Polygenic Influence on Educational Attainment

Domingue, Benjamin W.; Belsky, Daniel W.; Conley, Dalton; Harris, Kathleen Mullan; Boardman, Jason D.

doi:10.1177/2332858415599972

Cited by 151 publications

(103 citation statements)

References 58 publications

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“…Most existing studies work with an older, less predictive polygenic score based on the results of Rietveld et al (2013). Domingue et al (2015) demonstrate that this polygenic score predicts educational attainment in the Add Health study, and that this association appears to operate separately from childhood environments since it predicts higher education within sibling groups. Indeed, Rietveld et al (2014) find similar coefficients on a related polygenic score using either within-family variation or within and across family variation.…”

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confidence: 76%

Genes, Education, and Labor Market Outcomes: Evidence from the Health and Retirement Study

Papageorge

Thom

2017

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Recent advances have led to the discovery of specific genetic variants that predict educational attainment. We study how these variants, summarized as a genetic score variable, are associated with human capital accumulation and labor market outcomes in the Health and Retirement Study (HRS). We demonstrate that the same genetic score that predicts education is also associated with higher wages, but only among individuals with a college education. Moreover, the genetic gradient in wages has grown in more recent birth cohorts, consistent with interactions between technological change and labor market ability. We also show that individuals who grew up in economically disadvantaged households are less likely to go to college when compared to individuals with the same genetic score, but from higher socioeconomic status households. Our findings provide support for the idea that childhood socioeconomic status is an important moderator of the economic returns to genetic endowments. Moreover, the finding that childhood poverty limits the educational attainment of high-ability individuals suggests the existence of unrealized human potential. JEL Classification Codes: I24, J24Key Words: Human capital, inequality, education, genes Acknowledgments:We thank Aysu Okbay for constructing and sharing the polygenic score used for HRS respondents. For helpful comments and conversations, we thank Joseph Altonji, Robert Barbera, Daniel Belsky, Jonathan Beauchamp, Pietro Biroli, David Cesarini, Dora Costa, Stefanie Deluca, Jason Fletcher, Seth Gershenson, Barton Hamilton, Stephanie Heger, Erik Hurst, Steven Lehrer, Lance Lochner, Robert Moffitt, Aysu Okbay, Robert Pollak, Paul Romer, Victor Ronda, Petra Todd and Matthew Wiswall along with participants in seminars at Johns Hopkins University, New York University, the Census Bureau and SOLE 2016. We also thank Andrew Gray for excellent research assistance. The usual caveats apply. Abstract: Recent advances have led to the discovery of specific genetic variants that predict educational attainment. We study how these variants, summarized as a genetic score variable, are associated with human capital accumulation and labor market outcomes in the Health and Retirement Study (HRS). We demonstrate that the same genetic score that predicts education is also associated with higher wages, but only among individuals with a college education. Moreover, the genetic gradient in wages has grown in more recent birth cohorts, consistent with interactions between technological change and labor market ability. We also show that individuals who grew up in economically disadvantaged households are less likely to go to college when compared to individuals with the same genetic score, but from higher socioeconomic status households. Our findings provide support for the idea that childhood socioeconomic status is an important moderator of the economic returns to genetic endowments. Moreover, the finding that childhood poverty limits the educational attainment of high-ability individuals suggests the ex...

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confidence: 76%

Genes, Education, and Labor Market Outcomes: Evidence from the Health and Retirement Study

Papageorge

Thom

2017

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“…GRS derived from PGC results have been widely used in psychiatric research for generating patient strata, exploring diagnostic boundaries, identifying cognitive and behavioral correlates of genetic risk that predate clinical disorders, and evaluating the validity of putative cognitive or imaging phenotypes (47). Many social scientists have embraced the approach, seeing opportunities to study how genetic factors interact with the social environment (e.g., socio-economic status) to influence health and broader outcomes (48). …”

Section: Pgc An Agendamentioning

confidence: 99%

Psychiatric Genomics: An Update and an Agenda

Sullivan

Agrawal

Bulik

et al. 2018

AJP

594

383

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The Psychiatric Genomics Consortium (PGC) is the largest consortium in the history of psychiatry. In the past decade, this global effort has delivered an increasing flow of new knowledge about the fundamental basis of common psychiatric disorders, particularly given its dedication to rapid progress and open science. The PGC has recently commenced a program of research designed to deliver “actionable” findings – genomic results that (a) reveal the fundamental biology, (b) inform clinical practice, and (c) deliver new therapeutic targets. This is the central idea of the PGC: to convert the family history risk factor into biologically, clinically, and therapeutically meaningful insights. The emerging findings suggest that we are entering into a phase of accelerated genetic discovery for multiple psychiatric disorders. These findings are likely to elucidate the genetic portions of these truly complex traits, and this knowledge can then be mined for its relevance for improved therapeutics and its impact on psychiatric practice within a precision medicine framework.

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“…1000 Genomes phase 3), hence this issue is less important. Moreover, simulations found that the effect of LD decay on true causal variants was null to negligible [24]. The present study, by focusing only on the most significant hits, increased the likelihood of hitting on or very close to causal variants, hence reducing the artifact of LD decay.…”

Section: Discussionmentioning

confidence: 65%

“…It is sensitive to coverage and in older studies using low coverage genomic data (e.g. 1000 Genomes phase 1), it was found to reduce the reproducibility of findings [24]. However, contemporary GWAS use higher coverage data (e.g.…”

Section: Discussionmentioning

confidence: 99%

Evidence for Recent Polygenic Selection on Educational Attainment and Intelligence Inferred from GWAS Hits: A Replication of Previous Findings Using Recent Data

Piffer¹

2017

Preprint

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Abstract:Background: The genetic variants identified by three large genome-wide association studies (GWAS) of educational attainment and the largest intelligence GWAS were used to test a polygenic selection model. Methods: Average frequencies of alleles with positive effect (polygenic scores or PS) were compared across populations (N=26) using data from 1000 Genomes. Factor analysis was used to extract a signal of polygenic selection. Results: A polygenic selection factor of educational attainment GWAS hits is high among a handful of SNPs within genomic regions replicated across GWAS publications and it is highly correlated to the genetic intelligence factor (r= 0.96). These factors are both highly predictive of average population IQ (r=0.9), and are robust to tests of spatial autocorrelation. Several Monte Carlo simulations yielded highly significant p values. Furthermore, the polygenic selection model shows high replicability, with the EA and intelligence factor scores being virtually identical to those from an older study (r=0.96-0.99). A larger sample of populations (N=53) produced similar results. Conclusion: This study shows robust results after accounting for spatial autocorrelation and Monte Carlo simulation using random SNPs and shows robust reproducibility of results from a previous study.

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Polygenic Influence on Educational Attainment

Cited by 151 publications

References 58 publications

Genes, Education, and Labor Market Outcomes: Evidence from the Health and Retirement Study

Genes, Education, and Labor Market Outcomes: Evidence from the Health and Retirement Study

Psychiatric Genomics: An Update and an Agenda

Evidence for Recent Polygenic Selection on Educational Attainment and Intelligence Inferred from GWAS Hits: A Replication of Previous Findings Using Recent Data

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