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Social Competence in Parents Increases Children’s Educational Attainment: Replicable Genetically-Mediated Effects of Parenting Revealed by Non-Transmitted DNA

Published online by Cambridge University Press:  21 January 2019

Timothy C. Bates*
Affiliation:
Department of Psychology, University of Edinburgh, Edinburgh, UK
Brion S. Maher
Affiliation:
Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
Lucía Colodro-Conde
Affiliation:
QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
Sarah E. Medland
Affiliation:
QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
Kerrie McAloney
Affiliation:
QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
Margaret J. Wright
Affiliation:
Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia Centre for Advanced Imaging, University of Queensland, Brisbane, Queensland, Australia
Narelle K. Hansell
Affiliation:
Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia
Aysu Okbay
Affiliation:
Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
Kenneth S. Kendler
Affiliation:
Virginia Institute of Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
Nicholas G. Martin
Affiliation:
QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
Nathan A. Gillespie
Affiliation:
Virginia Institute of Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
*
Author for correspondence: Timothy C. Bates, Email: [email protected]

Abstract

We recently reported an association of offspring educational attainment with polygenic risk scores (PRS) computed on parent’s non-transmitted alleles for educational attainment using the second GWAS meta-analysis article on educational attainment published by the Social Science Genetic Association Consortium. Here we test the replication of these findings using a more powerful PRS from the third GWAS meta-analysis article by the Consortium. Each of the key findings of our previous paper is replicated using this improved PRS (N = 2335 adolescent twins and their genotyped parents). The association of children’s attainment with their own PRS increased substantially with the standardized effect size, moving from β = 0.134, 95% CI = 0.079, 0.188 for EA2, to β = 0.223, 95% CI = 0.169, 0.278, p < .001, for EA3. Parent’s PRS again predicted the socioeconomic status (SES) they provided to their offspring and increased from β = 0.201, 95% CI = 0.147, 0.256 to β = 0.286, 95% CI = 0.239, 0.333. Importantly, the PRS for alleles not transmitted to their offspring — therefore acting via the parenting environment — was increased in effect size from β = 0.058, 95% CI = 0.003, 0.114 to β = 0.067, 95% CI = 0.012, 0.122, p = .016. As previously found, this non-transmitted genetic effect was fully accounted for by parental SES. The findings reinforce the conclusion that genetic effects of parenting are substantial, explain approximately one-third the magnitude of an individual’s own genetic inheritance and are mediated by parental socioeconomic competence.

Type
Articles
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s) 2019

We recently reported an association of educational attainment with polygenic risk scores (PRS) computed using the 50% of DNA not transmitted to offspring by their parents (Bates et al., Reference Bates, Maher, Medland, McAloney, Wright, Hansell and Gillespie2018), a finding also demonstrated elsewhere (Koellinger & Harden, Reference Koellinger and Harden2018; Kong et al., Reference Kong, Thorleifsson, Frigge, Vilhjalmsson, Young, Thorgeirsson and Stefansson2018). Those findings were based on the second GWAS meta-analysis on educational attainment published by the Social Science Genetic Association Consortium (EA2; Okbay et al., Reference Okbay, Beauchamp, Fontana, Lee, Pers, Rietveld and Benjamin2016). Here, we use the pseudo-control method (Cordell et al., Reference Cordell, Barratt and Clayton2004) and the recently available third phase of educational attainment scores (EA3; Lee et al., Reference Lee, Wedow, Okbay, Kong, Maghzian, Zacher and Cesarini2018) to replicate this finding with a stronger polygenic predictor. We employed the same sample and high-quality measure of educational attainment, testing the replication of the hypotheses that: offspring EA3 PRS would positively predict their educational attainment (H1); parents’ EA3 PRS would positively predict their attained SES (H2); and offspring’s educational attainment would be significantly associated with parental EA3 PRS computed from alleles not transmitted to their offspring (H3). Finally, this cultural transmission effect would operate via socioeconomic competence at creating the extended phenotype of environmental niches aiding offspring development (H4).

Methods

Subjects

The sample consisted of all 2335 adolescent twins and their genotyped parents available within the Brisbane Adolescent Twin Study (Wright & Martin, Reference Wright and Martin2004) for whom educational attainment data and genotype data are available (1333 female, mean age at testing 17.15 years, SD = 0.39; 1002 male, mean age at testing 17.2 years, SD = 0.41), along with genotype and SES data for their parents. Exclusion criteria for entry to the cohort were significant head injury, neurological or psychiatric illness, substance dependence or chronic use of medications with central nervous system effects. Subjects were genotyped on the 610K and GSA Illumina genome-wide SNP platform and imputed to the Haplotype Reference Consortium (version 1.1). The study was approved by the QIMR Berghofer Medical Research Institute’s Human Research Ethics Committee.

Phenotypic Measures

Educational attainment was assessed using the overall score for the Queensland Core Skills Test (QCST; Queensland Studies Authority, 2003): a 7-h comprehensive assessment completed in the final year of high school (age ∼17 years) and providing a global index of achievement, standardized to the state-wide sample means for each year. Childhood SES was assessed using the Australian Socioeconomic Index 2006 (AUSEI06) occupational status scale (McMillan et al., Reference McMillan, Beavis and Jones2009) and was ascertained for each parent ∼12 months prior to the twins taking the QCST. For each twin pair, parental SES was set to the maximum of their maternal and paternal values.

Genetic Measures

Non-transmitted parental genomes were created using PseudoCons (Cordell et al., Reference Cordell, Barratt and Clayton2004; Howey, Reference Howey2014). Polygenic risk scores for EA3 were calculated for the offspring and parental pseudo-control genomes (best-guess imputed genotypes, excluding SNPs with low imputation quality r 2 < .6 and MAF <1%) using PLINK1.9 (Purcell et al., Reference Purcell, Neale, Todd-Brown, Thomas, Ferreira, Bender and Sham2007) and the method described by Wray et al. (Reference Wray, Lee, Mehta, Vinkhuyzen, Dudbridge and Middeldorp2014). A standardized PRS was computed using 79,339 SNPs: all SNPs associated p ≤ .05 recomputed from the cohorts of the third educational attainment GWAS (Lee et al., Reference Lee, Wedow, Okbay, Kong, Maghzian, Zacher and Cesarini2018) leaving out the QIMR and 23andMe samples, and using European population LD information from the 1000 Genomes reference set (pruning criteria LD r 2 < .1 within windows of 10 Mb).

Multilevel modeling was completed using lme from the nlme package (Pinheiro et al., Reference Pinheiro, Bates, DebRoy and Sarkar2017) to construct multilevel models with QCST as the dependent variable, with predictors nested within family and zygosity. Analyses controlled for age at test and for sex.

Results

We tested the replication of the four hypotheses outlined above. Results for hypotheses 1–3 are tabulated in Table 1 for convenience. Replicating hypothesis 1, educational attainment in the offspring was significantly related to their own EA3 PRS in a multi-level model with offspring QCST as the dependent variable, with offspring EA3 PRS, age and sex as predictors (β = 0.223, 95% CI = 0.169, 0.277, t(606) = 8.045, p < .001; see Figure 1, panel A). This exceeds the beta found using EA2 PRS (β = 0.133; 95% CI = 0.078, 0.187, t(606) = 4.805, p < .001). Controlling for parental SES did not materially alter this effect. Updating hypothesis 2, a linear model predicting parental attained SES from parental EA3 PRS was significant (β = 0.29, 95% CI = 0.24, 0.33, t = 11.98, p < .001). This was again larger than was found using EA2 (β = 0.20, 95% CI = 0.15, 0.26). Regarding an ancillary hypothesis that subjects’ EA3 PRS would interact with their parents’ SES (Tucker-Drob & Bates, Reference Tucker-Drob and Bates2016) — that is, a GPRS × SES interaction — no support for this was found (β = −0.02, 95% CI = −0.07, 0.04, t(593) = −0.57, p = .569), a result that comports with twin data from Australia (Bates et al., Reference Bates, Hansell, Martin and Wright2016; Grasby et al., Reference Grasby, Coventry, Byrne and Olson2017).

Table 1. Comparison of Effect Sizes between EA2 and EA3 Polygenic Risk Scores as Predictors of Offspring Educational Attainment and Parental SES

Fig. 1. Prediction of offspring educational attainment from offspring EA3 polygenic risk scores (A) and by parental non-transmitted EA3 polygenic risk scores (B).

Hypothesis 3, that offspring attainment would be significantly associated with parental EA3 PRS computed from alleles not transmitted to their offspring, reflecting cultural transmission, was updated using a multilevel model, with offspring EA3 PRS, parental non-transmitted EA3 PRS and age and sex as predictors. The effect was, again, replicated with increased magnitude (from β = 0.058, 95% CI = 0.002, 0.114 to β = 0.068, 95% CI = 0.014, 0.123, t(605) = 2.45, p = .014; see Figure 1, panel B). Supporting hypothesis 4, which predicts that the non-transmitted effect operates via parental socioeconomic competence, controlling parent SES wiped out the non-transmitted allele effect in the model (β = 0.00, 95% CI = −0.05, 0.06, t(594) = 0.17, p = .862).

Discussion

The findings of our initial investigation were replicated. In addition to increased magnitude of associations of offspring educational attainment with offspring EA3 PRS, the effect of parent genotype on a parental competence phenotype supporting child cognitive development was replicated, with higher attainment in offspring associated with non-transmitted elements of the EA3 PRS, and an effect size around one-third the magnitude of children’s own EA genetic score. The finding supports the view of EA3 gene polymorphisms as underlying a social competence phenotype of which education is just one component. The results also confirmed that the effects of parenting on attainment appear to mediate one or more of the aspects associated with the SES that parents provide their offspring. The useful foci for follow-up research include identifying specific physical and behavioral traits that are causal for improving offspring attainment. Examples of measurable mediators include wealth and housing (Duncan et al., Reference Duncan, Morris and Rodrigues2011; Galobardes et al., Reference Galobardes, Shaw, Lawlor, Lynch and Davey Smith2006), household chaos (Matheny et al., Reference Matheny, Wachs, Ludwig and Phillips1995), as well as parental responsiveness to offspring needs (Bradley & Corwyn, Reference Bradley and Corwyn2002). The method can also be extended to other outcomes, including psychiatric traits in which parental genotypes may be operating as an extended phenotype influencing offspring.

Acknowledgments

We are greatly appreciative of the support from the twins and their families, and their willingness to participate in our study. We thank Marlene Grace, Ann Eldridge and Natalie Garden for participant recruitment and data collection at QIMR Berghofer Medical Research Institute. We also thank the Queensland Curriculum and Assessment Authority for the provision of QCST data and statistics. Any analyses of QCST data constitutes our own assessment of the data and is not a representation of the agency. This collaborative research was supported by the Human Frontier Science Program (grant number RG0154/1998B) and grants from the Australian Research Council (A79600334, A79906588, A79801419, DP0212016, DP0664638, DP1093900) and the Netherlands Organisation for Scientific Research (NWO) 016.Veni.198.058. L.C-C. was supported by a QIMR Berghofer fellowship.

References

Bates, T. C., Hansell, N. K., Martin, N. G., & Wright, M. J. (2016). When does socioeconomic status (SES) moderate the heritability of IQ? No evidence for g × SES interaction for IQ in a representative sample of 1176 Australian adolescent twin pairs. Intelligence, 56, 1015.CrossRefGoogle Scholar
Bates, T. C., Maher, B. S., Medland, S. E., McAloney, K., Wright, M. J., Hansell, N. K., … Gillespie, N. A. (2018). The nature of nurture: Using a virtual-parent design to test parenting effects on children’s educational attainment in genotyped families. Twin Research and Human Genetics, 21, 7383.CrossRefGoogle ScholarPubMed
Bradley, R. H., & Corwyn, R. F. (2002). Socioeconomic status and child development. Annual Review of Psychology, 53, 371399.CrossRefGoogle ScholarPubMed
Cordell, H. J., Barratt, B. J., & Clayton, D. G. (2004). Case/pseudocontrol analysis in genetic association studies: A unified framework for detection of genotype and haplotype associations, gene-gene and gene-environment interactions, and parent-of-origin effects. Genetic Epidemiology, 26, 167185.CrossRefGoogle ScholarPubMed
Duncan, G. J., Morris, P. A., & Rodrigues, C. (2011). Does money really matter? Estimating impacts of family income on young children’s achievement with data from random-assignment experiments. Developmental Psychology, 47, 12631279.CrossRefGoogle Scholar
Galobardes, B., Shaw, M., Lawlor, D. A., Lynch, J. W., & Davey Smith, G. (2006). Indicators of socioeconomic position (part 1). Journal of Epidemiology and Community Health, 60, 712.CrossRefGoogle Scholar
Grasby, K. L., Coventry, W. L., Byrne, B., & Olson, R. K. (2017). Little evidence that socioeconomic status modifies heritability of literacy and numeracy in Australia. Child Development. Advance online publication. doi: 10.1111/cdev.12920Google ScholarPubMed
Howey, R. (2014). PseudoCons. Retrieved from http://www.staff.ncl.ac.uk/richard.howey/pseudocons/Google Scholar
Koellinger, P. D., & Harden, K. P. (2018). Using nature to understand nurture. Science, 359, 386387.CrossRefGoogle ScholarPubMed
Kong, A., Thorleifsson, G., Frigge, M. L., Vilhjalmsson, B. J., Young, A. I., Thorgeirsson, T. E., … Stefansson, K. (2018). The nature of nurture: Effects of parental genotypes. Science, 359, 424428.CrossRefGoogle ScholarPubMed
Lee, J. J., Wedow, R., Okbay, A., Kong, E., Maghzian, O., Zacher, M., … Cesarini, D. (2018). Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nature Genetics, 50, 11121121.CrossRefGoogle ScholarPubMed
Matheny, A. P. Jr., Wachs, T. D., Ludwig, J. L., & Phillips, K. (1995). Bringing order out of chaos: Psychometric characteristics of the confusion, hubbub, and order scale. Journal of Applied Developmental Psychology, 16, 429444.CrossRefGoogle Scholar
McMillan, J., Beavis, A., & Jones, F. L. (2009). A new socioeconomic index for Australia. Journal of Sociology, 45, 123149.CrossRefGoogle Scholar
Okbay, A., Beauchamp, J. P., Fontana, M. A., Lee, J. J., Pers, T. H., Rietveld, C. A., … Benjamin, D. J. (2016). Genome-wide association study identifies 74 loci associated with educational attainment. Nature, 533, 539542.CrossRefGoogle ScholarPubMed
Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D., & R Core Team. (2017). nlme: Linear and nonlinear mixed effects models (Version R package version 3.1-131). Retrieved from https://cran.r-project.org/web/packages/nlme/citation.htmlGoogle Scholar
Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A., Bender, D., … Sham, P. C. (2007). PLINK: A tool set for whole-genome association and population-based linkage analyses. American Journal of Human Genetics, 81, 559575.CrossRefGoogle ScholarPubMed
Queensland Studies Authority. (2003). The 2003 Queensland Core Skills Test retrospective. Brisbane, Australia: Queensland Studies Authority.Google Scholar
Tucker-Drob, E. M., & Bates, T. C. (2016). Large cross-national differences in gene x socioeconomic status interaction on intelligence. Psychological Science, 27, 138149.CrossRefGoogle ScholarPubMed
Wray, N. R., Lee, S. H., Mehta, D., Vinkhuyzen, A. A., Dudbridge, F., & Middeldorp, C. M. (2014). Research review: Polygenic methods and their application to psychiatric traits. Journal of Child Psychology and Psychiatry, 55, 10681087.CrossRefGoogle ScholarPubMed
Wright, M. J., & Martin, N. G. (2004). The Brisbane Adolescent Twin Study: Outline of study methods and research projects. Australian Journal of Psychology, 56, 6578.CrossRefGoogle Scholar
Figure 0

Table 1. Comparison of Effect Sizes between EA2 and EA3 Polygenic Risk Scores as Predictors of Offspring Educational Attainment and Parental SES

Figure 1

Fig. 1. Prediction of offspring educational attainment from offspring EA3 polygenic risk scores (A) and by parental non-transmitted EA3 polygenic risk scores (B).