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Houweling TAJ, Grünberger I. Intergenerational transmission of health inequalities: towards a life course approach to socioeconomic inequalities in health - a review. J Epidemiol Community Health 2024:jech-2022-220162. [PMID: 38955463 DOI: 10.1136/jech-2022-220162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 04/19/2024] [Indexed: 07/04/2024]
Abstract
Adult health inequalities are a persistent public health problem. Explanations are usually sought in behaviours and environments in adulthood, despite evidence on the importance of early life conditions for life course outcomes. We review evidence from a broad range of fields to unravel to what extent, and how, socioeconomic health inequalities are intergenerationally transmitted.We find that transmission of socioeconomic and associated health (dis)advantages from parents to offspring, and its underlying structural determinants, contributes substantially to socioeconomic inequalities in adult health. In the first two decades of life-from conception to early adulthood-parental socioeconomic position (SEP) and parental health strongly influence offspring adult SEP and health. Socioeconomic and health (dis)advantages are largely transmitted through the same broad mechanisms. Socioeconomic inequalities in the fetal environment contribute to inequalities in fetal development and birth outcomes, with lifelong socioeconomic and health consequences. Inequalities in the postnatal environment-especially the psychosocial and learning environment, physical exposures and socialisation-result in inequalities in child and adolescent health, development and behavioural habits, with health and socioeconomic consequences tracking into adulthood. Structural factors shape these mechanisms in a socioeconomically patterned and time-specific and place-specific way, leading to distinct birth-cohort patterns in health inequality.Adult health inequalities are for an important part intergenerationally transmitted. Effective health inequality reduction requires addressing intergenerational transmission of (dis)advantage by creating societal circumstances that allow all children to develop to their full potential.
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Affiliation(s)
- Tanja A J Houweling
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Ilona Grünberger
- Department of Public Health Sciences, Stockholm University, Stockholm, Sweden
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2
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Boardman JD, Harris KM, Finch BK. Pathways between a polygenic index for education and years of completed schooling: the presentation of self and assessment of others. BIODEMOGRAPHY AND SOCIAL BIOLOGY 2024; 69:102-109. [PMID: 38828740 PMCID: PMC11208116 DOI: 10.1080/19485565.2024.2355891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2024]
Abstract
Polygenic scores (PGS) are broadly misconstrued as reflecting direct causal genetic effects on their respective phenotypes. While this assumption might be accurate for some anthropometric traits like height, more complex traits such as educational attainment show very large indirect effects that stem from many sources. One unexplored source of confounding is the possibility of evocative gene-environment correlation (rGE). Using data from the National Longitudinal Study of Adolescent to Adult Health, we examine the relationship between interviewer assessments of respondent appearance as a function of education PGS. We show a bivariate association between educational PGS and 1) perceived grooming, 2) physical attractiveness, and 3) personality. We then regress years of education on the educational PGS and show that very little of the association (~1-2%) is mediated by attractiveness or personality but 7.5% of the baseline association is confounded with how others may perceive grooming. These results highlight the importance of social-behavioral mechanisms that may link specific genotypes to successful transitions through high school and college and continue to bridge research from the social and biological sciences.
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Affiliation(s)
- Jason D Boardman
- Institute of Behavioral Science, University of Colorado, Boulder, CO, USA
- Department of Sociology, University of Colorado, Boulder, CO, USA
| | - Kathleen Mullan Harris
- Carolina Population Center, University of North Carolina, Chapel Hill, NC, USA
- Department of Sociology, University of North Carolina, Chapel Hill, NC, USA
| | - Brian Karl Finch
- Center for Economic and Social Research, University of Southern California, Los Angeles, CA, USA
- Department of Sociology & Spatial Sciences, University of Southern California, Los Angeles, CA, USA
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3
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Mersha TB. From Mendel to multi-omics: shifting paradigms. Eur J Hum Genet 2024; 32:139-142. [PMID: 37468578 PMCID: PMC10853174 DOI: 10.1038/s41431-023-01420-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Revised: 05/24/2023] [Accepted: 06/22/2023] [Indexed: 07/21/2023] Open
Affiliation(s)
- Tesfaye B Mersha
- Cincinnati Children's Hospital Medical Center, Department of Pediatrics, College of Medicine, University of Cincinnati, Cincinnati, OH, USA.
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4
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Pettit RW, Byun J, Han Y, Ostrom QT, Coarfa C, Bondy ML, Amos CI. Heritable Traits and Lung Cancer Risk: A Two-Sample Mendelian Randomization Study. Cancer Epidemiol Biomarkers Prev 2023; 32:1421-1435. [PMID: 37530747 PMCID: PMC10651112 DOI: 10.1158/1055-9965.epi-22-0698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 02/14/2023] [Accepted: 07/24/2023] [Indexed: 08/03/2023] Open
Abstract
INTRODUCTION Lung cancer is a complex polygenic disorder. Analysis with Mendelian randomization (MR) allows for genetically predicted risks to be estimated between exposures and outcomes. METHODS We analyzed 345 heritable traits from the United Kingdom Biobank and estimated their associated effects on lung cancer outcomes using two sample MR. In addition to estimating effects with overall lung cancer, adenocarcinoma, small cell lung cancer, and squamous cell lung cancers, we performed conditional effect modeling with multivariate MR (MVMR) and the traits of alcohol use, smoking initiation, average pre-tax income, and educational attainment. RESULTS Univariate MR provided evidence for increased age at first sexual intercourse (OR, 0.55; P = 6.15 × 10-13), educational attainment (OR, 0.24; P = 1.07 × 10-19), average household income (OR, 0.58; P = 7.85 × 10-05), and alcohol usually taken with meals (OR, 0.19; P = 1.06 × 10-06) associating with decreased odds of overall lung cancer development. In contrast, a lack of additional educational attainment (OR, 8.00; P = 3.48 × 10-12), body mass index (OR, 1.28; P = 9.00 × 10-08), pack years smoking as a proportion of life span (OR, 9.93; P = 7.96 × 10-12), and weekly beer intake (OR, 3.48; P = 4.08 × 10-07) were associated with an increased risk of overall lung cancer development. CONCLUSIONS Many heritable traits associated with an increased or inverse risk of lung cancer development. Effects vary based on histologic subtype and conditional third trait exposures. IMPACT We identified several heritable traits and presented their genetically predictable impact on lung cancer development, providing valuable insights for consideration.
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Affiliation(s)
- Rowland W Pettit
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, Texas
| | - Jinyoung Byun
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, Texas
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, Texas
| | - Younghun Han
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, Texas
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, Texas
| | - Quinn T Ostrom
- Duke Cancer Institute, Duke University Medical Center, Durham, North Carolina
| | - Cristian Coarfa
- Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas
| | - Melissa L Bondy
- Department of Epidemiology and Population Health, School of Medicine, Stanford University, Stanford, California
| | - Christopher I Amos
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, Texas
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, Texas
- Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas
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van Kippersluis H, Biroli P, Dias Pereira R, Galama TJ, von Hinke S, Meddens SFW, Muslimova D, Slob EAW, de Vlaming R, Rietveld CA. Overcoming attenuation bias in regressions using polygenic indices. Nat Commun 2023; 14:4473. [PMID: 37491308 PMCID: PMC10368647 DOI: 10.1038/s41467-023-40069-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 07/11/2023] [Indexed: 07/27/2023] Open
Abstract
Measurement error in polygenic indices (PGIs) attenuates the estimation of their effects in regression models. We analyze and compare two approaches addressing this attenuation bias: Obviously Related Instrumental Variables (ORIV) and the PGI Repository Correction (PGI-RC). Through simulations, we show that the PGI-RC performs slightly better than ORIV, unless the prediction sample is very small (N < 1000) or when there is considerable assortative mating. Within families, ORIV is the best choice since the PGI-RC correction factor is generally not available. We verify the empirical validity of the simulations by predicting educational attainment and height in a sample of siblings from the UK Biobank. We show that applying ORIV between families increases the standardized effect of the PGI by 12% (height) and by 22% (educational attainment) compared to a meta-analysis-based PGI, yet estimates remain slightly below the PGI-RC estimates. Furthermore, within-family ORIV regression provides the tightest lower bound for the direct genetic effect, increasing the lower bound for the standardized direct genetic effect on educational attainment from 0.14 to 0.18 (+29%), and for height from 0.54 to 0.61 (+13%) compared to a meta-analysis-based PGI.
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Affiliation(s)
- Hans van Kippersluis
- Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, The Netherlands.
- Tinbergen Institute, Amsterdam, The Netherlands.
| | - Pietro Biroli
- Department of Economics, University of Bologna, Bologna, Italy
| | - Rita Dias Pereira
- Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, The Netherlands
- Tinbergen Institute, Amsterdam, The Netherlands
| | - Titus J Galama
- Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, The Netherlands
- Tinbergen Institute, Amsterdam, The Netherlands
- School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Center for Social and Economic Research, University of Southern California, Los Angeles, CA, USA
| | - Stephanie von Hinke
- Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, The Netherlands
- Tinbergen Institute, Amsterdam, The Netherlands
- School of Economics, University of Bristol, Bristol, UK
| | - S Fleur W Meddens
- Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, The Netherlands
- Statistics Netherlands, The Hague, The Netherlands
| | - Dilnoza Muslimova
- Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, The Netherlands
- Tinbergen Institute, Amsterdam, The Netherlands
| | - Eric A W Slob
- Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, The Netherlands
- Medical Research Council Biostatistics Unit, Cambridge University, Cambridge, UK
- Erasmus University Rotterdam Institute for Behavior and Biology, Rotterdam, The Netherlands
| | - Ronald de Vlaming
- Tinbergen Institute, Amsterdam, The Netherlands
- School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Cornelius A Rietveld
- Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, The Netherlands
- Tinbergen Institute, Amsterdam, The Netherlands
- Erasmus University Rotterdam Institute for Behavior and Biology, Rotterdam, The Netherlands
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6
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Hagenbeek FA, Hirzinger JS, Breunig S, Bruins S, Kuznetsov DV, Schut K, Odintsova VV, Boomsma DI. Maximizing the value of twin studies in health and behaviour. Nat Hum Behav 2023:10.1038/s41562-023-01609-6. [PMID: 37188734 DOI: 10.1038/s41562-023-01609-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 04/19/2023] [Indexed: 05/17/2023]
Abstract
In the classical twin design, researchers compare trait resemblance in cohorts of identical and non-identical twins to understand how genetic and environmental factors correlate with resemblance in behaviour and other phenotypes. The twin design is also a valuable tool for studying causality, intergenerational transmission, and gene-environment correlation and interaction. Here we review recent developments in twin studies, recent results from twin studies of new phenotypes and recent insights into twinning. We ask whether the results of existing twin studies are representative of the general population and of global diversity, and we conclude that stronger efforts to increase representativeness are needed. We provide an updated overview of twin concordance and discordance for major diseases and mental disorders, which conveys a crucial message: genetic influences are not as deterministic as many believe. This has important implications for public understanding of genetic risk prediction tools, as the accuracy of genetic predictions can never exceed identical twin concordance rates.
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Affiliation(s)
- Fiona A Hagenbeek
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
- Amsterdam Public Health Research Institute, Amsterdam, the Netherlands.
| | - Jana S Hirzinger
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Sophie Breunig
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Department of Psychology & Neuroscience, University of Colorado Boulder, Boulder, CO, USA
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA
| | - Susanne Bruins
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
| | - Dmitry V Kuznetsov
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Faculty of Sociology, Bielefeld University, Bielefeld, Germany
| | - Kirsten Schut
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Nightingale Health Plc, Helsinki, Finland
| | - Veronika V Odintsova
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Reproduction & Development (AR&D) Research Institute, Amsterdam, the Netherlands
- Department of Psychiatry, University Medical Center of Groningen, University of Groningen, Groningen, the Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
- Amsterdam Public Health Research Institute, Amsterdam, the Netherlands.
- Amsterdam Reproduction & Development (AR&D) Research Institute, Amsterdam, the Netherlands.
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7
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Liu S, Ludvigsson JF, Lichtenstein P, Gudbjörnsdottir S, Taylor MJ, Larsson H, Kuja-Halkola R, Butwicka A. Educational Outcomes in Children and Adolescents With Type 1 Diabetes and Psychiatric Disorders. JAMA Netw Open 2023; 6:e238135. [PMID: 37052917 PMCID: PMC10102872 DOI: 10.1001/jamanetworkopen.2023.8135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/14/2023] Open
Abstract
Importance Research shows that children and adolescents with type 1 diabetes (T1D), compared with their peers without diabetes, have a greater risk of psychiatric disorders. However, no study has comprehensively examined whether having psychiatric disorders is associated with educational outcomes in children and adolescents with T1D. Objective To investigate educational outcomes in children and adolescents with T1D with and without psychiatric disorders. Design, Setting, and Participants This cohort study used data from multiple Swedish registers. The main study cohort included individuals born in Sweden between January 1, 1973, and December 31, 1997, who were followed up from birth through December 31, 2013. Data analyses were conducted from March 1 to June 30, 2022. Exposures Type 1 diabetes and psychiatric disorders (including neurodevelopmental disorders, depression, anxiety disorders, eating disorders, bipolar disorder, psychotic disorder, and substance misuse) diagnosed before 16 years of age. Main Outcomes and Measures Achieving educational milestones (completing compulsory school [primary and lower secondary education], being eligible to and finishing upper secondary school, and starting and finishing university) and compulsory school performances. Results Of 2 454 862 individuals (51.3% male), 13 294 (0.5%; 53.9% male) were diagnosed with T1D (median [IQR] age at diagnosis, 9.5 [6.0-12.5] years), among whom 1012 (7.6%) also had at least 1 psychiatric disorder. Compared with healthy individuals (without T1D and psychiatric disorders), individuals with T1D alone had slightly lower odds of achieving the examined educational milestones. However, those with both T1D and any psychiatric disorder had much lower odds of achieving milestones, including completing compulsory school (odds ratio [OR], 0.17; 95% CI, 0.13-0.21), being eligible for (OR, 0.25; 95% CI, 0.21-0.30) and finishing (OR, 0.19; 95% CI, 0.14-0.26) upper secondary school, and starting (OR, 0.36; 95% CI, 0.29-0.46) and finishing (OR, 0.30; 95% CI, 0.20-0.47) university. They also showed lower grade point averages for compulsory school subjects. These findings remained similar in sibling comparison analyses, suggesting independence from familial confounding. Conclusions and Relevance In this cohort study of Swedish-born children and adolescents, those with T1D alone had minor difficulties with their educational outcomes, whereas those with both T1D and psychiatric disorders had universal long-term educational underachievement. These findings highlight the importance of identifying psychiatric disorders in pediatric patients with T1D and the need for targeted educational intervention and support to minimize the education gap between the affected children and their peers.
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Affiliation(s)
- Shengxin Liu
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden
| | - Jonas F Ludvigsson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden
- Department of Paediatrics, Örebro University Hospital, Örebro, Sweden
- Department of Medicine, Columbia University College of Physicians and Surgeons, New York, New York
| | - Paul Lichtenstein
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden
| | - Soffia Gudbjörnsdottir
- Swedish National Diabetes Register, Centre of Registers, Gothenburg, Sweden
- Department of Molecular and Clinical Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Mark J Taylor
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden
| | - Henrik Larsson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden
- School of Medical Sciences, Örebro University, Örebro, Sweden
| | - Ralf Kuja-Halkola
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden
| | - Agnieszka Butwicka
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden
- Child and Adolescent Psychiatry Stockholm, Stockholm Health Care Services, Region Stockholm, Sweden
- Department of Child Psychiatry, Medical University of Warsaw, Warsaw, Poland
- Department of Biostatistics and Translational Medicine, Medical University of Lodz, Lodz, Poland
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8
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Liu S, Larsson H, Lichtenstein P, Ludvigsson JF, Gudbjörnsdottir S, Serlachius E, Kuja-Halkola R, Butwicka A. Childhood-onset type 1 diabetes and attention-deficit/hyperactivity disorder with educational attainment: a population-based sibling-comparison study. Acta Paediatr 2022; 111:2131-2141. [PMID: 35897120 DOI: 10.1111/apa.16500] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 07/21/2022] [Accepted: 07/26/2022] [Indexed: 11/30/2022]
Abstract
AIM To examine the association of childhood-onset type 1 diabetes (T1D) and attention-deficit/hyperactivity disorder (ADHD) with educational outcomes from compulsory school to university. METHODS Using multiple Swedish nationwide registers, we followed up on 1,474,941 individuals born in Sweden from 1981-1995 to December 31, 2013. Associations of T1D and ADHD with achieving educational milestones (from compulsory school to university) and school performances were estimated using logistic and linear regression models and sibling comparison models. RESULTS Compared to their peers, children with both T1D and ADHD were less likely to achieve any of the educational attainments, including completing compulsory school (adjusted OR [aOR] [95% CI]: 0.43[0.26,0.72]), be eligible to and finishing upper secondary school (0.26[0.19,0.36], 0.24[0.17,0.35], respectively), and starting university (0.38[0.17,0.90]). The odds of achieving these educational milestones were substantially lower in children with ADHD alone (aORs: 0.14-0.44), but were slightly worse or no differences in children with T1D alone (aORs: 0.86-1.08). All associations above remained similar in the sibling comparison models. CONCLUSION Children and adolescents with both T1D and ADHD had long-term educational underachievement, with ADHD being the major contributor. Our findings suggest the importance of assessing ADHD in children with T1D and targeted support for minimizing the education gap between the affected children and their peers.
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Affiliation(s)
- Shengxin Liu
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden
| | - Henrik Larsson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden.,School of Medical Sciences, Örebro University, Örebro, Sweden
| | - Paul Lichtenstein
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden
| | - Jonas F Ludvigsson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden.,Department of Pediatrics, Örebro University Hospital, Örebro, Sweden.,Division of Epidemiology and Public Health, School of Medicine, University of Nottingham, Nottingham, UK.,Department of Medicine, Columbia University College of Physicians and Surgeons, New York, USA
| | - Soffia Gudbjörnsdottir
- Swedish National Diabetes Register, Centre of Registers, Gothenburg, Sweden.,Department of Molecular and Clinical Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Eva Serlachius
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Service, Sweden
| | - Ralf Kuja-Halkola
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden
| | - Agnieszka Butwicka
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden.,Child and Adolescent Psychiatry, Stockholm Health Care Service, Sweden.,Department of Child Psychiatry, Medical University of Warsaw, Warsaw, Poland.,Department of Biostatistics and Translational Medicine, Medical University of Lodz, Poland
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9
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Burt CH. Challenging the utility of polygenic scores for social science: Environmental confounding, downward causation, and unknown biology. Behav Brain Sci 2022; 46:e207. [PMID: 35551690 PMCID: PMC9653522 DOI: 10.1017/s0140525x22001145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
The sociogenomics revolution is upon us, we are told. Whether revolutionary or not, sociogenomics is poised to flourish given the ease of incorporating polygenic scores (or PGSs) as "genetic propensities" for complex traits into social science research. Pointing to evidence of ubiquitous heritability and the accessibility of genetic data, scholars have argued that social scientists not only have an opportunity but a duty to add PGSs to social science research. Social science research that ignores genetics is, some proponents argue, at best partial and likely scientifically flawed, misleading, and wasteful. Here, I challenge arguments about the value of genetics for social science and with it the claimed necessity of incorporating PGSs into social science models as measures of genetic influences. In so doing, I discuss the impracticability of distinguishing genetic influences from environmental influences because of non-causal gene-environment correlations, especially population stratification, familial confounding, and downward causation. I explain how environmental effects masquerade as genetic influences in PGSs, which undermines their raison d'être as measures of genetic propensity, especially for complex socially contingent behaviors that are the subject of sociogenomics. Additionally, I draw attention to the partial, unknown biology, while highlighting the persistence of an implicit, unavoidable reductionist genes versus environments approach. Leaving sociopolitical and ethical concerns aside, I argue that the potential scientific rewards of adding PGSs to social science are few and greatly overstated and the scientific costs, which include obscuring structural disadvantages and cultural influences, outweigh these meager benefits for most social science applications.
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Affiliation(s)
- Callie H Burt
- Department of Criminal Justice & Criminology, Center for Research on Interpersonal Violence (CRIV), Georgia State University, Atlanta, GA, USA ; www.callieburt.org
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10
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Herd P, Sicinski K. Using sibling models to unpack the relationship between education and cognitive functioning in later life. SSM Popul Health 2022; 17:100960. [PMID: 34984219 PMCID: PMC8693027 DOI: 10.1016/j.ssmph.2021.100960] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 11/05/2021] [Accepted: 11/06/2021] [Indexed: 01/11/2023] Open
Abstract
As the population ages and the prevalence of dementia increases, unpacking robust and persistent associations between educational attainment and later life cognitive functioning is increasingly important. We do know, from studies with robust causal designs, that policies that increase years of schooling improve later life cognitive functioning. Yet these studies don't illuminate why older adults with greater educational attainment have relatively preserved cognitive functioning. Studies focused on why, however, have been hampered by methodological limitations and inattention to some key explanations for this relationship. Consequently, we test explanations encompassing antecedent factors, specifically family environments, adolescent IQ, and genetic factors, as well as adult mediating mechanisms, specifically health behaviors and health. We employ the Wisconsin Longitudinal Study, which includes 80 years of prospectively collected data on a sample of 1 in every 3 high school graduates, and a selected sibling, from the class of 1957. Sibling models, and the inclusion of prospectively collected early and midlife covariates, allows us to address the explanatory and methodological limitations of the prior literature to better unpack the relationship between education and later life cognitive functioning. We find little evidence that early life genetic endowments and environments, or midlife health and health behaviors, explain the relationship. Adolescent cognition, however, does matter; higher educational attainment, linked to antecedent adolescent cognitive functioning, helps protect against lower levels of cognitive functioning in later life. Both adolescent cognition and education, however, independently associate with later life cognitive functioning at relatively similar magnitudes. Educational attainment's relationship to later life cognitive functioning is not simply a function of adolescent cognitive functioning.
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Affiliation(s)
- Pamela Herd
- Georgetown University, McCourt School of Public Policy, 37 and O Streets, NW. Old North, Suite 100, Washington, DC, 20057, USA
| | - Kamil Sicinski
- Wisconsin Longitudinal Study, University of Wisconsin-Madison, USA
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11
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Ujma PP, Eszlári N, Millinghoffer A, Bruncsics B, Török D, Petschner P, Antal P, Deakin B, Breen G, Bagdy G, Juhász G. Genetic effects on educational attainment in Hungary. Brain Behav 2022; 12:e2430. [PMID: 34843176 PMCID: PMC8785634 DOI: 10.1002/brb3.2430] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Revised: 06/08/2021] [Accepted: 10/25/2021] [Indexed: 01/12/2023] Open
Abstract
INTRODUCTION Educational attainment is a substantially heritable trait, and it has recently been linked to specific genetic variants by genome-wide association studies (GWASs). However, the effects of such genetic variants are expected to vary across environments, including countries and historical eras. METHODS We used polygenic scores (PGSs) to assess molecular genetic effects on educational attainment in Hungary, a country in the Central Eastern European region where behavioral genetic studies are in general scarce and molecular genetic studies of educational attainment have not been previously published. RESULTS We found that the PGS is significantly associated with the attainment of a college degree as well as the number of years in education in a sample of Hungarian study participants (N = 829). PGS effect sizes were not significantly different when compared to an English (N = 976) comparison sample with identical measurement protocols. In line with previous Estonian findings, we found higher PGS effect sizes in Hungarian, but not in English participants who attended higher education after the fall of Communism, although we lacked statistical power for this effect to reach significance. DISCUSSION Our results provide evidence that polygenic scores for educational attainment have predictive value in culturally diverse European populations.
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Affiliation(s)
- Péter P Ujma
- Institute of Behavioural Sciences, Semmelweis University, Budapest, Hungary.,National Institute of Clinical Neuroscience, Budapest, Hungary
| | - Nóra Eszlári
- Department of Pharmacodynamics, Faculty of Pharmacy, Semmelweis University, Budapest, Hungary.,NAP-2-SE New Antidepressant Target Research Group, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary
| | - András Millinghoffer
- NAP-2-SE New Antidepressant Target Research Group, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary.,Department of Measurement and Information Systems, Budapest University of Technology and Economics, Budapest, Hungary
| | - Bence Bruncsics
- Department of Measurement and Information Systems, Budapest University of Technology and Economics, Budapest, Hungary
| | - Dóra Török
- Department of Pharmacodynamics, Faculty of Pharmacy, Semmelweis University, Budapest, Hungary
| | - Péter Petschner
- Department of Pharmacodynamics, Faculty of Pharmacy, Semmelweis University, Budapest, Hungary.,MTA-SE Neuropsychopharmacology and Neurochemistry Research Group, Hungarian Academy of Sciences, Semmelweis University, Budapest, Hungary
| | - Péter Antal
- Department of Measurement and Information Systems, Budapest University of Technology and Economics, Budapest, Hungary
| | - Bill Deakin
- Division of Neuroscience and Experimental Psychology, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.,Manchester Academic Health Sciences Centre, Manchester, UK.,Greater Manchester Mental Health NHS Foundation Trust, Manchester, UK
| | - Gerome Breen
- Social, Genetic and Developmental Psychiatry Centre, King's College London, London, UK
| | - György Bagdy
- Department of Pharmacodynamics, Faculty of Pharmacy, Semmelweis University, Budapest, Hungary.,NAP-2-SE New Antidepressant Target Research Group, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary.,MTA-SE Neuropsychopharmacology and Neurochemistry Research Group, Hungarian Academy of Sciences, Semmelweis University, Budapest, Hungary
| | - Gabriella Juhász
- Department of Pharmacodynamics, Faculty of Pharmacy, Semmelweis University, Budapest, Hungary.,SE-NAP 2 Genetic Brain Imaging Migraine Research Group, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary
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12
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Burger K, Mortimer JT. Socioeconomic origin, future expectations, and educational achievement: A longitudinal three-generation study of the persistence of family advantage. Dev Psychol 2021; 57:1540-1558. [PMID: 34929097 DOI: 10.1037/dev0001238] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Expectations about the future direct effort in goal-oriented action and may influence a range of life course outcomes, including educational attainment. Here we investigate whether such expectations are implicated in the dynamics underlying the persistence of educational advantage across family generations, and whether such dynamics have changed in recent decades in view of historical change. Focusing on the role of domain-specific (educational) and general (optimism and control) expectations, we examine parallels across parent-child cohorts in (a) the relationships between parental socioeconomic status (SES) and children's future expectations and (b) the associations between children's future expectations and their academic achievement. We estimate structural equation models using data from the prospective multigenerational Youth Development Study (N = 422 three-generation triads [G1-G2-G3]; G1 Mage in 1988 = 41.0 years, G2 Mage in 1989 = 14.7 years, G3 Mage in 2011 = 15.8 years; G2 White in 1989 = 66.4%, G3 White in 2011 = 64.4%; G1 mean annual household income, converted to 2008 equivalents = $41,687, G2 mean annual household income in 2008 dollars = $42,962; G1 mode of educational attainment = high school, G2 mode of educational attainment = some college). We find intergenerational similarity in the relationships between parental educational attainment and children's future expectations. Children's educational expectations strongly predicted their academic achievement in the second generation, but not in the third generation. With educational expansion, the more recent cohort had higher educational expectations that were less strongly related to achievement. Overall, the findings reveal dynamics underlying the persistence of educational success across generations. The role of future expectations in this intergenerational process varies across historical time, confirming a central conclusion of life span developmental psychology and life course sociological research that individual functioning is influenced by sociocultural contexts. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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Affiliation(s)
- Kaspar Burger
- Jacobs Center for Productive Youth Development, University of Zurich
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13
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Becker J, Burik CAP, Goldman G, Wang N, Jayashankar H, Bennett M, Belsky DW, Karlsson Linnér R, Ahlskog R, Kleinman A, Hinds DA, Caspi A, Corcoran DL, Moffitt TE, Poulton R, Sugden K, Williams BS, Harris KM, Steptoe A, Ajnakina O, Milani L, Esko T, Iacono WG, McGue M, Magnusson PKE, Mallard TT, Harden KP, Tucker-Drob EM, Herd P, Freese J, Young A, Beauchamp JP, Koellinger PD, Oskarsson S, Johannesson M, Visscher PM, Meyer MN, Laibson D, Cesarini D, Benjamin DJ, Turley P, Okbay A. Resource profile and user guide of the Polygenic Index Repository. Nat Hum Behav 2021; 5:1744-1758. [PMID: 34140656 PMCID: PMC8678380 DOI: 10.1038/s41562-021-01119-3] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Accepted: 04/16/2021] [Indexed: 02/05/2023]
Abstract
Polygenic indexes (PGIs) are DNA-based predictors. Their value for research in many scientific disciplines is growing rapidly. As a resource for researchers, we used a consistent methodology to construct PGIs for 47 phenotypes in 11 datasets. To maximize the PGIs' prediction accuracies, we constructed them using genome-wide association studies-some not previously published-from multiple data sources, including 23andMe and UK Biobank. We present a theoretical framework to help interpret analyses involving PGIs. A key insight is that a PGI can be understood as an unbiased but noisy measure of a latent variable we call the 'additive SNP factor'. Regressions in which the true regressor is this factor but the PGI is used as its proxy therefore suffer from errors-in-variables bias. We derive an estimator that corrects for the bias, illustrate the correction, and make a Python tool for implementing it publicly available.
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Affiliation(s)
- Joel Becker
- Department of Economics, New York University, New York, NY, USA
| | - Casper A P Burik
- Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Grant Goldman
- National Bureau of Economic Research, Cambridge, MA, USA
| | - Nancy Wang
- National Bureau of Economic Research, Cambridge, MA, USA
| | | | | | - Daniel W Belsky
- Department of Epidemiology, Columbia University Mailman School of Public Health, New York, NY, USA
- Robert N. Butler Columbia Aging Center, Columbia University, New York, NY, USA
| | - Richard Karlsson Linnér
- Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Rafael Ahlskog
- Department of Government, Uppsala University, Uppsala, Sweden
| | | | | | - Avshalom Caspi
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA
- Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- Center for Genomic and Computational Biology, Duke University, Durham, NC, USA
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
| | - David L Corcoran
- Center for Genomic and Computational Biology, Duke University, Durham, NC, USA
| | - Terrie E Moffitt
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA
- Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- Center for Genomic and Computational Biology, Duke University, Durham, NC, USA
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
| | - Richie Poulton
- Dunedin Multidisciplinary Health and Development Research Unit, University of Otago, Dunedin, New Zealand
| | - Karen Sugden
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA
| | | | - Kathleen Mullan Harris
- Department of Sociology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Andrew Steptoe
- Department of Behavioural Science and Health, University College London, London, UK
| | - Olesya Ajnakina
- Department of Behavioural Science and Health, University College London, London, UK
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | - Lili Milani
- Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Tõnu Esko
- Institute of Genomics, University of Tartu, Tartu, Estonia
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - William G Iacono
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Matt McGue
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Patrik K E Magnusson
- Swedish Twin Registry, Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Travis T Mallard
- Department of Psychology, The University of Texas at Austin, Austin, TX, USA
| | - K Paige Harden
- Department of Psychology, The University of Texas at Austin, Austin, TX, USA
- Population Research Center, The University of Texas at Austin, Austin, TX, USA
| | - Elliot M Tucker-Drob
- Department of Psychology, The University of Texas at Austin, Austin, TX, USA
- Population Research Center, The University of Texas at Austin, Austin, TX, USA
| | - Pamela Herd
- McCourt School of Public Policy, Georgetown University, Washington, DC, USA
| | - Jeremy Freese
- Department of Sociology, Stanford University, Stanford, CA, USA
| | - Alexander Young
- UCLA Anderson School of Management, Los Angeles, CA, USA
- Human Genetics Department, UCLA David Geffen School of Medicine, Los Angeles, CA, USA
| | - Jonathan P Beauchamp
- Interdisciplinary Center for Economic Science and Department of Economics, George Mason University, Fairfax, VA, USA
| | - Philipp D Koellinger
- Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Robert M. La Follette School of Public Affairs, University of Wisconsin-Madison, Madison, WI, USA
| | - Sven Oskarsson
- Department of Government, Uppsala University, Uppsala, Sweden
| | - Magnus Johannesson
- Department of Economics, Stockholm School of Economics, Stockholm, Sweden
| | - Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Michelle N Meyer
- Center for Translational Bioethics and Health Care Policy, Geisinger Health System, Danville, PA, USA
| | - David Laibson
- National Bureau of Economic Research, Cambridge, MA, USA
- Department of Economics, Harvard University, Cambridge, MA, USA
| | - David Cesarini
- Department of Economics, New York University, New York, NY, USA.
- National Bureau of Economic Research, Cambridge, MA, USA.
| | - Daniel J Benjamin
- National Bureau of Economic Research, Cambridge, MA, USA.
- UCLA Anderson School of Management, Los Angeles, CA, USA.
- Human Genetics Department, UCLA David Geffen School of Medicine, Los Angeles, CA, USA.
| | - Patrick Turley
- Center for Economic and Social Research, University of Southern California, Los Angeles, CA, USA.
- Department of Economics, University of Southern California, Los Angeles, CA, USA.
| | - Aysu Okbay
- Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
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14
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Sabatello M, Martin B, Corbeil T, Lee S, Link BG, Appelbaum PS. Nature vs. Nurture in Precision Education: Insights of Parents and the Public. AJOB Empir Bioeth 2021; 13:79-88. [PMID: 34644234 DOI: 10.1080/23294515.2021.1983666] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
BACKGROUND The philosophical debate about the roles of nature versus nurture in human flourishing is not new. But the rise of precision education-a growing field of research that encourages the use of genetic data to inform educational trajectory and interventions to better meet student needs-has renewed historical and ethical concerns. A major worry is that "genetic hype" may skew public perceptions toward a deterministic perception of the child's educational trajectory, regardless of the child's capacities, and underestimation of environmental factors affecting educational outcomes. We tested this hypothesis with parents and adults from the general public in the US. METHODS A newly developed computerized implicit association test (IAT) to assess automatic associations between genetics or environments and student behaviors that are associated with educational achievement was administered to samples of parents of children below 21 years old (n = 450) and adults from the general public (n = 419). The samples were representative of the adult US population and adjusted to oversample Black/African American participants. An overall D score for participants' IATs (range: [-2, 2]) was calculated on the basis of the speed of participants' responses. RESULTS The mean IAT score for both samples indicated stronger association between the quality of being a good student and environment rather than genetics (parents: mean=-0.146, t = -6.56, p < 0.001; general public: mean = -0.249, t = -9.45, p < 0.0001). Younger participants from the general public showed a stronger association between genetics and educational success than middle-aged participants (β = -0.301, p = 0.006). CONCLUSION The views of parents and the general public on behavioral genetics and education are complex but call for investment in creating educational environments that are supportive of student success. Future research is needed to understand differences across age groups and to explore views of other stakeholders involved in determining children's educational trajectories about the roles of nature versus nurture in precision education.
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Affiliation(s)
- Maya Sabatello
- Center for Precision Medicine and Genomics, Department of Medicine and Division of Ethics, Department of Medical Humanities and Ethics, Columbia University, New York, New York, USA
| | - Bree Martin
- Division of General Pediatrics, Boston Children's Hospital, Boston, MA, USA
| | - Thomas Corbeil
- Division of Mental Health Data Science, New York State Psychiatric Institute, New York, New York, USA
| | - Seonjoo Lee
- Department of Biostatistics and Psychiatry, Columbia University Medical Center, New York, New York, USA.,New York State Psychiatric Institute, New York, New York, USA
| | - Bruce G Link
- School of Public Policy, University of California, Riverside, California, USA
| | - Paul S Appelbaum
- Center for Research on Ethical, Legal & Social Implications of Psychiatric, Neurologic & Behavioral Genetics, Department of Psychiatry, Columbia University, New York, USA
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15
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Wang B, Baldwin JR, Schoeler T, Cheesman R, Barkhuizen W, Dudbridge F, Bann D, Morris TT, Pingault JB. Robust genetic nurture effects on education: A systematic review and meta-analysis based on 38,654 families across 8 cohorts. Am J Hum Genet 2021; 108:1780-1791. [PMID: 34416156 PMCID: PMC8456157 DOI: 10.1016/j.ajhg.2021.07.010] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Accepted: 07/21/2021] [Indexed: 12/20/2022] Open
Abstract
Similarities between parents and offspring arise from nature and nurture. Beyond this simple dichotomy, recent genomic studies have uncovered "genetic nurture" effects, whereby parental genotypes influence offspring outcomes via environmental pathways rather than genetic transmission. Such genetic nurture effects also need to be accounted for to accurately estimate "direct" genetic effects (i.e., genetic effects on a trait originating in the offspring). Empirical studies have indicated that genetic nurture effects are particularly relevant to the intergenerational transmission of risk for child educational outcomes, which are, in turn, associated with major psychological and health milestones throughout the life course. These findings have yet to be systematically appraised across contexts. We conducted a systematic review and meta-analysis to quantify genetic nurture effects on educational outcomes. A total of 12 studies comprising 38,654 distinct parent(s)-offspring pairs or trios from 8 cohorts reported 22 estimates of genetic nurture effects. Genetic nurture effects on offspring's educational outcomes (βgenetic nurture = 0.08, 95% CI [0.07, 0.09]) were smaller than direct genetic effects (βdirect genetic = 0.17, 95% CI [0.13, 0.20]). Findings were largely consistent across studies. Genetic nurture effects originating from mothers and fathers were of similar magnitude, highlighting the need for a greater inclusion of fathers in educational research. Genetic nurture effects were largely explained by observed parental education and socioeconomic status, pointing to their role in environmental pathways shaping child educational outcomes. Findings provide consistent evidence that environmentally mediated parental genetic influences contribute to the intergenerational transmission of educational outcomes, in addition to effects due to genetic transmission.
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Affiliation(s)
- Biyao Wang
- Division of Psychology and Language Sciences, University College London, London WC1H 0AP, UK
| | - Jessie R Baldwin
- Division of Psychology and Language Sciences, University College London, London WC1H 0AP, UK; Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, King's College, London SE5 8AF, UK
| | - Tabea Schoeler
- Division of Psychology and Language Sciences, University College London, London WC1H 0AP, UK
| | - Rosa Cheesman
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, King's College, London SE5 8AF, UK; PROMENTA Research Center, Department of Psychology, University of Oslo, 0373 Oslo, Norway
| | - Wikus Barkhuizen
- Division of Psychology and Language Sciences, University College London, London WC1H 0AP, UK
| | - Frank Dudbridge
- Department of Health Sciences, University of Leicester, Leicester LE1 7RH, UK
| | - David Bann
- Centre for Longitudinal Studies, Social Research Institute, University College London, London WC1H 0AL, UK
| | - Tim T Morris
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2BN, UK
| | - Jean-Baptiste Pingault
- Division of Psychology and Language Sciences, University College London, London WC1H 0AP, UK; Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, King's College, London SE5 8AF, UK.
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16
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Genetic correlates of socio-economic status influence the pattern of shared heritability across mental health traits. Nat Hum Behav 2021. [PMID: 33686200 DOI: 10.1038/s41562-021-01053-4.genetic] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
Abstract
Epidemiological studies show high comorbidity between different mental health problems, indicating that individuals with a diagnosis of one disorder are more likely to develop other mental health problems. Genetic studies reveal substantial sharing of genetic factors across mental health traits. However, mental health is also genetically correlated with socio-economic status (SES), and it is therefore important to investigate and disentangle the genetic relationship between mental health and SES. We used summary statistics from large genome-wide association studies (average N ~ 160,000) to estimate the genetic overlap across nine psychiatric disorders and seven substance use traits and explored the genetic influence of three different indicators of SES. Using genomic structural equation modelling, we show significant changes in patterns of genetic correlations after partialling out SES-associated genetic variation. Our approach allows the separation of disease-specific genetic variation and genetic variation shared with SES, thereby improving our understanding of the genetic architecture of mental health.
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17
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Marees AT, Smit DJ, Abdellaoui A, Nivard MG, van den Brink W, Denys D, Galama TJ, Verweij KJ, Derks EM. Genetic correlates of socio-economic status influence the pattern of shared heritability across mental health traits. Nat Hum Behav 2021; 5:1065-1073. [PMID: 33686200 PMCID: PMC8376746 DOI: 10.1038/s41562-021-01053-4] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Accepted: 01/13/2021] [Indexed: 01/31/2023]
Abstract
Epidemiological studies show high comorbidity between different mental health problems, indicating that individuals with a diagnosis of one disorder are more likely to develop other mental health problems. Genetic studies reveal substantial sharing of genetic factors across mental health traits. However, mental health is also genetically correlated with socio-economic status (SES), and it is therefore important to investigate and disentangle the genetic relationship between mental health and SES. We used summary statistics from large genome-wide association studies (average N ~ 160,000) to estimate the genetic overlap across nine psychiatric disorders and seven substance use traits and explored the genetic influence of three different indicators of SES. Using genomic structural equation modelling, we show significant changes in patterns of genetic correlations after partialling out SES-associated genetic variation. Our approach allows the separation of disease-specific genetic variation and genetic variation shared with SES, thereby improving our understanding of the genetic architecture of mental health.
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Affiliation(s)
- Andries T. Marees
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands,QIMR Berghofer, Translational Neurogenomics group, Brisbane, Queensland, Australia,Department of Economics, School of Business and Economics, VU University Amsterdam, Amsterdam, the Netherlands,Correspondence: Andries T. Marees () Eske M. Derks ()
| | - Dirk J.A. Smit
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Abdel Abdellaoui
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Michel G. Nivard
- Department of Biological Psychology, VU University Amsterdam, Amsterdam, The Netherlands
| | - Wim van den Brink
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Damiaan Denys
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Titus J. Galama
- Department of Economics, School of Business and Economics, VU University Amsterdam, Amsterdam, the Netherlands,University of Southern California, Dornsife Center for Economic and Social Research (CESR), Los Angeles, CA, USA,Erasmus School of Economics, Erasmus University, Rotterdam, The Netherlands
| | - Karin J.H. Verweij
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Eske M. Derks
- QIMR Berghofer, Translational Neurogenomics group, Brisbane, Queensland, Australia,Correspondence: Andries T. Marees () Eske M. Derks ()
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18
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Grieneisen L, Dasari M, Gould TJ, Björk JR, Grenier JC, Yotova V, Jansen D, Gottel N, Gordon JB, Learn NH, Gesquiere LR, Wango TL, Mututua RS, Warutere JK, Siodi L, Gilbert JA, Barreiro LB, Alberts SC, Tung J, Archie EA, Blekhman R. Gut microbiome heritability is nearly universal but environmentally contingent. Science 2021; 373:181-186. [PMID: 34244407 PMCID: PMC8377764 DOI: 10.1126/science.aba5483] [Citation(s) in RCA: 104] [Impact Index Per Article: 34.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Revised: 01/25/2021] [Accepted: 05/17/2021] [Indexed: 12/31/2022]
Abstract
Relatives have more similar gut microbiomes than nonrelatives, but the degree to which this similarity results from shared genotypes versus shared environments has been controversial. Here, we leveraged 16,234 gut microbiome profiles, collected over 14 years from 585 wild baboons, to reveal that host genetic effects on the gut microbiome are nearly universal. Controlling for diet, age, and socioecological variation, 97% of microbiome phenotypes were significantly heritable, including several reported as heritable in humans. Heritability was typically low (mean = 0.068) but was systematically greater in the dry season, with low diet diversity, and in older hosts. We show that longitudinal profiles and large sample sizes are crucial to quantifying microbiome heritability, and indicate scope for selection on microbiome characteristics as a host phenotype.
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Affiliation(s)
- Laura Grieneisen
- Department of Genetics, Cell Biology, and Development, University of Minnesota, Minneapolis, MN 55455, USA.
| | - Mauna Dasari
- Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Trevor J Gould
- Department of Genetics, Cell Biology, and Development, University of Minnesota, Minneapolis, MN 55455, USA
| | - Johannes R Björk
- Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Jean-Christophe Grenier
- Department of Genetics, CHU Sainte Justine Research Center, Montréal, Quebec H3T 1C5, Canada
- Research Center, Montreal Heart Institute, Montréal, Quebec H1T 1C8, Canada
| | - Vania Yotova
- Department of Genetics, CHU Sainte Justine Research Center, Montréal, Quebec H3T 1C5, Canada
| | - David Jansen
- Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Neil Gottel
- Marine Biology Research Division, Scripps Institution of Oceanography, University of California, San Diego, La Jolla, CA 92093
| | - Jacob B Gordon
- Department of Biology, Duke University, Durham, NC 27708, USA
| | - Niki H Learn
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA
| | | | - Tim L Wango
- Amboseli Baboon Research Project, Amboseli National Park, Kenya
- The Department of Veterinary Anatomy and Animal Physiology, University of Nairobi, Kenya
| | | | | | - Long'ida Siodi
- Amboseli Baboon Research Project, Amboseli National Park, Kenya
| | - Jack A Gilbert
- Marine Biology Research Division, Scripps Institution of Oceanography, University of California, San Diego, La Jolla, CA 92093
| | - Luis B Barreiro
- Department of Genetics, CHU Sainte Justine Research Center, Montréal, Quebec H3T 1C5, Canada
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL 60637, USA
| | - Susan C Alberts
- Department of Biology, Duke University, Durham, NC 27708, USA
- Department of Evolutionary Anthropology, Duke University, Durham, NC 27708, USA
- Duke Population Research Institute, Duke University, Durham, NC 27708, USA
| | - Jenny Tung
- Department of Biology, Duke University, Durham, NC 27708, USA.
- Department of Evolutionary Anthropology, Duke University, Durham, NC 27708, USA
- Duke Population Research Institute, Duke University, Durham, NC 27708, USA
- Canadian Institute for Advanced Research, Toronto, Ontario M5G 1M1, Canada
| | - Elizabeth A Archie
- Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA.
| | - Ran Blekhman
- Department of Genetics, Cell Biology, and Development, University of Minnesota, Minneapolis, MN 55455, USA.
- Department of Ecology, Evolution, and Behavior, University of Minnesota, Minneapolis, MN 55455, USA
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19
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Hart SA, Little C, van Bergen E. Nurture might be nature: cautionary tales and proposed solutions. NPJ SCIENCE OF LEARNING 2021; 6:2. [PMID: 33420086 PMCID: PMC7794571 DOI: 10.1038/s41539-020-00079-z] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Accepted: 11/12/2020] [Indexed: 05/27/2023]
Abstract
Across a wide range of studies, researchers often conclude that the home environment and children's outcomes are causally linked. In contrast, behavioral genetic studies show that parents influence their children by providing them with both environment and genes, meaning the environment that parents provide should not be considered in the absence of genetic influences, because that can lead to erroneous conclusions on causation. This article seeks to provide behavioral scientists with a synopsis of numerous methods to estimate the direct effect of the environment, controlling for the potential of genetic confounding. Ideally, using genetically sensitive designs can fully disentangle this genetic confound, but these require specialized samples. In the near future, researchers will likely have access to measured DNA variants (summarized in a polygenic scores), which could serve as a partial genetic control, but that is currently not an option that is ideal or widely available. We also propose a work around for when genetically sensitive data are not readily available: the Familial Control Method. In this method, one measures the same trait in the parents as the child, and the parents' trait is then used as a covariate (e.g., a genetic proxy). When these options are all not possible, we plead with our colleagues to clearly mention genetic confound as a limitation, and to be cautious with any environmental causal statements which could lead to unnecessary parent blaming.
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Affiliation(s)
- Sara A Hart
- Department of Psychology, Florida State University, Tallahassee, FL, USA.
- Florida Center for Reading Research, Florida State University, Tallahassee, FL, USA.
| | - Callie Little
- Florida Center for Reading Research, Florida State University, Tallahassee, FL, USA
- Department of Psychology, University of New England, Armidale, NSW, Australia
| | - Elsje van Bergen
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
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20
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Ritchie SJ, Hill WD, Marioni RE, Davies G, Hagenaars SP, Harris SE, Cox SR, Taylor AM, Corley J, Pattie A, Redmond P, Starr JM, Deary IJ. Polygenic predictors of age-related decline in cognitive ability. Mol Psychiatry 2020; 25:2584-2598. [PMID: 30760887 PMCID: PMC7515838 DOI: 10.1038/s41380-019-0372-x] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/24/2018] [Revised: 11/13/2018] [Accepted: 01/11/2019] [Indexed: 12/11/2022]
Abstract
Polygenic scores can be used to distil the knowledge gained in genome-wide association studies for prediction of health, lifestyle, and psychological factors in independent samples. In this preregistered study, we used fourteen polygenic scores to predict variation in cognitive ability level at age 70, and cognitive change from age 70 to age 79, in the longitudinal Lothian Birth Cohort 1936 study. The polygenic scores were created for phenotypes that have been suggested as risk or protective factors for cognitive ageing. Cognitive abilities within older age were indexed using a latent general factor estimated from thirteen varied cognitive tests taken at four waves, each three years apart (initial n = 1091 age 70; final n = 550 age 79). The general factor indexed over two-thirds of the variance in longitudinal cognitive change. We ran additional analyses using an age-11 intelligence test to index cognitive change from age 11 to age 70. Several polygenic scores were associated with the level of cognitive ability at age-70 baseline (range of standardized β-values = -0.178 to 0.302), and the polygenic score for education was associated with cognitive change from childhood to age 70 (standardized β = 0.100). No polygenic scores were statistically significantly associated with variation in cognitive change between ages 70 and 79, and effect sizes were small. However, APOE e4 status made a significant prediction of the rate of cognitive decline from age 70 to 79 (standardized β = -0.319 for carriers vs. non-carriers). The results suggest that the predictive validity for cognitive ageing of polygenic scores derived from genome-wide association study summary statistics is not yet on a par with APOE e4, a better-established predictor.
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Affiliation(s)
- Stuart J Ritchie
- Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, Edinburgh, UK.
- Department of Psychology, The University of Edinburgh, Edinburgh, UK.
- Social, Genetic and Developmental Psychiatry Centre, King's College London, London, UK.
| | - W David Hill
- Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, Edinburgh, UK
- Department of Psychology, The University of Edinburgh, Edinburgh, UK
| | - Riccardo E Marioni
- Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, Edinburgh, UK
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, The University of Edinburgh, Edinburgh, UK
| | - Gail Davies
- Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, Edinburgh, UK
- Department of Psychology, The University of Edinburgh, Edinburgh, UK
| | - Saskia P Hagenaars
- Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, Edinburgh, UK
- Social, Genetic and Developmental Psychiatry Centre, King's College London, London, UK
| | - Sarah E Harris
- Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, Edinburgh, UK
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, The University of Edinburgh, Edinburgh, UK
| | - Simon R Cox
- Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, Edinburgh, UK
- Department of Psychology, The University of Edinburgh, Edinburgh, UK
| | - Adele M Taylor
- Department of Psychology, The University of Edinburgh, Edinburgh, UK
| | - Janie Corley
- Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, Edinburgh, UK
- Department of Psychology, The University of Edinburgh, Edinburgh, UK
| | - Alison Pattie
- Department of Psychology, The University of Edinburgh, Edinburgh, UK
| | - Paul Redmond
- Department of Psychology, The University of Edinburgh, Edinburgh, UK
| | - John M Starr
- Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, Edinburgh, UK
- Alzheimer Scotland Dementia Research Centre, The University of Edinburgh, Edinburgh, UK
| | - Ian J Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, Edinburgh, UK
- Department of Psychology, The University of Edinburgh, Edinburgh, UK
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21
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Domingue BW, Fletcher J. Separating Measured Genetic and Environmental Effects: Evidence Linking Parental Genotype and Adopted Child Outcomes. Behav Genet 2020; 50:301-309. [PMID: 32350631 PMCID: PMC7442617 DOI: 10.1007/s10519-020-10000-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Accepted: 04/24/2020] [Indexed: 12/14/2022]
Abstract
There has been widespread adoption of genome wide summary scores (polygenic scores) as tools for studying the importance of genetics and associated life course mechanisms across a range of demographic and socioeconomic outcomes. However, an often unacknowledged issue with these studies is that parental genetics impact both child environments and child genetics, leaving the effects of polygenic scores difficult to interpret. This paper uses multi-generational data containing polygenic scores for parents (n = 7193) and educational outcomes for adopted (n = 855) and biological (n = 20,939) children, many raised in the same families, which allows us to separate the influence of parental polygenic scores on children outcomes between environmental (adopted children) and environmental and genetic (biological children) effects. Our results complement recent work on "genetic nurture" by showing associations of parental polygenic scores with adopted children's schooling, providing additional evidence that polygenic scores combine genetic and environmental influences and that research designs are needed to separate these estimated impacts.
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Affiliation(s)
| | - Jason Fletcher
- La Follette School of Public Affairs, Department of Sociology, and Center for Demography of Health and Aging, University of Wisconsin-Madison, Madison, USA
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22
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Domingue BW, Fletcher J. Separating Measured Genetic and Environmental Effects: Evidence Linking Parental Genotype and Adopted Child Outcomes. Behav Genet 2020; 50:301-309. [PMID: 32350631 DOI: 10.1101/698464] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Accepted: 04/24/2020] [Indexed: 05/22/2023]
Abstract
There has been widespread adoption of genome wide summary scores (polygenic scores) as tools for studying the importance of genetics and associated life course mechanisms across a range of demographic and socioeconomic outcomes. However, an often unacknowledged issue with these studies is that parental genetics impact both child environments and child genetics, leaving the effects of polygenic scores difficult to interpret. This paper uses multi-generational data containing polygenic scores for parents (n = 7193) and educational outcomes for adopted (n = 855) and biological (n = 20,939) children, many raised in the same families, which allows us to separate the influence of parental polygenic scores on children outcomes between environmental (adopted children) and environmental and genetic (biological children) effects. Our results complement recent work on "genetic nurture" by showing associations of parental polygenic scores with adopted children's schooling, providing additional evidence that polygenic scores combine genetic and environmental influences and that research designs are needed to separate these estimated impacts.
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Affiliation(s)
| | - Jason Fletcher
- La Follette School of Public Affairs, Department of Sociology, and Center for Demography of Health and Aging, University of Wisconsin-Madison, Madison, USA
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23
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Domingue BW, Trejo S, Armstrong-Carter E, Tucker-Drob EM. Interactions between Polygenic Scores and Environments: Methodological and Conceptual Challenges. SOCIOLOGICAL SCIENCE 2020; 7:465-486. [PMID: 36091972 PMCID: PMC9455807 DOI: 10.15195/v7.a19] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Interest in the study of gene-environment interaction has recently grown due to the sudden availability of molecular genetic data-in particular, polygenic scores-in many long-running longitudinal studies. Identifying and estimating statistical interactions comes with several analytic and inferential challenges; these challenges are heightened when used to integrate observational genomic and social science data. We articulate some of these key challenges, provide new perspectives on the study of gene-environment interactions, and end by offering some practical guidance for conducting research in this area. Given the sudden availability of well-powered polygenic scores, we anticipate a substantial increase in research testing for interaction between such scores and environments. The issues we discuss, if not properly addressed, may impact the enduring scientific value of gene-environment interaction studies.
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24
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Lövdén M, Fratiglioni L, Glymour MM, Lindenberger U, Tucker-Drob EM. Education and Cognitive Functioning Across the Life Span. Psychol Sci Public Interest 2020; 21:6-41. [PMID: 32772803 PMCID: PMC7425377 DOI: 10.1177/1529100620920576] [Citation(s) in RCA: 370] [Impact Index Per Article: 92.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Cognitive abilities are important predictors of educational and occupational performance, socioeconomic attainment, health, and longevity. Declines in cognitive abilities are linked to impairments in older adults' everyday functions, but people differ from one another in their rates of cognitive decline over the course of adulthood and old age. Hence, identifying factors that protect against compromised late-life cognition is of great societal interest. The number of years of formal education completed by individuals is positively correlated with their cognitive function throughout adulthood and predicts lower risk of dementia late in life. These observations have led to the propositions that prolonging education might (a) affect cognitive ability and (b) attenuate aging-associated declines in cognition. We evaluate these propositions by reviewing the literature on educational attainment and cognitive aging, including recent analyses of data harmonized across multiple longitudinal cohort studies and related meta-analyses. In line with the first proposition, the evidence indicates that educational attainment has positive effects on cognitive function. We also find evidence that cognitive abilities are associated with selection into longer durations of education and that there are common factors (e.g., parental socioeconomic resources) that affect both educational attainment and cognitive development. There is likely reciprocal interplay among these factors, and among cognitive abilities, during development. Education-cognitive ability associations are apparent across the entire adult life span and across the full range of education levels, including (to some degree) tertiary education. However, contrary to the second proposition, we find that associations between education and aging-associated cognitive declines are negligible and that a threshold model of dementia can account for the association between educational attainment and late-life dementia risk. We conclude that educational attainment exerts its influences on late-life cognitive function primarily by contributing to individual differences in cognitive skills that emerge in early adulthood but persist into older age. We also note that the widespread absence of educational influences on rates of cognitive decline puts constraints on theoretical notions of cognitive aging, such as the concepts of cognitive reserve and brain maintenance. Improving the conditions that shape development during the first decades of life carries great potential for improving cognitive ability in early adulthood and for reducing public-health burdens related to cognitive aging and dementia.
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Affiliation(s)
- Martin Lövdén
- Aging Research Center, Department of Neurobiology, Care Sciences, and Society, Karolinska Institutet and Stockholm University, Stockholm, Sweden
- Department of Psychology, University of Gothenburg, Gothenburg, Sweden
| | - Laura Fratiglioni
- Aging Research Center, Department of Neurobiology, Care Sciences, and Society, Karolinska Institutet and Stockholm University, Stockholm, Sweden
- Stockholm Gerontology Research Center, Stockholm, Sweden
| | - M. Maria Glymour
- Department of Epidemiology and Biostatistics, University of California, San Francisco
| | - Ulman Lindenberger
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany, and London, United Kingdom
| | - Elliot M. Tucker-Drob
- Department of Psychology and Population Research Center, University of Texas at Austin
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25
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Holland D, Frei O, Desikan R, Fan CC, Shadrin AA, Smeland OB, Sundar VS, Thompson P, Andreassen OA, Dale AM. Beyond SNP heritability: Polygenicity and discoverability of phenotypes estimated with a univariate Gaussian mixture model. PLoS Genet 2020; 16:e1008612. [PMID: 32427991 PMCID: PMC7272101 DOI: 10.1371/journal.pgen.1008612] [Citation(s) in RCA: 95] [Impact Index Per Article: 23.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Revised: 06/04/2020] [Accepted: 01/15/2020] [Indexed: 12/27/2022] Open
Abstract
Estimating the polygenicity (proportion of causally associated single nucleotide polymorphisms (SNPs)) and discoverability (effect size variance) of causal SNPs for human traits is currently of considerable interest. SNP-heritability is proportional to the product of these quantities. We present a basic model, using detailed linkage disequilibrium structure from a reference panel of 11 million SNPs, to estimate these quantities from genome-wide association studies (GWAS) summary statistics. We apply the model to diverse phenotypes and validate the implementation with simulations. We find model polygenicities (as a fraction of the reference panel) ranging from ≃ 2 × 10-5 to ≃ 4 × 10-3, with discoverabilities similarly ranging over two orders of magnitude. A power analysis allows us to estimate the proportions of phenotypic variance explained additively by causal SNPs reaching genome-wide significance at current sample sizes, and map out sample sizes required to explain larger portions of additive SNP heritability. The model also allows for estimating residual inflation (or deflation from over-correcting of z-scores), and assessing compatibility of replication and discovery GWAS summary statistics.
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Affiliation(s)
- Dominic Holland
- Center for Multimodal Imaging and Genetics, University of California at San Diego, La Jolla, California, United States of America
- Department of Neurosciences, University of California, San Diego, La Jolla, California, United States of America
| | - Oleksandr Frei
- NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Rahul Desikan
- Department of Radiology, University of California, San Francisco, San Francisco, California, United States of America
| | - Chun-Chieh Fan
- Center for Multimodal Imaging and Genetics, University of California at San Diego, La Jolla, California, United States of America
- Department of Radiology, University of California, San Diego, La Jolla, California, United States of America
- Department of Cognitive Sciences, University of California at San Diego, La Jolla, California, United States of America
| | - Alexey A. Shadrin
- NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Olav B. Smeland
- NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - V. S. Sundar
- Center for Multimodal Imaging and Genetics, University of California at San Diego, La Jolla, California, United States of America
- Department of Radiology, University of California, San Diego, La Jolla, California, United States of America
| | - Paul Thompson
- Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America
| | - Ole A. Andreassen
- NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Anders M. Dale
- Center for Multimodal Imaging and Genetics, University of California at San Diego, La Jolla, California, United States of America
- Department of Neurosciences, University of California, San Diego, La Jolla, California, United States of America
- Department of Radiology, University of California, San Diego, La Jolla, California, United States of America
- Department of Psychiatry, University of California, San Diego, La Jolla, California, United States of America
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26
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Mitchell BL, Cuéllar-Partida G, Grasby KL, Campos AI, Strike LT, Hwang LD, Okbay A, Thompson PM, Medland SE, Martin NG, Wright MJ, Rentería ME. Educational attainment polygenic scores are associated with cortical total surface area and regions important for language and memory. Neuroimage 2020; 212:116691. [PMID: 32126298 DOI: 10.1016/j.neuroimage.2020.116691] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2019] [Revised: 02/06/2020] [Accepted: 02/26/2020] [Indexed: 02/01/2023] Open
Abstract
It is well established that higher cognitive ability is associated with larger brain size. However, individual variation in intelligence exists despite brain size and recent studies have shown that a simple unifactorial view of the neurobiology underpinning cognitive ability is probably unrealistic. Educational attainment (EA) is often used as a proxy for cognitive ability since it is easily measured, resulting in large sample sizes and, consequently, sufficient statistical power to detect small associations. This study investigates the association between three global (total surface area (TSA), intra-cranial volume (ICV) and average cortical thickness) and 34 regional cortical measures with educational attainment using a polygenic scoring (PGS) approach. Analyses were conducted on two independent target samples of young twin adults with neuroimaging data, from Australia (N = 1097) and the USA (N = 723), and found that higher EA-PGS were significantly associated with larger global brain size measures, ICV and TSA (R2 = 0.006 and 0.016 respectively, p < 0.001) but not average thickness. At the regional level, we identified seven cortical regions-in the frontal and temporal lobes-that showed variation in surface area and average cortical thickness over-and-above the global effect. These regions have been robustly implicated in language, memory, visual recognition and cognitive processing. Additionally, we demonstrate that these identified brain regions partly mediate the association between EA-PGS and cognitive test performance. Altogether, these findings advance our understanding of the neurobiology that underpins educational attainment and cognitive ability, providing focus points for future research.
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Affiliation(s)
- Brittany L Mitchell
- Department of Genetics & Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia; School of Biomedical Sciences, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD, Australia.
| | - Gabriel Cuéllar-Partida
- The University of Queensland Diamantina Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Katrina L Grasby
- Department of Genetics & Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Adrian I Campos
- Department of Genetics & Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia; Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Lachlan T Strike
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Liang-Dar Hwang
- The University of Queensland Diamantina Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Aysu Okbay
- Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Paul M Thompson
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Sarah E Medland
- Department of Genetics & Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Nicholas G Martin
- Department of Genetics & Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia; School of Biomedical Sciences, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD, Australia
| | - Margaret J Wright
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia; Centre for Advanced Imaging, The University of Queensland, Brisbane, QLD, Australia
| | - Miguel E Rentería
- Department of Genetics & Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia; School of Biomedical Sciences, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD, Australia
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27
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Etzel JA, Courtney Y, Carey CE, Gehred MZ, Agrawal A, Braver TS. Pattern Similarity Analyses of FrontoParietal Task Coding: Individual Variation and Genetic Influences. Cereb Cortex 2020; 30:3167-3183. [PMID: 32086524 DOI: 10.1093/cercor/bhz301] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
Pattern similarity analyses are increasingly used to characterize coding properties of brain regions, but relatively few have focused on cognitive control processes in FrontoParietal regions. Here, we use the Human Connectome Project (HCP) N-back task functional magnetic resonance imaging (fMRI) dataset to examine individual differences and genetic influences on the coding of working memory load (0-back, 2-back) and perceptual category (Face, Place). Participants were grouped into 105 monozygotic twin, 78 dizygotic twin, 99 nontwin sibling, and 100 unrelated pairs. Activation pattern similarity was used to test the hypothesis that FrontoParietal regions would have higher similarity for same load conditions, while Visual regions would have higher similarity in same perceptual category conditions. Results confirmed this highly robust regional double dissociation in neural coding, which also predicted individual differences in behavioral performance. In pair-based analyses, anatomically selective genetic relatedness effects were observed: relatedness predicted greater activation pattern similarity in FrontoParietal only for load coding and in Visual only for perceptual coding. Further, in related pairs, the similarity of load coding in FrontoParietal regions was uniquely associated with behavioral performance. Together, these results highlight the power of task fMRI pattern similarity analyses for detecting key coding and heritability features of brain regions.
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Affiliation(s)
- Joset A Etzel
- Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Ya'el Courtney
- Department of Biology, Kent State University, Kent, OH 44243, USA.,Division of Medical Sciences, Program in Neuroscience, Harvard Medical School, Boston, MA 02115, USA
| | - Caitlin E Carey
- Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO 63130, USA.,Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | - Maria Z Gehred
- Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO 63130, USA.,Department of Psychology and Neuroscience, Duke University, Durham, NC 27708, USA
| | - Arpana Agrawal
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110, USA
| | - Todd S Braver
- Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO 63130, USA
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28
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Harden KP, Domingue BW, Belsky DW, Boardman JD, Crosnoe R, Malanchini M, Nivard M, Tucker-Drob EM, Harris KM. Genetic associations with mathematics tracking and persistence in secondary school. NPJ SCIENCE OF LEARNING 2020; 5:1. [PMID: 32047651 PMCID: PMC7002519 DOI: 10.1038/s41539-020-0060-2] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/03/2019] [Accepted: 01/09/2020] [Indexed: 05/11/2023]
Abstract
Maximizing the flow of students through the science, technology, engineering, and math (STEM) pipeline is important to promoting human capital development and reducing economic inequality. A critical juncture in the STEM pipeline is the highly cumulative sequence of secondary school math courses. Students from disadvantaged schools are less likely to complete advanced math courses. Here, we conduct an analysis of how the math pipeline differs across schools using student polygenic scores, which are DNA-based indicators of propensity to succeed in education. We integrated genetic and official school transcript data from over 3000 European-ancestry students from U.S. high schools. We used polygenic scores as a molecular tracer to understand how the flow of students through the high school math pipeline differs in socioeconomically advantaged versus disadvantaged schools. Students with higher education polygenic scores were tracked to more advanced math already at the beginning of high school and persisted in math for more years. Analyses using genetics as a molecular tracer revealed that the dynamics of the math pipeline differed by school advantage. Compared to disadvantaged schools, advantaged schools buffered students with low polygenic scores from dropping out of math. Across all schools, even students with exceptional polygenic scores (top 2%) were unlikely to take the most advanced math classes, suggesting substantial room for improvement in the development of potential STEM talent. These results link new molecular genetic discoveries to a common target of educational-policy reforms.
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Affiliation(s)
- K. Paige Harden
- Department of Psychology and Population Research Center, University of Texas at Austin, Austin, TX USA
| | | | - Daniel W. Belsky
- Department of Epidemiology, Columbia University Mailman School of Public Health, New York, NY USA
| | - Jason D. Boardman
- Department of Sociology and Institute of Behavioral Science, University of Colorado at Boulder, Boulder, CA USA
| | - Robert Crosnoe
- Department of Sociology and Population Research Center, University of Texas at Austin, Austin, TX USA
| | - Margherita Malanchini
- Department of Psychology and Population Research Center, University of Texas at Austin, Austin, TX USA
| | - Michel Nivard
- Biological Psychology, VU University Amsterdam, Amsterdam, The Netherlands
| | - Elliot M. Tucker-Drob
- Department of Psychology and Population Research Center, University of Texas at Austin, Austin, TX USA
| | - Kathleen Mullan Harris
- Department of Sociology and Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC USA
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29
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30
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Smith-Woolley E, Selzam S, Plomin R. Polygenic score for educational attainment captures DNA variants shared between personality traits and educational achievement. J Pers Soc Psychol 2019; 117:1145-1163. [PMID: 30920283 PMCID: PMC6902055 DOI: 10.1037/pspp0000241] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Genome-wide polygenic scores (GPS) can be used to predict individual genetic risk and resilience. For example, a GPS for years of education (EduYears) explains substantial variance in cognitive traits such as general cognitive ability and educational achievement. Personality traits are also known to contribute to individual differences in educational achievement. However, the association between EduYears GPS and personality traits remains largely unexplored. Here, we test the relation between GPS for EduYears, neuroticism, and well-being, and 6 personality and motivation domains: Academic Motivation, Extraversion, Openness, Conscientiousness, Neuroticism, and Agreeableness. The sample was drawn from a U.K.-representative sample of up to 8,322 individuals assessed at age 16. We find that EduYears GPS was positively associated with Openness, Conscientiousness, Agreeableness, and Academic Motivation, predicting between 0.6% and 3% of the variance. In addition, we find that EduYears GPS explains between 8% and 16% of the association between personality domains and educational achievement at the end of compulsory education. In contrast, both the neuroticism and well-being GPS significantly accounted for between 0.3% and 0.7% of the variance in a subset of personality domains. Furthermore, they did not significantly account for any of the covariance between the personality domains and achievement, with the exception of the neuroticism GPS explaining 5% of the covariance between Neuroticism and achievement. These results demonstrate that the genetic effects of educational attainment relate to personality traits, highlighting the multifaceted nature of EduYears GPS. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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Affiliation(s)
- Emily Smith-Woolley
- King’s College London, MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, London. SE5 8AF, UK
| | - Saskia Selzam
- King’s College London, MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, London. SE5 8AF, UK
| | - Robert Plomin
- King’s College London, MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, London. SE5 8AF, UK
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31
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Rimfeld K, Malanchini M, Hannigan LJ, Dale PS, Allen R, Hart S, Plomin R. Teacher assessments during compulsory education are as reliable, stable and heritable as standardized test scores. J Child Psychol Psychiatry 2019; 60:1278-1288. [PMID: 31079420 PMCID: PMC6848749 DOI: 10.1111/jcpp.13070] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/03/2019] [Indexed: 11/27/2022]
Abstract
BACKGROUND Children in the UK go through rigorous teacher assessments and standardized exams throughout compulsory (elementary and secondary) education, culminating with the GCSE exams (General Certificate of Secondary Education) at the age of 16 and A-level exams (Advanced Certificate of Secondary Education) at the age of 18. These exams are a major tipping point directing young individuals towards different lifelong trajectories. However, little is known about the associations between teacher assessments and exam performance or how well these two measurement approaches predict educational outcomes at the end of compulsory education and beyond. METHODS The current investigation used the UK-representative Twins Early Development Study (TEDS) sample of over 5,000 twin pairs studied longitudinally from childhood to young adulthood (age 7-18). We used teacher assessment and exam performance across development to investigate, using genetically sensitive designs, the associations between teacher assessment and standardized exam scores, as well as teacher assessments' prediction of exam scores at ages 16 and 18, and university enrolment. RESULTS Teacher assessments of achievement are as reliable, stable and heritable (~60%) as test scores at every stage of the educational experience. Teacher and test scores correlate strongly phenotypically (r ~ .70) and genetically (genetic correlation ~.80) both contemporaneously and over time. Earlier exam performance accounts for additional variance in standardized exam results (~10%) at age 16, when controlling for teacher assessments. However, exam performance explains less additional variance in later academic success, ~5% for exam grades at 18, and ~3% for university entry, when controlling for teacher assessments. Teacher assessments also predict additional variance in later exam performance and university enrolment, when controlling for previous exam scores. CONCLUSIONS Teachers can reliably and validly monitor students' progress, abilities and inclinations. High-stakes exams may shift educational experience away from learning towards exam performance. For these reasons, we suggest that teacher assessments could replace some, or all, high-stakes exams.
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Affiliation(s)
- Kaili Rimfeld
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London UK
| | - Margherita Malanchini
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London UK,Department of Psychology, The University of Texas at Austin, Austin, TX, USA
| | - Laurie J. Hannigan
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London UK
| | - Philip S. Dale
- Department of Speech and Hearing Sciences, The University of New Mexico, Albuquerque, NM, USA
| | - Rebecca Allen
- Institute of Education, University College London, London, UK
| | - Sara Hart
- Department of Psychology, Florida State University, Tallahassee, FL, USA
| | - Robert Plomin
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London UK
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32
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Elliott ML, Belsky DW, Anderson K, Corcoran DL, Ge T, Knodt A, Prinz JA, Sugden K, Williams B, Ireland D, Poulton R, Caspi A, Holmes A, Moffitt T, Hariri AR. A Polygenic Score for Higher Educational Attainment is Associated with Larger Brains. Cereb Cortex 2019; 29:3496-3504. [PMID: 30215680 PMCID: PMC6645179 DOI: 10.1093/cercor/bhy219] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2018] [Revised: 08/09/2018] [Accepted: 08/16/2018] [Indexed: 01/20/2023] Open
Abstract
People who score higher on intelligence tests tend to have larger brains. Twin studies suggest the same genetic factors influence both brain size and intelligence. This has led to the hypothesis that genetics influence intelligence partly by contributing to the development of larger brains. We tested this hypothesis using four large imaging genetics studies (combined N = 7965) with polygenic scores derived from a genome-wide association study (GWAS) of educational attainment, a correlate of intelligence. We conducted meta-analysis to test associations among participants' genetics, total brain volume (i.e., brain size), and cognitive test performance. Consistent with previous findings, participants with higher polygenic scores achieved higher scores on cognitive tests, as did participants with larger brains. Participants with higher polygenic scores also had larger brains. We found some evidence that brain size partly mediated associations between participants' education polygenic scores and their cognitive test performance. Effect sizes were larger in the population-based samples than in the convenience-based samples. Recruitment and retention of population-representative samples should be a priority for neuroscience research. Findings suggest promise for studies integrating GWAS discoveries with brain imaging to understand neurobiology linking genetics with cognitive performance.
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Affiliation(s)
- Maxwell L Elliott
- Department of Psychology & Neuroscience, Duke University, Box 104410, Durham, NC, USA
| | - Daniel W Belsky
- Department of Population Health Sciences, Duke University School of Medicine, Durham, NC, USA
- Social Science Research Institute, Duke University, Durham, NC, USA
| | - Kevin Anderson
- Department of Psychology, Yale University, New Haven, CT, USA
| | - David L Corcoran
- Center for Genomic and Computational Biology, Duke University, Durham, NC, USA
| | - Tian Ge
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, 75 Ames Street, Cambridge, MA, USA
| | - Annchen Knodt
- Department of Psychology & Neuroscience, Duke University, Box 104410, Durham, NC, USA
| | - Joseph A Prinz
- Center for Genomic and Computational Biology, Duke University, Durham, NC, USA
| | - Karen Sugden
- Social Science Research Institute, Duke University, Durham, NC, USA
- Center for Genomic and Computational Biology, Duke University, Durham, NC, USA
| | - Benjamin Williams
- Social Science Research Institute, Duke University, Durham, NC, USA
- Center for Genomic and Computational Biology, Duke University, Durham, NC, USA
| | - David Ireland
- Department of Psychology, Dunedin Multidisciplinary Health and Development Research Unit, University of Otago, 163 Union St E, Dunedin, New Zealand
| | - Richie Poulton
- Department of Psychology, Dunedin Multidisciplinary Health and Development Research Unit, University of Otago, 163 Union St E, Dunedin, New Zealand
| | - Avshalom Caspi
- Department of Psychology & Neuroscience, Duke University, Box 104410, Durham, NC, USA
- Center for Genomic and Computational Biology, Duke University, Durham, NC, USA
- Social, Genetic, & Developmental Psychiatry Research Centre, Institute of Psychiatry, Psychology, & Neuroscience, King’s College London, De Crespigny Park, Denmark Hill, London, UK
- Department of Psychiatry & Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
| | - Avram Holmes
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Terrie Moffitt
- Department of Psychology & Neuroscience, Duke University, Box 104410, Durham, NC, USA
- Center for Genomic and Computational Biology, Duke University, Durham, NC, USA
- Social, Genetic, & Developmental Psychiatry Research Centre, Institute of Psychiatry, Psychology, & Neuroscience, King’s College London, De Crespigny Park, Denmark Hill, London, UK
- Department of Psychiatry & Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
| | - Ahmad R Hariri
- Department of Psychology & Neuroscience, Duke University, Box 104410, Durham, NC, USA
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33
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Sallis H, Davey Smith G, Munafò MR. Genetics of biologically based psychological differences. Philos Trans R Soc Lond B Biol Sci 2019; 373:rstb.2017.0162. [PMID: 29483347 PMCID: PMC5832687 DOI: 10.1098/rstb.2017.0162] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/22/2017] [Indexed: 01/21/2023] Open
Abstract
In recent years, substantial effort has gone into disentangling the genetic contribution to individual differences in behaviour (such as personality and temperament traits). Heritability estimates from twin and family studies, and more recently using whole genome approaches, suggest a substantial genetic component to these traits. However, efforts to identify the genes that influence these traits have had relatively little success. Here, we review current work investigating the heritability of individual differences in behavioural traits and provide an overview of the results from genome-wide association analyses of these traits to date. In addition, we discuss the implications of these findings for the potential applications of Mendelian randomization.This article is part of the theme issue 'Diverse perspectives on diversity: multi-disciplinary approaches to taxonomies of individual differences'.
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Affiliation(s)
- Hannah Sallis
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK .,UK Centre for Tobacco and Alcohol Studies, School of Experimental Psychology, University of Bristol, Bristol, UK.,Centre for Academic Mental Health, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | | | - Marcus R Munafò
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.,UK Centre for Tobacco and Alcohol Studies, School of Experimental Psychology, University of Bristol, Bristol, UK
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Yengo L, Robinson MR, Keller MC, Kemper KE, Yang Y, Trzaskowski M, Gratten J, Turley P, Cesarini D, Benjamin DJ, Wray NR, Goddard ME, Yang J, Visscher PM. Imprint of assortative mating on the human genome. Nat Hum Behav 2018; 2:948-954. [PMID: 30988446 DOI: 10.1038/s41562-018-0476-3] [Citation(s) in RCA: 60] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2018] [Accepted: 10/22/2018] [Indexed: 11/09/2022]
Abstract
Preference for mates with similar phenotypes; that is, assortative mating, is widely observed in humans1-5 and has evolutionary consequences6-8. Under Fisher's classical theory6, assortative mating is predicted to induce a signature in the genome at trait-associated loci that can be detected and quantified. Here, we develop and apply a method to quantify assortative mating on a specific trait by estimating the correlation (θ) between genetic predictors of the trait from single nucleotide polymorphisms on odd- versus even-numbered chromosomes. We show by theory and simulation that the effect of assortative mating can be quantified in the presence of population stratification. We applied this approach to 32 complex traits and diseases using single nucleotide polymorphism data from ~400,000 unrelated individuals of European ancestry. We found significant evidence of assortative mating for height (θ = 3.2%) and educational attainment (θ = 2.7%), both of which were consistent with theoretical predictions. Overall, our results imply that assortative mating involves multiple traits and affects the genomic architecture of loci that are associated with these traits, and that the consequence of mate choice can be detected from a random sample of genomes.
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Affiliation(s)
- Loic Yengo
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia.
| | - Matthew R Robinson
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia.,Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
| | - Matthew C Keller
- Department of Psychology and Neuroscience, Institute for Behavioral Genetics, University of Colorado at Boulder, Boulder, CO, USA
| | - Kathryn E Kemper
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Yuanhao Yang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Maciej Trzaskowski
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Jacob Gratten
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia.,Mater Research, Translational Research Institute, Brisbane, Queensland, Australia
| | - Patrick Turley
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.,Stanley Centre for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - David Cesarini
- National Bureau of Economic Research, Cambridge, MA, USA.,Department of Economics, New York University, New York, NY, USA.,Center for Experimental Social Science, New York University, New York, NY, USA
| | - Daniel J Benjamin
- National Bureau of Economic Research, Cambridge, MA, USA.,Center for Economic and Social Research, University of Southern California, Los Angeles, CA, USA.,Department of Economics, University of Southern California, Los Angeles, CA, USA
| | - Naomi R Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia.,Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia
| | - Michael E Goddard
- Faculty of Veterinary and Agricultural Science, University of Melbourne, Melbourne, Victoria, Australia.,Biosciences Research Division, Department of Economic Development, Jobs, Transport and Resources Government of Victoria, Bundoora, Victoria, Australia
| | - Jian Yang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia.,Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia
| | - Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia. .,Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia.
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35
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Belsky DW, Domingue BW, Wedow R, Arseneault L, Boardman JD, Caspi A, Conley D, Fletcher JM, Freese J, Herd P, Moffitt TE, Poulton R, Sicinski K, Wertz J, Harris KM. Genetic analysis of social-class mobility in five longitudinal studies. Proc Natl Acad Sci U S A 2018; 115:E7275-E7284. [PMID: 29987013 PMCID: PMC6077729 DOI: 10.1073/pnas.1801238115] [Citation(s) in RCA: 134] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
A summary genetic measure, called a "polygenic score," derived from a genome-wide association study (GWAS) of education can modestly predict a person's educational and economic success. This prediction could signal a biological mechanism: Education-linked genetics could encode characteristics that help people get ahead in life. Alternatively, prediction could reflect social history: People from well-off families might stay well-off for social reasons, and these families might also look alike genetically. A key test to distinguish biological mechanism from social history is if people with higher education polygenic scores tend to climb the social ladder beyond their parents' position. Upward mobility would indicate education-linked genetics encodes characteristics that foster success. We tested if education-linked polygenic scores predicted social mobility in >20,000 individuals in five longitudinal studies in the United States, Britain, and New Zealand. Participants with higher polygenic scores achieved more education and career success and accumulated more wealth. However, they also tended to come from better-off families. In the key test, participants with higher polygenic scores tended to be upwardly mobile compared with their parents. Moreover, in sibling-difference analysis, the sibling with the higher polygenic score was more upwardly mobile. Thus, education GWAS discoveries are not mere correlates of privilege; they influence social mobility within a life. Additional analyses revealed that a mother's polygenic score predicted her child's attainment over and above the child's own polygenic score, suggesting parents' genetics can also affect their children's attainment through environmental pathways. Education GWAS discoveries affect socioeconomic attainment through influence on individuals' family-of-origin environments and their social mobility.
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Affiliation(s)
- Daniel W Belsky
- Department of Population Health Sciences, Duke University School of Medicine, Durham, NC 27710;
- Social Science Research Institute, Duke University, Durham, NC 27708
| | | | - Robbee Wedow
- Institute of Behavioral Science and Department of Sociology, University of Colorado, Boulder, CO 80309
| | - Louise Arseneault
- Social, Genetic, and Developmental Psychiatry Research Centre, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, SE5 8AF London, United Kingdom
| | - Jason D Boardman
- Institute of Behavioral Science and Department of Sociology, University of Colorado, Boulder, CO 80309
| | - Avshalom Caspi
- Social, Genetic, and Developmental Psychiatry Research Centre, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, SE5 8AF London, United Kingdom
- Department of Psychology and Neuroscience, Duke University, Durham, NC 27708
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC 27708
- Center for Genomic and Computational Biology, Duke University, Durham, NC 27708
| | - Dalton Conley
- Department of Sociology, Princeton University, Princeton, NJ 08544
| | - Jason M Fletcher
- La Follette School of Public Policy, University of Wisconsin-Madison, Madison, WI 53706
- Center for Demography of Health and Aging, University of Wisconsin-Madison, Madison, WI 53706
| | - Jeremy Freese
- Department of Sociology, Stanford University, Stanford, CA 94305
| | - Pamela Herd
- La Follette School of Public Policy, University of Wisconsin-Madison, Madison, WI 53706
| | - Terrie E Moffitt
- Social, Genetic, and Developmental Psychiatry Research Centre, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, SE5 8AF London, United Kingdom
- Department of Psychology and Neuroscience, Duke University, Durham, NC 27708
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC 27708
- Center for Genomic and Computational Biology, Duke University, Durham, NC 27708
| | - Richie Poulton
- Dunedin Multidisciplinary Health and Development Research Unit, Department of Psychology, University of Otago, 9016 Dunedin, New Zealand
| | - Kamil Sicinski
- Center for Demography of Health and Aging, University of Wisconsin-Madison, Madison, WI 53706
| | - Jasmin Wertz
- Department of Psychology and Neuroscience, Duke University, Durham, NC 27708
| | - Kathleen Mullan Harris
- Department of Sociology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27516;
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27516
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36
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The Nature of Nurture: Using a Virtual-Parent Design to Test Parenting Effects on Children's Educational Attainment in Genotyped Families. Twin Res Hum Genet 2018. [DOI: 10.1017/thg.2018.11] [Citation(s) in RCA: 103] [Impact Index Per Article: 17.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Research on environmental and genetic pathways to complex traits such as educational attainment (EA) is confounded by uncertainty over whether correlations reflect effects of transmitted parental genes, causal family environments, or some, possibly interactive, mixture of both. Thus, an aggregate of thousands of alleles associated with EA (a polygenic risk score; PRS) may tap parental behaviors and home environments promoting EA in the offspring. New methods for unpicking and determining these causal pathways are required. Here, we utilize the fact that parents pass, at random, 50% of their genome to a given offspring to create independent scores for the transmitted alleles (conventional EA PRS) and a parental score based on allelesnot transmittedto the offspring (EA VP_PRS). The formal effect of non-transmitted alleles on offspring attainment was tested in 2,333 genotyped twins for whom high-quality measures of EA, assessed at age 17 years, were available, and whose parents were also genotyped. Four key findings were observed. First, the EA PRS and EA VP_PRS were empirically independent, validating the virtual-parent design. Second, in this family-based design, children's own EA PRS significantly predicted their EA (β = 0.15), ruling out stratification confounds as a cause of the association of attainment with the EA PRS. Third, parental EA PRS predicted the SES environment parents provided to offspring (β = 0.20), and parental SES and offspring EA were significantly associated (β = 0.33). This would suggest that the EA PRS is at least as strongly linked to social competence as it is to EA, leading to higher attained SES in parents and, therefore, a higher experienced SES for children. In a full structural equation model taking account of family genetic relatedness across multiple siblings the non-transmitted allele effects were estimated at similar values; but, in this more complex model, confidence intervals included zero. A test using the forthcoming EA3 PRS may clarify this outcome. The virtual-parent method may be applied to clarify causality in other phenotypes where observational evidence suggests parenting may moderate expression of other outcomes, for instance in psychiatry.
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Abstract
SummaryResearch has established that genetic differences among people explain a greater or smaller proportion of the variation in life outcomes in different environmental conditions. This review evaluates the results of recent educationally relevant behavioural genetic studies and meta-analyses in the context of recent trends in income and wealth distribution. The pattern of results suggests that inequality and social policies can have profound effects on the heritability of educational attainment and achievement in a population (Gene–Gini interplay). For example, heritability is generally higher at greater equality levels, suggesting that inequality stifles the expression of educationally relevant genetic propensities. The review concludes with a discussion of the mechanisms of Gene–Gini interplay and what the findings mean for efforts to optimize education for all people.
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