Marmot, 2005). data derived from testing DNA samples can now be used in To better understand how intelligence, education, and socio- unrelated, population-based samples to estimate the degree to economic status are related, we can examine the genetic foun- which phenotypic similarities can be explained by genetic dations of these variables and their associations. Twin studies similarities. The present study's sample, Generation Scotland: indicate that intelligence has a high heritability in adulthood the Scottish Family Health Study (Smith et al., 2006, 2012), is a (Deary, Johnson, & Houlihan, 2009)(around70%)andthat large (n ~ 24,000), family-structured, population-based cohort there is a strong genetic correlation between intelligence and that allows us to examine both the molecular and the pedigree- education (Calvin et al., 2012). However, twin studies have long based approaches. been subjected to criticisms on methodological grounds (Plomin, 2012; Trzaskowski et al., 2013) and researchers are now turning 2. Methods to analyses based on DNA differences to obtain heritability estimates of such complex traits. For example, when considered Generation Scotland: the Scottish Family Health Study simultaneously, variants in linkage disequilibrium with common (Smith et al., 2006, 2012) is a family-structured, population- single nucleotide polymorphisms (SNPs) explain around 40–50% based cohort study recruited between 2006 and 2011. Regional of the variation in cognitive ability in older people (Davies et al., sampling occurred in Glasgow, Tayside, Ayrshire, Arran, and 2011). North-East Scotland. Participants were recruited through Although educational attainment is often treated as an general medical practitioners (GPs) (95% of probands) and environmental factor (Deary & Johnson, 2010), common genetic via word-of-mouth or direct publicity. We refer to the first variants explain around 15% of this variation (highest degree member of any one family to be recruited into GS:SFHS as the completed) (Benjamin et al., 2013) and 22.4% of the variation in proband. Probands (n = 7953) were aged between 35 and years of education (Rietveld et al., 2013). A GWAS meta-analysis 65 years and
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